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.

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.