Research on Demand Responsive Transit Route Optimization, Scheduling Models, and Solution Algorithms

Abstract

Nowadays, urban traffic congestion is a serious issue, and the rise of demand responsive transit systems improves this problem to a certain extent. This paper delves into the route optimization, scheduling, and modeling of demand-responsive transit, exploring its seamless integration into urban transportation planning. This bus system aims to improve the efficiency of the transportation system, reduce congestion, and improve the urban environment, thus enhancing the quality of life of the residents. This study reveals the potential benefits of a demand responsive transit system in urban transportation planning, offering the possibility of increased flexibility and efficiency. Future research directions could further explore how demand responsive systems can be implemented in a targeted manner under different urban environments and demands to promote sustainable urban transport development. Considering the differences in cities, such as transportation structure, population density, and cultural characteristics, it will be crucial to develop appropriate implementation strategies. Meanwhile, combining emerging technologies, such as artificial intelligence and big data analytics, will provide strong support for further enhancement of the demand responsive transit system to better meet the travel needs of the residents and promote the development of urban transportation more sustainably. This study provides a valuable reference for urban planners and points out the direction for the improvement and development of urban transportation in the future.