Abstract
Reasonably optimizing the urban rail transit stop plan from the perspective of bounded rational cognition of passengers regarding their travel choices is an important way to improve riding environment, transportation capacity, and service quality. Based on the analysis of the characteristics of passengers’ bounded rational choice behaviour, starting from two aspects of travel time and congestion, the prospect theory is applied to describe optimization goals: maximum travel time savings and maximum congestion costs savings. Subsequently, an urban rail transit stop plan optimization model is constructed, and a non-dominated sorting genetic algorithm with the elite strategy is designed to solve the model, and stop plans based on the calculated Pareto solution set are selected. A case study of an urban rail transit with 13 station nodes verifies the research conclusion as follows: compared with the traditional station stopping plan, the skip-stop plan has better passenger flow adaptability, in which the average travel time of passengers is shortened by 22.57 min at most, and the congestion degree is reduced by 10.93% at most. Based on this, the impact of different factors on the stop plan, such as the target weight, passenger flow, and behavioural parameters, is further discussed.
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