Journal of Shandong University(Engineering Science) ›› 2023, Vol. 53 ›› Issue (4): 1-17.doi: 10.6040/j.issn.1672-3961.0.2023.045

• Transportation Engineering—Special Issue for Intelligent Transportation •     Next Articles

Intelligent scheduling technology of highway emergency rescue vehicle

Xiuguang SONG1(),Xinming GUO1,2,*(),Fang YAN2,Guoqiang LI2,Yuan TIAN1   

  1. 1. Qilu College of Transportation, Shandong University, Jinan 250002, Shandong, China
    2. Shandong High-speed Dongying Development Co., Ltd., Dongying 257000, Shandong, China
  • Received:2023-03-09 Online:2023-08-20 Published:2023-08-18
  • Contact: Xinming GUO E-mail:songxiuguang@sdu.edu.cn;202115385@mail.sdu.edu.cn

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Abstract:

Traffic accidents had suddenness, complexity and severity. Efficient and reasonable dispatch of emergency rescue vehicles played a vital role in reducing casualties and property losses. The influencing factors of rescue route planning were introduced. The model was described from the single objective model and the multi-objective model. Combined with the characteristics of emergency rescue in sudden traffic accidents, the route optimization algorithm of emergency rescue vehicles was summarized from two aspects: precise algorithm and meta-heuristic algorithm. The current emergency rescue vehicle scheduling technology and development trend were summarized and prospected.

Key words: emergency rescue, path optimization, intelligent scheduling, mathematical modeling, simulation

CLC Number: 

  • U491.1+2

Fig.1

Schematic diagram of the shortest path for rescue vehicles"

Table 1

Characteristics and shortcomings of path planning model"

模型类别 影响因素 特点 不足
单目标模型 救援距离 应急状态下, 所有车辆行驶路径的总长度最短 多事件非同步发生时, 响应途中的应急救援车辆无法根据实际情况调整
救援成本 通常考虑每公里的行驶成本和车辆启用数, 尽可能减少运输费用 可能会导致救援时间延误和救援物资提供不足
响应时间 提高服务效率, 减少车辆行驶时间, 减少突发事件造成的资产和生命损失 缩短响应时间的同时, 也增加了救援过程的风险性, 增大了二次事故发生的概率
多目标模型 综合考虑多个因素的影响, 使模型与实际问题更相符 目标增多会增加决策选择的复杂度

Fig.2

Principle of genetic algorithm"

Fig.3

The process of solid annealing"

Table 2

Definition of simulated annealing"

固体退火 模拟退火算法
粒子状态 可行解
粒子能量最低状态 最优解
设置初始温度 初始解
能量 目标函数
冷却 控制参数的下降
等温过程 Metropolis抽样过程

Fig.4

The map of ant colony behavior"

Table 3

The advantage and disadvantage of route planning algorithm for emergency rescue vehicles"

算法 优势 不足
精确算法 对于小规模的车辆优化调度问题具有求解速度快、求解精度高等优点 计算复杂度与问题规模之间呈几何级数变化关系, 不能满足实际应用需求
元启发式算法 适用于求解大规模复杂的实际问题, 处理速度相对精确算法更快 无法保证得到某优化问题的全局最优解; 无法有效地处理约束条件, 可能导致最终解为不可行解

Table 4

The advantage and disadvantage of meta-heuristic algorithm"

算法 优势 不足
蚁群算法 具有强大的鲁棒性和寻找较优解的能力 收敛速度慢，容易陷入最优解
粒子群算法 全局搜索能力强, 收敛速度快, 简单容易实现, 并且没有许多参数需要调整 容易陷入过早收敛, 局部搜索能力弱
模拟退火算法 能适应于许多组合和连续优化问题 收敛缓慢, 调参问题复杂
遗传算法 具有很强的鲁棒性和优化能力 在进化后期搜索效率较低
布谷鸟算法 可以在全局范围内进行更有效的探索, 提高寻优效率 面对多峰复杂函数时对空间的探索仍具有一定的盲目性
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