People and companies today are connected around the world, which has led to a growing importance of the aviation industry. As flight delays are a big challenge in aviation, machine learning algorithms can be used to forecast those. This paper investigates the prediction of the occurrence of flight arrival delays with three promi-nent machine learning algorithms for a data set of do-mestic flights in the USA. The task is regarded as a clas-sification problem. The focus lies on the investigation of the influence of short-term features on the quality of the results. Therefore, three scenarios are created that are characterised by different input feature sets. When for-going the inclusion of short-term information in order to shift the prediction timing to an early point in time, an accuracy of 69.5% with a recall of 68.2% is achieved. By including information on the delay that the aircraft had on its previous flight, the prediction quality increases slightly. Hence, this is a compromise between the early prediction timing of the first model and the good predic-tion quality of the third model, where the departure delay of the aircraft is added as an input feature. In this case, an accuracy of 89.9% with a recall of 83.4% is obtained. The desired timing of prediction therefore determines which features to use as inputs since short-term features significantly improve the prediction quality.

European airspace is marked by fragmentation, and from fragmentation originated congestion. As an answer to congestion, flexible use of airspace is created to ful-fil all users’ requirements optimally, and at its very core lies civil-military cooperation and the coordination of airspace use. Such an approach is enabled by collabo-rative decision-making, which presumes timely and com-plete exchange of complete relevant information between airspace users. To participate in the process, the mili-tary should be transparent regarding its restrictions and capabilities of adaptation to other airspace users’ de-mands. Flight training syllabus structure and its specific impact on proactive planning of high-performance mili-tary aircraft pilot training operations in flexible airspace structures is the subject of this research. Significant di-versity in the hourly distribution of civil traffic demand in flexible airspace structures in Zagreb FIR proves them applicable for a proactive approach to high-performance aircraft pilot training operations planning and manage-ment. Noticeable reduction in the number of civil aircraft affected by high-performance aircraft pilot training op-erations in flexible airspace structures at congested flight levels is corroborated. Simulations of proactive planning of high-performance aircraft pilot training operations reveal indications of flight syllabus structure’s impact on pilot flight training duration, as well as the possibility to manage its restrictions.

Data-driven forecasting methods have the problems of complex calculations, poor portability and need a large amount of training data, which limits the application of data-driven methods in small cities. This paper propos-es a traffic flow forecasting method using a Nonlinear AutoRegressive model with eXogenous variables (NARX model), which uses a dynamic neural network Focused Time-Delay Neural Network (FTDNN) with a Tapped Delay Line (TDL) structure as a nonlinear function. The TDL structure enables the FTDNN to have short-term memory capabilities. At the same time, before the data is input into the FTDNN, the use of trend decomposition or differential calculation on the traffic data sequence can make the NARX model maintain long-term predictive ca-pabilities. Compared with common nonlinear models, the FTDNN has structural advantages. It uses a simple TDL structure without the memory mechanism and the gated structure, which can reduce the parameters of the model and reduce the scale of data. Through the four-day data of Guilin City, the traffic volume forecast for five minutes is verified, and the performance of the NARX model is better than that of the SARIMA model and the Holt-Win-ters model.

With the rapid growth of flight volume, the impact of convective weather on flight operations in the terminal area has become more and more serious. In this paper, the typical flight paths (TFPs) are used to replace flight procedures as the routine flight paths in the terminal area, and the TFP of each flight is predicted by Random Forest (RF), Boosting Tree (BT) and K-Nearest Neigh-bour (KNN) algorithms based on the weather and flight plan characteristics. A multi-flight rerouting optimisa-tion model by bi-level programming is established, which contains a flight flow optimisation model in the upper layer and a single flight path optimisation model in the lower layer. The simulated annealing algorithm and the bidirectional A* algorithm are used to solve the upper and lower models. This paper uses the terminal area of Guangzhou Baiyun Airport (ZGGG) and Wuhan Tianhe Airport (ZHHH) for case analysis. The RF algorithm has better performance in predicting TFPs compared with the BT and KNN algorithms. Compared to the historical radar trajectory, the flight path optimisation results show that for the Guangzhou terminal area, while meeting the Terminal Airspace Availability (TAA) as constraint, the flight flow increases and the flight distance reduces, ef-fectively improving the operational efficiency within the terminal.

The article reviews qualitative and quantitative indi-cators to measure transport demand. After the review of the indicators and taking into account the specificity of the analysed region and the availability of data, the se-lected indicators were divided into five indicators groups: demographic, public transport usage description, public transport service and infrastructure, automobilisation and economics. A database of relevant indicators has been de-veloped to execute the evaluation. The study yielded three separate results: data of city municipalities, circular mu-nicipalities and regional municipalities. The purpose of this article is to identify the most important indicators that influence the passenger flows in regional public transport and to identify the interfaces between the indicators. The main raised hypothesis was that different groups of munic-ipalities will have different key indicators influencing the use of public transport and that public transport planning cannot follow the same methods. Multiple Variable Analy-sis and Simple Regression Analysis were chosen to test the hypotheses and clarify the most important indicators. The analysis shows that unemployment has the greatest impact on the number of passengers on suburban routes, while bus mileage on suburban routes has the smallest impact. The number of buses also has an impact on suburban pas-senger flows.

Establishing the desired quality of service (QoS) of the airport passenger terminal in order to improve operational performance is a challenge for every air-port. Recent international research indicates a gradual recovery in air transport and, accordingly, the need to develop additional transport infrastructure. If the pas-senger terminal design in terms of infrastructure and operational capacity is not approached correctly, the level of service provided to passengers may decline. This research will focus on how the IATA Level of Ser-vice (LoS), which is provided to airport users can con-tribute to the optimisation of the level of service of the passenger terminal. Additionally, the impact of level of service on passenger terminal capacity assessment in relation to the diversity of air carrier business model will be analysed. Since there is no common link to uni-formly describe and solve this problem, this paper will review the relevant literature in the field of passenger terminal capacity research and will analyse different approaches to solving this problem with the aim to de-velop a new unified concept in observing and optimising the capacity of the airport passenger terminal taking into account the types of air carrier business models.

Modern environmental and economic challenges in waste management require transition from linear to cir-cular economic flow. In practice, this entails consider-able challenges that include the change of material circle flux, the application of mathematical modelling and the use of life cycle thinking – also in the field of recovered wood (RW). To this end, the reverse logistics process model CATWOOD (CAscade Treatment of WOOD) with mechanistic modelling for detailed planning of the RW reverse flow with regular collection, innovative (cas-cade) sorting based on RW quality and environmentally sound recovery has been designed. As a decision support, the quantitative methods of life-cycle assessment (LCA) and societal life-cycle costing (SLCC) have been incor-porated into the CATWOOD, which can choose among a few alternative scenarios. A case study has been per-formed in the Posavje region in Slovenia, which has discovered that reverse logistics scenarios for reuse are environmentally friendlier than those for recycling or en-ergy recovery, but also more costly, mainly because of extensive manual labour needed and less heavy technol-ogy involved in sorting and recovery processes. Sensitiv-ity analysis has exposed that modifying the values of the input parameters may change the final LCA and SLCC results in scenarios observed.

Air traffic complexity indicators play an essential role in measuring operational performance and control-ler workload. However, current studies mainly depend on the manual scoring method to scale performance or workload. This paper focuses on arrival operations and presents a data-driven strategy to establish the correla-tion between complexity and performance to avoid the subjectivity of the currently used manual scoring method. Firstly, we present twenty-six indicators for describing air traffic complexity and two indicators for arrival op-erational performance. Secondly, the clustering method distinguishes peak and off-peak situations for arrival operation. Moreover, clustering results are compared to investigate the correlation between complexity and per-formance initially. Thirdly, the classification method is adopted to determine such correlation further. In addi-tion, we also identify the affecting factors which could influence operational performance. Finally, trajectories of arrival aircraft landing at Guangzhou Baiyun Inter-national Airport (ZGGG) are used for case validation. The results indicate that there is a strong correlation be-tween complexity and performance. The accuracy and precision of classification are approximately 90%. Fur-thermore, the number of aircraft significantly impacts the arrival operational performance within TMA.

Few existing research studies have explored the re-lationship of road section level, local area level and ve-hicle level risks within the highway traffic safety system, which can be important to the formation of an effective risk event prediction. This paper proposes a framework of multi-level risks described by a set of carefully select-ed or designed indicators. The interrelationship among these latent multi-level risks and their observable indica-tors are explored based on vehicle trajectory data using the structural equation model (SEM). The results show that there exists significant positive correlation between the latent risk constructs that each have adequate con-vergent validity, and it is difficult to completely separate the local traffic level risk from both the road section level risk and vehicle level risk. The local and road level in-dicators are also found to be of more importance when risk prediction time gets earlier based on feature impor-tance scoring of the LightGBM. The proposed conceptual multi-level indicator based latent risk framework gener-ally fits with the observed results and emphasises the im-portance of including multi-level indicators for risk event prediction in the future.

Vehicular and flying ad hoc networks (VANETs and FANETs) are becoming increasingly important with the development of smart cities and intelligent transporta-tion systems (ITSs). The high mobility of nodes in these networks leads to frequent link breaks, which complicates the discovery of optimal route from source to destination and degrades network performance. One way to over-come this problem is to use machine learning (ML) in the routing process, and the most promising among different ML types is reinforcement learning (RL). Although there are several surveys on RL-based routing protocols for VANETs and FANETs, an important issue of integrating RL with well-established modern technologies, such as software-defined networking (SDN) or blockchain, has not been adequately addressed, especially when used in complex ITSs. In this paper, we focus on performing a comprehensive categorisation of RL-based routing pro-tocols for both network types, having in mind their simul-taneous use and the inclusion with other technologies. A detailed comparative analysis of protocols is carried out based on different factors that influence the reward func-tion in RL and the consequences they have on network performance. Also, the key advantages and limitations of RL-based routing are discussed in detail.

People and companies today are connected around the world, which has led to a growing importance of the aviation industry. As flight delays are a big challenge in aviation, machine learning algorithms can be used to forecast those. This paper investigates the prediction of the occurrence of flight arrival delays with three prominent machine learning algorithms for a data set of domestic flights in the USA. The task is regarded as a classification problem. The focus lies on the investigation of the influence of short-term features on the quality of the results. Therefore, three scenarios are created that are characterised by different input feature sets. When forgoing the inclusion of short-term information in order to shift the prediction timing to an early point in time, an accuracy of 69.5% with a recall of 68.2% is achieved. By including information on the delay that the aircraft had on its previous flight, the prediction quality increases slightly. Hence, this is a compromise between the early prediction timing of the first model and the good prediction quality of the third model, where the departure delay of the aircraft is added as an input feature. In this case, an accuracy of 89.9% with a recall of 83.4% is obtained. The desired timing of prediction therefore determines which features to use as inputs since short-term features significantly improve the prediction quality.

European airspace is marked by fragmentation, and from fragmentation originated congestion. As an answer to congestion, flexible use of airspace is created to fulfil all users’ requirements optimally, and at its very core lies civil-military cooperation and the coordination of airspace use. Such an approach is enabled by collaborative decision-making, which presumes timely and complete exchange of complete relevant information between airspace users. To participate in the process, the military should be transparent regarding its restrictions and capabilities of adaptation to other airspace users’ demands. Flight training syllabus structure and its specific impact on proactive planning of high-performance military aircraft pilot training operations in flexible airspace structures is the subject of this research. Significant diversity in the hourly distribution of civil traffic demand in flexible airspace structures in Zagreb FIR proves them applicable for a proactive approach to high-performance aircraft pilot training operations planning and management. Noticeable reduction in the number of civil aircraft affected by high-performance aircraft pilot training operations in flexible airspace structures at congested flight levels is corroborated. Simulations of proactive planning of high-performance aircraft pilot training operations reveal indications of flight syllabus structure’s impact on pilot flight training duration, as well as the possibility to manage its restrictions.

The article reviews qualitative and quantitative indicators to measure transport demand. After the review of the indicators and taking into account the specificity of the analysed region and the availability of data, the selected indicators were divided into five indicators groups: demographic, public transport usage description, public transport service and infrastructure, automobilisation and economics. A database of relevant indicators has been developed to execute the evaluation. The study yielded three separate results: data of city municipalities, circular municipalities and regional municipalities. The purpose of this article is to identify the most important indicators that influence the passenger flows in regional public transport and to identify the interfaces between the indicators. The main raised hypothesis was that different groups of municipalities will have different key indicators influencing the use of public transport and that public transport planning cannot follow the same methods. Multiple Variable Analysis and Simple Regression Analysis were chosen to test the hypotheses and clarify the most important indicators. The analysis shows that unemployment has the greatest impact on the number of passengers on suburban routes, while bus mileage on suburban routes has the smallest impact. The number of buses also has an impact on suburban passenger flows.

Establishing the desired quality of service (QoS) of the airport passenger terminal in order to improve operational performance is a challenge for every airport. Recent international research indicates a gradual recovery in air transport and, accordingly, the need to develop additional transport infrastructure. If the passenger terminal design in terms of infrastructure and operational capacity is not approached correctly, the level of service provided to passengers may decline. This research will focus on how the IATA Level of Service (LoS), which is provided to airport users can contribute to the optimisation of the level of service of the passenger terminal. Additionally, the impact of level of service on passenger terminal capacity assessment in relation to the diversity of air carrier business model will be analysed. Since there is no common link to uniformly describe and solve this problem, this paper will review the relevant literature in the field of passenger terminal capacity research and will analyse different approaches to solving this problem with the aim to develop a new unified concept in observing and optimising the capacity of the airport passenger terminal taking into account the types of air carrier business models.

Modern environmental and economic challenges in waste management require transition from linear to circular economic flow. In practice, this entails considerable challenges that include the change of material circle flux, the application of mathematical modelling and the use of life cycle thinking – also in the field of recovered wood (RW). To this end, the reverse logistics process model CATWOOD (CAscade Treatment of WOOD) with mechanistic modelling for detailed planning of the RW reverse flow with regular collection, innovative (cascade) sorting based on RW quality and environmentally sound recovery has been designed. As a decision support, the quantitative methods of life-cycle assessment (LCA) and societal life-cycle costing (SLCC) have been incorporated into the CATWOOD, which can choose among a few alternative scenarios. A case study has been performed in the Posavje region in Slovenia, which has discovered that reverse logistics scenarios for reuse are environmentally friendlier than those for recycling or energy recovery, but also more costly, mainly because of extensive manual labour needed and less heavy technology involved in sorting and recovery processes. Sensitivity analysis has exposed that modifying the values of the input parameters may change the final LCA and SLCC results in scenarios observed.

Vehicular and flying ad hoc networks (VANETs and FANETs) are becoming increasingly important with the development of smart cities and intelligent transportation systems (ITSs). The high mobility of nodes in these networks leads to frequent link breaks, which complicates the discovery of optimal route from source to destination and degrades network performance. One way to overcome this problem is to use machine learning (ML) in the routing process, and the most promising among different ML types is reinforcement learning (RL). Although there are several surveys on RL-based routing protocols for VANETs and FANETs, an important issue of integrating RL with well-established modern technologies, such as software-defined networking (SDN) or blockchain, has not been adequately addressed, especially when used in complex ITSs. In this paper, we focus on performing a comprehensive categorisation of RL-based routing protocols for both network types, having in mind their simultaneous use and the inclusion with other technologies. A detailed comparative analysis of protocols is carried out based on different factors that influence the reward function in RL and the consequences they have on network performance. Also, the key advantages and limitations of RL-based routing are discussed in detail.

With the rapid growth of flight volume, the impact of convective weather on flight operations in the terminal area has become more and more serious. In this paper, the typical flight paths (TFPs) are used to replace flight procedures as the routine flight paths in the terminal area, and the TFP of each flight is predicted by Random Forest (RF), Boosting Tree (BT) and K-Nearest Neighbour (KNN) algorithms based on the weather and flight plan characteristics. A multi-flight rerouting optimisation model by bi-level programming is established, which contains a flight flow optimisation model in the upper layer and a single flight path optimisation model in the lower layer. The simulated annealing algorithm and the bidirectional A* algorithm are used to solve the upper and lower models. This paper uses the terminal area of Guangzhou Baiyun Airport (ZGGG) and Wuhan Tianhe Airport (ZHHH) for case analysis. The RF algorithm has better performance in predicting TFPs compared with the BT and KNN algorithms. Compared to the historical radar trajectory, the flight path optimisation results show that for the Guangzhou terminal area, while meeting the Terminal Airspace Availability (TAA) as constraint, the flight flow increases and the flight distance reduces, effectively improving the operational efficiency within the terminal.

Air traffic complexity indicators play an essential role in measuring operational performance and controller workload. However, current studies mainly depend on the manual scoring method to scale performance or workload. This paper focuses on arrival operations and presents a data-driven strategy to establish the correlation between complexity and performance to avoid the subjectivity of the currently used manual scoring method. Firstly, we present twenty-six indicators for describing air traffic complexity and two indicators for arrival operational performance. Secondly, the clustering method distinguishes peak and off-peak situations for arrival operation. Moreover, clustering results are compared to investigate the correlation between complexity and performance initially. Thirdly, the classification method is adopted to determine such correlation further. In addition, we also identify the affecting factors which could influence operational performance. Finally, trajectories of arrival aircraft landing at Guangzhou Baiyun International Airport (ZGGG) are used for case validation. The results indicate that there is a strong correlation between complexity and performance. The accuracy and precision of classification are approximately 90%. Furthermore, the number of aircraft significantly impacts the arrival operational performance within TMA.

Data-driven forecasting methods have the problems of complex calculations, poor portability and need a large amount of training data, which limits the application of data-driven methods in small cities. This paper proposes a traffic flow forecasting method using a Nonlinear AutoRegressive model with eXogenous variables (NARX model), which uses a dynamic neural network Focused Time-Delay Neural Network (FTDNN) with a Tapped Delay Line (TDL) structure as a nonlinear function. The TDL structure enables the FTDNN to have short-term memory capabilities. At the same time, before the data is input into the FTDNN, the use of trend decomposition or differential calculation on the traffic data sequence can make the NARX model maintain long-term predictive capabilities. Compared with common nonlinear models, the FTDNN has structural advantages. It uses a simple TDL structure without the memory mechanism and the gated structure, which can reduce the parameters of the model and reduce the scale of data. Through the four-day data of Guilin City, the traffic volume forecast for five minutes is verified, and the performance of the NARX model is better than that of the SARIMA model and the Holt-Winters model.

Few existing research studies have explored the relationship of road section level, local area level and vehicle level risks within the highway traffic safety system, which can be important to the formation of an effective risk event prediction. This paper proposes a framework of multi-level risks described by a set of carefully selected or designed indicators. The interrelationship among these latent multi-level risks and their observable indicators are explored based on vehicle trajectory data using the structural equation model (SEM). The results show that there exists significant positive correlation between the latent risk constructs that each have adequate convergent validity, and it is difficult to completely separate the local traffic level risk from both the road section level risk and vehicle level risk. The local and road level indicators are also found to be of more importance when risk prediction time gets earlier based on feature importance scoring of the LightGBM. The proposed conceptual multi-level indicator based latent risk framework generally fits with the observed results and emphasises the importance of including multi-level indicators for risk event prediction in the future.

The application of predicting bus travel time with re-al-time information, including Global Positioning System (GPS) and Electronic Smart Card (ESC) data is effec-tive to advance the level of service by reducing wait time and improving schedule adherence. However, missing information in the data stream is inevitable for various reasons, which may seriously affect prediction accuracy. To address this problem, this research proposes a Long Short-Term Memory (LSTM) model to predict bus travel time, considering incomplete data. To improve the model performance in terms of accuracy and efficiency, a Genet-ic Algorithm (GA) is developed and applied to optimise hyperparameters of the LSTM model. The model perfor-mance is assessed by simulation and real-world data. The results suggest that the proposed approach with hybrid data outperforms the approaches with ESC and GPS data individually. With GA, the proposed model outperforms the traditional one in terms of lower Root Mean Square Error (RMSE). The prediction accuracy with various com-binations of ESC and GPS data is assessed. The results can serve as a guideline for transit agencies to deploy GPS devices in a bus fleet considering the market penetration of ESC.

The present study explores whether the European Union’s transport policy measures of the last decade have fulfilled the expectations, i.e. whether there has been a positive change in the field of rail freight transport in the region. Data on the volumes of freight transport in the recent period have been analysed with freight trans-port intensity as an indicator. The values have been then translated into a spatial econometric model, looking for spatiality in the European Economic Region, including countries such as Norway, Switzerland or even Russia, extending the scope of the study to 37 countries. It has been proven that there is a spatial correlation between rail freight transport performance and GDP in Europe, which has a positive effect on countries with high GDP and a negative effect on low GDP countries in terms of performance. There is a particularly high intensity of rail freight in the Baltic region, as well as in Ukraine and Russia. Furthermore, it can be stated that rail freight has not undergone any significant changes in the last 10 years.

Portions of dynamic traffic volumes consisting of multiple vehicle classes are accurately monitored with-out vehicle detectors using vehicle-to-infrastructure (V2I) communication systems. This offers the feasibility of online monitoring of the total traffic volumes with multi-vehicle classes without any advanced vehicle de-tectors. To evaluate this prospect, this article presents a method of monitoring dynamic multi-class vehicu-lar traffic volumes in a road location where road-side equipment (RSE) for V2I communication is in opera-tion. The proposed method aims to estimate dynamic total traffic volume data for multiple vehicle classes us-ing the V2I sensing probe volume (i.e. partial vehicular traffic volumes) collected through the RSE. An experi-mental study was conducted using real-world V2I sens-ing probe volume data. The results showed that traffic volumes for vehicle types I and II (i.e. cars and heavy vehicles, respectively) can be effectively monitored with average errors of 6.69% and 10.89%, respectively, when the penetration rates of the in-vehicle V2I device for the two vehicle types average 0.384 and 0.537, re-spectively. The performance of the method in terms of detection error is comparable to those of widely used vehicle detectors. Therefore, V2I sensing probe data for multi-vehicle classes can complement the functions of vehicle detectors because the penetration rate of in-ve-hicle V2I devices is currently high.

Supply chain collaboration management is a system-atic, integrated and agile advanced management mode, which helps to improve the competitiveness of enterprises and the entire supply chain. In order to realise the synergy of supply chain, the most important is to realise the dynam-ic synergy of information. Here we proposed a strategy to integrate system dynamics and multi-agent system model-ling methods. Based on the strategy of supply chain infor-mation sharing and coordination, a two-level aggregation hybrid model was designed and established. Through the computer simulation analysis of the two modes before and after information collaboration, it is found that under the information collaboration mode, the change trend of or-der or inventory of suppliers and manufacturers always closely matches that of retailers. After the implementation of supply chain information coordination, ordering and inventory can be reasonably planned and matched, and problems such as over-stocking or short-term failure to meet order demands caused by poor information commu-nication will no longer occur, which can greatly reduce the “bullwhip effect”.

The negative effects of goods flows realization are most visible in urban areas - the places of the greatest concentration of economic and social activities. The application of succeeded classical technologies in the realization of new demands causes significant negative effects on all city functions and the quality of life. Considering the ongoing trends and new demands, smart sustainable city logistics (CL) solutions are defined in this article to mitigate the unsustainable effects of logistics. The solutions represent combinations of different initiatives, technologies and concepts of CL from one side, and the technologies of industry 4.0 from the other. Such an approach in defining smart CL solutions represents the main contribution of this article. The defined solutions are evaluated according to different stakeholder groups through the application of a novel hybrid multi-criteria decision-making (MCDM) model, based on BWM (Best-Worst Method) and CODAS (COmbinative Distance-based ASsessment) methods in grey environment, which is another contribution of the article. The results of the model’ application imply that the potentially best sustainable smart CL solution is the one that is based on the combination of the concepts of micro-consolidation centers and autonomous vehicles with the support of artificial intelligence and internet of things technologies.

The article focuses on the up-to-date subject from the practical as well as scientific point of view. It specif-ically discusses a proposal of an approach concerning transport or distribution problems in the range of city logistics and investigates possibilities to use opted oper-ations research methods in this particular area. Specific suggestions lie first and foremost in using selected tools of operations research (i.e. a set of methods concerning vehicle routing problem) to model multiple variants of distribution paths from a determined hub to multiple spokes in order to minimise the overall travelled distance in an urban area. As far as the very research goes, to de-fine distribution paths to supply multiple logistics objects in the range of city logistics, ensuing methods are step by step used: Clarke-Wright algorithm, Mayer algorithm and the nearest neighbour algorithm. The article con-sists of a conceptual section, describing the relevant the-ory as well as data and methods used, the practical part and the section encompassing an assessment of the key findings, along with the discussion. A suitable combina-tion of adequate operations research methods and their application to city logistics issues is where an innovative solution of this research lies.

Mobility as a Service (MaaS) has been proposed as a user-centric, data-driven and personalised ser-vice. However, full personalisation is not available yet. Customisation settings are developed in mobile appli-cations, and several semi-personalised functionalities are also involved. The quantitative analysis of relation between these two could be the reference for further de-velopment tendency of interface functions in mobile ap-plications. Thus, the research objective is identified as: the quantitative correlation analysis between semi-per-sonalisation functionalities and customisation settings. Accordingly, the multi-criteria qualitative analysis method is applied to identify the assessment aspects regarding mobile applications. The scoring method is also introduced. Then the correlation quantitative anal-ysis method is applied to calculate the correlation coef-ficient. We have assessed 25 MaaS applications regard-ing determined aspects. The correlation coefficients for each application together with the overall coefficient are calculated, the assessment results are summarised, and the correlation tendency is interpreted. According to assessment results, the correlation between custom-isation settings and semi-personalisation is not strong at current stage. Selected MaaS mobile applications are customisation setting oriented applications. Fewer manual selections are expected in further personalised services. Our results facilitate development of further personalised functions in MaaS mobile applications.

In traffic monitoring data analysis, the magnitude of traffic density plays an important role in determin-ing the level of traffic congestion. This study proposes a data imputation method for spatio-functional principal component analysis (s-FPCA) and unifies anomaly curve detection, outlier confirmation and imputation of traf-fic density at target intersections. Firstly, the detection of anomalous curves is performed based on the binary principal component scores obtained from the function-al data analysis, followed by the determination of the presence of outliers through threshold method. Secondly, an improved method for missing traffic data estimation based on upstream and downstream is proposed. Final-ly, a numerical study of the actual traffic density data is carried out, and the accuracy of s-FPCA for imputation is improved by 8.28%, 8.91% and 7.48%, respective-ly, when comparing to functional principal component analysis (FPCA) with daily traffic density data missing rates of 5%, 10% and 20%, proving the superiority of the method. This method can also be applied to the detection of outliers in traffic flow, imputation and other longitudi-nal data analysis with periodic fluctuations.

Non-standard unsignalised intersections are very common in European countries with old street networks. The major road often bends at an angle at the centre of an intersection, which makes the intersection non-standard. There are very few papers about the capacity analysis and headway values at these intersections, even though non-standard intersections are widespread not only in Europe but also in the rest of the world. Regarding the fact that priority at the non-standard unsignalised inter-section (NSUI) differs from the standard unsignalised in-tersection (SUI) and the conflict flows, it can be expected that headways are not the same as those at the SUI. Con-sequently, the capacity at the NSUI differs from that at the SUI. This paper gives critical headway and follow-up headway values at 3-leg and 4-leg NSUI collected by on-field measurement. Recommendations for the values used for the capacity analysis are given, and recommended values are compared at SUI and NSUI.

The extreme traffic measures during the COVID-19 lockdown provided a unique opportunity to gain better insight into the relationship between traffic characteris-tics and NO2 concentrations in Maribor, a small Slove-nian city. NO2, traffic and meteorological data were sta-tistically processed in detail for March and April 2018, 2019 and 2020 to get a historical insight and to exclude the specifics of the lockdown period. The extreme event resulted in an average reduction of road traffic of 42%. The decrease in the number of passenger cars ranged from 33.9 to 60.3% per day with the largest decrease on the motorway. Daily averages of heavy goods traffic de-clined on the motorway and the expressway by 24.6% and 7%, respectively. Traffic characteristics were reflect-ed in a 24–27% decrease in NO2 concentrations at the urban station. The change is smaller than the change in traffic volume, which could be explained by the change in the composition of the vehicle fleet due to the increase in NO2-dominant traffic sources, e.g. diesel heavy goods vehicles. The presented results are relevant for improv-ing air quality and sustainable mobility management in small cities. They highlight the important role of reor-ganisation of heavy goods traffic in urban logistics.

Motivating people to switch to public transport from using their own car is one of the most important parts on the way to accomplishing the Green Deal 2050 challenge. In the Czech Republic, where the number of passengers was rapidly rising in the pre-pandemic time, individual car transport still offers many more travel benefits than railway lines for most long-distance rela-tions. How to strategically develop the railway infra-structure? Will the planned high-speed railways really be the appropriate solution to this problem in time? Will they satisfy all the different requirements of passengers who are potentially able to switch from car to train?

The pandemic caused by the coronavirus COVID-19 is having a worldwide impact that affects health, econo-my and air pollution in cities indirectly. In Slovenia, as well as in all other countries, the number of cases of in-fected people increased continually in 2020, which affect-ed the health system and caused movement restrictions, which, in turn, affected the air pollution in the country. This article presents the indirect effect produced by this pandemic on air pollution in Maribor, Slovenia. Traffic and air quality data were used to perform the evaluation, in particular PM10 and PM2.5 daily concentrations from the monitoring station in Maribor. By observing the de-tailed traffic data and particulate matter concentrations acquired in the Maribor city centre before and during the pandemic times, we show the influence of COVID-19 on particulate matter concentrations in that part of the town. The results show slightly lower particulate matter con-centrations, which could be explained by the significantly lower traffic volume values in the lockdown months.

The application of predicting bus travel time with real-time information, including Global Positioning System (GPS) and Electronic Smart Card (ESC) data is effective to advance the level of service by reducing wait time and improving schedule adherence. However, missing information in the data stream is inevitable for various reasons, which may seriously affect prediction accuracy. To address this problem, this research proposes a Long Short-Term Memory (LSTM) model to predict bus travel time, considering incomplete data. To improve the model performance in terms of accuracy and efficiency, a Genetic Algorithm (GA) is developed and applied to optimise hyperparameters of the LSTM model. The model performance is assessed by simulation and real-world data. The results suggest that the proposed approach with hybrid data outperforms the approaches with ESC and GPS data individually. With GA, the proposed model outperforms the traditional one in terms of lower Root Mean Square Error (RMSE). The prediction accuracy with various combinations of ESC and GPS data is assessed. The results can serve as a guideline for transit agencies to deploy GPS devices in a bus fleet considering the market penetration of ESC.

The present study explores whether the European Union’s transport policy measures of the last decade have fulfilled the expectations, i.e. whether there has been a positive change in the field of rail freight transport in the region. Data on the volumes of freight transport in the recent period have been analysed with freight transport intensity as an indicator. The values have been then translated into a spatial econometric model, looking for spatiality in the European Economic Region, including countries such as Norway, Switzerland or even Russia, extending the scope of the study to 37 countries. It has been proven that there is a spatial correlation between rail freight transport performance and GDP in Europe, which has a positive effect on countries with high GDP and a negative effect on low GDP countries in terms of performance. There is a particularly high intensity of rail freight in the Baltic region, as well as in Ukraine and Russia. Furthermore, it can be stated that rail freight has not undergone any significant changes in the last 10 years.

Portions of dynamic traffic volumes consisting of multiple vehicle classes are accurately monitored without vehicle detectors using vehicle-to-infrastructure (V2I) communication systems. This offers the feasibility of online monitoring of the total traffic volumes with multi-vehicle classes without any advanced vehicle detectors. To evaluate this prospect, this article presents a method of monitoring dynamic multi-class vehicular traffic volumes in a road location where road-side equipment (RSE) for V2I communication is in operation. The proposed method aims to estimate dynamic total traffic volume data for multiple vehicle classes using the V2I sensing probe volume (i.e. partial vehicular traffic volumes) collected through the RSE. An experimental study was conducted using real-world V2I sensing probe volume data. The results showed that traffic volumes for vehicle types I and II (i.e. cars and heavy vehicles, respectively) can be effectively monitored with average errors of 6.69% and 10.89%, respectively, when the penetration rates of the in-vehicle V2I device for the two vehicle types average 0.384 and 0.537, respectively. The performance of the method in terms of detection error is comparable to those of widely used vehicle detectors. Therefore, V2I sensing probe data for multi-vehicle classes can complement the functions of vehicle detectors because the penetration rate of in-vehicle V2I devices is currently high.

Supply chain collaboration management is a systematic, integrated and agile advanced management mode, which helps to improve the competitiveness of enterprises and the entire supply chain. In order to realise the synergy of supply chain, the most important is to realise the dynamic synergy of information. Here we proposed a strategy to integrate system dynamics and multi-agent system modelling methods. Based on the strategy of supply chain information sharing and coordination, a two-level aggregation hybrid model was designed and established. Through the computer simulation analysis of the two modes before and after information collaboration, it is found that under the information collaboration mode, the change trend of order or inventory of suppliers and manufacturers always closely matches that of retailers. After the implementation of supply chain information coordination, ordering and inventory can be reasonably planned and matched, and problems such as over-stocking or short-term failure to meet order demands caused by poor information communication will no longer occur, which can greatly reduce the “bullwhip effect”.

The negative effects of goods flows realisation are most visible in urban areas as the places of the greatest concentration of economic and social activities. The main goals of this article were to identify the applicable Industry 4.0 technologies for performing various city logistics (CL) operations, establish smart sustainable CL solutions (SSCL) and rank them in order to identify those which will serve as the base points for future plans and strategies for the development of smart cities. This kind of problem requires involvement of multiple stakeholders with their opposing goals and interests, and thus multiple criteria. For solving it, this article proposed a novel hybrid multi-criteria decision-making (MCDM) model, based on BWM (Best-Worst Method) and CODAS (COmbinative Distance-based ASsessment) methods in grey environment. The results of the model application imply that the potentially best SSCL solution is based on the combination of the concepts of micro-consolidation centres and autonomous vehicles with the support of artificial intelligence and Internet of Things technologies. The main contributions of the article are the definition of original SSCLs, the creation of a framework and definition of criteria for their evaluation and the development of a novel hybrid MCDM model.

The article focuses on the up-to-date subject from the practical as well as scientific point of view. It specifically discusses a proposal of an approach concerning transport or distribution problems in the range of city logistics and investigates possibilities to use opted operations research methods in this particular area. Specific suggestions lie first and foremost in using selected tools of operations research (i.e. a set of methods concerning vehicle routing problem) to model multiple variants of distribution paths from a determined hub to multiple spokes in order to minimise the overall travelled distance in an urban area. As far as the very research goes, to define distribution paths to supply multiple logistics objects in the range of city logistics, ensuing methods are step by step used: Clarke-Wright algorithm, Mayer algorithm and the nearest neighbour algorithm. The article consists of a conceptual section, describing the relevant theory as well as data and methods used, the practical part and the section encompassing an assessment of the key findings, along with the discussion. A suitable combination of adequate operations research methods and their application to city logistics issues is where an innovative solution of this research lies.

In traffic monitoring data analysis, the magnitude of traffic density plays an important role in determining the level of traffic congestion. This study proposes a data imputation method for spatio-functional principal component analysis (s-FPCA) and unifies anomaly curve detection, outlier confirmation and imputation of traffic density at target intersections. Firstly, the detection of anomalous curves is performed based on the binary principal component scores obtained from the functional data analysis, followed by the determination of the presence of outliers through threshold method. Secondly, an improved method for missing traffic data estimation based on upstream and downstream is proposed. Finally, a numerical study of the actual traffic density data is carried out, and the accuracy of s-FPCA for imputation is improved by 8.28%, 8.91% and 7.48%, respectively, when comparing to functional principal component analysis (FPCA) with daily traffic density data missing rates of 5%, 10% and 20%, proving the superiority of the method. This method can also be applied to the detection of outliers in traffic flow, imputation and other longitudinal data analysis with periodic fluctuations.

Mobility as a Service (MaaS) has been proposed as a user-centric, data-driven and personalised service. However, full personalisation is not available yet. Customisation settings are developed in mobile applications, and several semi-personalised functionalities are also involved. The quantitative analysis of relation between these two could be the reference for further development tendency of interface functions in mobile applications. Thus, the research objective is identified as: the quantitative correlation analysis between semi-personalisation functionalities and customisation settings. Accordingly, the multi-criteria qualitative analysis method is applied to identify the assessment aspects regarding mobile applications. The scoring method is also introduced. Then the correlation quantitative analysis method is applied to calculate the correlation coefficient. We have assessed 25 MaaS applications regarding determined aspects. The correlation coefficients for each application together with the overall coefficient are calculated, the assessment results are summarised, and the correlation tendency is interpreted. According to assessment results, the correlation between customisation settings and semi-personalisation is not strong at current stage. Selected MaaS mobile applications are customisation setting oriented applications. Fewer manual selections are expected in further personalised services. Our results facilitate development of further personalised functions in MaaS mobile applications.

Non-standard unsignalised intersections are very common in European countries with old street networks. The major road often bends at an angle at the centre of an intersection, which makes the intersection non-standard. There are very few papers about the capacity analysis and headway values at these intersections, even though non-standard intersections are widespread not only in Europe but also in the rest of the world. Regarding the fact that priority at the non-standard unsignalised intersection (NSUI) differs from the standard unsignalised intersection (SUI) and the conflict flows, it can be expected that headways are not the same as those at the SUI. Consequently, the capacity at the NSUI differs from that at the SUI. This paper gives critical headway and follow-up headway values at 3-leg and 4-leg NSUI collected by on-field measurement. Recommendations for the values used for the capacity analysis are given, and recommended values are compared at SUI and NSUI.

The extreme traffic measures during the COVID-19 lockdown provided a unique opportunity to gain better insight into the relationship between traffic characteristics and NO2 concentrations in Maribor, a small Slovenian city. NO2, traffic and meteorological data were statistically processed in detail for March and April 2018, 2019 and 2020 to get a historical insight and to exclude the specifics of the lockdown period. The extreme event resulted in an average reduction of road traffic of 42%. The decrease in the number of passenger cars ranged from 33.9 to 60.3% per day with the largest decrease on the motorway. Daily averages of heavy goods traffic declined on the motorway and the expressway by 24.6% and 7%, respectively. Traffic characteristics were reflected in a 24–27% decrease in NO2 concentrations at the urban station. The change is smaller than the change in traffic volume, which could be explained by the change in the composition of the vehicle fleet due to the increase in NO2-dominant traffic sources, e.g. diesel heavy goods vehicles. The presented results are relevant for improving air quality and sustainable mobility management in small cities. They highlight the important role of reorganisation of heavy goods traffic in urban logistics.

Motivating people to switch to public transport from using their own car is one of the most important parts on the way to accomplishing the Green Deal 2050 challenge. In the Czech Republic, where the number of passengers was rapidly rising in the pre-pandemic time, individual car transport still offers many more travel benefits than railway lines for most long-distance relations. How to strategically develop the railway infrastructure? Will the planned high-speed railways really be the appropriate solution to this problem in time? Will they satisfy all the different requirements of passengers who are potentially able to switch from car to train?

The pandemic caused by the coronavirus COVID-19 is having a worldwide impact that affects health, economy and air pollution in cities indirectly. In Slovenia, as well as in all other countries, the number of cases of infected people increased continually in 2020, which affected the health system and caused movement restrictions, which, in turn, affected the air pollution in the country. This article presents the indirect effect produced by this pandemic on air pollution in Maribor, Slovenia. Traffic and air quality data were used to perform the evaluation, in particular PM10 and PM2.5 daily concentrations from the monitoring station in Maribor. By observing the detailed traffic data and particulate matter concentrations acquired in the Maribor city centre before and during the pandemic times, we show the influence of COVID-19 on particulate matter concentrations in that part of the town. The results show slightly lower particulate matter concentrations, which could be explained by the significantly lower traffic volume values in the lockdown months.

Traffic control approaches, in particular Variable Speed Limit (VSL), are often studied as solutions to improve the level of service on urban motorways. However, the efficiency of VSL strongly depends on the spatiotemporal arrangement of VSL zones. It is crucial to determine the lengths and locations of VSL zones for best VSL efficiency before deployment in a real system, as the optimal length of the VSL zone and its distance from the bottleneck directly affects traffic dynamics and, thus, bottleneck control. Therefore, in this study, we perform the analysis of different VSL zones lengths and their positions by using a closed-loop Simple Proportional Speed Controller for VSL (SPSC-VSL). We evaluate the different VSL zone configurations and their impact on traffic flow control and vehicle emissions in a SUMO microscopic simulation on a high traffic demand scenario. The results support the observations of previous researchers on the significant dependence of VSL zone placement on VSL efficiency. Additionally, new data-based (traffic parameters and vehicle emissions) evidence of the performance of the SPSC-VSL design are provided regarding the best placement of consecutive VSL zones for motorway bottleneck control not analysed in previous research.

Traffic control approaches, in particular Variable Speed Limit (VSL), are often studied as solutions to improve the level of service on urban motorways. However, the efficiency of VSL strongly depends on the spatiotemporal arrangement of VSL zones. It is crucial to determine the lengths and locations of VSL zones for best VSL efficiency before deployment in a real system, as the optimal length of the VSL zone and its distance from the bottleneck directly affects traffic dynamics and, thus, bottleneck control. Therefore, in this study, we perform the analysis of different VSL zones lengths and their positions by using a closed-loop Simple Proportional Speed Controller for VSL (SPSC-VSL). We evaluate the different VSL zone configurations and their impact on traffic flow control and vehicle emissions in a SUMO microscopic simulation on a high traffic demand scenario. The results support the observations of previous researchers on the significant dependence of VSL zone placement on VSL efficiency. Additionally, new data-based (traffic parameters and vehicle emissions) evidence of the performance of the SPSC-VSL design are provided regarding the best placement of consecutive VSL zones for motorway bottleneck control not analysed in previous research.

The centrality of stations is one of the most important issues in urban transit systems. The central stations of such networks have often been identified using network to-pological centrality measures. In real networks, passenger flows arise from an interplay between the dynamics of the individual person movements and the underlying physical structure. In this paper, we apply a two-layered model to identify the most central stations in the Beijing Subway System, in which the lower layer is the physical infrastruc-ture and the upper layer represents the passenger flows. We compare various centrality indicators such as degree, strength and betweenness centrality for the two-layered model. To represent the influence of exogenous factors of stations on the subway system, we reference the al-pha centrality. The results show that the central stations in the geographic system in terms of the betweenness are not consistent with the central stations in the network of the flows in terms of the alpha centrality. We clarify this difference by comparing the two centrality measures with the real load, indicating that the alpha centrality approx-imates the real load better than the betweenness, as it can capture the direction and volume of the flows along links and the flows into and out of the systems. The empirical findings can give us some useful insights into the node cen-trality of subway systems.

Traffic flow forecast is critical in today’s transportation system since it is necessary to construct a traffic plan in order to determine a travel route. The goal of this research is to use time-series forecasting models to estimate future traffic in order to reduce traffic congestion on roadways. Minimising prediction error is the most difficult task in traffic prediction. In order to anticipate future traffic flow, the system also requires real-time data from vehicles and roadways. A hybrid autoregressive integrated moving av-erage with multilayer perceptron (ARIMA-MLP) model and a hybrid autoregressive integrated moving average with recurrent neural network (ARIMA-RNN) model are proposed in this paper to address these difficulties. The transportation data are used from the UK Highways data-set. The time-series data are preprocessed using a random walk model. The forecasting models autoregressive inte-grated moving average (ARIMA), recurrent neural net-work (RNN), and multilayer perceptron (MLP) are trained and tested. In the proposed hybrid ARIMA-MLP and ARI-MA-RNN models, the residuals from the ARIMA model are used to train the MLP and RNN models. Then the ef-ficacy of the hybrid system is assessed using the metrics MAE, MSE, RMSE and R2 (peak hour forecast-0.936763, non-peak hour forecast-0.87638 on ARIMA-MLP model and peak hour forecast-0.9416466, non-peak hour fore-cast-0.931917 on ARIMA-RNN model).

This study introduces a novel methodological frame-work for extracting integral vehicle trajectories from several consecutive pictures automatically. The frame-work contains camera observation, eliminating image distortions, video stabilising, stitching images, identify-ing vehicles and tracking vehicles. Observation videos of four sections in South Fengtai Road, Nanjing, Jiangsu Province, China are taken as a case study to validate the framework. As key points, six typical tracking algorithms, including boosting, CSRT, KCF, median flow, MIL and MOSSE, are compared in terms of tracking reliability, operational time, random access memory (RAM) usage and data accuracy. Main impact factors taken into con-sideration involve vehicle colours, zebra lines, lane lines, lamps, guide boards and image stitching seams. Based on empirical analysis, it is found that MOSSE requires the least operational time and RAM usage, whereas CSRT presents the best tracking reliability. In addition, all tracking algorithms produce reliable vehicle trajecto-ry and speed data if vehicles are tracked steadily.

Ancillary services in air transport represent a set of services provided to passengers to choose from, enabling them to enhance their travel experience while accumu-lating additional airline revenue. Low-cost airlines pi-oneered the practice, but the separation of ancillary services from the basic service has become an intense-ly growing trend in the air transport industry over the last decade. This practice has enabled low-cost airlines to significantly reduce the price of the basic service. To remain competitive in an era of transparency provided by search engines, traditional airlines offer ancillary ser-vices in addition to the basic service. To meet the passen-ger’s needs, a whole range of ancillary services has been created. However, existing revenue management systems do not take this ancillary revenue into account when cal-culating reservation limits. If the airline knew that an in-dividual passenger is willing to pay more for ancillary services, the system would be able to adjust the availabil-ity of the service for that passenger during the booking process. A review of research on passengers’ willingness to pay for ancillary services is presented in the paper, as well as a review on research on the personalisation of ancillary services and challenges of integrating person-alised pricing into existing revenue management systems.

The sugar-energy sector is extremely important to the Brazilian economy, with several other production chains derived from it, generating some of the main products linked to food and energy sources. This study proposes an integration model for sugarcane harvesting logistics processes, focusing on optimisation of industrial plant production capacity. Dynamic modelling has been applied to study a broad range of the productive phases of the sugar-energy chain. This paper proposes indicators to evaluate the degree of efficiency of the production logistics processes. Preliminary results showed that phase times in the production logistics processes can be significantly reduced in the harvest phase. When analysed as a coordination-oriented flow having chained activities, the production logistics processes optimise the speeds and travel times during the harvest phase. The developed model uses data set of the production and logistics processes phases of a sugarcane industry. A future study will focus on more detailed and complex stakeholder behaviours based on the model proposed.

Passenger exchange coefficient is a significant factor which has great impact on the pricing model of urban rail transit. This paper introduces passenger exchange coefficient into a bi-level programming model with time differential pricing for urban rail transit by analysing variation regularity of passenger flow characteristics. Meanwhile, exchange cost coefficient is also considered as a restrictive factor in the pricing model. The improved particle swarm optimisation algorithm (IPSO) was applied to solve the model, and simulation results show that the proposed improved pricing model can effectively realise stratification of fares for different time periods with different routes. Taking Line 2 and Line 8 of the Beijing rail transit network as an example, the simulation result shows that passenger flows of Line 2 and Line 8 in peak hours decreased by 9.94% and 19.48% and therefore increased by 32.23% and 44.96% in off-peak hours, respectively. The case study reveals that dispersing passenger flows by means of fare adjustment can effectively drop peak load and increase off-peak load. The time differential pricing model of urban rail transit proposed in this paper has great influences on dispersing passenger flow and ensures safety operation of urban rail transit. It is also a valuable reference for other metropolitan rail transit operating companies.

The goal of the paper is to investigate the impact of tire tread depth on road accident risk and to develop an accident rate prediction model. The state of 4288 vehicle tires using tread depth gauge was inspected and processed statistically. The tread depth of the most worn tire from each vehicle was registered for further analysis. Based on the collected data, a statistical tire tread depth model for an insurance company vehicle fleet had been developed. The conformity of the gamma distribution to the data was verified upon applying the Pearson compatibility criterion. The paper provides the histograms of the frequencies of tire tread depths and the theoretical curves of the distribution density. The probability of the accident risk depending on the tire tread depth (adaptive risk index) was calculated applying the formed distributions and risk index dependence on the tire tread depth for the inspected vehicle fleet. According to the developed prediction model, an upgrade of the regulation for the minimum allowed tire tread depth by 2 mm (up to 3.6 mm) could reduce road accident risk (caused by poor adhesion to road surface) to 19.3% for the chosen vehicle fleet. Such models are useful for road safety experts, insurance companies and accident cost evaluation specialists by predicting expenses related to insurance events.

In 1991, the European Union decided on setting up a liberalised and single railway market. However, in the atomised European region, more than a half of railways can be designated as small railways. For the very reason of significant differences between the national railway systems, the EU legislation has laid broad grounds for track access charge (TAC) modelling, thus resulting in many different TAC models. Out of numerous papers in respect of TAC modelling, only a small number consider the specificities and the needs of small railways. The paper aims to answer the questions of how to design or set up an efficient TAC structure when it comes to small countries. Another objective is to answer how to develop a TAC structure allowing the infrastructure manager to manage its costs. The answers to these questions are provided through the case study of railway in Montenegro – small railways in the Western Balkans. The main contribution of this paper is in developing the TAC model based on the efficient ratio of the capacity and infrastructure wear and tear components.

Visualisation helps explain the operating mechanisms of deep learning models, but its applications are rarely seen in traffic analysis. This paper employs a convolu-tional neural network (CNN) to evaluate road network performance level (NPL) and visualises the model to en-lighten how it works. A dataset of an urban road network covering a whole year is used to produce performance maps to train a CNN. In this process, a pretrained network is introduced to overcome the common issue of inadequa-cy of data in transportation research. Gradient weighted class activation mapping (Grad-CAM) is applied to vi-sualise the CNN, and four visualisation experiments are conducted. The results illustrate that the CNN focuses on different areas when it identifies the road network as dif-ferent NPLs, implying which region contributes the most to the deteriorating performance. There are particular visual patterns when the road network transits from one NPL to another, which may help performance prediction. Misclassified samples are analysed to determine how the CNN fails to make the right decisions, exposing the model’s deficiencies. The results indicate visualisation’s potential to contribute to comprehensive management strategies and effective model improvement.

In search for measures to reduce greenhouse gas emissions from transport, insights into the characteristics of all sorts of trips and specifically trips by car are needed. This paper focuses on everyday leisure trips for social and recreational purposes. Travel behaviour for these purposes is analysed considering individual and household factors as well as properties of the trip, based on Swedish national travel survey data. The analysis reveals that everyday leisure trips are often of joint character and that the average distance travelled per person and day increases with, for example, income, cohabitation, children in the household and residence in rural areas. The result also shows that the studied characteristics vary between studied trip purposes, influencing the sustainability potential of a reduction in car use and suggested measures. For instance, the largest share of passenger mileage comes from social trips, whereas trips for exercise and outdoor life have the largest share of car trips below 5 km. Several characteristics indicate difficulties in transferring trips by car to, for example, bicycle or public transport due to convenience, economy, start times, company etc. The study indicates that there is a need to take a broader view of the effective potential.

Traffic control approaches, in particular Variable Speed Limit (VSL), are often studied as solutions to improve the level of service on urban motorways. However, the efficiency of VSL strongly depends on the spatiotemporal arrangement of VSL zones. It is crucial to determine the lengths and locations of VSL zones for best VSL efficiency before deployment in a real system, as the optimal length of the VSL zone and its distance from the bottleneck directly affects traffic dynamics and, thus, bottleneck control. Therefore, in this study, we perform the analysis of different VSL zones lengths and their positions by using a closed-loop Simple Proportional Speed Controller for VSL (SPSC-VSL). We evaluate the different VSL zone configurations and their impact on traffic flow control and vehicle emissions in a SUMO microscopic simulation on a high traffic demand scenario. The results support the observations of previous researchers on the significant dependence of VSL zone placement on VSL efficiency. Additionally, new data-based (traffic parameters and vehicle emissions) evidence of the performance of the SPSC-VSL design are provided regarding the best placement of consecutive VSL zones for motorway bottleneck control not analysed in previous research.

The centrality of stations is one of the most important issues in urban transit systems. The central stations of such networks have often been identified using network to-pological centrality measures. In real networks, passenger flows arise from an interplay between the dynamics of the individual person movements and the underlying physical structure. In this paper, we apply a two-layered model to identify the most central stations in the Beijing Subway System, in which the lower layer is the physical infrastruc-ture and the upper layer represents the passenger flows. We compare various centrality indicators such as degree, strength and betweenness centrality for the two-layered model. To represent the influence of exogenous factors of stations on the subway system, we reference the al-pha centrality. The results show that the central stations in the geographic system in terms of the betweenness are not consistent with the central stations in the network of the flows in terms of the alpha centrality. We clarify this difference by comparing the two centrality measures with the real load, indicating that the alpha centrality approx-imates the real load better than the betweenness, as it can capture the direction and volume of the flows along links and the flows into and out of the systems. The empirical findings can give us some useful insights into the node cen-trality of subway systems.

Traffic flow forecast is critical in today’s transportation system since it is necessary to construct a traffic plan in order to determine a travel route. The goal of this research is to use time-series forecasting models to estimate future traffic in order to reduce traffic congestion on roadways. Minimising prediction error is the most difficult task in traffic prediction. In order to anticipate future traffic flow, the system also requires real-time data from vehicles and roadways. A hybrid autoregressive integrated moving av-erage with multilayer perceptron (ARIMA-MLP) model and a hybrid autoregressive integrated moving average with recurrent neural network (ARIMA-RNN) model are proposed in this paper to address these difficulties. The transportation data are used from the UK Highways data-set. The time-series data are preprocessed using a random walk model. The forecasting models autoregressive inte-grated moving average (ARIMA), recurrent neural net-work (RNN), and multilayer perceptron (MLP) are trained and tested. In the proposed hybrid ARIMA-MLP and ARI-MA-RNN models, the residuals from the ARIMA model are used to train the MLP and RNN models. Then the ef-ficacy of the hybrid system is assessed using the metrics MAE, MSE, RMSE and R2 (peak hour forecast-0.936763, non-peak hour forecast-0.87638 on ARIMA-MLP model and peak hour forecast-0.9416466, non-peak hour fore-cast-0.931917 on ARIMA-RNN model).

This study introduces a novel methodological frame-work for extracting integral vehicle trajectories from several consecutive pictures automatically. The frame-work contains camera observation, eliminating image distortions, video stabilising, stitching images, identify-ing vehicles and tracking vehicles. Observation videos of four sections in South Fengtai Road, Nanjing, Jiangsu Province, China are taken as a case study to validate the framework. As key points, six typical tracking algorithms, including boosting, CSRT, KCF, median flow, MIL and MOSSE, are compared in terms of tracking reliability, operational time, random access memory (RAM) usage and data accuracy. Main impact factors taken into con-sideration involve vehicle colours, zebra lines, lane lines, lamps, guide boards and image stitching seams. Based on empirical analysis, it is found that MOSSE requires the least operational time and RAM usage, whereas CSRT presents the best tracking reliability. In addition, all tracking algorithms produce reliable vehicle trajecto-ry and speed data if vehicles are tracked steadily.

Based on the existing safe distance cellular automata model, an improved cellular automata model based on realistic human reactions is proposed in this paper, which aims to reproduce the characteristics of congested traffic flow. In the proposed model, the stochastic noise param-eter is optimised by considering driving behavioural dif-ference. The relative speed, gap and acceleration of the front vehicle are introduced into the optimised stochastic noise parameter oriented to describing the asymmetric acceleration behaviour of drivers in congestion. The sim-ulation results show that an uneven distribution of accel-eration trajectories of vehicles experiencing congestion exhibited on the spatial-temporal diagram of the pro-posed model is reproduced. Based on the analysis of the NGSIM, compared with the model with traditional sto-chastic noise parameter, the vehicles that move accord-ing to the proposed model can follow more easily and more realistically. Then the actual gap of vehicles can be better reflected by the proposed model and the change of vehicle speed is more stable. Additionally, the traffic efficiency from two aspects of flow and speed shows that the proposed model can significantly improve the traffic efficiency in the medium high density region.

Ancillary services in air transport represent a set of services provided to passengers to choose from, enabling them to enhance their travel experience while accumu-lating additional airline revenue. Low-cost airlines pi-oneered the practice, but the separation of ancillary services from the basic service has become an intense-ly growing trend in the air transport industry over the last decade. This practice has enabled low-cost airlines to significantly reduce the price of the basic service. To remain competitive in an era of transparency provided by search engines, traditional airlines offer ancillary ser-vices in addition to the basic service. To meet the passen-ger’s needs, a whole range of ancillary services has been created. However, existing revenue management systems do not take this ancillary revenue into account when cal-culating reservation limits. If the airline knew that an in-dividual passenger is willing to pay more for ancillary services, the system would be able to adjust the availabil-ity of the service for that passenger during the booking process. A review of research on passengers’ willingness to pay for ancillary services is presented in the paper, as well as a review on research on the personalisation of ancillary services and challenges of integrating person-alised pricing into existing revenue management systems.

The sugar-energy sector is extremely important to the Brazilian economy, with several other production chains derived from it, generating some of the main products linked to food and energy sources. This study proposes an integration model for sugarcane harvesting logistics processes, focusing on optimisation of industrial plant production capacity. Dynamic modelling has been applied to study a broad range of the productive phases of the sugar-energy chain. This paper proposes indicators to evaluate the degree of efficiency of the production logistics processes. Preliminary results showed that phase times in the production logistics processes can be significantly reduced in the harvest phase. When analysed as a coordination-oriented flow having chained activities, the production logistics processes optimise the speeds and travel times during the harvest phase. The developed model uses data set of the production and logistics processes phases of a sugarcane industry. A future study will focus on more detailed and complex stakeholder behaviours based on the model proposed.

Passenger exchange coefficient is a significant factor which has great impact on the pricing model of urban rail transit. This paper introduces passenger exchange coefficient into a bi-level programming model with time differential pricing for urban rail transit by analysing variation regularity of passenger flow characteristics. Meanwhile, exchange cost coefficient is also considered as a restrictive factor in the pricing model. The improved particle swarm optimisation algorithm (IPSO) was ap-plied to solve the model, and simulation results show that the proposed improved pricing model can effectively re-alise stratification of fares for different time periods with different routes. Taking Line 2 and Line 8 of the Beijing rail transit network as an example, the simulation result shows that passenger flows of Line 2 and Line 8 in peak hours decreased by 9.94% and 19.48% and therefore increased by 32.23% and 44.96% in off-peak hours, re-spectively. The case study reveals that dispersing pas-senger flows by means of fare adjustment can effectively drop peak load and increase off-peak load. The time dif-ferential pricing model of urban rail transit proposed in this paper has great influences on dispersing passenger flow and ensures safety operation of urban rail transit. It is also a valuable reference for other metropolitan rail transit operating companies.

The goal of the paper is to investigate the impact of tire tread depth on road accident risk and to develop an accident rate prediction model. The state of 4288 vehicle tires using tread depth gauge was inspected and processed statistically. The tread depth of the most worn tire from each vehicle was registered for further analy-sis. Based on the collected data, a statistical tire tread depth model for an insurance company vehicle fleet had been developed. The conformity of the gamma distribu-tion to the data was verified upon applying the Pearson compatibility criterion. The paper provides the histo-grams of the frequencies of tire tread depths and the theoretical curves of the distribution density. The prob-ability of the accident risk depending on the tire tread depth (adaptive risk index) was calculated applying the formed distributions and risk index dependence on the tire tread depth for the inspected vehicle fleet. Accord-ing to the developed prediction model, an upgrade of the regulation for the minimum allowed tire tread depth by 2 mm (up to 3.6 mm) could reduce road accident risk (caused by poor adhesion to road surface) to 19.3% for the chosen vehicle fleet. Such models are useful for road safety experts, insurance companies and accident cost evaluation specialists by predicting expenses related to insurance events.

In 1991, the European Union decided on setting up a liberalised and single railway market. However, in the atomised European region, more than a half of railways can be designated as small railways. For the very reason of significant differences between the national railway systems, the EU legislation has laid broad grounds for track access charge (TAC) modelling, thus resulting in many different TAC models. Out of numerous papers in respect of TAC modelling, only a small number consider the specificities and the needs of small railways. The paper aims to answer the questions of how to design or set up an efficient TAC structure when it comes to small countries. Another objective is to answer how to develop a TAC structure allowing the infrastructure manager to manage its costs. The answers to these questions are provided through the case study of railway in Montenegro – small railways in the Western Balkans. The main contribution of this paper is in developing the TAC model based on the efficient ratio of the capacity and infrastructure wear and tear components.

Visualisation helps explain the operating mechanisms of deep learning models, but its applications are rarely seen in traffic analysis. This paper employs a convolu-tional neural network (CNN) to evaluate road network performance level (NPL) and visualises the model to en-lighten how it works. A dataset of an urban road network covering a whole year is used to produce performance maps to train a CNN. In this process, a pretrained network is introduced to overcome the common issue of inadequa-cy of data in transportation research. Gradient weighted class activation mapping (Grad-CAM) is applied to vi-sualise the CNN, and four visualisation experiments are conducted. The results illustrate that the CNN focuses on different areas when it identifies the road network as dif-ferent NPLs, implying which region contributes the most to the deteriorating performance. There are particular visual patterns when the road network transits from one NPL to another, which may help performance prediction. Misclassified samples are analysed to determine how the CNN fails to make the right decisions, exposing the model’s deficiencies. The results indicate visualisation’s potential to contribute to comprehensive management strategies and effective model improvement.

In search for measures to reduce greenhouse gas emissions from transport, insights into the characteristics of all sorts of trips and specifically trips by car are needed. This paper focuses on everyday leisure trips for social and recreational purposes. Travel behaviour for these purposes is analysed considering individual and household factors as well as properties of the trip, based on Swedish national travel survey data. The analysis reveals that everyday leisure trips are often of joint character and that the average distance travelled per person and day increases with, for example, income, cohabitation, children in the household and residence in rural areas. The result also shows that the studied characteristics vary between studied trip purposes, influencing the sustainability potential of a reduction in car use and suggested measures. For instance, the largest share of passenger mileage comes from social trips, whereas trips for exercise and outdoor life have the largest share of car trips below 5 km. Several characteristics indicate difficulties in transferring trips by car to, for example, bicycle or public transport due to convenience, economy, start times, company etc. The study indicates that there is a need to take a broader view of the effective potential.

In recent years, the public’s interaction with street green spaces has been increasing, leading to much more concern about its design. By using stated preference data from a discrete choice experiment and the multinomial logit model, this study investigates pedestrians’ and cy-clists’ landscape preference regarding street green space through an online survey based on a virtual street envi-ronment. The results show that trees are the most suitable to be planted symmetrically between the cycle track and sidewalk. Large size trees with large crown width and tall height are more preferred than common size trees. There are considerable differences in preferences for lo-cations of shrubs, hedges, flowers, and grass between cy-clists and pedestrians. Cyclists prefer grass by the cycle track the most and grass by the sidewalk the least. But for pedestrians, flowers, hedges, and grass by the sidewalk are positively significant. Buildings with green plants in their front yards are preferred over a monotonous facade or coffee seats. This study enriches the understanding of the public’s landscape preferences for streets sharing non-motorised lanes. The results also play a guiding role in people-oriented street green space designs of land-scape architects and governments.

Traffic congestion has become a severe problem, af-fecting travellers both mentally and economically. To al-leviate traffic congestion, this paper proposes a method using a concept of future time windows to estimate the future state of the road network for navigation. Through our method, we can estimate the travel time not only based on the current traffic state, but the state that ve-hicles will arrive in the future. To test our method, we conduct experiments based on Simulation of Urban MO-bility (SUMO). The experimental results show that the proposed method can significantly reduce the overall travel time of all vehicles, compared to the benchmark Dijkstra algorithm. We also compared our method to the Dynamic User Equilibrium (DUE) provided by SUMO. The experimental results show that the performance of our method is a little better than the DUE. In practice, the proposed method takes less time for computation and is insensitive to low driver compliance: with as low as 40% compliance rate, our method can significantly im-prove the efficiency of the unsignalised road network. We also verify the effectiveness of our method in a signalised road network. It also demonstrates that our method can assign traffic efficiently.

Roundabout capacity estimation has been the subject of several types of research in recent decades. Most of the analyses are based on the empirical or analytical models (e.g. gap acceptance theory) considering various kinds of conflicting flows, namely entry, circulating, and exit-ing flow. The drivers on the exiting flow either obey the traffic rule (use the right-turn indicator) or disobey the traffic rule (do not use the right-turn indicator). Accord-ing to the reviewed literature, the impact of these driv-ers on the roundabout capacity has not been studied to a greater extent. Therefore, this study aims to develop an analytical roundabout capacity estimation model that also takes into account a share of exiting flow. It extends Brilon-Wu’s model, by including the impact of exiting drivers who disobey the traffic rule on the gap accep-tance of the entering drivers. The proposed model was validated using the quasi-observation data generated by a microscopic model. The results obtained by our model were compared with Bovy’ and Yaps’ empirical models as well as Brilon-Wu’s analytical model for a single-lane roundabout. Using the RMSE and regression analysis, it is proved that the proposed model outperforms the exist-ing models in terms of estimating the capacity and delays of roundabouts.

Short-term traffic flow prediction is to automatically predict the traffic flow changes in a period of future time based on the extraction of the spatiotemporal features in the road network. For governments, timely and accurate traffic flow prediction is crucial to plan road manage-ment and improve traffic efficiency. Recent advances in deep learning have shown their dominance on short-term traffic flow prediction. However, previous methods based on deep learning are mainly limited to temporal features and have so far failed to predict the bidirectional con-textual spatiotemporal relationship correctly. Besides, the precision and the practicality are limited by the road network scale and the single time scale. To remedy these issues, a Bidirectional Context-aware and Multi-scale fusion hybrid Network (BCM-Net) is proposed, which is a novel short-term traffic flow prediction framework to predict timely and accurate traffic flow changes. In BCM-Net, the Bidirectional Context-aware (BCM) block is added to the feature extraction structure to effective-ly integrate spatiotemporal features. The Interpolation Back Propagation sub-network is used to merge multi-scale information, which further improves the robustness of the model. Experiment results on diverse datasets demonstrated that the proposed method outperformed the state-of-the-art methods.

Work-related road accidents are estimated to con-tribute to at least one quarter to over one third of all work-related deaths. Changing the vehicle has a major impact on traffic safety. Some studies have shown that drivers’ knowledge and practical driver training can improve traffic safety when changing vehicles. The aim of this paper is to determine whether there is an impact of the vehicle change on traffic safety. The research was conducted at the location with cylinders, braking coeffi-cient sensors, and brake pedal force detector, as well as with ten different passenger car brands and types. At the time of the research, all cars were registered and used daily in traffic. Prior to the research, the precision of the measuring instruments at the research site was checked. On the basis of the results, it can be concluded that there are two significant factors: the vehicle and the driver who needs to be trained before starting to drive a new ve-hicle. When changing the vehicle brand and type within the company, it is necessary to conduct systemic training of drivers which would include theoretical and practical parts and involve at least braking, driver distraction, and active and passive vehicle safety.

In the current world of increasing density of unmanned aerial vehicle operations in the airspace, there is an enhanced emphasis on their safety due to the potential for mid-air collision, either with another aircraft or with each other. At the same time, unmanned aerial vehicles are also being used in the context of introducing smart technologies into maintenance processes, where there is also a need to prevent a potentially possible conflict when two drones come close together. The paper introduces a mathematical model for tactical prediction of a conflict between a pair of drones. The tactical prediction of drone conflict is intended to alert the drone operator to an immediate potentially dangerous situation. The mathematical simulation in this paper extrapolates the 3D trajectory in the direction of the relative velocity vector of the convergence over the advance time. If the extrapolated trajectory has at least one point in common with the conflict space of the other drone, the conflict is signalled to the drone operator. This model can then be used in practice to simulate flight operations in shared airspace or to develop the currently required rules in selected situations.

Driver distraction has been identified as a contributing factor to road crashes, among which the most common is the use of mobile phones while driving. For this reason, the aim of this paper is to analyse the behaviour of young drivers while they use mobile phones (answering a telephone call, texting, and browsing the internet) and drive in a simulated urban environment. In total 28 volunteers participated in the study. Several variables were recorded for each participant: driving speed, acceleration, deceleration, and eye movement. The results show that the difference in driving speed, acceleration, and deceleration was relatively small for each task and for the control condition (no use of mobile phone). However, when looking at the total time required for conducting each task, participants spent 26.44% of the time looking at the phone when texting, 37.01% when browsing the internet, and 2.27% when talking on the phone. In addition, participants viewed on average 66.45% traffic signs when distracted, compared to 79.22% during undistracted driving. Based on the results, a proactive approach to reduce the problem related to the use of mobile phones while driving is proposed.

Interlockings are an essential element of the railway system. They are necessary to command and control devices, such as points and signals in order to route trains within the bounds of railway stations. Their design must ensure the highest level of safety for all involved parties. The European continent has an extensive railway network which has slowly grown over more than 150 years. Interlockings have evolved over the same period from large mechanical devices requiring physical force to operate to computerised systems capable of complex operations. Despite the technological leap, many interlockings using older technologies are still in use in the present. This review aims to paint an accurate picture of the current state of interlockings in Europe by evaluating the share of each interlocking generation (mechanical, relay and electronic). The study covers 15 countries and over 200,000 km of railway tracks, representing over two thirds of the entire EU railway network. A brief presentation is given for each country, while comparisons made between the researched countries highlight certain key findings. The focus is only on station interlockings, not including line signalling. The conclusions of this analysis include recommendations for current and future development of the railway sector.

Passenger choice behaviour of buying tickets has a great impact on the high-speed rail (HSR) revenue management. It is very critical to find out the sensitive factors that prevent passengers with high willingness to pay for a ticket from buying low-price tickets. The literature on passenger choice behaviour mainly focuses on travel mode choice, choice between a conventional train and a high-speed train and choice among high-speed trains. To extend the literature and serve revenue management, this paper investigates passenger choice behaviour of buying high-speed railway tickets. The data were collected by the stated preference (SP) survey based on Beijing-Hohhot high-speed railway. The conditional logit model was established to analyse influencing factors for business travel and non-business travel. The results show that: business passengers have the higher inherent preference for full-price tickets, while non-business passengers have the higher inherent preference for discount tickets; the number of days booked in advance and frequent passenger points have a significant impact on the ticket choice of business travellers, but not on non-business travellers; passengers are unwilling to buy tickets that depart after 16:00 for non-business travel; factors have different effects on the passengers' choice in business travel and non-business travel. The results can provide parameters for revenue management models and references for the ticket-product design.

This paper presents the modelling of the saturation flow rate of the permitted left turn in an exclusive lane. In the proposed model, the total permitted left-turn saturation flow rate is determined as a sum of saturation flow rates during the effective green time and the intergreen period. Primarily, the permitted left-turn saturation flow rate during the effective green time is modelled based on the opposing through-flow degree of saturation and the number of opposing through-flow lanes. The relation between the permitted left-turn saturation flow during the effective green time and these variables was examined using data from the simulation experiments in VISSIM. To our knowledge, this is the first study of the permitted left-turn saturation flow modelling based on the opposing through-flow degree of saturation instead of the opposing through-flow rate and signal-timing parameters. The proposed model was validated based on data collected at seven intersections with a permitted left turn served in an exclusive lane. The permitted left-turn saturation flow rate could be accurately determined based on the opposing through-flow degree of saturation and the number of opposing lanes according to the RMSE of 58.4 pcu/h.

Customised bus (CB) is a cutting-edge mean of transportation and has been implemented worldwide. To support the spread of the CB system, methodologies for CB network design have been conducted. However, a majority of them cannot be adopted directly for multi-modal transportation environment. In this paper, we proposed a bi-level programming model to fill this gap. The upper-level problem is to maximise the usage of the CB system with the limitation of operation constraints. Meanwhile, the lower-level problem is to capture the traveller’s choice by minimising traveller’s generalised cost during travel. A solving procedure via genetic algorithm is further proposed and validated via the metro data at Shanghai. The results indicated that the proposed CB route network would attract nearly 5,000 users during morning peak period under the given metro transaction data. We further studied the features of the selected routes and found that the CB network mainly served residence to commercial or industrial parks travellers and would provide travel service with fewer stops, and higher travel efficiency by travelling through expressway.

Support vector machine (SVM) models have good performance in predicting daily traffic volume at toll stations, however, they cannot accurately predict holiday traffic volume. Therefore, an improved SVM model is proposed in this paper. The paper takes a toll station in Heilongjiang, China as an example, and uses the daily traffic volume as the learning set. The current and previous 7-day traffic volumes are used as the dependent and independent variables for model learning, respectively. This paper found that the basic SVM model is not  accurate enough to forecast the traffic volume during holidays. To improve the model accuracy, this paper first used the SVM model to forecast non-holiday traffic volumes, and proposed a prediction method using quarterly conversion coefficients combined with the SVM model to construct an improved SVM model. The result of the prediction showed that the improved SVM model in this paper was able to effectively improve accuracy, making it better than in the basic SVM and GBDT model, thus proving the feasibility of the improved SVM model.

Different types of pedestrians exhibit different speed characteristics and heterogeneity. In the case of mixed pedestrian flow at signalised intersections, pedestrian traffic flow modelling is important in research of the con-ditions at signalised intersections and the evaluation of services for pedestrians. The characteristics of pedestri-an traffic flow at signalised intersections were investi-gated in this study against the background of pedestrian heterogeneity using videos of pedestrians crossing three signalised intersections in Chongqing recorded in a field survey. The pedestrian walking speeds were manually ex-tracted from the videos and used as the data basis for dis-tinguishing pedestrian heterogeneity. The walking speed data of three types (young, middle-aged, and elderly) of pedestrians at different pedestrian flows were obtained by using a microsimulation software. Based on this, a pe-destrian traffic flow model for mixed-type pedestrians at signalised intersections was established and verified by actual cases. In comparison with the HCM model, the model outperforms the HCM model overall in practical applications, indicating its strong applicability and reli-ability.

Most studies investigate the benefit of public trans-port service from either the perspective of the operators or the public individually, failing to bind them together. Furthermore, they have not considered the significance of the government in quantifying the benefit. This pa-per explores the comprehensive benefit of public trans-port service from the perspectives of three stakeholders; namely, the operators, the public, and government. We develop a comprehensive benefit evaluation tool that is able to quantify production efficiency, service effect, and environmental effect, and test the effectiveness of the tool through a case study in 36 central cities of China. A network data envelopment analysis (NDEA) is used to evaluate the efficiency of the production and service sub-process, and comprehensive benefits. The results re-veal the following: (1) during the period 2010–2017, the production efficiency in 36 central cities showed a down-ward trend; (2) the service effectiveness did not change considerably from 2010 to 2013 but declined gradual-ly during the period 2014–2017; (3) the comprehensive benefits rarely changed during the period 2010–2013, but gradually got worse in response to reductions in the production efficiency and service effectiveness during the period 2014–2017. This study offers a robust tool to mea-sure the benefits of public transport in China for better decision-making, in terms of transit operation and man-agement.

Unconventional geometric designs such as continu-ous-flow intersections, U-turns, and contraflow left-turn lanes have been proposed to reduce left-turn conflicts and improve intersection efficiency. Having a waiting area at a signalised intersection is an unconventional de-sign that is used widely in China and Japan to improve traffic capacity. Many studies have shown that waiting areas improve traffic capacity greatly, but few have con-sidered how to improve the benefits of this design from the aspect of signal optimisation. Comparing the start-up process of intersections with and without waiting areas, this work explores how this geometric design influenc-es vehicle transit time, proposes two signal optimisation strategies, and establishes a unified capacity calculation model. Taking capacity maximisation as the optimisation function, a cycle optimisation model is derived for over-saturated intersections. Finally, the relationship among waiting-area storage capacity, cycle time, and traffic ca-pacity is discussed using field survey data. The results of two cases show that optimising the signal scheme helps reduce intersection delays by 10–15%.

The problem of choosing only one relevant safety performance indicator for the purpose of comparing and assessing road safety situations has been the subject of many recent research studies. This paper shows the concept of creating a composite exposure index based on available data. The procedure of creating a model for calculating this indicator is based on the analysis of quality of individual exposure indicators and the size of their impact on the direct safety performance indicators – number of road crashes and their consequences. The following four models (TOPSIS EQUAL, TOPSIS CRIT-IC, PROMETHEE EQUAL, PROMETHEE CRITIC) for determining weighted coefficients of the individual indi-cators that participate in the creation of the composite exposure index have been analysed in this paper. The method used for defining the composite exposure index is the “high-efficiency method” based on which the final shape of the model for defining the composite exposure index has been defined. The main aim of this paper is to create a model for defining the composite index of traffic exposure. The final outcome is to provide an opportuni-ty to evaluate and rank traffic safety levels based on the unique road traffic risk.

Origin-destination (OD) matrices provide transportation experts with comprehensive information on the number and distribution of trips. For comparing two OD matrices, it is vital to consider not only the numerical but also the structural differences, including trip distribution priorities and travel patterns in the study region. The mean structural similarity (MSSIM) index, geographical window-based structural similarity index (GSSI), and socioeconomic, land-use, and population structural similarity index (SLPSSI) have been developed for the structural comparison of OD matrices. These measures have undeniable drawbacks that fail to correctly detect differences in travel patterns, therefore, a novel measure is developed in this paper in which geographical, socioeconomic, land-use, and population characteristics are simultaneously considered in a structural similarity index named GSLPSSI for comparison of OD matrices. The proposed measure was evaluated using OD matrices of smartphone Global Positioning System (GPS) data in Tehran metropolitan. Also, the robustness of the proposed measure was verified using sensitivity analysis. GSLPSSI was found to have up to 21%, 15%, and 9% higher accuracy than MSSIM, GSSI, and SLPSSI, respectively, regarding structural similarity calculation. Furthermore, the proposed measure showed 7% higher accuracy than SLPSSI in the structural similarity index of two sparse OD matrices.

The taxi system is one of the most famous and de-veloped subsystems of flexible passenger transport. To reach the goal of the system achieving maximum produc-tion efficiency, the management focus is directed at users and service quality (SQ). The SQ can have several forms: expected, targeted, delivered, and perceived SQ. We ex-amine the expected SQ, expressed through the users’ at-titudes about the importance of the defined parameters of the SQ, which represent the users’ expectations from the taxi system. The analysis included the data from the conducted studies in three selected taxi systems. The aim of this paper was to determine the effect of market and selected user characteristics on the user expectations, applying the Chi-Square Test. We conclude that the spe-cific market and certain user characteristics affect the user expectations of the taxi system. There is a moderate effect on the employed users, pensioners, and daily users of the taxi system. When it comes to the users who use the taxi system several times a month and week, there is a less significant effect. Other user categories have no sig-nificant correlation with the selection of the parameters of the SQ in the taxi system.

Evaluating air transport service quality is fundamen-tal to ensure acceptable quality standards for users and improve the services offered to passengers and tourists. In the transportation literature there is a wide range of studies about the evaluation of public transport service quality based on passengers’ perceptions; however, more recently, the evaluation of air transport service quality is becoming a relevant issue. Evaluating service quality in air transport sector represents a more stimulating chal-lenge, given the complexity of air transport system in re-gards to the other systems; in fact, air transport service is characterised by a great variety of service aspects relat-ing to services offered by the airlines and provided by the companies managing airports. The complexity of such a service requires a deep investigation on the methods adopted for collecting and analysing the data regarding passengers’ perceptions. We propose this paper just for treating these interesting aspects and to provide an ex-haustive literature review of the studies analysing ser-vice quality from the passengers’ point of view, where the opinions of the passengers are collected by the Customer Satisfaction Surveys (CSS). We decided to select papers published within the last decade (2010–2020) in journals indexed in important databases such as Scopus and WoS.

Walking is an environment-friendly trip mode and can help ease the congestion caused by automobiles. Proper design of pedestrian facilities that promotes effi-ciency and safety can encourage more people to choose walking. Upstream detection (UD) strategy is proposed by previous studies to reduce pedestrian waiting time at mid-block crosswalk (MBC). This paper applied UD strategy to MBC under mixed traffic circumstance where the crosswalk serves both pedestrians and non-motor us-ers. Traffic data was collected from an MBC in the city of Nanjing, China. Simulation models were developed by using the VISSIM software and its add-on module Vehicle Actuated Programming (VAP). The models were catego-rised by the volume and composition of pedestrians and non-motor users. Models were simulated according to different experimental schemes to explore the effective-ness of the UD strategy under mixed traffic circumstance. T-test and analysis of variance (ANOVA) were used to interpret the simulation results. The main conclusions of this paper are that the UD strategy is still effective at the MBC with a mixed traffic circumstance despite the pro-portion of non-motor users. However, as the proportion of non-motor users becomes higher, the average delay of pedestrians and non-motor users will increase compared to pure pedestrian flow.

The reasonable placement of the advance guide signs (AGSs) is important in improving driving efficiency and safety when exiting an expressway. By analysing the lane-changing process when approaching an exit on new two-way eight-lane expressways, we modified the tradi-tional AGS model lane-change distance formula. To this end, a field experiment was designed to explore the lane-change traversal time at the free flow condition (LOS 1). Considering the limitations of the experimental equip-ment, lane change distance at the worst levels of service was explored using VISSIM simulation. The results show that the eight-lane changing distance based on modified theoretical calculations, revealed a minor difference with VISSIM simulation in free flow condition. Further-more, placement distance at the worst levels of service are discussed. Then placement distance of all-level AGSs is recommended to be 3 km, 2 km, 1.2 km, and 0.8 km, considering the driver's short-term memory attenuation calculation formula. Determining the two-way eight-lane AGS placement distance from the perspective of LOS can provide a basis on which to supplement the existing stan-dards and references for the AGS placement distance af-ter the expressway expansion in China.

According to models commonly used in practice, the capacity of roundabouts largely depends on the value of critical headway. The value of critical headway depends on the characteristics of vehicles, driving conditions, and geometric characteristics of intersections, but also on driver behaviour. Driver behaviour is the result of many factors that depend on the influence of the local environment, driver habits, mentality, etc. Accordingly, to calculate the capacity of roundabouts within the op-erational and planning analyses of roundabouts more accurately, it is necessary to use data that correspond to local conditions. In this paper, the critical headway was estimated at five urban single-lane roundabouts using five methods: Harders’, Logit, Raff’s, Wu’s, and the max-imum likelihood method. In order to determine which of the stated methods provides the most realistic estimate of critical headway, a comparison of field capacity values with theoretical capacity values was performed. Based on the comparative analysis performed in MATLAB, as well as the calculation of percentage prediction error, it was found that the Harders' method provides the most accurate estimate of critical headway at observed round-abouts in two cities in Bosnia and Herzegovina. Due to the similarity in the design of roundabouts and driver be-haviour, the results obtained in this paper can be applied in the surrounding countries, i.e., Southeast Europe

Level of service (LOS) classifications of traffic oper-ational conditions play a significant role in roadway-im-provement funding decisions. Traveller perception of LOS should be consistent with traffic analysis values to avoid undermining the public confidence in the transpor-tation agency decisions. Research methods to study trav-eller perceptions range from in-vehicle videos to focus groups and surveys. These methods have different advan-tages, but all suffer from time and/or cost inefficiencies for collecting data sets across a wide range of operating conditions. This paper describes a novel method to study this topic with increased time and cost efficiency. This new method combines traffic microsimulation and 3-D visualisation capabilities. The focus of this paper is to provide guidance on how to apply traffic microsimula-tion and computer 3-D visualisation to evaluate highway trip quality from a traveller’s perspective. It discusses the creation of the simulation environment to produce a real-istic view from the vehicle’s cabin interior, including the network creation, landscaped area, dashboard speedom-eter, and rear-view mirror. The authors also propose an automated method for choosing an appropriate vehicle within the simulated traffic stream, such that the desired overall traffic stream conditions are conveyed to the trav-eller vehicle within the field of view.

The contribution includes the necessity to analyse the potential of the regions in terms of rail passenger transport. The main research goal is to propose a new concept of determination and evaluation of the particu-lar factors, which evaluate the region potential. Several research methods are used in research, for example the brainstorming methods, methods of expert estimate, and especially point evaluation method. Firstly, the factors influencing the traffic potential are defined and exam-ined. Secondly, specific ways of monitoring and evaluat-ing particular factors are proposed. The research results form the basis of a new methodology for traffic potential determination in regions. Subsequently the mentioned methodology is applied in practice to the selected region-al railway line in Slovak Republic. The potential of this region is expressed by coefficient values of the chosen factors. For example, coefficient of the number of inhab-itants is 2.69 and the average value of the adjacent co-efficient of the railway stations and stops availability is 1.92. The details are explained in the fourth chapter. The proposals and outputs including practical application represent a new innovative way for region evaluation which has so far not been used anywhere. It can help plan and organise traffic service in the regions.

The aim of this work is to investigate the simplifica-tion of public transport networks (PTNs) for megacities and the optimisation of route planning based on the de-mand density of complex networks. A node deletion rule for network centre areas and a node merging rule for net-work border areas in the PTN are designed using the de-mand density of complex networks. A transit route plan-ning (TRP) model is established, which considers the demands of direct passengers, transfer passengers at the same stop and transfer passengers at different stops, and aims at maximising the transit demand density of a PTN. An optimisation process for TRP is developed based on the ant colony optimisation (ACO). The proposed method was validated through a sample application in Handan City in China. The results indicate that urban PTNs can be simplified while retaining their local attributes to a great extent. The hierarchical structure of the network is more obvious, and the layer-by-layer planning of routes can be effectively used in TRP. Moreover, the operating efficiency and service level of urban PTNs can be en-hanced effectively.

The vehicular Adhoc Network has unique charac-teristics of frequent topology changes, traffic rule-based node movement, and speculative travel pattern. It leads to stochastic unstable nature in forming clusters. The re-liable routing process and load balancing are essential to improve the network lifetime. Cluster formation is used to split the network topology into small structures. The reduced size network leads to accumulating the topology information quickly. Due to the absence of centralised management, there is a pitfall in network topology man-agement and optimal resource allocation, resulting in ineffective routing. Hence, it is necessary to develop an effective clustering algorithm for VANET. In this paper, the Genetic Algorithm (GA) and Dynamic Programming (DP) are used in designing load-balanced clusters. The proposed Angular Zone Augmented Elitism-Based Im-migrants GA (AZEIGA) used elitism-based immigrants GA to deal with the population and DP to store the out-come of old environments. AZEIGA ensures clustering of load-balanced nodes, which prolongs the network lifetime. Experimental results show that AZEIGA works appreciably well in homogeneous resource class VANET. The simulation proves that AZEIGA gave better perfor-mance in packet delivery, network lifetime, average de-lay, routing, and clustering overhead.

The efficiency of urban transportation system is un-der the influence of weather conditions. It is necessary to incorporate these impacts into transport system analysis, in order to prepare adequate mitigation measures. Trans-port models are often used in different types of transport system analysis and forecasting of its future characteris-tics. This paper focuses on implementation of the impact of rain in transport modelling, particularly into a traffic assignment process as a part of a macroscopic transport model. This aspect of modelling is important because it can indicate parts of the network where this impact leads to a high volume/capacity ratio, which is a good input for defining mitigation measures. Commonly, transport models do not consider weather impacts in its standard procedures. The paper presents a methodology for cali-brating volume-delay function in order to improve traf-fic assignment modelling in case of rain. The impact of different rain categories on capacity and free-flow speed was quantified and implemented in the volume-delay function. Special attention is given to the calibration of the part of volume-delay function for over-saturated traf-fic conditions. Calibration methodology is applicable for different types of volume-delay functions and presents a solid approach to incorporate weather conditions into common engineering practice.

The main competitor of air transportation is High-Speed Railway (HSR). However, in an oil-exporting country with low fuel prices and strong car dependence, HSR can face fierce competition with private cars and even buses. There is little previous research that forecast modal share in this situation. The case study of this research is the Tehran-Hamedan route in Iran that has high travel demand due to several historical and economic reasons and in the absence of air transportation, building the HSR in this route attracted foreign investment. To analyse the travel behaviour of passengers after the introduction of HSR, 409 stated and revealed preferences were collected in a self-designed questionnaire. Multinomial logit (MNL) model and mixed logit (ML) model were developed and modal share of each mode of transportation were forecasted up to 2045. HSR modal share is compared with other routes of the world to see the impact of air competition. The overall modal share of railway in this route is estimated to reach 64%, which is close to the average of major HSR routes globally (around 60%). Therefore, private cars can be a fierce competitor for HSR when there is no air link on the route and fuel is rather cheap.

In this study, a mobile application-based service providing information on the reduction in the air pollution source emissions due to the replacement of conventional scooters by electric scooters (e-scooters) was proposed to increase user awareness of air quality and purchase intention towards e-scooters. The extended unified theory of acceptance and use of technology was employed and an explanatory variable of environmental awareness (EA) was incorporated for enhancing constructs to investigate the factors that may influence the user acceptance of text-based mobile information on the reduced carbon emissions, in comparison with that of histogram-based mobile information on the reduction in emissions of six air pollution sources. A within-subjects experimental design was employed to evaluate both information contents. The results indicate that the model constructs of habit and EA are useful predictors of the behavioural intention (BI) to use app services. Furthermore, providing different mobile information contents demonstrated no statistically significant difference in the user’s acceptance and intentions. However, providing different mobile information contents on the same information spindle may trigger different constructs and intensities of influence on users’ purchase intentions for e-scooters. Based on these findings, several recommendations for app managers and developers and suggestions for future research have been provided.

Passenger stations are transit hubs where several railway lines interchange. They have important roles in providing train operations and passenger services. Interrelations between track layouts and technological performances are important for reducing bottleneck effects and raising the operational effectiveness of rail networks. To the best of our knowledge, in previous research the assessment of track layouts has not been considered with respect to various technological aspects including railway operations, safety, and passenger services but rather as a single criterion for analysis of different individual performance indicators. We propose a new two-phase decision making approach for the complex evaluation of track layout alternatives. The first phase model is a VIKOR method for ranking track layouts by criteria related to: railway capacity, safety issue, and passenger-pedestrian fluctuations. Next, in the second phase, we use marginal analysis to find Pareto front and compare the alternatives ratings by calculating performance-benefit coefficients. To show the applicability of the proposed model, we employ an illustrative example of a passenger rail station and evaluate six different track layout alternatives. The effectiveness of the proposed model is demonstrated comparing the proposed two-phase model with traditional VIKOR.

In this research, field and laboratory testing of three commercially available brake pads with the lowest, mid-dle, and highest price were performed. Complex field testing, where brake pads were tested in real extreme conditions on a loaded van vehicle and laboratory tests were performed. The field testing intended to investigate the temperatures that occur during the braking process and to determine the stopping distance, deceleration, and stopping time separately on the type of brake pads. Labo-ratory tests included the determination of the friction co-efficient according to ASTM G77, the structure of brake pad surfaces before and after the testing, and quantitative chemical analysis of brake pads. The aim of this study was to determine the influence of brake pad temperature on braking time depending on their purchase prices. The obtained results show a significant difference between the temperature, friction coefficient, chemical composi-tion, and braking time of the brake pads and their price.

At un-signalised at-grade intersections or roundabouts, motorcyclists have to make a quick decision to manoeuvre and avoid crash. Many studies show that risk-taking behaviour is the major cause of accidents in young motorcyclists. In this study, we analysed various factors that are involved in the risk-taking behaviour of young motorcyclists at un-signalised intersections. Online questionnaires were distributed among university and college students in Islamabad and Rawalpindi. The data of 490 respondents were collected to test the research model. Partial least square structural equation modelling approach was used to evaluate the measurement model, structural model and importance-performance map analysis. In this study, we assumed that risk-taking behaviour of young motorcyclists at un-signalised intersections could be influenced by several factors, i.e., demographic, past crash involvement, and peer influence. The results revealed that past crash involvement, confidence level, and peer influence were the significant factors that affect the risk-taking behaviour. Peer influence has the highest effect on the risk-taking behaviour. The person whose friends encourage them to take risk and accept challenges is more likely to exhibit the risk-taking behaviour. Those people who are more confident while riding a motorcycle are more likely to take risks.

Traffic congestion problems have dramatically esca-lated with the increasing volume of vehicles, pedestrians, and cyclists in the face of limited road capacity. This re-search aims to reduce the time road users spend in the system (school-zone area) and improve the efficiency of the process of dropping off and collecting children from a crowded school area. The study integrates discrete-event simulation (DES) and multi-criterion decision-mak-ing (MCDM) techniques to comprehensively evaluate the proposed alternatives to select an optimal solution based on many performance measures. A real-world case study of the traffic and congestion problems experienced by parents when they drop off and fetch their children from school during peak hours is presented. A heuristic algorithm was developed to simulate the random and un-predictable behaviour of road users. A cost-benefit anal-ysis considered the impact of waiting time, traffic den-sity, number of accidents, additional fuel expenses, and emission reduction. The technique for order of preference by similarity to ideal solution (TOPSIS) and preference selection index (PSI) methods were utilised to select the most appropriate option for parents. The study found that the integration of simulation techniques with MCDM methods could efficiently solve traffic problems.

The complexity of urban congestion requires policy-makers to adopt different congestion control measures that suit the characteristics of the city at the proper time. The paper focuses on the most controversial congestion pricing and offers methods to judge the efficacy of the policy by game theoretic approaches. It is found that congestion pricing is not merely a Pigouvian tax that internalizes drivers’ externalities, but also a powerful means to enhance public traffic proportion and balance road utilization on the premise of maximized social util-ity. Meanwhile, the embedded multiple case study shows that theoretical correctness of the policy is a necessary, but not sufficient condition for its effectiveness because the valid operation of the policy further requires cities to hold certain attributes in some aspects, such as econom-ic level, population density, proper pricing mechanism, and the ability to limit access to and from certain areas. Moreover, the authority should pay attention to matching the policy goal and its functions for successful implementation.

The advancement of data collection technologies has brought an upsurge in GPS applications. For example, travel behaviour research has benefited from the integration of multiple sources of Global Positioning System (GPS) data. However, the effective use of such data is still impeded by the challenge in data processing. For instance, GPS data, despite providing detailed spatial movement information, do not label the starting and finishing points of a trip, especially for commercial trucks. Hence, there is a critical need to develop a trip identification method to effectively use the trajectory data provided by GPS without additional information. This paper focused on identifying trips from the raw GPS data. Specifically, a systematic method is proposed to extract trips on the basis of origin-destination (OD) pairs by using a 5-step procedure. An application was provided on estimating the performance of travel time reliability using three metrics based on the OD trips for each dedicated truck. The application showed that, in general, trucks on long-distance routes have less reliable travel times compared to trucks on short-distance routes. This paper provides an example of using GPS data, without further information, to study travel time for freight performance and similar needs of punctuality in logistics.

The outbreak of COVID-19 disrupted our everyday life. Many local authorities enforced a cordon sanitaire for the protection of sensitive areas. Travellers can only pass the cordon after tested. This paper aims to propose a method to design an on-ramp control scheme to maximise urban freeway network throughput with a predetermined queuing delay constraint at all off-ramps around cordon sanitaire. A bi-level programming model is formulated where the lower-level is a transportation system equilibrium to predict traffic flow, and the upper-level is onramp metering optimisation that is nonlinear programming. A stochastic queuing model is used to represent the waiting phenomenon at each off-ramp where testing is conducted, and a heuristic algorithm is designed to solve the proposed bi-level model where a method of successive averages (MSA) is adopted for the lower-level model; A genetic algorithm (GA) with elite strategy is adopted for the upper-level model. An experimental study is conducted to demonstrate the effectiveness of the proposed method and algorithm. The results show that the methods can find a good heuristic optimal solution. These methods are useful for freeway operators to determine the optimal on-ramp control for disease control and prevention.