基于排队论的道路交通警力资源配置研究

doi:10.16265/j.cnki.issn1003-3033.2024.07.0153

摘要/Abstract

摘要：

为缓解有限的交通警力资源与道路交通事故处理不及时之间的矛盾,提出道路网格化管理模式下基于排队论模型的交通警力优化配置方法。首先,利用城市路网卡口系统获取的车牌识别数据,提取车辆的历史出行轨迹,建立路段之间的相似度模型;其次,采用谱聚类算法对路段进行聚类划分,形成路段之间关联度最高的集合,作为城市道路网格划分的结果;然后,针对网格范围内实时发生的交通事故,进一步提出利用排队论模型来计算每个网格所需的最少警员数量,以及相应的警力资源配置优化方案;最后,以宁波市鄞州区为例验证所提方法。结果表明:与传统的“事故即发生即派发警员”的方法相比,提出的警力资源配置优化方法能够减少18.18%的警员配置数量和10.87%的出警巡航路程,使事故处置的响应速度提高10.68%,表现出良好的优化性能。

关键词: 排队论, 交通警力, 资源配置, 交通事故, 网格化管理

Abstract:

To alleviate the contradiction between limited traffic police resources and the untimely handling of road traffic accidents, a traffic police resource optimization allocation approach was proposed based on a queuing theory model under a grid management mode of roads. Firstly, license plate recognition data obtained from the city's road network bayonet system was used to extract historical travel trajectories of vehicles and develop a similarity model between road segments. Secondly, the spectral clustering algorithm was adopted to cluster the road segments and form a set with the highest association between the segments, serving as the result of the road network division. Then, for the real-time traffic accidents within the grid, a queuing theory model was further proposed to calculate the minimum number of police officers required for each grid, along with an optimized allocation scheme for police resources. Finally, the proposed method was validated in Yinzhou District of Ningbo City. The results showed that the proposed optimization method for police allocation reduced the number of police officers by 18.18% and patrol mileage by 10.87% compared to the traditional method of dispatching police officers as soon as an accident occurs. Furthermore, the proposed method increased the accident handling response speed by 10.68%, demonstrating excellent optimization performance.

Key words: queuing theory, traffic police, resource allocation, traffic accidents, grid-based management

中图分类号:

X951交通运输安全

胡正华, 周继彪, 郭旭, 马昌喜. 基于排队论的道路交通警力资源配置研究[J]. 中国安全科学学报, 2024, 34(7): 178-185.

HU Zhenghua, ZHOU Jibiao, GUO Xu, MA Changxi. Allocation of traffic police resources based on queuing theory[J]. China Safety Science Journal, 2024, 34(7): 178-185.

图/表 8

表1

索引名称及含义

类目	变量名	含义
索引	S	网格(即交警中队的管辖区域)
	I	网格的总数
	i	网格S的索引
	J	时间窗内网格S_i总共发生的交通事故数量
	j	在网格S_i内发生事故的索引
	k	在网格S_i内派遣警员的索引
参数	$Z (S i)$	网格S_i需要派遣的警员数
	$A j S i$	网格S_i内发生的第j起事故
	L	警员当前所在的位置
	$v -$ /(km·h^-1)	警员的平均行驶速度
	P(A,B)/km	地点A与B之间的最短路径
	$C k S i$	第k个警员在网格S_i内累计处理的交通事故数
	N⁺	非零自然数集合
	Q	需要处理的交通事故队列
	T/h	处理一起事故的平均时间
	T(M)/h	处理“物损事故”所需的时间
	T(C)/h	处理“伤亡事故”所需的时间
	T( $A c S i$ ,k)/h	网格S_i内第k个警员处理完第c 起事故还需要的时间

表1

表2

图1

图2

图3

表3

排队论模型的警员出警方案与传统模式的出警方案比较

序号	发生时间	出警的警员编号		所属网格		出行距离/m		到达时间			结束时间				等待时间
序号	发生时间	(a)	(b)	所属网格		(a)	(b)	(a)	(b)		(a)		(b)		(a)	(b)
1	7:10:00	1	1	网格1		899	899	7:11:04	7:11:04		7:31:04		7:31:04		0:01:04	0:01:04
2	7:20:00	2	2	网格1		7 075	7 075	7:28:29	7:28:29		7:38:29		7:38:29		0:08:29	0:08:29
3	7:24:00	3	3	网格1		2 633	2 633	7:27:09	7:27:09		7:37:09		7:37:09		0:03:09	0:03:09
4	7:38:00	6	7	网格4		6 336	6 336	7:45:36	7:45:36		7:55:36		7:55:36		0:07:36	0:07:36
5	7:49:00	8	9	网格3		2 441	2 441	7:51:55	7:51:55		8:01:55		8:01:55		0:02:55	0:02:55
6	8:00:00	4	5	网格2		6 487	6 487	8:07:47	8:07:47		8:17:47		8:17:47		0:07:47	0:07:47
7	8:01:00	9	10	网格3		2 895	2 895	8:04:28	8:04:28		8:14:28		8:14:28		0:03:28	0:03:28
8	8:04:00	6	8	网格4		3 226	5 852	8:07:52	8:11:01		8:17:52		8:21:01		0:03:52	0:07:01
9	8:04:00	8	11	网格3		2 602	3 312	8:07:07	8:07:58		8:17:07		8:17:58		0:03:07	0:03:58
10	8:10:00	5	6	网格5		4 651	4 651	8:15:35	8:15:35		8:25:35		8:25:35		0:05:35	0:05:35
11	8:15:00	9	9	网格3		4 860	3 630	8:20:50	8:19:21		8:30:50		8:29:21		0:05:50	0:04:21
12	8:20:00	8	10	网格3		2 338	3 410	8:22:48	8:24:05		8:32:48		8:34:05		0:02:48	0:04:05
13	8:22:00	1	1	网格1		5 955	5 635	8:29:09	8:28:45		8:39:09		8:38:45		0:07:09	0:06:45
14	8:24:00	6	7	网格4	1 106		5 479	8:25:19		8:30:34		8:45:19		8:50:34	0:01:19	0:06:34
15	8:30:00	2	2	网格1	4 945		2 633	8:35:56		8:33:09		8:45:56		8:43:09	0:05:56	0:03:09
16	8:32:00	9	11	网格3	7 494		4 116	8:40:59		8:36:56		8:50:59		8:46:56	0:08:59	0:04:56
17	8:35:00	7	8	网格4	4 504		4 504	8:40:24		8:40:24		8:50:24		8:50:24	0:05:24	0:05:24
18	8:36:00	3	3	网格1	3 920		4 781	8:40:42		8:41:44		8:50:42		8:51:44	0:04:42	0:05:44
19	8:40:00	1	4	网格1	4 213		1 633	8:45:03		8:41:57		8:55:03		8:51:57	0:05:03	0:01:57
20	8:40:00	4	5	网格2	15		6 503	8:40:01		8:47:48		8:50:01		8:57:48	0:00:01	0:07:48
21	8:46:00	5	6	网格5	1 850		5 353	8:48:13		8:52:25		8:58:13		9:02:25	0:02:13	0:06:25
—				出行总路程	80 445		90 258	—						平均等待时间	0:04:36	0:05:09
—				提升占比/%	—		10.87	—						提升占比/%	—	10.68

表3

图4

图5

参考文献 22

[1]	唐炉亮, 阚子涵, 任畅, 等. 利用GPS轨迹的转向级交通拥堵精细分析[J]. 测绘学报, 2019, 48(1): 75-85. doi: 10.11947/j.AGCS.2019.20170448
	TANG Luliang, KAN Zihan, REN Chang, et al. Fine-grained analysis of traffic congestions at the turning level using GPS traces[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(1): 75-85. doi: 10.11947/j.AGCS.2019.20170448
[2]	姬浩, 王永东, 李佩, 等. 事故车辆影响下的城市三车道道路交通流仿真[J]. 中国安全科学学报, 2021, 31(3): 112-120. doi: 10.16265/j.cnki.issn1003-3033.2021.03.016
	JI Hao, WANG Yongdong, LI Pei, et al. Traffic flow simulation of urban three-lane road considering influence of accident vehicle[J]. China Safety Science Journal, 2021, 31(3): 112-120. doi: 10.16265/j.cnki.issn1003-3033.2021.03.016
[3]	CHU Wenhui, WU Chaozhong, ATOMBO C, et al. Traffic climate, driver behaviour, and accidents involvement in China[J]. Accident Analysis & Prevention, 2019, 122: 119-126.
[4]	WELDESLASSIE F M, TAREKEGN T K, GAIM T K, et al. Assessment of magnitude and associated factors of road traffic accidents among minibus taxi drivers in Megenagna, Torhailoch and Saris, Addis Ababa, Ethiopia[J]. Journal of Health and Environmental Research, 2022, 8(3): 197-211.
[5]	杨洋, 王文慧, 吴先宇, 等. 高速公路非常规交通事故研究综述[J]. 应用基础与工程科学学报, 2024, 32(3): 601-626.
	YANG Yang, WANG Wenhui, WU Xianyu, et al. Review of the research toward freeway unconventional traffic accidents[J]. Journal of Basic Science and Engineering, 2024, 32(3): 601-626.
[6]	王婷婷. 智慧交通背景下成都市公共出行服务的问题与对策研究[D]. 成都: 电子科技大学, 2022.
	WANG Tingting. A study on the problems of and solutions to the public travel service of Chengdu in the background of intelligent transportation[D]. Chengdu: University of Electronic Science and Technology of China, 2022.
[7]	王启超. 当前警务改革背景下辅警管理研究[D]. 济南: 山东大学, 2022.
	WANG Qichao. Research on auxiliary police team management under the background of current police reform[D]. Ji'nan: Shandong University, 2022.
[8]	STEDEN V R, MEHLBAUM S M. Police volunteers in the Netherlands: a study on policy and practice[J]. Policing and Society, 2019, 29(4): 420-433.
[9]	NEWSTEAD S V, CAMERON M H, LEGGETT L M W. The crash reduction effectiveness of a network-wide traffic police deployment system[J]. Accident Analysis & Prevention, 2001, 33(3): 393-406.
[10]	李志恒, 赵宁宇, 谭墍元. 考虑信号灯影响的交通警力巡逻模型研究[J]. 交通运输系统工程与信息, 2015, 15(4): 118-122,128.
	LI Zhiheng, ZHAO Ningyu, TAN Jiyuan. Model of traffic police patrol considering the influence of traffic lights[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(4): 118-122,128.
[11]	ADLER N, HAKKERT A S, KORNBLUTH J, et al. Location-allocation models for traffic police patrol vehicles on an interurban network[J]. Annals of Operations Research, 2014, 221(1): 9-31.
[12]	胡子峰, 陈洋, 郑秀娟, 等. 空地异构机器人系统协作巡逻路径规划方法[J]. 控制理论与应用, 2022, 39(1): 48-58.
	HU Zifeng, CHEN Yang, ZHENG Xiujuan, et al. Cooperative patrol path planning method for air-ground heterogeneous robot system[J]. Control Theory & Applications, 2022, 39(1): 48-58.
[13]	DUNNETT S, LEIGH J, JACKSON L. Optimising police dispatch for incident response in real time[J]. Journal of the Operational Research Society, 2019, 70(2): 269-279. doi: 10.1080/01605682.2018.1434401
[14]	CHASE J D, NGUYEN D T, SUN H, et al. Improving law enforcement daily deployment through machine learning-informed optimization under uncertainty[C]. The Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019: 5815-5821.
[15]	LI Wenguang, ZHEN Zhiming. Intelligent surveillance and reconnaissance mode of police UAV based on grid[C]. 2021 7^th International Symposium on Mechatronics and Industrial Informatics (ISMII), 2021: 292-295.
[16]	胡立伟, 吕一帆, 赵雪亭, 等. 基于数据驱动的交通事故伤害程度影响因素及其耦合关系研究[J]. 交通运输系统工程与信息, 2022, 22(5): 117-124,134.
	HU Liwei, LYU Yifan, ZHAO Xueting, et al. Influence factors and coupling relationship of traffic accident injury degree based on a data-driven approach[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(5): 117-124,134.
[17]	缪明月, 曹玉锋, 李强, 等. 大数据背景下道路交通应急警力资源科学配置的路径与方法[J]. 中国人民公安大学学报:自然科学版, 2022, 28(4): 39-46.
	MIAO Mingyue, CAO Yufeng, LI Qiang, et al. Path and method of scientific allocation for road traffic emergency police resources under the background of big data[J]. Journal of People's Public Security University of China :Science and Technology, 2022, 28(4): 39-46.
[18]	杨文臣, 周燕宁, 田毕江, 等. 基于聚类分析和SVM的二级公路交通事故严重度预测[J]. 中国安全科学学报, 2022, 32(5): 163-169. doi: 10.16265/j.cnki.issn1003-3033.2022.05.1263
	YANG Wenchen, ZHOU Yanning, TIAN Bijiang, et al. Traffic accident severity prediction for secondary highways based on cluster analysis and SVM model[J]. China Safety Science Journal, 2022, 32(5): 163-169. doi: 10.16265/j.cnki.issn1003-3033.2022.05.1263
[19]	PRADOS-GARZON J, AMEIGEIRAS P, RAMOS-MUNOZ J J, et al. Performance modeling of softwarized network services based on queuing theory with experimental validation[J]. IEEE Transactions on Mobile Computing, 2019, 20(4): 1558-1573.
[20]	MODI M, AGARWAL G, PATIL V, et al. Minimization of traffic congestion by using queuing theory[J]. International Journal of Scientific and Technology Research, 2019, 8(10):532-541.
[21]	GÜRLER Ü, BERK E. Decision sciences: theory and practice[M]. Boca Raton: Taylor & Francis, 2016: 303-364.
[22]	KRISHNAMOORTHY A, SHAJIN D, NARAYANAN W. Inventory with positive service time: a survey[J]. Advanced Trends in Queueing Theory, 2021, 2: 201-238.

序号	约束条件的内容
1	根据宁波市公安局交通警察局的规定 (简称规定),交警在接到当事人报警后, 须在10 min之内到达现场
2	每一位交警不跨格网执行任务
3	根据规定,每位交警一天最多只能处理8起事故
4	根据规定,物损事故按照10 min处理完毕, 伤亡事故按照20 min处理完毕
5	如果发生伤亡事故,直接插入Q的头部优先处理;否则按照发生的时间顺序进入Q