基于信令数据的中型城市通勤公交站点优化方法OA北大核心CSTPCD

Due to significant differences in density of base stations and travel patterns of commuters between medi-um and larger-sized cities,the deployment of bus stops shows notable variations.Based on this fact,a method for optimizing the deployment of bus stops for commuting in medium-sized cities is proposed,utilizing an improved Mean Shift clustering algorithm.Next,this method is adopted and tested based on the commuting records from the signaling data in the central area of Jingzhou,in which the main evaluation criterion is total system cost that encom-passes both operating cost and walking time of passengers.Based on the commuter travel demand during the morn-ing peak in the central area,an optimization scheme about deployment of bus stops for commuters is formulated.By comparing the results of the optimization scheme to the existing deployment of bus stops,the effectiveness of the optimization method is validated.Through a comparative analysis for different clustering algorithms,the superior performance of the improved Mean Shift algorithm is demonstrated.Additionally,by considering the influence of base stations and isochrones,the necessity of evaluating both factors in various scenarios is proved.The results show that:①Based on the travel demand in the morning peak in the research area of Jingzhou,28 bus stops are ob-tained,which results in a remarkable reduction of 51.98%in passenger walking time and a 17.82%decrease in the total system cost.This indicates the effectiveness of the optimization method in achieving a deployment scheme of bus stops with reduced total system cost and walking time of passengers.②In comparison with different clustering algorithms,the solution obtained from the improved Mean Shift algorithm shows a significant enhancement.Specifi-cally,the total system cost is 8.73%lower than that achieved using the K-means clustering algorithm and 2.48%lower than the Affinity Propagation clustering algorithm.③When comparing scenarios with and without the consid-eration of base stations and isochronous circles,the results that considering these factors results in reduced walking time.These analyses highlight the superiority of the optimization method in terms of clustering quality and can pro-vide valuable insights for planning of bus lines in medium-sized cities.