北京交通大学学报2024，Vol.48Issue(6)：22-29,8.DOI:10.11860/j.issn.1673-0291.20230162
基于时空网络建模的公交场站泊位分配优化
Optimization of bus station berth allocation based on a time-space network model
摘要
Abstract
With the increasing demand for urban public transportation,existing bus stations struggle to provide adequate vehicle docking services.This study addresses the optimization of berth allocation in bus stations.First,to depict the complete process of selecting berths and completing bus docking within a hub station,four types of spatiotemporal network arcs are constructed:entrance waiting arc,berthing travel arc,berth docking arc,and unparking travel arc.Next,based on these spatiotemporal network arcs,an integer programming model for bus berth allocation is developed,integrating the number of berths,characteristics of bus routes,and aiming to minimize entrance waiting time and bal-ance berth resource utilization.A linearization model with auxiliary 0-1 decision variables is introduced to enhance decision-making.Finally,taking the Sihui Bus Hub as an example,the model's accuracy and effectiveness are verified through a Python-implemented program and solved using the Gurobi op-timization software.The results demonstrate that the reallocating bus parking platforms and berths can maximize existing resources and significantly enhance bus system operational efficiency,reducing bus waiting time costs and berth utilization variance by 26 minutes and 724 minutes,respectively,repre-senting an overall improvement of nearly 48%compared to the original scheme.When new bus routes are added,optimizing the berth allocation scheme for the existing routes yields superior outcomes,par-ticularly in achieving balanced berth usage.关键词
城市交通/公交场站/泊位分配/时空网络模型Key words
urban transportation/bus station/berth allocation/spatiotemporal network model分类
交通工程引用本文复制引用
于怀智,康柳江,杨喜梅,毛雪莉..基于时空网络建模的公交场站泊位分配优化[J].北京交通大学学报,2024,48(6):22-29,8.基金项目
国家自然科学基金(72471022,72288101,72331001)National Natural Science Foundation of China(72471022,72288101,72331001) （72471022,72288101,72331001）
