郝正博

入学时间：2018级

答辩时间：2024年

论文题目：考虑通行优先的城市道路应急交通路径优化方法研究

中文摘要

摘要

城市应急车辆路径优化是一个长期存在的社会热点、难点和痛点问题，同样也是典型的应用型学理问题。应急车辆（如消防救援车辆、医疗救护车辆、警用特勤车辆、工程抢险车辆和国防保障车辆等执行救援任务的专用车辆）在拯救人民生命和减少财产损失方面发挥着重要作用。与社会车辆不同，应急车辆稍有延误便可能造成严重后果。由于城市道路交通系统的高度复杂性和动态性等特征，应急车辆救援的效率性、安全性、可靠性和低社会成本性难以得到保障。

因此，秉承问题导向、需求牵引的研究原则，本文从提高应急车辆救援效率、保障其安全可靠和降低因其优先通行所造成的社会车辆负面成本等实际工作需求出发，依托车路协同环境下大数据、车联网等新兴技术快速发展优势，利用常发性较高的我国消防救援事件及其多元数据构建实证分析场景，以我国应急车辆所享有的法定优先通行权为核心切入点，探究考虑通行优先的城市道路应急交通路径优化方法。针对以往研究中偏向依赖模型或强数理假定、时空优先策略缺乏协同、社会车辆负面成本考虑较少等不足，本文开展如下四个部分的研究：

1. 基于多元数据的应急车辆救援通行特征解析。针对以往研究中偏向依赖模型或强数理假定等不足，为减少理论与应用之间的差异，增强研究成果的可应用性，该部分首先对我国应急车辆历史轨迹数据等相关多元数据进行融合分析，并提出考虑多特征约束的两阶段行程识别方法，用以构建其救援通行全过程。其后，从应急救援需求特征分析、应急救援供给特征分析、应急救援供需匹配分析、行驶速度影响因素分析、现有被动优先效果分析、主动通行优先效果模拟等多个方面解析其救援通行特征。通过特征解析，可获得大部分救援车队规模为1-3辆；92.1%的救援行程距离分布在0-4公里区间内；车道数量、路段限速等因素在99%的置信水平下显著影响应急车辆速度等诸多实用性结论。该成果有助于为后续章节中应急车辆路径规划及沿途优先方案提供研究场景与真实数据支撑。

2. 考虑时空协同优先的应急车辆路径优化方法。针对以往研究中时空优先策略缺乏协同等不足，该部分立足于为应急车辆提供无条件优先通行策略的核心思路，即为应急车辆赋予最高等级优先权，并考虑其与社会车辆在优先通行政策、路径优化目标等多方面的特征差异，提出考虑时间优先策略与空间优先策略相互协同的两阶段应急车辆路径动态优化方法。第一阶段，将用于求解静态K最短路径的Yen算法与路网时空动态性等特征相结合，增加基于层次聚类算法的路径安全风险约束与基于偏斜度指数等综合评价指标的可靠性约束，以获取备选路径集合。第二阶段，基于车道数、路段限速和拥堵延迟指数等静、动态属性特征，获得在时空协同优先条件下的最佳路径。实证研究显示，与其历史轨迹数据相比，该方法能够缩短救援时间约67.8%，并提高安全性与可靠性。此外，各备选路径在实施超过路段限速20%的超速策略后，原有次优路径的救援时间低于了原有最优路径，并随着超速策略实施强度增加而进一步扩大优势。因此，适当实施超速驾驶策略具有进一步减少救援时间，并改变最佳路径选择结果的潜能。

3. 考虑不同优先等级的应急车辆路径优化方法。针对以往研究中社会车辆负面成本考虑较少等不足，该部分立足于为应急车辆提供有条件优先通行策略的核心思路，即在为应急车辆规划路径过程中，不仅满足其救援需求，同时尽可能降低因其优先通行造成的社会车辆延误。首先，考虑应急事件自身特征、发展态势和交通及救援供给能力，构建基于多级灰度评价法的路径级优先通行等级评价模型。其后，构建以最大化应急车辆救援时间节约值和最小化社会车辆行程时间总延误值为目标的多目标优化模型，并综合考虑时变道路运行状态等参数，估算不同优先通行等级下的策略实施效果，系统优化救援路径及优先策略方案，以进一步实现应急车辆快速救援（个体最优）与社会车辆整体出行（系统最优）之间的最佳协同。实证研究显示，与推荐最佳路径相比，该方法能够缩短救援时间约50.5%。同时基于帕累托最优解集合能够获得在该时间节约比例下，社会车辆所承担的最小负面成本及其对应优先策略方案。此外，当多个（以3个为例）应急车辆形成连续车队进行救援时，能够进一步压缩单位车辆救援时间约1.7%。

4. 应急车辆救援主动交通管理即服务系统应用。针对现有应急救援实际工作中缺乏和其他部门在系统层面的协同联动等不足，该部分在总结前述章节理论模型构建和技术方法成果的基础上，面向未来城市交通高度数字化、信息化、网联化、智能化发展趋势，借鉴以往已有系统理念（如出行即服务、交通管理即服务），讨论城市应急车辆救援主动交通管理即服务系统应用，包括系统基本构思、系统需求分析、系统框架设计等内容，为未来打通业务壁垒提供系统平台思路。

本学位论文在丰富城市道路应急交通路径优化方法体系方面主要具有三点创新：1. 基于我国应急车辆多元数据赋能其救援水平提升，包括救援通行特征解析等；2. 在路径寻优算法中融入应急车辆多维度特征差异，包括综合优化救援服务水平等；3. 实施全过程主动协同型应急车辆优先通行策略集，包括构建分级分类的优先通行等级等。本文研究成果对于提升我国城市应急车辆救援出行服务水平、降低因应急车辆优先通行造成的社会车辆负面成本和提高我国城市部门联动应急处置管理水平具有较强的理论意义和应用价值。

关键词：智能交通系统，城市应急车辆，通行优先，路径优化，多元数据

英文摘要

ABSTRACT

Route optimization for urban emergency vehicles is a long-standing hotspot, challenging, and painful problem in society, and it is also a typical applied theoretical problem. Emergency vehicles (e.g., fire rescue vehicles, emergency medical vehicles, police special duty vehicles, engineering rescue vehicles, and national defense vehicles) are essential in saving people's lives and reducing property losses. Unlike social vehicles, the slight delay of emergency vehicles may cause serious consequences. Owing to the highly complex and dynamic characteristics of urban road traffic systems, it is difficult to guarantee the efficiency, safety, reliability, and low social vehicles impact of emergency vehicle rescue.

Therefore, adhering to the research principles of problem-oriented and demand-driven, this dissertation starts from the practical demand of improving the rescue efficiency of urban emergency vehicles, guaranteeing their safety and reliability, and reducing the negative social vehicles impact caused by priority strategies of emergency vehicles. Relying on the advantages of the rapid development of emerging technologies such as big data and Internet of Vehicles in vehicle infrastructure cooperative system, and utilizing the multivariate data of China's fire trucks to construct the empirical analysis scenario, and taking the statutory priority strategies of emergency vehicles as the core entry point, this dissertation explores the optimization methods for urban emergency vehicles routes considering traffic priority strategies. Aiming at the shortcomings of previous studies, such as relying on models or strong assumptions, lack of synergy between spatial and temporal priority strategies, and less consideration of the negative costs of social vehicles, this paper carries out the following four parts of the research content:

Part 1. Rescue passage characteristics analysis of emergency vehicle based on multivariate data. Aiming at decreasing biased reliance on models or strong assumptions, to reduce the discrepancy between theory and application and enhance the applicability of research results, Part 1 initially fuses and analyzes China's emergency vehicles multivariate data (e.g., historical trajectory data) and proposes a two-phase trip identification method considering multi-feature constraints, which can be used to construct the whole process of emergency vehicle rescue. Then, Part 1 analyzes the rescue passage characteristics of China’s emergency vehicles from several aspects, such as analysis of emergency rescue demand characteristics, analysis of emergency rescue supply characteristics, analysis of emergency rescue supply-demand matching, analysis of driving speed influencing factors, analysis of the existing passive priority strategies effect, and simulation of the active priority strategies effect, and so on. By analyzing the characteristics, we can obtain the practical findings that most of the rescue fleet size ranges from 1-3 vehicles; 92.1% of the rescue travel distance is within a 0-4 km range; and the number of lanes, speed limit, and other factors significantly affect the speed of emergency vehicles at 99% confidence level. These conclusions of Part 1 provide research scenarios and real-world data support for emergency vehicle route optimization and prioritization schemes along the route in subsequent chapters.

Part 2. Route optimization method for emergency vehicle considering spatio-temporal collaborative priority. Aiming at the shortcomings, such as the lack of collaboration between temporal and spatial priority strategies in previous studies, Part 2 is based on the core idea of providing unconditional priority strategies for emergency vehicles, that is, gives the highest level of priority for emergency vehicles, and taking into account the differences (such as priority policies, route optimization objectives) between their characteristics and those of the social vehicles. Part 2 proposes a two-phase dynamic route optimization method for emergency vehicles, which takes into account the collaboration of temporal priority and spatial priority strategies. In the first stage, the Yen algorithm for solving the static K-shortest path is combined with the dynamic characteristics of the road network, and the route safety constraints based on the hierarchical clustering algorithm and reliability constraints based on the skewness index and other comprehensive evaluation indexes are added in order to obtain the set of alternative routes. In the second stage, the optimal route under spatio-temporal collaborative priority conditions is obtained based on dynamic and static attribute features such as the number of lanes, the speed limit, and the congestion delay index. Empirical analysis results show that the proposed method can reduce the time by about 67.8% and improve safety and reliability compared to its historical trajectory data. Moreover, the time of the original suboptimal route becomes lower than that of the original optimal route after the implementation of the speeding strategy that exceeds the speed limit by 20% for each alternative route, and the advantage further expands with the increase of the implementation intensity of the speeding strategy. Therefore, proper implementation of the speeding strategy has the potential to reduce the time further and change the outcome of the optimal route selection.

Part 3. Route optimization method for emergency vehicle considering different priority levels. Aiming at the shortcomings, such as less consideration of the negative impacts of social vehicles in previous studies, Part 3 is based on the core idea of providing the conditional priority strategy for emergency vehicles, that is, in the process of planning routes for emergency vehicles, not only to meet their rescue demand but also to minimize the delays for social vehicles due to priority strategies implementation as much as possible. Initially, considering the characteristics of the emergency event, the development trend, and the traffic and rescue supply capacity, Part 3 constructs a route-level priority level evaluation model based on the multi-step associated gray evaluation model. Subsequently, a multi-objective optimization model is constructed to maximize the rescue time saving value of emergency vehicles and minimize the total delay value of social vehicle travel time, and the time-varying road operation state and other parameters are considered comprehensively to estimate the strategy implementation effect under different priority levels, and to systematically optimize the rescue routes and the prioritization schemes, to realize the optimal collaboration between the rapid rescue of emergency vehicles (user optimum) and the overall travel time of social vehicles (system optimum). The empirical analysis results indicate that the proposed method can shorten the rescue time by about 50.5% compared with the recommended optimal route. Meantime, based on the set of Pareto-optimal solutions, we can obtain the minimum negative cost borne by the social vehicles and the corresponding prioritization schemes under a given time-saving ratio. In addition, when multiple emergency vehicles (three, for example) form a continuous fleet for rescue driving, the rescue time per vehicle can be further compressed by approximately 1.7%.

Part 4. Application of the Emergency Vehicle Rescue Active Traffic Management as a Service System. Aiming at the shortcomings, such as lack of collaboration with other departments in the system level in the existing emergency rescue practice, Part 4 builds on the theoretical model construction and technical method research outcomes of the preceding chapters and faces the future trends of highly digitalized, informatized, networked, and intelligent urban traffic development, and draws on previous concepts of systems (e.g. Mobility as a Service, Traffic Management as a Service) to discuss the application of the Urban Emergency Vehicle Rescue Active Traffic Management as a Service System. This includes the basic concept of the system, system requirements analysis, and system framework design, among other aspects, for the future to open up the collaboration barriers to provide a system platform ideas.

This dissertation primarily contributes three innovations to the enhancement of urban road emergency traffic route optimization method system: 1. Leveraging multivariate data of China's emergency vehicles to enhance their rescue performance, including the analysis of rescue passage characteristics; 2. Integrating multi-dimensional characteristics of emergency vehicles into route optimization algorithms, including incorporating comprehensive optimization of rescue service levels; and 3. Implementing a full-process proactive collaboration of priority strategies for emergency vehicles, including the establishment of hierarchical classifications for priority passage levels. The research findings of this dissertation are of significant theoretical and practical value in improving the performance of rescue services for emergency vehicles in China's cities, reducing the social vehicles impact caused by the priority strategies and enhancing the management and collaboration among departments in China's cities to cope with emergencies.

Key Words: Intelligent Transportation System, Urban Emergency Vehicle, Traffic Priority,Route Optimization,Multimodal Data