Low-carbon transport is a priority in addressing climate change. Transport is still almost totally dependent on fossil fuels (96%) and accounts for almost 60% of global oil use. Sustainable transport systems, both passenger and freight, should be economically and technically feasible, but also low-carbon and environmentally friendly. The calculation of greenhouse gas emissions in transport projects is becoming a primary target of transport companies as a part of an endeavor for low-carbon strategies to reduce the energy demand and environmental impact. This paper investigates the CO2 impact of construction and operation of the main highway and railway line infrastructure in Greece, which connects Athens and Thessaloniki, the capital and the second biggest city in Greece respectively and provides a comparative analysis in roadway and railway transport.
Measuring carbon emissions is an essential step in taking required action to fight global warming. This research presents a computational method for measuring transport related carbon emissions in a healthcare supply network. The network configuration significantly impacts carbon emissions. First, a multi-objective mathematical programing model is developed for designing a healthcare supply network in the form of a two-graph location routing problem under demand and fuel consumption uncertainty. Objective functions are minimizing total cost and minimizing total fuel consumption. In the presented model, the demand of each customer must be completely satisfied in each time period, and backlog is not permitted. The number and capacity of vehicles are determined, and vehicles are heterogeneous. Furthermore, fuel consumption depends on traveling distance, vehicle and road conditions, and the load of a vehicle. The centroid method is applied to face demand uncertainty. Next, a multi-objective non-dominated ranked genetic algorithm (M-NRGA) is proposed to solve the model. Then, a Monte Carlo based approach is presented for measuring
transport-related carbon emissions based on fuel consumption in supply network. Finally, the proposed approach is applied to the case of a healthcare supply network in the Fars province in Iran. The obtained results illustrate that the proposed approach is a practical tool in designing healthcare supply networks and measuring transport-related carbon emissions in the network.
Train timetable is the key document to regulate railway traffic through sequencing train movements to keep the appropriate order. Timetable stability and on-schedule rate are closely related. Delays caused by disturbances in train operations can be absorbed by a high quality timetable with high stability, and the on-schedule rate then can be assured. This paper improves the stability of timetables of several connected railway sections to assure the on-schedule rate with a simulation method. Firstly, we build a macroscopic network model of train operation in a railway network using the Petri net theory. Then we design the train tracking subnet model, the station subnet model and arrival-departure track subnet model. At last we propose a computing case, simulating the train operation process based on the presented models, and the simulation results prove the feasibility and availability of the models. The approach presented in this paper can offer valuable decision-support information for railway operators preparing train timetables.
In many countries, bus operators are private companies whose service has been leased by government agencies. These agencies develop service compliance indices or measures to keep track of factors such as passenger satisfaction, frequency, and regularity but do not necessarily include the objectives of the operators in the assessment. In this paper, we used slack-based measure data envelopment analysis (SBM) to investigate whether it is possible for a bus operator to be efficient (from a private perspective) and match required standards of frequency and regularity. In doing so, data collected from two major bus operators in Santiago, Chile has been used comprising 99 services. The results show that when private objectives, namely revenues, are included in the analysis, bus operators do not necessarily seek to improve the regularity of their service. Moreover, it was found that some bus services are on the efficient frontier while keeping low performance measure standards. Using the shadow prices of the models, it was also found that improving the performance measures will be hard for many bus services unless there is a significant change in factors that are not under control of the operators (i.e., number of stops, length of the route, etc.). This shows the difficulty of correctly aligning the private objectives of operators with agencies’ objectives.
Reasonable selection of passenger flow routes considering
different transportation organization modes can meet
the demands of adapting to large-scale high-speed railway
networks and improving network efficiency. Passenger flow
routing models are developed to find and optimize a set
of passenger flow routes for a high-speed railway network considering different transportation organization modes. In this paper, we presented a new approach minimizing the operating costs, including traveling cost, cost of travel time differences between different lines, and penalties for the inter-line. The network was reconstructed to solve the directed graph with four nodes (node-in-up, node-in-down, nodes-outup, and nodes-out-down) indicating one station. To tackle our problem, we presented an integer non-linear programming model, and direct passenger demand was guaranteed owing to volume constraints. Binary variables were introduced to simplify the model, and the algorithm process was optimized. We suggested a global optimal algorithm by Lingo 11.0. Finally, the model was applied to a sub-network of the Northeast China railway system. Passenger flow routes were optimized and the transportation organization mode was discussed based on passenger volume, traveling distance, and infrastructure.
A driver’s reaction time encountering hazards on roads involves different sections, and each section must occur at the right time to prevent a crash. An appropriate reaction starts with hazard detection. A hazard can be detected on time if it is completely visible to the driver. It is assumed in this paper that hazard properties such as size and color, the contrast between the environment and a hazard, whether the hazard is moving or fixed, and the presence of a warning are effective in improving driver hazard detection. A driving simulator and different scenarios on a two-lane rural road are used for assessing novice and experienced drivers’ hazard detection, and a Sugeno fuzzy model is used to analyze the data. The results show that the hazard detection ability of novice and experienced drivers decreases by 35% and 64%, respectively, during nighttime compared to daytime. Also, moving hazards increase hazard detection ability by 9% and 180% for experienced and novice drivers, respectively, compared to fixed hazards. Moreover, increasing size, contrast, and color difference affect hazard detection under nonlinear functions. The results could be helpful in safety improvement solution prioritization and in preventing vehicle-pedestrian, vehicle-animal, and vehicle-object crashes, especially for novice drivers.
For a long time, many researchers have investigated the continuous network design problem (CNDP) to distribute equitably additional capacity between selected links in a road network, to overcome traffic congestion in urban roads. In addition, CNDP plays a critical role for local authorities in tackling traffic congestion with a limited budget. Due to the mutual interaction between road users and local authorities, CNDP is usually solved using the bilevel modeling technique. The upper level seeks to find the optimal capacity enhancements of selected links, while the lower level is used to solve the traffic assignment problem. In this study, we introduced the enhanced differential evolution algorithm based on multiple improvement strategies (EDEMIS) for solving CNDP. We applied EDEMIS first to a hypothetical network to show its ability in finding the global optimum solution, at least in a small network. Then, we used a 16-link network to reveal the capability of EDEMIS especially in the case of high demand. Finally, we used the Sioux Falls city network to evaluate the performance of EDEMIS according to other solution methods on a medium-sized road network. The results showed that EDEMIS produces better solutions than other considered algorithms, encouraging transportation planners to use it in large-scale road networks.
The growth in air traffic in recent decades in Europe has consequently caused aircraft delays due to insufficient capacities of airspace and airports. Primary and reactionary delays at certain European and Croatian coastal airports in 2014 are analyzed in the paper according to CODA methodology and classified according to main flight delays causes. The largest share of delay minutes at the Croatian coastal airports (75%) are related to reactionary delays, ranging from around 20% to 60% of total delay at the most congested European airports. Special emphasis is given to the analyses of rotational reactionary delay, and the results indicate that the share of reactionary delay in total delay at the Split airport is significantly higher compared to selected European airports, which may be explained by delays propagated from air traffic network and limited airport capacity. The total recorded delay at Croatian coastal airports is minor compared to total recorded delay in the European air traffic system, but delay patterns are quite similar, especially during peak summer months.
Queue discharge flow is the most frequently observed phenomenon on urban motorways when demand exceeds capacity. Once a queue is formed, congestion arises, and the number of vehicles that can pass from downstream reduces. This reduction phenomenon is defined as the capacity drop and calculated by taking the difference between capacity and discharge flow at a road section. Obviously, this capacity drop exists after an onset of congestion and may increase in relation to weather conditions, such as rain, snow, or fog, which cause longer queues and delays. In this paper, the effect of rain on discharge flows is investigated and compared with sunny days on Istanbul urban motorways. Besides, rain precipitation during congestion is considered and related to discharge flow. Four different motorway sections were analyzed, and up to 37% discharge flow reduction was determined between sunny and rainy conditions. Motorway sections with higher free flow speed (FFS) were found to be more affected by rain, and discharge flow reduction was bigger compared to the section with the lowest FFS. For 1 mm/m2/h of precipitation, the discharge flow is estimated as 1,719 pcu/h/lane when FFS is 84 km/h, and as 1,560 pcu/h/lane if FFS is 104 km/h.
Designing tariff systems in public passenger transport is a complex issue of optimization by means of satisfying the wants and needs of all the engaged parties. An integrated passenger transport system (IPTS) stands for the concept of organization and management of public passenger transport based on a uniform tariff system. The issue of transport disadvantage of certain peri-urban and rural areas is the result of poorly organized transport systems. Social and spatial isolation in the framework of mobility is partly the fault of the way in which tariff systems have been designed with no regard to the social factors of the engaged parties for which such systems are designed – its users. Special emphasis in the research of tariff systems is placed on resolving issues of designing tariff zones, maximizing social welfare, transport equity, and transport disadvantage. An outline of the existing research and a review of literature concerning tariffs in integrated passenger transport systems is given, and proposals are put forward for future research due to the need for designing socially beneficial tariff systems, which would eliminate social exclusion, i.e., the transport disadvantage of individuals or parts of society.
In the past few years, numerous mobile applications have made it possible for public transit passengers to find routes and learn about the expected arrival times of their transit vehicles. Previous studies show that provision of accurate real-time bus information is vital to passengers for reducing their anxieties and wait times at bus stops. Inadequate and/or inaccurate real-time information not only confuses passengers but also reinforces the bad image of public transit. However, almost all methods of real-time information optimization are aimed at predicting bus arrival or travel times. In order to make up for the lack of information accuracy, this paper proposes a new approach to optimize mobile real-time information for each transit route based on robust linear optimization. An error estimation is added to current bus arrival time information as a new element of mobile bus applications. The proof process of the robust optimization model is also presented in this paper. In the end, the model is tested on two comparable bus routes in Shanghai. The real-time information for these two routes was obtained from Shanghai Bus, a mobile application used in Shanghai City. The test results reflect the validity, disadvantages, and risk costs of the model.
Near Field Communication (NFC) is a very short-range type of radio communication that is compatible with other contactless communication technologies. It provides enormous possibilities, particularly given that it does not require any particular communication infrastructure. NFC technology has found possible application in contactless cards and mobile phone devices as a communication infrastructure which provides a platform for the development of NFC-based business services. This paper proposes a novel approach to forecasting the number of new users of NFC mobile phones based on fuzzy logic and the Norton-Bass diffusion model. The proposed approach is demonstrated through the case study.
In subway stations, bottlenecks are the narrowed areas that reduce pedestrian flows in channels. Because pedestrians at bottlenecks are forced to dense together, bottlenecks decrease flow efficiency and pedestrians’ transfer comfort and may trigger serious crowd disasters such as trampling. This study used pedestrian experiments to investigate the methods of optimizing pedestrian traffic at bottlenecks of subway stations. Three optimization measures were proposed and evaluated by analyzing the characteristics of pedestrian flows, including efficiency, smoothness, and security. In this paper, setting the rear sides of the bottleneck entrance as straight and surface funnel shapes is called straight funnel shape and surface funnel shape, respectively. Setting a column at a bottleneck is called the column obstacle. The results showed that when efficiency or security come first, a column on the left is recommended; when comfort comes first, a concave funnel is recommended; when comprehensiveness is prioritized, a column on the left is recommended. Moreover, the larger the volume, the optimization is more obvious. Although many bottlenecks cannot be prevented when subway stations are constructed, the proposed optimization measures may help ease their adverse effects by improving facility efficiency, smoothness, and security, and by providing recommendations for designing and managing subway stations.
Urban rail transit trips usually involve multiple stages, which can be differentiated in terms of transfers that may involve distinct access and egress modes. Most studies on access and egress mode choices of urban rail transit have separately examined the two mode choices. However, in reality, the two choices are temporally correlated. This study, therefore, has sequentially applied the mixed logit to examine the contributors of access and egress mode choices of urban metro commuters using the data from a recent survey conducted in Nanjing, China. 9 typical multimodal combinations constituted by 5 main access modes (walk, bike, electric bike, bus, and car) and 2 main egress modes (walk and bus) are included in the study. The result proves that the model is reliable and reproductive in analyzing access/egress mode choices of metro commuters. Estimation results prove the existence of time constraint and service satisfaction effect of access trip on commuters’ egress mode choice and reveal the importance of transfer infrastructure and environments that serve for biking, walking, bus riding, and car parking in commuter’s connection choice. Also, policy implications are segmentally concluded for the transfer needs of commuters in different groups to encourage the use of metro multimodal trips.
According to the European Committee (EC) on Transport, the future road transport strategy lies in creating a strong road transport sector which is based on a well-functioning internal market, fair competition and workers’ rights, decarbonization, and use of digital technologies. Urban and suburban passenger transportation systems, according to the principles of the EC, have a key role in achieving the goal of sustainable development and sustainable transport in cities. The fare, ticketing, and payment modes have a significant impact on public urban transport systems, primarily in terms of collecting transport service fees, and represent the basic source of financing of such systems, in addition to subsidies and grants from city budgets. This paper presents the selection methodology of the optimal fare system for urban public transport, applicable for all cities with an organized public city passenger transport (PCPT) system. Based on the established criteria with respect to setting tariff limits and fare systems, passenger demand, and the enterprise organizing the transport, the tariff system was selected. The presented method is that of multi- criteria optimization of the tariff system with numerical results on the example of the City of Novi Sad.
Intelligent traffic systems attempt to solve the problem of traffic congestion, which is one of the most important environmental and economic issues of urban life. In this study, we approach this problem via prediction of traffic status using past average traveler speed (ATS). Five different algorithms are proposed for predicting the traffic status. They are applied to real data provided by the Traffic Control Center of Istanbul Metropolitan Municipality. Algorithm 1 predicts future ATS on a highway section based on the past speed information obtained from the same road section. The other proposed algorithms, Algorithms 2 through 5, predict the traffic status as fluent, moderately congested, or congested, again using past traffic state information for the same road segment. Here, traffic states are assigned according to predetermined intervals of ATS values. In the proposed algorithms, ATS values belonging to past five consecutive 10-minute time intervals are used as input data. Performances of the proposed algorithms are evaluated in terms of root mean square error (RMSE), sample accuracy, balanced accuracy, and processing time. Although the proposed algorithms are relatively simple and require only past speed values, they provide fairly reliable results with noticeably low prediction errors.
This paper aims to find the optimal depot locations and vehicle routings for spare parts of an automotive company considering future demands. The capacitated location-routing problem (CLRP), which has been practiced by various methods, is performed to find the optimal depot locations and routings by additionally using the artificial neural network (ANN). A novel multi-stage approach, which is performed to lower transportation cost, is carried out in CLRP. Initially, important factors for customer demand are tested with an univariate analysis and used as inputs in the prediction step. Then, genetic algorithm (GA) and ANN are hybridized and applied to provide future demands. The location of depots and the routings of the vehicles are determined by using the variable neighborhood descent (VND) algorithm. Five neighborhood structures, which are either routing or location type, are implemented in both shaking and local search steps. GA-ANN and VND are applied in the related steps successfully. Thanks to the performed VND algorithm, the company lowers its transportation cost by 2.35% for the current year, and has the opportunity to determine optimal depot locations and vehicle routings by evaluating the best and the worst cases of demand quantity for ten years ahead.
The U.S. Highway Capacity Manual (HCM 2010) methodology is used in Spain to evaluate traffic operation and quality of service. In two-lane undivided highways, the effect of limiting where drivers could pass slower vehicles, or passing restrictions, is considered through the percentage of no-passing zones. This measure does not account for how passing opportunities are distributed along the road. The objective of this research was to evaluate the effect percentage of no-passing zones and average passing zone length on a two-lane highway and, if significant, incorporate them in the analysis methodology,. The TWOPAS microsimulation program was calibrated and validated to the Spanish conditions. Passing restrictions had little effect on average traffic speed (ATS), with differences lower than 6 km/h between a road segment with no passing restrictions and a road segment with a passing restriction on 100% of its length. Conversely, passing restrictions can increase the percent time spent following (PTSF) up to 30%. Increasing the passing zone length beyond 2,000 m does not improve PTSF. The new models could be used to better estimate traffic operation on Spanish two-lane highways.
Improving urban mobility is crucial to the sustainable development of a city. Well-managed movement of individuals, goods, and services is essential to increase citizens’ welfare, not only by reducing travel times and congestion levels, but also by minimizing air pollution, noise, accidents, etc. To achieve the desired results, the objectives and scope of the optimization efforts have become broader in recent years. Instead of focusing only on the flows and on the network itself, research and projects have connected various areas of economy to traffic management, such as public health and logistic optimization. In this work we discuss the interconnections between urban mobility and city logistics, and present a case study showing how the mobility plan implemented in Ghent (Belgium) in April 2017 affected its logistic system.
The article investigates human capital in national postal providers (NPPs), which is an area that has yet to be underpinned by research. There is a number of works dealing with human capital, but only few address human capital in the postal service sector. The main aim of the article is to define differences in approach to human capital management in selected NPPs. Differences are expressed via the overall Human Capital Index (HCI). Due to the fact that so far there has been no complex research analyzing activities related to human capital in NPPs, it was necessary to carry out a content analysis of annual reports of selected NPPs. Results of the analysis served as a basis for expression of overall HCI. This study brings new insights into the topic and contributes to the sustainable development of the field.
With the development and application of modern information and communication equipment and services (ICES), global society has undergone significant development and progress in everyday life. Along with the positive, negative aspects of their usage have evolved. One of the problems is the new sources of driver distraction, which ultimately results in a serious, growing daily threat to traffic safety. Simultaneously with the development of ICES, the number of distracters (mobile terminal devices, laptops, smart watches, and others) has increased as well, which also reduces the level of safety and affects the quality of driving. With the increase in participation and ownership of mobile terminal devices, as well as the implementation of modern multimedia information and communication systems in vehicles, this problem will further escalate in the coming years. The unfavorable factor comes from the fact that distraction factors influenced by using ICES belong to bio-mechanical, cognitive, auditory, and visual categories, which directly affects essential human functions for successful and safe vehicle operation. This paper provides insight into the issue of distraction factors that affect drivers with an emphasis on those caused by using modern ICES.
Departure time choice is critical for subway passengers to avoid congestion during morning peak hours. In this study, we propose a Bayesian network (BN) model to capture departure time choice based on data learning. Factors such as travel time saving, crowding, subway fare, and departure time change are considered in this model. K2 algorithm is then employed to learn the BN structure, and maximum likelihood estimation (MLE) is adopted to estimate model parameters, according to the data obtained by a stated preference (SP) survey. A real-world case study of Beijing subway is illustrated, which proves that the proposed model has higher prediction accuracy than typical discrete choice models. Another key finding indicates that subway fare discount higher than 20% will motivate some passengers to depart 15 to 20 minutes earlier and release the pressure of crowding during morning peak hours.
It is critical to implement accurate short-term traffic forecasting in traffic management and control applications. This paper proposes a hybrid forecasting method based on neural networks combined with the K-nearest neighbor (K-NN) method for short-term traffic flow forecasting. The procedure of training a neural network model using existing traffic input-output data, i.e., training data, is indispensable for fine-tuning the prediction model. Based on this point, the K-NN method was employed to reconstruct the training data for neural network models while considering the similarity of traffic flow patterns. This was done through collecting the specific state vectors that were closest to the current state vectors from the historical database to enhance the relationship between the inputs and outputs for the neural network models. In this study, we selected four different neural network models, i.e., back-propagation (BP) neural network, radial basis function (RBF) neural network, generalized regression (GR) neural network, and Elman neural network, all of which have been widely applied for short-term traffic forecasting. Using real world traffic data, the experimental results primarily show that the BP and GR neural networks combined with the K-NN method have better prediction performance, and both are sensitive to the size of the training data. Secondly, the forecast accuracies of the RBF and Elman neural networks combined with the K-NN method both remain fairly stable with the increasing size of the training data. In summary, the proposed hybrid forecasting approach outperforms the conventional forecasting models, facilitating the implementation of short-term traffic forecasting in traffic management and control applications.
In this paper, the relation of the velocity of a vehicle in the slip mode to the parameters of the tire marks on the road surface is examined. During traffic accident reconstructions, the initial velocity of a sideslipping vehicle is established according to the tire mark trajectory radius, and calculations highly depend on the directly measured parameters of the tire marks, in particular cases known as yaw marks. In this work, a developed and experimentally validated 14-degree-of-freedom mathematical model of a vehicle is used for an investigation of the relation between velocity and trajectories. The dependence of initial vehicle velocity on tire yaw mark length and trajectory radius was found as a characteristic relation. Hence, after approximation of the permanent slipping part by a polynomial, the parameters of the latter were related to vehicle velocity. The dependences were established by specific experimental tests and computer-aided simulation of the developed model.
Small cities with less than 200,000 inhabitants do not usually suffer from chronic congestion problems. However, private vehicles are used excessively, making it necessary to implement measures to encourage further use of public transport and pedestrian mobility to make it more sustainable. Bypasses improve level of service (LOS) by removing cars from the city center, leading to significant reductions in overall travel time. Most studies so far have been conducted in large cities suffering chronic congestion problems, so the aim of this research is to analyze the effects of bypasses in small and non-congested cities through the construction of a traffic model in Badajoz (Spain), starting with the allocation of the origin-destination travel matrix derived from surveys and traffic counts conducted at the southern and eastern accesses. The traffic model describes the mobility in potentially-capturable future southern traffic relationships and allows insights into different alternatives in the construction of a new high LOS road. This research concludes that small cities with no chronic congestion problems should plan bypasses as close as possible to the city, since they are the most economical, produce greater traffic capture, greater time savings, and eliminate the largest number of CO2 emissions from the urban center. The more distant alternatives have a higher LOS, however, these are longer and more expensive solutions that also capture less traffic and thus eliminate less CO2 emissions.
To apply the experimental data measured in a wind tunnel for a scaled aircraft to a free-flying model, conditions of dynamical similarity must be met or scaling procedures introduced. The scaling methods should correct the wind tunnel data regarding model support, wall interference, and lower Reynolds number. To include the necessary corrections, the current scaling techniques use computational fluid dynamics (CFD) in combination with measurements in cryogenic wind tunnels. There are a few methods that enable preliminary calculations of typical corrections considering specific measurement conditions and volume limitation of test section. The purpose of this paper is to present one possible approach to estimating corrections due to sting interference and difference in Reynolds number between the real airplane in cruise regime and its 1:100 model in the small wind tunnel AT-1. The analysis gives results for correction of axial and normal force coefficients. The results of this analysis indicate that the Reynolds number effects and the problem of installation of internal force balance are quite large. Therefore, the wind tunnel AT-1 has limited usage for aerodynamic coefficient determination of transport airplanes, like Dash 8 Q400 analyzed in this paper.
The importance of the Port of Ploče lies in serving the majority of the Bosnian market. However, the Pan-European Corridor Vc provides access to a much wider market in Central and South-Eastern Europe. The purpose of this paper is to express views on the future development of the Corridor and its consequential impact on the Port. This was conducted by means of analysis, comparison, and synthesis of cargo flow data and the dynamics data of the Corridor Vc construction. It covers the relations between Bosnia and Herzegovina (BiH) and the Republic of Croatia, and the assessment of importance of the Corridor in those countries. Statistical indicators show the importance of the Corridor completion for the successful execution of port development plans. The analysis of the Corridor status points to the current prevailing circumstances in BiH that make its realization ultimately uncertain, especially its railway component. The findings show that the most significant obstacles for a successful realization of Port of Ploče development plans are not only within the BiH internal geo-political relationships but in the disputes between BiH and the Republic of Croatia (RH) as well. Consequently, it has been shown that the Port of Ploče is not able to define and carry out the necessary measures toward BiH on its own but necessarily with the participation of the RH Government. The analysis offers observations and recommendations for improving relations with BiH, which would significantly advance the completion of the Corridor in BiH. In this way, it would allow for a full establishment of the Port of Ploče on target markets.
The aim of this study is to develop a framework for investigating a comprehensive set of Key Performance Indicators (KPIs) for the assessment of railway Intelligent Transportation Systems (ITS). The framework is established through four main steps: (1) development of a comprehensive set of KPIs for railway ITS; (2) validation of developed KPIs and collection of judgments from experts through a Delphi questionnaire; (3) evaluation of KPIs weights for assessing railway ITS with the Group Analytical Hierarchy Process (GAHP); and (4) presentation of a SWOT analysis for the developed KPIs by the authors. The results of the framework are presented as a set of 25 indicators for evaluation of railway ITS and their impacts. The framework could be helpful for selecting KPIs of ITS in another mode of transportation. Monitoring of the contributions of ITS towards sustainable railway can be achieved by a developed set of indicators which are classified in accordance with sustainable dimensions.
Safety is the key point of railway transportation, and railway traffic accident prediction is the main content of safety management. There are complex nonlinear relationships between an accident and its relevant indexes. For this reason, triangular gray relational analysis (TGRA) is used for obtaining the indexes related to the accident and the deep auto-encoder (DAE) for finding out the complex relationships between them and then predicting the accident. In addition, a nonlinear weight changing particle swarm optimization algorithm, which has better convergence and global searching ability, is proposed to obtain better DAE structure and parameters, including the number of hidden layers, the number of neurons at each hidden layer and learning rates. The model was used to forecast railway traffic accidents at Shenyang Railway Bureau, Guangzhou Railway Corporation, and Nanchang Railway Bureau. The results of the experiments show that the proposed model achieves the best performance for predicting railway traffic accidents.
A major problem connected with planning the organization of trains on a railway network is the optimization of the scheme of movement, which determines the routing and the number of trains. In this paper, an integrated approach of fuzzy linear programming method and multi-criteria analysis including three steps is proposed. In the first step, we defined the schemes of transportation of intercity trains and optimized each scheme in terms of direct operating costs by taking into account the uncertainty of passenger flows and utilization of train capacity using the fuzzy linear programming method. In the second step we determined the additional technological criteria to assess the variant schemes. The Fuzzy AHP method was applied to determine the weights of criteria. Using the results obtained from Fuzzy AHP, we prioritized the variant schemes of transportation by applying the PROMETHEE method. The third step presents the optimal choice of transportation of trains on a railway network based on minimum ratio of normalized costs and normalized PROMETHEE net outranking flow. In this step, the model uses the results obtained in the first and second steps. The practicability of the integrated approach is demonstrated
through the case study of Bulgaria’s railway network, and nine schemes were investigated. The model results and the real situation were compared. It was found out that the optimal scheme of intercity train transportation improves the service and reduces direct operating costs.
Drinking-driving behaviors are important causes of road traffic injuries, which are serious threats to the lives and property of traffic participants. Therefore, reducing the occurrences of drinking-driving behaviors has become an important problem of traffic safety research. Forty-eight male drivers and six female drivers who could drink moderate alcohol were chosen as participants. The drivers’ physiological data, operation behavior data, car running data, and driving environment data were collected by designing various virtual traffic scenes and organizing drivers to conduct driving simulation experiments. The original variables were analyzed by the Principal Component Analysis (PCA), and seven principal components were extracted as the input vector of the Radial Basis Function (RBF) neural network. The principal component data was used to train and verify the RBF neural network. The Levenberg-Marquardt (LM) algorithm was chosen to train the parameters of the neural network and build a drinking-driving recognition model based on PCA and RBF neural network to realize an accurate recognition of drinking-driving behaviors. The test results showed that the drinking-driving recognition model based on PCA and RBF neural network could identify drinking drivers accurately during driving process with a recognition accuracy of 92.01%, and the operation efficiency of the model was high. The research can provide useful reference for prevention and treatment of drinking and driving and traffic safety maintenance.
To scientifically and accurately evaluate the status of the development of green airports in China, evaluation methods of green, ecological airports are established in this paper. To address the shortcomings in subjective and objective weighting methods, we propose a combination weighting method based on Spearman’s rank correlation coefficient and evaluation grades based on interval approximation. At the same time, by taking into account resource conservation, environmental friendliness, operation efficiency, and people-oriented service, we propose an evaluation index system and an interval number for each index. Lastly, the theory is applied to five large airports in different regions of China. Analysis of the evaluation results shows that Shanghai Pudong International Airport (PVG) and Guangzhou Baiyun International Airport (CAN) have the highest scores for the resource conservation and environmental friendliness indexes, thus indicating that the development of a green ecological airport is closely related to its passenger transportation scale and economic strength. All considered airports showed the need for upgrading public service facilities and constructing intelligent equipment. The method proposed in this paper is reasonable and reliable; therefore, it can provide guidance for the evaluation and construction of green, ecological airports.
In this paper, a methodology for creating and testing new proposed transport infrastructure is presented. It is based on microscopic traffic simulation of current and forecasted traffic demand and in-depth analysis of traffic flow. The most congested boulevard in Skopje has been chosen as a use case. Real-world traffic flow data was collected and used in the calibration and validation of a microscopic simulation model. Three possible configurations of new urban mobility infrastructure have been proposed and best one chosen using appropriately defined criteria. The proposed configurations were evaluated from the aspect of traffic performances, suitability for forecasted future traffic demand, and vehicle emissions. The obtained results prove the effectiveness of the presented methodology in reducing delays and vehicle emissions and significantly improving the level of service of the chosen use case.
Having come into effect, the International Convention for the Control and Management of Ships’ Ballast Water and Sediments of 2004 requires ships to process their ballast water in accordance with specific standards. Different processing methods require different use of ship power, thus affecting fuel oil consumption, ships’ energy efficiency, and the ship economics in general. This paper presents the analysis and comparison of the economic viability of systems using two dominant ballast water treatment methods on merchant ships. The cost effectiveness of these methods, UV irradiation and electrochlorination, is compared to the standard efficiency of ballast water exchange using sequential flow method as a reference. The process efficiency is measured through fuel oil consumption on board. Taking into account possible variations in efficiency due to different designs and environmental constraints and assumptions, the findings are in favor of the electrochlorination method.
In this paper, the term “corporate social responsibility” (CSR) was first observed based on the existing pyramid, which defines CSR as a set of economic, legal, ethical, and philanthropic activities. Then the dimensioning of the model of corporate responsibility in postal system was performed, where seven categories of the CSR model were defined. Only one category (out of seven) represents a set of all four activities defined by the existing pyramid. Based on this, a new model of CSR in the postal system was developed, that is seen through the development of three dimensions of the postal network: physical (PH), electronic (E), and financial (F). The main objective of the paper is to define a CSR model that will ensure the economic, social, and environmental development of the postal market by synergistic operation of all three dimensions of the postal network. An analysis of the existing state of the postal services market was carried out, and then the level of the future development of the postal network was determined. Through evaluation or systematic and objective assessment of the CSR model based on the determined parameters, measurability of the CSR model is assured. This paper deals with the case of the public postal operator (PPO) in the Republic of Serbia (RS).
To explore efficient strategies of adjusting travel mode structure and support scientific implements of public transit system, this paper investigated travelers’ mode choice behavior in a multimodal network incorporating inertia in utility specifications. Comprehensive stated preference surveys considering four modes and four key decisive variables were designed, and face-to-face investigations were conducted to collect reliable data in Shanghai. The discrete choice technique considering mode-specific inertias was employed for modeling. The influencing factors of car stickiness were particularly explored. The results show that there are significant and mode-specific inertias in travelers’ choices of travel mode. The inertia of car users shifting to other modes is considerably large compared to inertias of public transit users. Travel time reliability and crowdedness in public transit are identified to be crucial factors influencing car users’ willingness to use public transit. Demographic attributes (age, income, education level and gender), spatial context features (commuting duration) and the regime of flexible work time are found to be significant influential variables of car stickiness. Moreover, direct and cross elasticity analyses were executed to show practical implications of shifting car users to public transit. The results provide serviceable support for transport planning and strategy making.
Environmental sustainability of the transport sector is a highly important issue today. The European Commission has made a goal of delivering a minimum 60% reduction in greenhouse gas emissions from transport by 2050. Part of this reduction will come from the railway sector by making the maintenance processes more environmentally friendly. This paper presents the results of the environmental assessment of the self-propelled bulk carriage (SPBC), an innovative new product aiming to decrease the environmental impact of the railway maintenance processes. The life cycle assessment (LCA) methodology was used in the study, and environmental impact is given in five impact categories based on the CML 2001 method through three main modules of the self-propelled bulk carriage life cycle: upstream, core, and downstream. The novelty of the research includes the fact that this is the first life cycle assessment study done for the bulk carriage, as well in that the authors have proposed the use of a new functional unit in the category of freight railway vehicles. The biggest environmental impact of the self-propelled bulk carriage across all five categories is in the use and maintenance phase of its life cycle and mainly due to diesel fuel use. The SPBC uses significantly less fuel than a conventional diesel locomotive.
The macroscopic fundamental diagram (MFD) is a graphical method used to characterize the traffic state in a road network and to monitor and evaluate the effect of traffic management. For the determination of an MFD, both traffic volumes and traffic densities are needed. This study introduces a methodology to determine an MFD using combined data from probe vehicles and loop detector counts. The probe vehicles in this study were taxis with GPS. The ratio of taxis in the total traffic was determined and used to convert taxi density to the density of all vehicles. This ratio changes over the day and between different links. We found evidence that the MFD was rather similar for days in the same year based on real data collected in Changsha, China. The difference between MFDs made of data from 2013 and 2015 reveals that the modification of traffic control can influence the MFD significantly. A macroscopic fundamental diagram could also be drawn for an area with incomplete data gained from a sample of loop detectors. An MFD based on incomplete data can also be used to monitor the emergence and disappearance of congestion, just as an MFD based on complete traffic data.
In populated cities with high traffic congestion, traffic information may play a key role in choosing the fastest route between origins and destinations, thus saving travel time. Several research studies investigated the effect of traffic information on travel time. However, little attention has been given to the effect of traffic information on travel time according to trip distance. This paper aims to investigate the relation between real-time traffic information dissemination and travel time reduction for medium-distance trips. To examine this relation, a methodology is applied to compare travel times of two types of vehicle, with and without traffic information, travelling between an origin and a destination employing probe vehicles. A real case study in the metropolitan city of Tehran, the capital of Iran, is applied to test the methodology. There is no significant statistical evidence to prove that traffic information would have a significant impact on travel time reduction in a medium-distance trip according to the case study.
The aim of this paper is to improve the process of physical distribution of consumer goods in the Western Balkans region through defining and analyzing key indicators of physical distribution. Theoretical research identified the most important indicators that affect physical distribution, such as: transport costs, quality of delivery, condition of vehicles, customer relations, and institutional/regulatory factors. The empirical study was conducted on a sample of 166 respondents in the distribution centers and transport companies and 146 end customers. Multiple regression analysis defined the individual contribution of each of these indicators to the process of physical distribution of goods. A comparison of results between the Western Balkan countries that are EU member states and those that are non-EU countries showed statistically significant differences in the impact of these indicators. Based on the obtained results, a model of physical distribution of consumer goods was presented. The results show to managements of distribution centers and transport companies which indicators should be developed to ensure timely and complete delivery of goods according to the 7P concept and thereby create a base of satisfied and loyal end users of transport services. Recommendations for future research are provided in the paper.
With urban rail transit noise becoming an increasingly serious issue, accurate and quick analysis of the low to medium frequency spectral characteristics of this noise has become important. Based on the FE-SEA (Finite Element - Statistical Energy Analysis) hybrid method, a vibration prediction model of a U-beam was established using a frequency-dividing strategy. The frequency domain and spatial characteristics of the vibration and structural noise of the U-beam within the 1.25-500 Hz frequency range, when subjected to vertical wheel-rail interaction forces, were analyzed. Compared with other methods described in the literature, the proposed FE-SEA hybrid method improves the calculation efficiency while ensuring better accuracy for a wide frequency range of structural noise and vibration. It was found that the excitation frequencies of the wheel-rail force dominate the spectra of the vibration and structural noise of the U-beam. Therefore, the frequency band containing the excitation frequencies should be the target for noise and vibration reduction when implementing strategies. The results show that the bottom plate contributes the most to the sound pressure level at all prediction points, and therefore should be the focus for noise and vibration reduction.
This manuscript analyzes two methods for Global Navigation Satellite System positioning error determination for positioning performance assessment by calculation of the distance between the observed and the true positions: one using the Cartesian 3D rectangular coordinate system, and the other using the spherical coordinate system, the Cartesian reference frame distance method, and haversine formula for distance calculation. The study shows unresolved issues in the utilization of position estimates in geographical reference frame for GNSS positioning performance assessment. Those lead to a recommendation for GNSS positioning performance assessment based on original WGS84-based GNSS position estimates taken from recently introduced data access from GNSS software-defined radio (SDR) receivers.
Agencies that have large-scale traffic signal systems under their purview often have to face asset upgrade decisions. As one of the most advanced traffic control technologies, Adaptive Traffic Control Systems (ATCS) are among the options that must be taken into consideration. Having in mind the complexity of benefits and costs stemming from ATCS investments, there is a need for information-rich performance measures (PM) used in the evaluation and decision-making. However, individual PMs are often not suitable for evaluating the multidimensionality of ATCS operations, due the inherent variability of ATCS control parameters. To expand the range of PMs used in ATCS evaluation, this research develops a new PM, i.e., average arrivals on green ratio, and proposes a refinement of average delay PM to account for queue formation. The paper also presents an application framework for a multi-criteria analysis, assuming a combination of the proposed and existing PMs. In addition to presenting the analytical PM formulation, the evaluation methodology uses microsimulation for a case study comparison between actuated-coordinated and ATCS operations. The results include a comparison between previous and proposed PMs, based on the processed simulation data as well as field data. In conclusion, the proposed PMs have a high transferability potential, low data collection cost, and high data quality, thus being suitable for use in decision processes for signal asset investment. Finally, this research opens up further opportunities for advancing decision-support methods for traffic operations asset management.
Biofuel is considered to be an important alternative energy in the future transportation. Its development is supported by the rest of the world. However, biofuel industry development is still very slow. From the previous research it is known that the supply chain coordination and other problems need to be solved to promote the supply chain ability. This paper studies biodiesel supply chain coordination problem from the view of disturbance management. It gives a disturbed coordination strategy which contains the optimal order quantity and the contract parameters. This paper has then verified the disturbed coordination strategy through using the actual data of Jiangsu Yueda Kate New Energy Co. Ltd. The result shows that when the market demand and the recovery cost are simultaneously disturbed, the coordination can make the biodiesel supply chain robust and the new strategy under the revenue sharing contract is better than the original one.
This paper proposes a mathematical model to regulate the acceleration (deceleration) applied by self-driving vehicles in car-following situations. A virtual environment is designed to test the model in different circumstances: (1) the followers decelerate in time if the leader decelerates, considering a time delay of up to 5 s to refresh data (vehicles position coordinates) required by the model, (2) with the intention of optimizing space, the vehicles are grouped in platoons, where 3 s of time delay (to update data) is supported if the vehicles have a centre-to-centre spacing of 20 m and a time delay of 1 s is supported at a spacing of 6 m (considering a maximum speed of 20 m/s in both cases), and (3) an algorithm is presented to manage the vehicles’ priority at a traffic intersection, where the model regulates the vehicles’ acceleration (deceleration) and a balance in the number of vehicles passing from each side is achieved.
The intersecting of pedestrian streams is a common phenomenon which would lead to the pedestrian deceleration, stopping, and even threat to the safety of walking. The organization of pedestrian flow is a critical factor which influences the intersection traffic. The aim of this paper is to study the characteristics of oblique pedestrian streams by a set of pedestrian experiments. Two groups of experiment participants, three volume levels and five intersecting angles were tested. The qualitative analysis and quantitative analysis methods were applied to find out the relationship between the pedestrian streams angle and pedestrian characteristics. The results indicated that the mean and median speed, exit traffic efficiency decreased initially and increased afterwards with the increase of intersecting angles when the volume was 1,000 p/h/m and 3,000 p/h/m, while the speed standard deviation changing inversely. However, these four factors show the opposite variation tendency in volume 5,000 p/h/m. Meanwhile, the quadratic function was selected to fit them. It is found that the worst speeds of pedestrian streams were 131° and 122° in volume 1,000 p/h/m and 3,000 p/h/m, respectively, and the greatest influence on pedestrian streams was 125° in volume 5,000 p/h/m. The results of this research can help establish the foundation for the organization and optimization of intersecting pedestrian streams.
The number of registered commercial freight vehicles is constantly increasing, increasing therefore as well the traffic load on the roads in Bosnia and Herzegovina. A significant part of freight vehicles moving along the main and regional roads are overloaded and cause significant damage to road infrastructure, affect road safety and result in an increase of emissions of harmful gases for people and the environment. The overloading rate is extremely high, in particular with 5-axle trucks representing 58.7%. The research showed that the increased overload level ranges from 10-20% of the maximum permissible weight. The importance of load limits was recognized early in the history of road development. This interrelation led directly to limitations on vehicle loads, and laws were enacted in many countries to establish the maximum allowable motor vehicle sizes and weights. Strict enforcement of motor vehicle size and weight laws is a step toward reducing motor vehicle size and weight violations, heavy truck accidents, and, even more, improving road maintenance, rehabilitation expenditures and road safety. Thus, based on the applied model the objective of this paper is to evaluate and optimize the locations of truck weigh stations on the road network of Bosnia and Herzegovina.
Short-term forecasting of the remaining parking space is important for urban parking guidance systems (PGS). The previous methods like polynomial equations and neural network methods are difficult to be applied in practice because of low accuracy or lengthy initial training time which is unfavourable if real-time training is carried out on adapting to changing traffic conditions. To forecast the remaining parking space in real-time with higher accuracy and improve the performances of PGS, this study develops an online forecasting model based on a time series method. By analysing the characteristics of data collected in Nanjing, China, an autoregressive integrated moving average (ARIMA) model has been established and a real-time forecasting procedure developed. The performance of this proposed model has been further analysed and compared with the performances of a neural network method and the Markov chain method. The results indicate that the mean error of the proposed model is about 2 vehicles per 15 minutes, which can meet the requirements for general PGS. Furthermore, this method outperforms the neural network model and the Markov chain method both in individual and collective error analysis. In summary, the proposed online forecasting method appears to be promising for forecasting the remaining parking space in supporting the PGS.
The shipping market is an economic derivative of global production and trade, being precariously subject of their cyclic changes, depressions and expansions. This paper analyses the condition of global container shipping market, caused by long-lasting economic and financial crisis that begun in 2008, but is still much visible within the container industry, particularly through overcapacity and low freight rates. It also deals with major changes of maritime container carrier’s management strategies, development and application of advanced transportation, technological, technical, economical, organizational and commercial measures in order to adapt and cope with new business environment. Finally, an attempt is made to forecast the market, potential difficulties and to propose problem-solving measures.
Safety systems detect unsafe conditions and provide warnings for travellers to take action and avoid crashes. Estimation of the geographical location of a moving vehicle as to where it will be positioned next with high precision and short computation time is crucial for identifying dangers. To this end, navigational and dynamic data of a vehicle are processed in connection with the data received from neighbouring vehicles and infrastructure in the same vicinity. In this study, a vehicular location prediction model was developed using an artificial neural network for cooperative active safety systems. The model is intended to have a constant, shorter computation time as well as higher accuracy features. The performance of the proposed model was measured with a real-time testbed developed in this study. The results are compared with the performance of similar studies and the proposed model is shown to deliver a better performance than other models.
With the continuous expansion of urban scales and the constant growth of traffic demands, it has become important to accurately predict the distribution of traffic flow so as to relieve the traffic jams and lower the energy consumption. This research mainly focuses on the distribution problem of traffic flow in the urban traffic network. A minimization program has been provided as an alternative formulation for the turning delay stochastic user equilibrium problem. The paper derives the Weibull distribution-based node-link random loading mechanism of turning delay for direct calculation of link and turning flows that are consistent with the
path flow, thus avoiding the enumeration of turning paths. Numerical examples are provided to illustrate the turning delay stochastic user equilibrium (SUE) model and the nodelink- based algorithm. The experiment demonstrates that the present method can reflect the relative performance of link and turning costs well, while presenting its advantages in the simulation of large-scale turning delay flow ssignment.
This paper presents a procedure for analysing safety and operational improvements made possible by converting traffic circles to modern roundabouts. An Italian case study is presented for alternative layouts under various traffic demand scenarios. In the application of the procedure, the average waiting times and queue lengths at entries are computed with an analytical capacity model, using default values for gap parameters. Then, the roundabout is dynamically simulated. The simulation results in a revised set of gap parameters that are in turn used as inputs to a second trial of the capacity model, and in turn fed back into the simulation. The two steps are repeated until the parameters reach a pre-selected convergence criterion, so that gap parameter values for both the static capacity and dynamic microsimulation models are in equilibrium. Therefore, the applied procedure can conduct both static and dynamic roundabout design, usually applied separately. One can start with default values in guidelines and couple them with limited field data, improving both the expected results and cost-effectiveness of solutions. Next, safety is estimated using dynamic simulation software and a compatible conflict counting model to acquire surrogate measures of safety. Level-of-service
and surrogate safety indicators for the existing and redesigned roundabouts are then compared. The procedure is first demonstrated on an old “ultra-large” roundabout. The procedure is tested on this roundabout using the Highway Capacity Manual 2016 (HCM2016), AimsunTM, and Surrogate Safety Assessment Model (SSAM) software. A redesign is shown to be far superior in efficiency and safety. Finally, two cases are described where large first generation roundabouts
were upgraded to modern standards.
It is well known that road accidents tend to be more frequent in locations where a sudden change in road characteristics violates the driver's expectations. Most methods used to assess the design consistency are based on simplified speed profiles that give a coarse description of the vehicle dynamics along the road. This paper presents a new approach to quantify the road design consistency, based on continuous operating speed profiles. These profiles are based on the Gipps’ car-following equations, adapted to simulate the driver behaviour in the vicinity of horizontal curves under free-flow conditions. A methodology to calibrate and validate the Gipps’ behavioural parameters from field data is presented and applied to predict the speed profiles of three drivers for a set of rural road segments. The calibration is based on trajectory data collected with an instrumented vehicle and it follows an automated procedure that aims to minimize the differences between the predicted and observed speed profiles. The new consistency index is based on the deceleration distances and it allows to overcome some limitations of the existing methods.
The Panama Canal (PC) expansion will have an impact on trading patterns and the manner in which goods are transported around the world. Once the third set of locks at the Canal began their operation, it was clear that the way in which vessels transited the canal and their maximum dimensions were going to change. As such, the expanded Canal will undoubtedly mean that a new kind of vessel will come into existence. In terms of dimensions, these Neopanamax ships will be adapted to how the locks operate. However, this effect will not be the same across the full range of traffic. After the first transit on 26 June 2016, it was possible to obtain access to transit data for Neopanamax ships. A thorough statistical study of these new datasets would involve analysing how these new locks impact the vessel size and seaborne transport.
The paper presents a system that recognizes the make, colour and type of the vehicle. The classification has been performed using low quality data from real-traffic measurement devices. For detecting vehicles’ specific features three methods have been developed. They employ several image and signal recognition techniques, e.g. Mamdani Fuzzy Inference System for colour recognition or Scale Invariant Features Transform for make identification. The obtained results are very promising, especially because only on-site equipment, not dedicated for such application, has been employed. In case of car type, the proposed system has better performance than commonly used inductive loops. Extensive information about the vehicle can be used in many fields of Intelligent Transport Systems, especially for traffic supervision.
More than 16,500 people lose their lives each year due to traffic crashes in Iran, which reflects one of the highest road traffic fatality rates in the world. The aim of the present study is to investigate the factors structure of an extended Driver Behaviour Questionnaire (DBQ) and to examine the gender differences in the extracted factors among Iranian drivers. Further, the study tested the association between DBQ factors, demographic characteristics, and self-reported crashes. Based on Iranian driving culture, an extended (36 items) Internet-based version of the DBQ was distributed among Iranian drivers. The results of Exploratory Factor Analysis based on a sample of 632 Iranians identified a five-factor solution named “Speeding and Pushing Violations”, “Lapses and Errors”, “Violations Causing Inattention”, “Aggressive Violations” and “Traffic Violations” which account for 44.7 percent of the total variance. The results also revealed that females were more prone to Lapses and Errors, whereas males reported more violations than females. Logistic regression analysis identified Violations Causing Inattention, Speeding and Pushing Violations as predictors of self-reported crashes in a three-year period. The results were discussed in line with road traffic safety countermeasures suitable for the Iranian context.
With the aging of population in the world, understanding the travel demands of the elderly is important. In China, the aging society is in the process of forming. Meanwhile the urban motorization has just started. The aim of this paper is to investigate the dependence of the future elderly on private cars. The data used here come from a stated preference (SP) survey of the young and middle-aged residents in the capital of China, Beijing. The influencing factors on the car ownership and mode choices of the future elderly are analysed based on the ordered logit model and MNL model, respectively. The effect of uncertainty in respondents’ statements on the car usage has been also investigated. The results show that the future elderly in Beijing become increasingly dependent on private cars. It is also found that younger people have higher propensities to own private cars and to make use of driving after the age of 65. Moreover, improving public transport services contributes to an increased ridership of public transport by the future elderly. The findings in this paper can provide valuable references for the aging society when making transport policies in Beijing.
The paper integrates Rough Sets (RS) and Bayesian Networks (BN) for roadway traffic accident analysis. RS reduction of attributes is first employed to generate the key set of attributes affecting accident outcomes, which are then fed into a BN structure as nodes for BN construction and accident outcome classification. Such RS-based BN framework combines the advantages of RS in knowledge reduction capability and BN in describing interrelationships among different attributes. The framework is demonstrated using the 100-car naturalistic driving data from Virginia Tech Transportation Institute to predict accident type. Comparative evaluation with the baseline BNs shows the RS-based BNs generally have a higher prediction accuracy and lower network complexity while with comparable prediction coverage and receiver operating characteristic curve area, proving that the proposed RS-based BN overall outperforms the BNs with/without traditional feature selection approaches. The proposed RS-based BN indicates the most significant attributes that affect accident types include pre-crash manoeuvre, driver’s attention from forward roadway to centre mirror, number of secondary tasks undertaken, traffic density, and relation to junction, most of which feature pre-crash driver states and driver behaviours that have not been extensively researched in literature, and could give further insight into the nature of traffic accidents.
Level of service (LOS) is used as the main indicator of transport quality on urban roads and it is estimated based on the travel speed. The main objective of this study is to determine which of the existing models for travel speed calculation is most suitable for local conditions. The study uses actual data gathered in travel time survey on urban streets, recorded by applying second by second GPS data. The survey is limited to traffic flow in saturated conditions. The RMSE method (Root Mean Square Error) is used for research results comparison with relevant models: Akcelik, HCM (Highway Capacity Manual), Singapore model and modified BPR (the Bureau of Public Roads) function (Dowling - Skabardonis). The lowest deviation in local conditions for urban streets with standardized intersection distance (400-500 m) is demonstrated by Akcelik model. However, for streets with lower signal density (<1 signal/km) the correlation between speed and degree of saturation is best presented by HCM and Singapore model. According to test results, Akcelik model was adopted for travel speed estimation which can be the basis for determining the level of service in urban streets with standardized intersection distance and coordinated signal timing under local conditions.
This paper brings a proposal for a timetable optimization model for minimizing the passenger travel time and congestion for a single metro line under time-dependent demand. The model is an integer-programming model that systemically considers the passenger travel time, the capacity of trains, and the capacity of platforms. A multi-objective function and a recursive optimization method are presented to solve the optimization problem. Using the model we can obtain an efficient timetable with minimal passenger travel time and minimal number of congestion events on platforms. Moreover, by increasing the number of dispatches, the critical point from congestion state to free-flow state and the optimal timetable with minimal cost for avoiding congestion on platforms can be obtained. The effectiveness of the model is evaluated by a real example. A half-regular timetable with fixed headways in each operation period and an irregular timetable with unfixed headway are investigated for comparison.
Emerging info-communication and vehicle technologies (especially vehicle automation) facilitate evolvement of autonomous road freight transportation. The entire transport system and its operation undergo a major change. New service concepts are growing and the existing ones are being transformed. The changing is particularly significant in city logistics. However, there are debates about the ways of automation of processes targeting improvement of capacity utilisation and decrease of expenditures. The main research questions of the paper are therefore: what properties of the future autonomous freight transportation are presumed; what system structure is to be constructed and how the system is to be operated? After introducing the basic notions and reviews of the current systems and developments, the shifting from traditional freight transportation to autonomous system is investigated by several aspects. A system- and process-oriented analytical modelling method has been applied. The main system constituents and their connections are modelled. Finally, we elaborate the operational model of road freight transportation, which is applicable principally in metropolitan areas. In conclusion, the presented
results appoint the research and innovation trends towards the automation of freight transportation.
High-occupancy vehicle (HOV) lanes, which are designed so as to encourage more people to use high-capacity travel modes and thus move more people in a single roadway lane, have been implemented as a lane management measure to deal with the growing traffic congestion in practice. However, the implementation has shown that some HOV lanes are not able to achieve the expected effects without proper HOV lane settings. In this study, the tradable credits scheme (TCS) is introduced to improve the HOV lane management and an optimal capacity of HOV lanes in a multilane highway is investigated to match TCSs. To approach the investigation, a bilevel programming model is proposed. The upper-level represents the decision of the highway authority and the lower-level follows the commuters’ user equilibrium with deterministic demand. The potential influence of TCSs is further investigated within the proposed framework. A modified genetic algorithm is proposed to solve the bilevel programming model. Numerical examples demonstrate that combining TCSs with the HOV lane management can obviously mitigate traffic congestion.
The choice of a particular mode of transport as an alternative to another one is subjective and usually based on an individual passenger’s approach to the evaluation of advantages and disadvantages of some particular means of transport. The paper presents the methods of analysing the reasons for passengers’ choice of travelling by train as an alternative to using air transport and the results obtained in the research. The 16 criteria (sub-criteria), describing the advantages of travelling by rail over air travel, are defined. The data of the survey questionnaire filled by 52 passengers of the Vilnius–Moscow train and the ranks assigned by them to the considered criteria are described. The average ranks of all 16 criteria and their normalized subjective weights are calculated by using a new method of average rank transformation into weight (ARTIW). The average ranks assigned by the passengers of the train to sub-criteria and the calculated global weights show what criteria are most important. Using the inverse hierarchy model based on the sub-criteria weights, the most and the least important groups of criteria are determined. The institutions and companies engaged in passenger transportation by rail, which give priority to improving the services described by the most important criteria, can make this mode of transport more attractive to people.
Several factors affect the lane choices made by motorway drivers. According to the driving rules, the nearside lane is the one that is primarily used. The main reasons for lane changes are overtaking, congestion, or restrictions on other lanes. The empirical research presented in this paper presents comprehensive traffic characteristics observed in different traffic lanes on four-lane motorways in Slovenia. The research was focused on the influence of adverse weather conditions on the lane flow distribution, and on the speed of vehicles in different lanes. The lane flow and speed distributions both directly affect road capacity and safety; therefore, estimating these characteristics could improve the reliability of active traffic control when traffic flow perturbation is detected. Field test results show that lane flow distributions and lane speed distributions at a particular site vary depending on weather conditions, namely, dry, wet (rain), low-visibility, and snow conditions.
The delivery of the right product, at the right time to the retail store, only seems to be an easy process. The smallest problem can cause the out-of-stock (OOS) situation, which may prevent customers to buy products they were looking for. Consequently, it affects retailers and their suppliers through potential operational inefficiencies, sale losses and eventually the losses of their loyal customers. Starting from these problems, by using the data of a large Serbian retailer, this paper analyses out-of-stocks in the context of two alternative delivery systems, centralized and direct. For calculating OOS rates the perpetual inventory aggregation metrics was used, while the occurrence of out-of-stocks was modelled by the application of probit regression analysis. The results have shown that delivery system has a significant impact on the probability of a stock-out, indicating potential problems in the centralized system. In addition, the analysis included certain product and store characteristics that also significantly affect the average probability of stock-outs.
The transport policy of the European Union is based on the mission of restructuring road traffic into other and energy-favourable transport modes which have not been sufficiently represented yet. Therefore, the development of the inland waterway and rail transport, and connectivity in the intermodal transport network are development planning priorities of the European transport strategy. The aim of this research study was to apply the scientific methodology and thus analyse the factors that affect the distribution of the goods flows and by using the fuzzy logic to make an optimization model, according to the criteria of minimizing the costs and negative impact on the environment, for the selection of the optimal transport route. Testing of the model by simulation, was performed on the basis of evaluating the criteria of the influential parameters with unprecise and indefinite input parameters. The testing results show that by the distribution of the goods flow from road transport network to inland waterways or rail transport, can be predicted in advance and determine the transport route with optimal characteristics. The results of the performed research study will be used to improve the process of planning the transport service, with the aim of reducing the transport costs and environmental pollution.