面向交通事件處置的交通警力資源調(diào)度方法的研究
發(fā)布時間：2018-11-27 18:34
【摘要】：道路交通事件的頻繁發(fā)生使警力資源調(diào)度指揮管理逐漸引起了相關(guān)交通管理部門和社會的關(guān)注,同時也暴露出當交通事件發(fā)生時警力資源調(diào)度指揮在應急體系建設和實施方面存在著許多薄弱環(huán)節(jié)。北京市龐大的人口數(shù)量與機動車數(shù)量對北京市交通警力帶來巨大壓力,特別是在冰雪災害和各類突發(fā)交通事件中,跨區(qū)域、跨路網(wǎng)、跨部門應急指揮等方面仍然存在諸多不足。掌握各種實時信息,及時處理交通業(yè)務及交通事件,以求在最短時間內(nèi)保證交通暢通,把交通事件帶來的損失降到最低,這是對管理交通的公安交警部門的必然要求。 本文以北京市122交通事故報警臺海量數(shù)據(jù)為基礎,以有關(guān)事故、擁堵、反映類交通事件數(shù)據(jù)特征的深入分析為切入點,從事故、擁堵、反映類三種情形給出交通事件投量投向的建議。在深入學習了大量應用交通警力資源處置交通事件知識的前提下,從靜態(tài)和動態(tài)兩個方面對警力資源配置方法進行研究。 一方面對遺傳算法理論進行深入學習,在理解遺傳算法的基本原理、基本操作及特征的基礎上,提出一種面向交通事件處置的警力資源靜態(tài)部署方法——基于遺傳算法的警力資源靜態(tài)部署方法,用以提高部署效率,并實現(xiàn)最小部署警力和最大部署性能的優(yōu)化部署。 另一方面對GIS進行深入學習與對遺傳算法進行深入思考,以及利用決策樹的方法對警員狀態(tài)進行辨識分析,提出一種面向交通事件處置的動態(tài)警力資源調(diào)度方法——基于GIS的動態(tài)警力資源調(diào)度方法,特別對事故頻發(fā)區(qū)域而警力資源不夠調(diào)度時,采用遺傳算法提供最優(yōu)的調(diào)度方案,以減少人力成本的基礎上,保證調(diào)度的準確性與及時性。
[Abstract]:With the frequent occurrence of road traffic events, the police resource dispatching command and management have gradually aroused the concern of the relevant traffic management departments and the society. At the same time, it also reveals that there are many weak links in the construction and implementation of emergency system. The huge population of Beijing and the number of motor vehicles bring great pressure to traffic police in Beijing, especially in the ice and snow disasters and all kinds of sudden traffic events, there are still many deficiencies in cross-regional, cross-road network, inter-departmental emergency command and so on. To grasp all kinds of real-time information and deal with traffic operations and traffic events in time in order to ensure the smooth flow of traffic in the shortest time and to minimize the losses caused by traffic events is an inevitable requirement for the traffic police department of traffic management. Based on the mass data of the 122 traffic accident alarm station in Beijing, this paper takes the in-depth analysis of the characteristics of accident, congestion and reflecting the data of traffic events as the breakthrough point, from the accident, congestion, Reflecting the three kinds of cases, the author gives the suggestion of the traffic incident investment. On the premise of studying a large number of traffic police resources to deal with traffic events, the method of police resource allocation is studied from static and dynamic aspects. On the one hand, the theory of genetic algorithm is deeply studied, and on the basis of understanding the basic principle, basic operation and characteristics of genetic algorithm, In this paper, a static deployment method of police resources for traffic incident management is proposed, which is based on genetic algorithm to improve the deployment efficiency and achieve the optimal deployment of the minimum deployed police force and the maximum deployment performance. On the other hand, we study GIS deeply and think deeply about genetic algorithm, and use the method of decision tree to identify and analyze the state of police officers. This paper presents a dynamic police resource scheduling method based on GIS, which is a dynamic police resource scheduling method for traffic incident management. Especially, genetic algorithm (GA) is used to provide the optimal scheduling scheme for the frequent accident area and insufficient police resource scheduling. On the basis of reducing labor costs, ensure the accuracy and timeliness of scheduling.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：D631.5
本文編號：2361686
[Abstract]:With the frequent occurrence of road traffic events, the police resource dispatching command and management have gradually aroused the concern of the relevant traffic management departments and the society. At the same time, it also reveals that there are many weak links in the construction and implementation of emergency system. The huge population of Beijing and the number of motor vehicles bring great pressure to traffic police in Beijing, especially in the ice and snow disasters and all kinds of sudden traffic events, there are still many deficiencies in cross-regional, cross-road network, inter-departmental emergency command and so on. To grasp all kinds of real-time information and deal with traffic operations and traffic events in time in order to ensure the smooth flow of traffic in the shortest time and to minimize the losses caused by traffic events is an inevitable requirement for the traffic police department of traffic management. Based on the mass data of the 122 traffic accident alarm station in Beijing, this paper takes the in-depth analysis of the characteristics of accident, congestion and reflecting the data of traffic events as the breakthrough point, from the accident, congestion, Reflecting the three kinds of cases, the author gives the suggestion of the traffic incident investment. On the premise of studying a large number of traffic police resources to deal with traffic events, the method of police resource allocation is studied from static and dynamic aspects. On the one hand, the theory of genetic algorithm is deeply studied, and on the basis of understanding the basic principle, basic operation and characteristics of genetic algorithm, In this paper, a static deployment method of police resources for traffic incident management is proposed, which is based on genetic algorithm to improve the deployment efficiency and achieve the optimal deployment of the minimum deployed police force and the maximum deployment performance. On the other hand, we study GIS deeply and think deeply about genetic algorithm, and use the method of decision tree to identify and analyze the state of police officers. This paper presents a dynamic police resource scheduling method based on GIS, which is a dynamic police resource scheduling method for traffic incident management. Especially, genetic algorithm (GA) is used to provide the optimal scheduling scheme for the frequent accident area and insufficient police resource scheduling. On the basis of reducing labor costs, ensure the accuracy and timeliness of scheduling.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：D631.5
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