In the initial stage of an emergency, rescue resources, such as rescuers, are often scarce, and victims at disaster sites under the condition of bounded rationality demonstrate competitive psychology in relation to the dispatch of rescue workers. Based on this, this study incorporates factors such as the bounded rationality of the disaster-stricken populations and their competitive psychology in terms of the number of rescuers into the analytical framework from the perspective of the disaster-stricken people and constructs an evolutionary game model with a multi-strategy set. The dynamic evolution process (EP) and the best rescuer dispatching scheme for each disaster site are shown through an example, and a disturbance analysis of the identified parameters is then carried out. The results reveal that the proposed rescuer dispatching model considers the multistage dynamic features of the emergency rescue process and the bounded rational game psychology among disaster sites, resulting in a more realistic dispatch scheme. For three different scenarios, such as the distance between the rescue point and the disaster site being relatively far and relatively close, and the subsequent rescue stage when the disaster situation is preliminarily alleviated, decision makers should make dispatch plans with timeliness, demand satisfaction, and comprehensive consideration of timeliness and demand satisfaction as the main decisions.

The occurrence of emergencies and secondary disasters causes varying degrees of obstruction on roads used by actors in an emergency rescue logistics network, and the bounded rationality of rescuers in the face of road risks considerably affects the choice of emergency rescue paths. In this regard, this study considered the traffic obstruction caused by emergencies and secondary disasters and the bounded rationality of rescue workers using a framework that combines cumulative prospect theory (CPT) and evolutionary game (EG) theory. The concept of a replicator was used to dynamically describe the game learning behaviors reflected in rescuers’ path selection (PS) decisions, and an EG model was constructed to represent the multi-strategy set of limited rational rescuers. An example is presented to illustrate the dynamic evolution of PS and conduct a sensitivity analysis of parameters. The results showed that the EG model could determine the optimal path (stability strategy) on the basis of road conditions and the number of rescue vehicles traveling along a road network. Factors such as the type and severity of a secondary disaster, the time-related risks faced by rescuers, and the perception of road conditions tremendously affect the PS strategies of rescuers.

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.