面向乘客需求实时响应的定制公交线路优化
Optimization of Customized Bus Routes Based on Passengers’ Dynamic Demand
DOI: 10.12677/aam.2026.155248, PDF,   
作者: 高梦娜, 翟金刚:鲁东大学数学与统计科学学院,山东 烟台;姚 琛*:鲁东大学交通学院,山东 烟台
关键词: 定制公交动态需求响应加权二部图KM算法Customized Bus Dynamic Demand Response Weighted Bipartite Graph KM Algorithm
摘要: 针对定制公交运营阶段新增乘客请求实时、随机到达,传统静态线路方案难以及时响应的问题,研究基于乘客动态需求响应的定制公交线路优化方法。在静态线路规划结果基础上,采用滚动时域策略,将动态调度过程划分为若干连续决策周期;结合可行插入判定,构建以系统综合成本最小为目标的乘客–车辆加权二部图匹配模型,并通过引入虚拟节点与权值转换,利用KM算法求解各周期最优匹配方案。以济南市主城区早高峰通勤场景为例进行验证,所提方法能够在保证既有乘客服务质量的前提下,提高新增请求响应能力,降低系统综合成本,可为定制公交动态调度提供理论参考和方法支持。
Abstract: Aiming at the problem that the traditional static route scheme cannot respond timely to the real-time and random arrival of new passenger requests during the operation of customized buses, this paper studies a customized bus route optimization method based on passengers’ dynamic demand response. On the basis of the static route planning results, a rolling horizon strategy is adopted to divide the dynamic scheduling process into several consecutive decision cycles. Combined with feasible insertion judgment, a passenger-vehicle weighted bipartite graph matching model is constructed with the goal of minimizing the comprehensive system cost. By introducing virtual nodes and weight transformation, the KM algorithm is used to solve the optimal matching scheme for each cycle. Taking the morning peak commuting scenario in the main urban area of Jinan City as an example for verification, the proposed method can improve the response ability to new requests and reduce the comprehensive system cost on the premise of ensuring the service quality of existing passengers, which can provide theoretical reference and method support for the dynamic scheduling of customized buses.
文章引用:高梦娜, 姚琛, 翟金刚. 面向乘客需求实时响应的定制公交线路优化[J]. 应用数学进展, 2026, 15(5): 523-536. https://doi.org/10.12677/aam.2026.155248

参考文献

[1] Li, P., Jiang, L., Zhang, S., et al. (2022) Demand Response Transit Scheduling Research Based on Urban and Rural Transportation Station Optimization. Sustainability, 14, 13328. [Google Scholar] [CrossRef
[2] Yu, Q., Zhang, H., Li, W., Song, X., Yang, D. and Shibasaki, R. (2020) Mobile Phone GPS Data in Urban Customized Bus: Dynamic Line Design and Emission Reduction Potentials Analysis. Journal of Cleaner Production, 272, Article 122471. [Google Scholar] [CrossRef
[3] Wang, K. and Chen, W. (2024) Customized Bus Route Planning Based on Taxi Order Data in a ‘Many-to-One’ Scenario. International Conference on Smart Transportation and City Engineering (STCE 2023), Chongqing, 16-18 December 2023, 490-500. [Google Scholar] [CrossRef
[4] Tong, L., Zhou, L., Liu, J. and Zhou, X. (2017) Customized Bus Service Design for Jointly Optimizing Passenger-to-Vehicle Assignment and Vehicle Routing. Transportation Research Part C: Emerging Technologies, 85, 451-475. [Google Scholar] [CrossRef
[5] Lyu, Y., Chow, C., Lee, V.C.S., Ng, J.K.Y., Li, Y. and Zeng, J. (2019) CB-Planner: A Bus Line Planning Framework for Customized Bus Systems. Transportation Research Part C: Emerging Technologies, 101, 233-253. [Google Scholar] [CrossRef
[6] Li, W., Zheng, L., Wu, X., Tang, X., Xiao, S., Zhao, M., et al. (2024) Exploring Potential Customized Bus Passengers across Private Car Trajectory Data. IEEE Transactions on Intelligent Transportation Systems, 25, 21278-21296. [Google Scholar] [CrossRef
[7] 靳文舟, 胡为洋, 邓嘉怡, 等. 基于混合算法的需求响应公交灵活调度模型[J]. 华南理工大学学报(自然科学版), 2021, 49(1): 123-133.
[8] Sun, J., Chen, Y., Huang, J., Wei, P. and Song, C. (2021) Flexible Bus Route Optimization Scheduling Model. Advances in Civil Engineering, 2021, Article ID: 8816965. [Google Scholar] [CrossRef
[9] 李欣, 林小敬, 许航, 等. 需求响应公交网络化运营优化模型[J]. 计算机应用, 2023, 43(S1): 288-292.
[10] Zhan, Z. and Chen, S. (2024) Research on Customized Bus Route Planning Considering Multiple Path Selection. International Conference on Smart Transportation and City Engineering (STCE 2023), Chongqing, 16-18 December 2023, 442-450. [Google Scholar] [CrossRef
[11] Cai, Y. and Sun, S. (2024) Optimizing Customized Bus Routing and Maximum Seat Occupancy Rate under the Influence of Epidemic Outbreaks. IEEE Access, 12, 172368-172384. [Google Scholar] [CrossRef
[12] Wu, B., Zuo, X., Chen, G., Ai, G. and Wan, X. (2024) Multi-Agent Deep Reinforcement Learning Based Real-Time Planning Approach for Responsive Customized Bus Routes. Computers & Industrial Engineering, 188, Article 109840. [Google Scholar] [CrossRef
[13] 陈程, 石超峰, 熊敏. 考虑实时需求的定制公交在线调度算法研究[J]. 计算机与数字工程, 2024, 52(9): 2555-2560.
[14] Yu, B., Wang, H., Shan, W. and Yao, B. (2018) Prediction of Bus Travel Time Using Random Forests Based on near Neighbors. Computer-Aided Civil and Infrastructure Engineering, 33, 333-350. [Google Scholar] [CrossRef
[15] Mouwen, A. (2015) Drivers of Customer Satisfaction with Public Transport Services. Transportation Research Part A: Policy and Practice, 78, 1-20. [Google Scholar] [CrossRef