Study on Decision Process and Strategy Choice Behavior under Multimode Choice

Information Display Board is used to design an experimental survey and dynamic decision process data are retrieved under a multimode choice scenario with car, bus and subway, and park and ride. It is concluded that car travelers who will switch to choosing park and ride need more decision time to compare it with the existing modes (car, bus and subway) and tend to mainly use compensatory decision strategies in the decision process. The most commonly used decision strategy for travelers is the combined strategy under the multimode choice scenario. Correlation analysis shows that the influencing factors of age, whether the travelers have ever used park and ride, and decision time highly correlate with decision strategy choice. These conclusions have a certain reference value for theory research of decision process under multimode choice

Analysis on passenger flow evolution and service facility configuration for large-scale events in outer suburbs

As more and more large-scale winter events are held in different areas, a reasonable configuration of service facilities is crucial for ensuring the successful execution of these events. Based on an analysis of the passenger flow for large-scale events in outer suburbs, this study has developed a dynamic evolution model to well simulate passenger arrival distribution among nodes and queue performance over time. Subsequently, an optimization model for service facility configuration based on node linkage is proposed. Using a large-scale winter event as a case study, we conclude that assigning a higher objective weight to spectators’ queuing time cost in the optimization model leads to an increase in the number of configured service facilities among nodes, thereby enhancing service quality. Different facility layouts for security checks and ticket checks have no significant effects on the optimal number of configured service facilities and spectator queuing time costs. However, implementing a remote security check can alleviate passenger congestion at downstream nodes and reduce the overall queuing time cost. The dynamic evolution model and the service facilities configuration model are suitable for coordinating passenger flow under limited-service facility provision along with measures such as adjusting facility layouts and controlling passenger flow. Thus, a good match between passenger flow distribution and facility service capacity can be achieved. The research conclusions can provide a reference for the analysis of passenger flow, service facility configuration, and passenger flow organization for large-scale events held in the outer suburbs

Analysis of the Travel Intent for Park and Ride Based on Perception

As a multimodal travel behavior, park and ride includes several trip modes such as car, walking, bus, or railway. And people’s choice of park and ride is influenced by many factors. This paper, based on the park and ride behavior survey in Beijing, will analyze the relationship between the perception of the influencing factors and the behavior intent for park and ride by using structural equation modeling. The conclusions suggest that the park and ride choice for travelers is a passive behavior which means giving up driving the car is mainly caused by the serious traffic congestion. Furthermore, improving the service level of the park and ride facilities and the comfort for riding bus or railway will increase the utilization of park and ride facilities. The perceptions of the influencing factors have both direct and indirect effects on the travel intent for park and ride by the interaction among the influencing factors

Bilevel Programming Model of Private Capital Investment in Urban Public Transportation: Case Study of Jinan City

Increasing public transportation subsidies have created fiscal pressures for governments. To ease this financial pressure, Chinese government strongly encourages private capital investment in public transportation. However, previous private capital investments in public transportation operations have largely failed, mainly due to low ticket fares that cannot support sustainable operations. To address this issue, several previous research projects have developed methods to facilitate private capital investment. The majority of the research focuses on qualitative analysis and value for money analysis. Our research proposed a new method of private capital investment in public transportation operations based on the concept of “passenger value.” The feasibility of the proposed method of private investment was analyzed quantitatively by constructing a bilevel programming model. The model was verified based on a sample analysis of Jinan city traffic. Results showed that effective private capital investment increases the total societal benefit from the public transportation system and additionally that the investment method considering “passenger value” is superior to the traditional one. A quantitative tool was provided by the model to evaluate private capital investment effects, design investment policies, and develop further research

A Traffic Mode Choice Model for the Bus User Groups based on SP and RP Data

Enhancing the bus share rate is a major measure to relieve the traffic congestion. To analyze the effect of public transit policy, this paper establishes MNL models based on both SP data and combining SP and RP data, which was collected in Jinan city. Then the paper analyzes how the influencing factors affect the choice proportion of bus travel mode for the bus user groups. In the end, the paper obtains some significant conclusions and proposes measures which would enhance the bus attraction

Towards a more flexible demand responsive transit service with compensation mechanism considering boundedly rational passengers

Abstract Demand responsive transit (DRT) with app‐based reservation platforms is experiencing a renaissance to bring the tremendous potential for mobility in the urban universe. Nevertheless, how to attract and retain passengers for long‐term use has become one of the most significant problems. The decision‐making psychology of passengers is often overlooked but incredibly critical in the practical applicability of DRT services. This paper proposes a more flexible DRT service with soft time windows considering boundedly rational passengers. A compensation mechanism is developed to alleviate the dissatisfaction of passengers while considerably promoting the system efficiency. A two‐stage model is designed to incorporate bounded rationality into the optimization process of mixed demand, including the static phase for reservation passengers and the dynamic phase for real‐time passengers. To enhance the computational efficiency, a hybrid heuristic algorithm combining spatiotemporal clustering and non‐dominated sorting genetic algorithm (NSGA)‐II is constructed to obtain the Pareto solutions set. An illustrative example of the Nguyen–Dupuis network is presented to demonstrate the validity of the algorithm. Subsequently, a large‐scale case study in Beijing evaluated the applicability of DRT in the real‐world network. The results reveal that dynamic DRT with compensation mechanism can substantially improve the system performance while ensuring the service quality. The response rate of passengers has been dramatically promoted to 80%. The operating profit has been enormously improved by up to 73%. Therefore, this study is radically conducive to understanding the passenger's decision‐making psychology while constructing a more cost‐efficient flexible strategy for the service provider

Estimating heterogeneity of car travelers on mode shifting behavior based on discrete choice models

In order to understand the mode shift behavior of car travelers and relieve traffic congestion, a Stated Preference survey has been conducted in the city of Ji\u27nan in China to analyze bus choice behavior and the heterogeneity of car travelers. Several discrete choice models, including multinomial logit, mixed logit and latent class model (LCM) are developed based on these survey data. A comparative analysis indicates that the LCM has the highest precision and is more suitable to analyze the heterogeneity of car travelers. The LCM divides car travelers into three classes. Different classes have different sets of influencing factors in the model. Policy recommendations are also proposed for those classes to promote bus shift from car travelers based on the model results. Finally, sensitivity analysis on parking fees and fuel cost is carried out on the LCMs under different bus service levels. Car travelers have different sensitivities to the influencing factors. The conclusions indicate that the LCM can reflect the heterogeneity and preferences of car travelers and can be used to understand how to shift the behavior of car travelers and make more effective traffic policy

A Slack Departure Strategy for Demand Responsive Transit Based on Bounded Rationality

Demand responsive transit (DRT) is emerging as one of the most potential travel modes to satisfy flexible travel demands. Nevertheless, how to attract more passengers has become a critical problem in the success of DRT projects. It is necessary to take into account the psychological factors impacting passengers’ choices. The study proposes a slack departure strategy considering boundedly rational passengers, which introduces passengers’ decision-making psychology into the optimization process. The strategy can adjust the departure time of passengers to adjacent time windows. The discount-incentive mechanism is presented to attract passengers to accept the changes while maintaining the quality of service. On this basis, the theory of bounded rationality is applied to describe the decision-making process of passengers. We construct a multiobjective programming model to analyze the operator-passenger interactive effect. To address the multiobjective problem, a two-phase heuristic algorithm is established to get the Pareto solution for the model. A numerical experiment is carried out on the Sioux Falls network. The case study of Beijing is discussed to evaluate the effectiveness of the strategy. The results indicate that the slack departure strategy can significantly benefit both the operators and passengers. The operating profit substantially increases by up to 63%. Meanwhile, the passenger’s general travel cost declines by 12%. The optimal discount rate of the incentive mechanism is 20%. Therefore, the study contributes to comprehending the passenger’s decision-making psychology and providing a new optimization strategy for the operator of DRT

Analysis on context change and repetitive travel mode choices based on a dynamic, computational model

Research on individual decision-making process is fundamentally critical to explore the macroscopic behavioral rules for travel mode choice. In this paper, a behavioral experiment under different contexts was designed by a process-tracing method to obtain data regarding repetitive travel mode choices. Based on the Decision Field Theory, a stochastic, dynamic model was proved to be reliable and used to reproduce and analyze the repeated decision-making process. It is concluded that in a stable context, travelers would gradually establish and use some new decision rules to make a travel mode choice during the repetitive decision-making process. When travelers have developed a travel mode habit, environmental cues become the key factors that trigger travelers to make travel mode choices. Context change and traffic policies can make travelers consider, weigh and compare the relevant information again and interrupt their previous habitual choice behavior, enhancing the use of Park and Ride. Meanwhile, travelers with a faster learning speed and better memory develop a travel mode habit in a stable context and change the existing car use habit in a new context more quickly. These results would help to enrich the existing theoretical study of travel behavior and provide an interesting starting point for the development of practical strategies to promote the use of public transport instead of a private car. Traffic management techniques such as congestion pricing, along with behavior intervention and guidance strategies for different groups can strengthen this effect