Optimization Method for Autonomous Bus Operation Considering Adaptive Control Points

doi:10.16097/j.cnki.1009-6744.2025.05.004

Abstract

Abstract: To address the issue of uncertain operation times for autonomous buses in time-varying traffic environments, this study proposes an optimization method for multi-objective operation that incorporates adaptive control points. The method overcomes the limitations of traditional line-level control schemes for human-driven buses, by constructing a stochastic mixed-integer nonlinear programming model, jointly optimizing the number and locations of time control points along with the scheduled arrival times, and minimizing the weighted sum of negative utilities from both arrival time deviations and travel time deviations. This approach leverages the synergistic advantages of adaptive control point strategies and autonomous driving technology to enhance bus service quality under uncertain conditions. Through the linearization techniques and Monte Carlo simulation, the proposed model is further transformed into a deterministic mixed-integer linear programming model, which is efficiently solved by using a branch-and-bound algorithm. The proposed method is validated through multiple comparative experiments on a bus route with 19 stops and 12 consecutive trips. The results demonstrate that, compared to fixed control point strategies, the proposed variable control point strategy and optimization method reduce mean arrival time deviation disutility by 11.35% and mean travel time deviation disutility by 58.87% during peak hours. During off-peak hours, the reductions reach 18.21% and 38.68%, respectively. Sensitivity analysis further reveals that the optimal operational performance is achieved when the number of regular stops between adjacent control points is set to 20% of the total stops on the bus route, under the experimental conditions specified in this study.

Key words: intelligent transportation, operation schedule, mixed-integer programming, autonomous buses, time control points; stochasticity

摘要： 针对时变交通环境下自动驾驶公交运行时间不确定的问题，本文提出一种考虑自适应控制点的自动驾驶公交多目标运行优化方法。该方法突破传统人工驾驶公交线路级控制方案的局限性，通过构建随机混合整数非线性规划模型，联合优化时间控制点数量、位置与车辆计划到站时刻，最小化到站时刻偏差负效用与运行时间偏差负效用加权和，发挥自适应控制点策略与自动驾驶技术协同优势，提升不确定环境下线路运行的可靠性与平稳性。通过线性化技术与蒙特卡洛仿真，将原模型转化为确定性混合整数线性规划模型，采用分支定界法高效求解。以一条包含19个站点和连续运行12个班次的公交线路为例，设计多组对比实验验证方法的有效性。结果表明：相较于固定控制点策略，本文提出的自适应控制点策略及运行优化方法在高峰时段可降低11.35%的平均到站时刻偏差负效用和58.87%的平均运行时间偏差负效用；在平峰时段降幅分别达18.21%和38.68%。敏感性分析进一步发现，在本文设定的实验条件下，当企业期望的相邻控制点间普通站点数为线路总站点数的20%时，可获得最佳运行效果。

关键词: 智能交通, 运行计划, 混合整数规划, 自动驾驶公交, 时间控制点, 随机性

CLC Number:

U495

DOU Xueping, YANG Minghui, XIONG Jie. Optimization Method for Autonomous Bus Operation Considering Adaptive Control Points[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(5): 50-58.

窦雪萍, 杨明慧, 熊杰. 考虑自适应控制点的自动驾驶公交运行优化方法[J]. 交通运输系统工程与信息, 2025, 25(5): 50-58.

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URL: http://www.tseit.org.cn/EN/10.16097/j.cnki.1009-6744.2025.05.004

http://www.tseit.org.cn/EN/Y2025/V25/I5/50

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