Train dwell time is one of the most unpredictable components of railway operations, mainly because of the varying volumes of alighting and boarding passengers. However, for reliable estimations of train running times and route conflicts on main lines, it is necessary to obtain accurate estimations of dwell times at the intermediate stops on the main line, the so-called short stops. This is a great challenge for a more reliable, efficient and robust train operation. Previous research has shown that the dwell time is highly dependent on the number of boarding and alighting passengers. However, these numbers are usually not available in real time. This paper discusses the possibility of a dwell time estimation model at short stops without passenger demand information by means of a statistical analysis of track occupation data from the Netherlands. The analysis showed that the dwell times are best estimated for peak and off-peak hours separately. The peak-hour dwell times are estimated using a linear regression model of train length, dwell times at previous stops and dwell times of the preceding trains. The off-peak-hour dwell times are estimated using a non-parametric regression model, in particular, the k-nearest neighbor model. There are two major advantages of the proposed estimation models. First, the models do not need passenger flow data, which is usually impossible to obtain in real time in practice. Second, detailed parameters of rolling stock configuration and platform layout are not required, which makes the model more generic and eases implementation. A case study at Dutch railway stations shows that the estimation accuracy is 85.8%-88.5% during peak hours and 80.1% during off-peak hours, which is relatively high. We conclude that the estimation of dwell times at short stop stations without passenger data is possible.@en

Effective traffic signal control strategies are critical for traffic management in urban traffic networks. Most existing optimization-based urban traffic control approaches update the traffic signal at regular time instants, where the length of the fixed update time interval is determined based on a trade-off between the computational efficiency and the control performance. Since event-triggered control (ETC) allows for more flexible and more efficient control than conventional time-triggered control by triggering the control action by events, and since it can refrain from redundant optimization while retaining a satisfactory behavior, we use an ETC scheme for traffic light control. In addition, based on the geographically distributed feature of traffic networks, a distributed paradigm is adopted to reduce the computational complexity for the optimization. We propose a distributed threshold-based event-triggered control strategy, where the independent triggering of agents leads to an asynchronous update of traffic signals in the system. The triggered agent then solves a mixed-integer linear programming problem and updates its traffic signals. The proposed approach is evaluated under various traffic demands by simulation, and is shown to yield the best trade-off between control performance and computational complexity compared to other control strategies. @en

Effective traffic signal control strategies are critical for traffic management in urban traffic networks. Most existing optimization-based urban traffic control approaches update the traffic signal at regular time instants, where the length of the fixed update time interval is determined based on a trade-off between the computational efficiency and the control performance. Since event-triggered control (ETC) allows for more flexible and more efficient control than conventional time-triggered control by triggering the control action by events, and since it can refrain from redundant optimization while retaining a satisfactory behavior, we use an ETC scheme for traffic light control. In addition, based on the geographically distributed feature of traffic networks, a distributed paradigm is adopted to reduce the computational complexity for the optimization. We propose a distributed threshold-based event-triggered control strategy, where the independent triggering of agents leads to an asynchronous update of traffic signals in the system. The triggered agent then solves a mixed-integer linear programming problem and updates its traffic signals. The proposed approach is evaluated under various traffic demands by simulation, and is shown to yield the best trade-off between control performance and computational complexity compared to other control strategies. @en

This article is concerned with the design and performance optimization of feedback controllers for state-based switching bilinear systems (SBLSs), where subsystems take the form of bilinear systems in different state space polyhedra. First, by further dividing the subregions into smaller regions and designing region-dependent feedback controllers in the resulting regions, the SBLSs can be transformed into corresponding switching linear systems (SLSs). Then, for these SLSs, by imposing contractility conditions on the Lyapunov functions, an upper bound on the infinite horizon quadratic cost can be obtained. Optimizing this upper bound yields the controller design. The optimization problem is formulated as a linear matrix inequalities optimization problem, which can be solved efficiently. Finally, the stability of the close-loop system under the proposed controller is established step by step through a decreasing overall Lyapunov function.@en

This article is concerned with the design and performance optimization of feedback controllers for state-based switching bilinear systems (SBLSs), where subsystems take the form of bilinear systems in different state space polyhedra. First, by further dividing the subregions into smaller regions and designing region-dependent feedback controllers in the resulting regions, the SBLSs can be transformed into corresponding switching linear systems (SLSs). Then, for these SLSs, by imposing contractility conditions on the Lyapunov functions, an upper bound on the infinite horizon quadratic cost can be obtained. Optimizing this upper bound yields the controller design. The optimization problem is formulated as a linear matrix inequalities optimization problem, which can be solved efficiently. Finally, the stability of the close-loop system under the proposed controller is established step by step through a decreasing overall Lyapunov function.@en

Through calculating the sharing rate of each traffic mode inside inter-city transport corridor by using binomial Logit model, the utility function model was established and its parameters were determined by using AHP method and combining with questionnaire data, and then, the model was applied on Nanchang-Jiujiang inter-city transport corridor and was taken example analysis.Through calculation, this paper points out the passenger traffic sharing rate of inter-city railway and highway inside the corridor are 59.01% and 40.99% respectively, analyzes the sensitivity of enterprise’s revenue on influence factors, and puts forward relative conclusions and suggestions for transport enterprises.@en

In this paper, controllers are designed for stabilizing state-based switching bilinear systems. The controller is designed based on the special features of bilinear systems comparing with linear systems, and is carried out through three steps: first, deriving the state-based switching linear system corresponding to the switching bilinear system; then, state-feedback controllers are designed to asymptotically stabilize the corresponding switching linear system; finally, stabilizing controllers are obtained for the original system, the switching bilinear system. The stability of the controller is proved step by step through the decreasing of the overall Lyapunov function.@en

Understanding pedestrian wayfinding behavior is crucial for traffic management and building design. The use of virtual reality technology presents an efficient approach for investigating pedestrian wayfinding behavior in large public spaces, offering numerous advantages for data collection. However, the impact of different scenario dimensions on pedestrian wayfinding behavior in large public spaces remains unclear. Additionally, the selection of virtual experiment scenario dimensions currently relies primarily on researchers’ experience and practical conditions, lacking sufficient evidence to support their rational. Another challenge is the limited focus on spatial knowledge’s effect on wayfinding behavior, with insufficient analysis of the utility of pedestrian visual information and a lack of precise methods to quantify visual field information accurately. This study addresses these gaps by incorporating spatial knowledge at multiple scales and pedestrian visual field information as influencing factors in the analysis of wayfinding behavior. Furthermore, it distinguishes between three-dimensional and two-dimensional scenarios to compare the impact of dimensional differences on pedestrian wayfinding behavior. By analyzing behavior data from non-immersive wayfinding experiments, this research employs statistical analysis methods and a deep learning framework to derive results regarding the factors influencing wayfinding behavior. The findings demonstrate that considering both spatial and visual field information effectively enhances the predictive ability of the wayfinding model. Additionally, dimensional differences significantly influence the pedestrian wayfinding process. These results offer empirical evidence to guide researchers in selecting experimental scenarios of pedestrian behavior and provide insights for public space layout, signage design, and improving pedestrian efficiency.@en

Existing literature suggested that pedestrian flow is largely effected by the nature of pedestrian group, which is a common phenomenon in crowds. However, the influence of social groups with heterogeneity on the crowd movement has not yet been fully elucidated. In order to recognize the movement characteristics of groups with heterogeneity and its influence on the pedestrian crowd, this paper takes the social-groups with heterogeneity as the research object, particularly for two types of groups, namely, Friend-group and Family-group. The groups of heterogeneity were analysed by observing group size, velocity, interpersonal distance, step frequency and walking pattern. The results shown that groups do have effects on pedestrian movement and the degree of influence depends on the heterogeneity of groups. Based on these observation, this paper developed a conceptual model on the basis of the existing social force model which obtaining the qualitative relationship between the parameter of model and groups’ heterogeneity.@en

Social groups with heterogeneous characteristics are very common and have obvious effects on pedestrian movements. However, a limited number of models investigated the relationship of social groups with heterogeneous characteristics on the people movements. Based on several controlled experiments which took the social-groups with heterogeneous characteristics in terms of family-group and friend-group as the research objectives, the heterogeneous groups are characterized by different group size, velocity, interpersonal distance, step frequency, and walking pattern. The results showed that heterogeneous groups do have effects on pedestrian movements and the effect degrees vary with the heterogeneous attributes of groups. This paper filled the gap by developing an improved social force model which take in account the movement characteristics of groups and its influence on the pedestrians.@en

For dense railway lines, timetables are commonly designed periodically to provide regular services. However, such timetables may lead to many low-occupancy departures and make it inefficient to a railway company. We combine the features of periodic timetabling with those of demand-responsive timetabling design for a bidirectional rail transit line considering rolling stock circulation. A mixed-integer programming model with the objective of minimizing total passenger waiting time and weighted total train travel time is formulated and a three-phase heuristic algorithm is developed. Five performance indicators, namely, total train travel time, passenger average waiting time, event time deviation, event frequency deviation, and train connection time are introduced to evaluate the performance of the model. A case study on Jinshan railway corridor in Shanghai demonstrates that the proposed demand responsive periodic timetabling model can reduce passengers’ waiting time and keep periodicity in the meantime with short computational time.@en