The fare adjustment method can be classified into incentive policy and direct adjustment of fares. We considered solving this problem from the perspective of transportation demand management by adjusting fares to change the travel time of passengers and reasonably loading the passenger flow to each train during the peak period to alleviate the problem of congestion during the peak period, balance the utilization rate of in-service trains, and improve the quality of urban rail transit services. From a practical perspective, owing to the confusion of passengers boarding and alighting during peak hours and a few uncontrollable factors, certain risks are posed when continuing to reduce headways. From a technical perspective, continuing the reduction in headways poses high requirements in terms of both signals and vehicles. Currently, the minimum headway is approximately 1 min. As a dynamic input, the train schedule should be changed with the change in passenger flow however, the continued increase in subway passenger flow has resulted in a saturated train schedule, which prevents optimization. The primary methods include constructing new subway lines and optimizing train schedules. The high degree of congestion in the section and the insufficient experience of passengers on the subway have significantly curbed the enthusiasm of urban residents in opting for the subway as a transport mode.Ĭurrently, the passenger flow problem of urban rail transit during peak periods is primarily considered in terms of two aspects: traffic supply management and traffic demand management, which aim to improve the supply of transportation capacity through the construction of transportation infrastructure and optimization of transportation organization to satisfy the travel requirements of passengers. In particular, during the peak period, the subway platform is crowded, which poses a severe safety hazard. In some large cities, peak and low periods are evident owing to the uneven distribution of daily passenger flow.

As the main tool to alleviate urban traffic congestion, the subway can transport passengers rapidly. In this context, the development of public transportation has been prioritized to alleviate urban traffic problems. In recent years, traffic congestion caused by the increasingly prominent contradiction between urban traffic demand and supply has become a social problem in countries worldwide. Finally, relevant parameters are discussed.

The results show that the objective function is reduced by 17.5%, the congested section is reduced by 9.1% when the full-load rate is 90% of the train loading capacity, and the passenger flow shifts to both ends of the peak period. Based on an example, we obtain the specific fare and passenger flow distribution of each train after fare adjustment. A simulated annealing algorithm is introduced to solve the bilevel model. To achieve a more realistic model, the range of acceptable train numbers for each passenger is considered. The upper level of the model determines the discount factor to minimize the total number of passengers exceeding the full-load rate, and the lower level of the model determines the distribution of passengers on the line, in which the cost-minimizing behavior of each passenger is considered using the allocation method based on the probability of selection. A bilevel model is proposed herein to solve the congestion problem for an urban rail line. In urban rail transit, adjusting fares to satisfy passenger flow requirements is a new method to relieve urban congestion.

Peakhour urban technologies