Bus system optimization for timetables, routes, charging, and facilities: a summary

Bus systems plays an important role in reducing urban traffic congestion, developing green transportation, and providing more equitable transportation services. However, the rising demand for service quality from passengers and the large number of new energy buses have posed new challenges to the operation of bus systems. Integrating resources and optimizing bus systems is a challenge that requires collaboration between the government and bus operators to find effective solutions. This paper reviews the research related to bus system optimization from four aspects: bus timetable optimization, bus route optimization, bus charging optimization, and bus facility optimization. For bus timetable optimization, most of the existing research takes the minimum cost, the number of vehicles, and the maximum passenger capacity as the optimization objectives, which are solved by classical statistical methods and deep learning methods. For bus route optimization, existing research mostly focuses on different scenarios such as customized buses and autonomous buses, and uses heuristic algorithms and exact algorithms to analyze them with the objectives of cost, travel time, and carbon emission minimization. For bus charging optimization, integer programming models and heuristic algorithms are typically applied to address the optimization problems of charging stations and charging schedules, with minimum charging cost and charging time as the optimization objectives. For bus facility optimization, the research is mostly carried out from the optimization of bus stops and bus lanes. This paper can provide suggestions for the optimization and development of bus systems.