| 城市道路网公交车最优路径自适应挖掘算法 |
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| 引用本文：姚欣锟1，刘庆华2，张雪梅3.城市道路网公交车最优路径自适应挖掘算法[J].计算技术与自动化,2026,(2):17-22 |
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| 中文摘要:针对公交车任务空间较为复杂的情况，提出了城市道路网公交车最优路径自适应挖掘算法。采用双层决策模型，描述理想城市道路网。基于新路径点行驶方向与目标点之间连线夹角最小和新路径点与目标点之间距离最小，分别构建上层和下层决策目标函数，在威胁约束和公交车自身性能约束两个方面构建约束惩罚函数，结合双层决策目标函数和约束惩罚函数特点挖掘待决策新参考路径点和下一段行驶路径。对不存在威胁的参考路径，进行平滑处理，精简路径中冗余点，利用B样条曲线法进一步平滑路径，实现城市道路网公交车最优路径自适应挖掘算法。实验结果表明，平均加速度和最大加速度更小、最小路径长度更短。 |
| 中文关键词:城市道路网 公交车 最优路径 自适应挖掘 双层决策模型 |
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| Adaptive Mining Algorithm for Optimal Bus Routing in Urban Road Networks |
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| Abstract:Aiming at the complex task space of buses, an adaptive mining algorithm for optimal bus paths in urban road networks is proposed. Using a double layer decision model to describe the ideal urban road network, based on the minimum angle between the direction of travel of the new path point and the target point, as well as the minimum distance between the new path point and the target point, upper and lower level decision objective functions are constructed respectively. Constraint penalty functions are constructed in terms of threat constraints and bus performance constraints, and combined with the characteristics of the double layer decision objective function and constraint penalty function, new reference path points and the next driving path to be decided are excavated. For reference paths without threats, smoothing is carried out to streamline redundant points in the path, and B-spline curve method is used to further smooth the path, achieving an adaptive mining algorithm for the optimal bus path in the urban road network. The experimental results indicate that the average acceleration and maximum acceleration are smaller, and the minimum path length is shorter. |
| keywords:urban road network bus optimal path adaptive mining double layer decision model |
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