Differentiated fares depend on bus line and time for urban public transport network based on travelers’ day-to-day group behavior

Abstract

Considering travelers’ day-to-day group behavior based on social interaction, this paper proposes a differentiated fare strategy depending on bus line and time. Based on the analysis of travelers’ generalized cost, a day-to-day group travel behavior evolution model with social interaction is established, and the corresponding OD matrix evolutionary model based on the complexity of the group behavior is designed. Set the differentiated fares of bus lines, private car parking fees and bus departure frequency as the optimization variables, the multi-objective optimization model is established to maximize the profit of the public transportation system and travelers’ utility On this basis, the improved particle swarm multi-objective optimization algorithm is introduced to solve the model. Finally, the proposed model and algorithm are applied to a bus network under real case. The numerical results show that: (1) The implementation of differentiated fares depends on bus line and time based on travelers’ day-to-day group behavior can improve the Pareto frontier, in which the travelers obtain higher utility and the public transportation system achieves higher profit than the model based on perfect rationality; (2) Compared with the traditional traffic flow model, the day-to-day evolution model of group behavior under differentiated fares can reduce the congestion of the network; (3) Properly reducing the information interaction range of travelers can effectively reduce the travel time under the differentiated fares.