The level of greenhouse gas emissions is one of the most important issues today, both professionally and politically, because a lower level of greenhouse gas emission is mandatory for a sustainable economy. Besides industry and households, the transport sector is also responsible for these emissions. For this reason, it may be essential to set up a model with which the amount of CO2 emissions could be estimated or predicted. This article presents a model that examines the extent of economic development and CO2 emissions in European countries. The result is establishing a pattern requiring a longer time series. If the pattern is proven, a clear reassessment of the current relationship between economic development and environmental protection should be made.

Instrument flight procedures are essential and critical components of the global aviation system. They are designed for all phases of flight, i.e.  the standard instrument departures, standard instrument arrivals, instrument approaches and the en-route phase of flight. Instrument flight procedures are designed from various aeronautical data, information, dimensions, etc., which are named instrument flight procedure elements according to this paper. Development of air navigation systems affect design of instrument flight procedures and flexible use of airspace. The design process is carried out within a framework defined by international and national standards, organizational norms and economic aspects. Instrument flight procedure elements are a fundamental part of the process. Deviations of these elements from full compliance with international regulations can significantly and negatively affect air traffic safety. The objective of this paper was to investigate the basic prerequisites for statistical analysis of the design quality of instrument flight procedures, which have not been explored before. Six prerequisites were proposed for acquiring the data and preparing them for further statistical use.

The paper presents a simpler and more precise model of lumbar moment prediction based on single linear, or multiple linear regression with two predictors. The body mass index (BMI) as the predictor contains two of the most important static anthropometric measures, height and mass, whose separated role in lumbar moment prediction, as well as their mutual relations, have not been sufficiently investigated. This study analysed mass, height, age and BMI as lumbar moment predictors, on a sample of 50 Croatian male engine drivers. Two prediction models were compared: (1) multiple linear regression prediction with mass and height as predictors; (2) single linear regression with mass as the only predictor. Results confirmed the multiple regression model as the best one (R2= 0.9015 with standard error of prediction 1.26), having the mass of the best predictor. Surprisingly, the single regression model with mass as predictor explained only 3.6% of lumbar moment variance less than multiple regression model, with related standard error of prediction 1.46 (mean percentage value of the relative error was only 0.8% higher than at multiple regression model). The obtained findings suggest high prediction potential of mass and height that should be verified on various subject samples.

This paper focuses on daily freight train scheduling and dynamic railcar routing problems for rail freight transportation at the operational level.  Two mixed integer linear programming models that adopted different strategies were formulated based on a continuous two-layer time-space network. We simultaneously considered the benefits of railroad company and service quality when setting the objective function. By solving the  models, we can distribute the dynamic railcar flows to the train paths in the basic train timetable to obtain the daily train operation plan over a short time horizon (e.g. a day), which will be helpful for dispatchers to make decisions such as the empty railcar distribution and car routing (trip planning). Finally, we compared two models on a part of the Chinese railroad network. The results show that the second model can effectively improve the efficiency of railroad freight transportation.

Logistics is playing a significant role in supporting economic growth and material security during the epidemic period and it has been  experiencing a rapid development in recent years. With the issues of personalisation and cost, the economy and society ask for higher requirements for logistics storage systems. The rational design of the functional area layout is an essential step to improve the operational efficiency of the logistics warehousing system. In reality, due to warehouse design and equipment application, there has been a gradual increase in irregular warehouses. By taking an irregular warehouse as an example, combining the operation status quo, this paper clarifies the functional area settings and constructs a 0–1 integer planning model based on the grid and systematic layout planning method with constraints, such as the unique functional attributes of the grid. We optimised the genetic algorithm based on the warehouse irregularity factor and the grids factor, and then solve it through MATLAB. Finally, by using the Flexsim software, simulation metrics were selected for evaluation, the method feasibility is verified.

Aiming at two aircraft conflict scenario in the pre-tactical stage, by converting the uncertain flight trajectory of the target aircraft into a spatio-temporal trajectory under its performance constraints, a conflict detection model based on truncated normal distribution was proposed,
and influencing factors affecting the overall conflict probability were analysed by numerical simulation. For the conflict scenario, nonlinear  particle swarm optimisation (NPSO) algorithm was applied to solve the optimal separation configuration strategy for the ownship. The simulation results show that, in comparison to conventional PSO algorithm, the improved NPSO algorithm improves the optimal value by 14.88% and decreases the maximum velocity change by 19.84%. The simulation also shows that the algorithm can maintain the minimum interval requirements under different initial parameters, demonstrating its strong adaptability.

Traffic violations are a major cause of traffic accidents, yet current research falls short in comprehensively analysing these violations and the  named entity method fails to extract the name of traffic violation events from records, thereby lacking in providing guidance for managing urban traffic violations. By expanding the People’s Daily dataset from 71,456 words to 95,291 words, the BERT-CRF (Bidirectional Encoder Representations from Transformers-Conditional Random Field) model achieves an accuracy rate of 88.53%, a recall rate of 92.90% and an F1 score of 90.66%, successfully identifying event, time and location named entities within traffic violations. The data of traffic violations is then enhanced through forward geocoding and the Bayesian formula, and traffic violations are analysed from time, space, administrative region, gender and weather, to provide support for the dynamic allocation of law enforcement forces on traffic scenes and the precise management of
traffic violations.

With the continuous increase of urban vehicles, traffic congestion becomes severe in the metropolitan areas and higher car utilisation areas. The traffic signal timing scheme can effectively alleviate traffic congestion at intersections. We need to make a profound study in the traffic signal timing. An optimisation model is established, which not only takes the average delay time of vehicles, the number of vehicle stops and the traffic capacity, but also takes the exhaust emissions as the evaluation indexes. The model is too complex and involves too many variables to be solved by using multi-objective programming. Thus, the Harris Hawks Optimisation (HHO) with few parameters and high search accuracy was used to solve the model. To avoid the disadvantages of poor search performance and easy to fall into local optimisation of the Harris Hawks Algorithm, multi-strategy improvements were introduced. The experimental effects show that during the peak hours of traffic flow, the improved algorithm can reduce the average vehicle delay by 36.7%, the exhaust emission by 31.2% and increase the vehicle capacity by 41.6%. The above indicators have also been upgraded during the low peak stage.

The transportation industry is a key area for ecological civilisation construction and low-carbon development. As the core support of the  national integrated transportation system, the ecological development level of integrated transportation hub (ITH) is crucial for enhancing 
the sustainable development capacity of the national integrated transportation. An eco-efficiency evaluation index system of ITH is established  in this study and the eco-efficiencies of twenty international ITHs in China are comprehensively evaluated based on the super-efficient epsilon-based measure (EBM) model. Then the panel Tobit regression model is adopted to analyse the influencing factors of eco-efficiency. The results  show that the average eco-efficiency of ITHs in China during 2011–2021 declines first and then rises, with a relatively high level overall but not efficient yet, and there is an obvious gradient distribution characteristic in all eco-efficiencies. Among them, Guangzhou ranks first, followed by  Haikou, and Harbin ranks last. It is found that integrated transportation efficiency, urban green coverage, level of opening-up and economic development improve eco-efficiency significantly, while urbanisation rate, industrial structure and technology input have a negative impact. The  results are consistent with the actual situation, verifying the practicality of models, and can be used to promote the sustainable development of integrated transportation.

The current development of urban agglomeration greatly promotes the intercity connection and elevates the significance of intercity mobility system. However, intercity mobility often exhibits extreme spatiotemporal imbalances due to the diverse urban characteristics. This poses a huge challenge for traffic management and reveals the necessity on understanding the urban attractiveness for intercity mobility, which is represented as spatial interaction gravity in this study. While recent works have explored relevant aspects, they failed to provide insights into temporal variations in spatial interaction gravity or capture the determining factors from multiple perspectives. To fill this gap, this study proposed a two-phase framework to measure the urban spatial interaction gravity and developed determination approaches utilising the large-scale location-based services (LBS) dataset. Specifically, the inverse gravity model was adopted for the measure within multiple urban agglomerations and city sets during weekdays, weekends and holidays. Then, we developed the fitting equations of spatial interaction gravity by incorporating the correlated features associated with social, economic, network accessibility and land use. The findings present spatial interaction gravity across different periods and substantiate the distinct determination effects of features, with a high fitting accuracy. They provide promising supports for the intercity mobility prediction and pre-emptive traffic management.

With the emergence of novel transportation trends, regular buses have experienced a significant decline in passenger numbers. Consequently, it becomes imperative to conduct studies on passengers’ intentions. This particular investigation employed a meticulously designed survey questionnaire to gather data, and developed a new model that integrates the theory of planned behaviour, technology acceptance model and expectation confirmation theory. The primary aim was to explore the key factors that influence residents’ ongoing behavioural intentions towards regular public bus travel. Furthermore, a gender-based multi-group analysis was conducted to investigate the impact mechanism of gender differences on ongoing behavioural intentions. The new model demonstrates various degrees of positive or negative influences among the variables, thereby confirming its universal applicability. Moreover, the multi-group analysis reveals that compared to gender, travel satisfaction has a stronger impact on women’s intentions, while travel attitude has a stronger impact on men’s intentions to travel by certain mean of transport. Simultaneously, perceived behavioural control does not significantly affect persistent intention for women but has a significant positive impact on persistent intention for men. Furthermore, perceived ease of use does not significantly impact perceived usefulness for women but has a significant positive effect on perceived usefulness for men. These research findings bear great significance in promoting environmentally-friendly travel practices.

The main objective of the transport reliability and maintenance analysis is to improve the understanding of accidents through incident investigations. This research focuses on composite pneumatic tyres used in transportation engineering and presents both theoretical and experimental studies. The finite element method used for numerical simulation combined with pre-experimental measurements based on optimisation by material vibration response is presented for tyre material modelling. Piezoelectric vibration test was used for the pre-experimental test of the tyre quarters. The simulation results indicate that the pneumatic tyre with the recommended air pressure inflation shows the least amount of deformation. In comparison, pneumatic tyres with recommended and reduced air pressure inflation of 0.25, 0.5 and 0.75 bar are under research. Additionally, it was established that, when subjected to external forces that exceed the tyre’s maximum weight capacity, as determined by the manufacturer, the tyre exhibits significant stiffening and internal stress. The research suggests that this methodology can be used to obtain a realistic model of vehicle tyre dynamic processes and assess the impact on road traffic safety with different inflation pressures and loads.

This article deals with the highly topical issue of greening air transport chains. It is important to consider the environmental aspect of the current performance of air transport in regions with less intensive air transport chains, such as the Eastern Adriatic. The regional airports of Ljubljana, Zagreb and Belgrade are dependent on European air cargo hubs and at the same time have the task of connecting the national airports in Sarajevo, Podgorica, etc., which complicates the functioning of air transport chains regionally. A comprehensive consideration of air cargo chains is important in terms of price and transport time, but also in terms of the GHG footprint. The results show that an environmental assessment of air transport chains is necessary for a more comprehensive decision on sustainable supply chains. The study enriches the scientific understanding of air transport chains in the Eastern Adriatic region from the point of view of carbon footprint and energy efficiency of transport, and highlights the need to use the already developed IT tools in the assessment and modelling of transport chains when different options are presented to cargo owners. Integrating the above approaches and data into current business models enables the gradual regional decarbonisation of air transport.

The role of transportation is becoming increasingly important in the world economy, and road transport in particular plays a very important role in all types of transportation. For this reason, it is extremely important to monitor its performance regularly. Very often, this is done using Data Envelopment Analysis (DEA) performance evaluation models, and consequently, there are numerous articles in the literature on DEA evaluation of road transport systems. In this study, we first summarise these articles and classify them according to different characteristics (environmental, safety, economic, energy). Finally, we use them as a basis for developing a novel DEA framework, which is used for the evaluation of the efficiency and ranking of road transport systems that also takes into account undesirable outputs, i.e. environmental and safety outputs. As a case study, we evaluate 28 European countries from technical, safety and environmental aspects. The CCR and SBM models are used to evaluate the efficiency of these countries for the last two years of published data. The results show that Denmark ranks first and Cyprus last for both years. It was also found that safety efficiency is generally rated lower than other criteria. Finally, the results and reasons for the efficiency and inefficiency of specific decision-making units, i.e. countries, are discussed.

The article discusses the results of studies of railway line capacity relative to the application of additional block division using virtual blocks in the process of positioning a train reporting its position and train set integrity. The studies were conducted using the authors’ original simulation software enabling extensive parameterisation of infrastructure, including configuration of the train control system and signalling principles, by taking the actual characteristics of train movements into account based on data obtained from real-life measurements.

Autonomous vehicles (AVs) and human-driven vehicles (HDVs) will share the roads for a long time, hence the need to study traffic flows mixing AVs and HDVs, especially during the AV introduction period. This paper aims to investigate the roadway and traffic characteristics that affect the impact of AVs on freeway traffic operations, using an adapted version of the HCM6 truck passenger-car equivalent (PCE) methodology. A large number of scenarios comprising different roadway characteristics, AV types and traffic flow compositions were simulated using Vissim to obtain AV PCEs. The results indicated that, for all scenarios considered, an AV has a 20% lower impact on the quality of service and operation than an HDV. A CART decision tree indicated that the most important factors affecting the AVs’ impact on traffic operations are vehicle-to-vehicle connectivity level and the capability of travelling in platoons. Maximum platoon length did not matter, and the increase in the number of traffic lanes reduced the positive impact of AVs on service quality.

Non-synchronised timetables of the first hour trains can lead to longer waiting times for passengers wishing to transfer at the transfer station. This study aims to reduce the waiting time of passengers by synchronising the timetables of first hour trains using actual transfer times. The transfer times of the passengers are obtained from the observations and are used in this synchronisation study. The genetic and simulated annealing algorithms are implemented to solve the first train synchronisation model. Finally, a case study is conducted on a section of the Istanbul metro network to test the synchronisation model. The results show that the total waiting time of the first hour trains transfer passengers is reduced by 35% by applying the proposed model. Another result of the study shows that using the actual transfer time instead of the average transfer time of the passengers reduces the average waiting time of the passengers by 19%.

With the popularity of electric vehicles, they have become an indispensable part of traffic flow on the road network. This paper presents a reliability-based network equilibrium model to realise the traffic flow pattern prediction on the road network with electric vehicles and gasoline vehicles, which incorporates travel time reliability, electric vehicles’ driving range and recharge requirement. The mathematical expression of reliable path travel time is derived, and the reliability-based network equilibrium model is formulated as a variational inequality problem. Then a multi-criterion labelling algorithm is proposed to solve the reliable shortest path problem, and a column-generation-based method of the successive average algorithm is proposed to solve the reliability-based network equilibrium model. The applicability and efficiency of the proposed model and algorithm are verified on the Nguyen-Dupuis network and the real road network of Sioux Falls City. The proposed model and algorithm can be extended to other road networks and help traffic managers analyse traffic conditions and make sustainable traffic policies.

Effectively equilibrating passenger distribution on metro platforms and carriages is important for relieving local congestion. This paper explores the role of incentive mechanisms in encouraging passenger queuing behaviours. To quantitatively analyse passenger compliance with the policy, a questionnaire survey was conducted in Fuzhou, China. According to the preliminary analysis of the survey data, passengers have various moving distance preferences under the incentive scenarios, namely, no movement, smaller distance and greater distance. Additionally, this paper establishes a nested logit model that considers travel purposes and moving distances. The empirical results show that although monetary and point-system incentives can effectively enhance passenger compliance with transfer queue-positioning requirements, when the moving distance is very small, people pay less attention to rewards. Compared to those commuting on weekends, passengers commuting on weekdays comply with policies more strongly, and the effect of implementing incentive policies is better; however, the effect of those policies is reduced among those travelling for leisure. Meanwhile, when travelling for leisure, as the number of companions increases, people’s willingness to follow guidance on where to wait increases. According to the results, the implementation of incentive-based waiting encouragement policies during peak working days can result in good compliance.

At present, interest in the application of unmanned aerial vehicles (UAV) for delivery is growing. A new “multi-type of UAV collaborative delivery” mode has been proposed. Through a combination of large, medium and small UAVs, the delivery capabilities of the UAV logistics system are significantly improved. Sometimes there is high demand, resulting in planned delivery routes that are no longer feasible, and even cause a shortage of distribution centre capacity and drones. This study explores logistics delivery strategies to solve problems caused by high demand. In this study, a multitype and multidistribution UAV model was established with the objective of minimising the total cost of distribution by considering factors such as the UAV energy consumption, load and distribution centre conditions. An improved ant colony algorithm was designed and its effectiveness was verified through the variability of the calculation time and multiple calculation results of different-scale examples. Finally, the classic vehicle routing problem (VRP) case is used in three scenarios to analyse the UAV scheduling optimisation problem. The results indicate that assisted delivery can reduce costs by 3% while ensuring delivery timeliness. The results of this study can provide guidance and benchmarks for the application of UAVs in urban logistics delivery systems.

The structural deficiencies of the terminal delivery path often make it the main culprit of urban traffic congestion and environmental pollution. Traditional studies of express networks regarded them as an independent entity, ignoring the endogenous role of urban road network morphology and structure. To solve this problem, this paper explored the spatial dependency of terminal delivery routes in Xi'an City based on the idea of a bipartite graph network. A spatial dependency matrix of delivery paths–urban roads was constructed by abstracting delivery paths as node-set A and urban roads as node-set B. In addition, three spatial dependencies indexes, including degree centrality, betweenness centrality and closeness centrality were introduced to analyse the coupling features of these two objects. The results show that these dependency measures can reflect the coupling features of urban terminal delivery paths and urban roads. Firstly, degree centrality demonstrates terminal delivery path coverage and coupling hierarchy and scale-free nature. Secondly, betweenness centrality presents the road utilisation balance of terminal delivery paths. Thirdly, closeness centrality explains how easy it is for delivery paths to connect with others.

Based on two-sided market theory, this paper has studied the pricing problem of ride-hailing platforms with a combination of inter-group network externality and inner-group network externality. Two scenarios of user structure are considered. In scenario 1, both travellers and drivers are single-homing. In scenario 2, travellers are single-homing while drivers are multi-homing. Moreover, time sensitive factors and driver’s commission rate are introduced to reflect the characteristics of transport industry. Finally, the impact of network externality, time sensitivity, driver’s commission rate and entry cost on ride-hailing platform pricing, user scale and profits are analysed. The results show that inter-group network externality and inner-group network externality have a negative effect on platform prices charged to both travellers and drivers. However, when travellers are multi-homing, the price charged to travellers is positive with respect to the inter-group network externality from drivers. In the relationship between travellers’ scale and inter-group network externality, inner-group network externality is positive. Further, in both scenarios, the network externalities from the two sides affect platform profits negatively.

Connected and autonomous vehicles (CAVs) are recognised as a technology trend in the transportation engineering arena. As one of the most popular capabilities of CAVs, trajectory planning attracts extensive attention and interest from both academia and the industry. Segmented trajectory planning is gaining popularity for its simplicity and robustness in computation and deployment. Constructive recommendations and guidelines can be provided by exploring the effects of segmented trajectories in different settings of CAVs and intersections. This research proposes a control strategy for segmented trajectory planning in a fixed signal timing environment. To test the effects of this control strategy, this research designs simplified fixed signalised intersection scenarios and implements segmented trajectory planning features of CAVs with different traffic demand scenarios, distances and speed limits. The results show that the proposed control strategy has stable superior performances in different traffic scenarios especially when the traffic volume is near capacity.

Tram signal priority control is a crucial approach for enhancing the reliability of tram operations and has been implemented in various cities. Nevertheless, unpredictable tram operations influenced by tram dwell time during station stops can cause signal priority control failure at intersections. It is challenging to precisely predict tram dwell time at stations that offer multiple lines. To address this issue, the proposed research presents a capped robust optimal control (CRC) technique for tram signal priority. This method entails considering the stochastic number of passengers boarding and alighting at stations with multiple lines. Furthermore, tram delay calculation models at intersections are established and integrated into an objective function. The main objective of this strategy is to enhance tram operation reliability and maximise tram operation efficiency while reducing the adverse impact of tram priority on other vehicles at the intersection. A case study was conducted to evaluate the effectiveness of the CRC method. The results indicate that the CRC technique significantly improves tram operation reliability and efficiency.