本文選題：橋下積水　切入點：交通擁堵　出處：《北京交通大學》2014年碩士論文　論文類型：學位論文

【摘要】：近年來,因雨雪等惡劣天氣造成的交通擁堵頻發(fā),這不僅導(dǎo)致路網(wǎng)大范圍交通擁堵事件發(fā)生,還造成了嚴重的經(jīng)濟財產(chǎn)損失,如何應(yīng)對惡劣天氣造成的橋下積水交通擁堵已成為北京等城市面臨的新難題。目前,學者和專家對常發(fā)性交通擁堵的各方面研究較多,而利用浮動車數(shù)據(jù)針對暴雨天氣這一具體原因所導(dǎo)致的偶發(fā)性交通擁堵研究相對較少。在此背景下,本文以北京市為例,針對重點橋下積水道路開展了多角度的浮動車數(shù)據(jù)分析,歸納總結(jié)橋下積水交通擁堵演變規(guī)律。在此基礎(chǔ)上,提出了基于三個指標的橋下積水交通擁堵點段識別方法,構(gòu)建了橋下積水交通擁堵的蔓延和消散速度模型。 首先,從交通擁堵定義和交通擁堵特性入手,論述了道路交通運行等級速度劃分以及常發(fā)性擁堵與偶發(fā)性擁堵的異同點。然后,在國內(nèi)外交通事件自動檢測及擁堵點段識別的研究基礎(chǔ)上,分析了國內(nèi)外交通事件自動檢測方法在橋下積水交通擁堵識別的適應(yīng)性。結(jié)合現(xiàn)有的交通擁堵特性,同時開展了惡劣天氣對交通影響的研究綜述�；诖�,明確了本文研究的目標、思路和技術(shù)路線。 其次,對比分析正常情況和積水當日的交通流數(shù)據(jù)特性,發(fā)現(xiàn)了橋下積水交通擁堵會導(dǎo)致浮動車樣本量顯著缺失的特征,并提出了浮動車數(shù)據(jù)樣本量比率概念。同時,對比分析了積水交通擁堵與交通事故、交通管制的樣本量比率異同點,結(jié)果表明,北京市西三環(huán)快速路的缺失樣本量比例近達80%,西四環(huán)快速路的缺失樣本量比例高達97%。然后,通過計算分析橋下積水交通擁堵的蔓延和消散速度以及擁堵路段的車輛行駛里程(VKT),歸納總結(jié)了橋下積水深度與擁堵蔓延的時間關(guān)系規(guī)律和擁堵空間的影響范圍。 最后,根據(jù)橋下積水交通擁堵的空間影響范圍、擁堵路段的速度變化和時空分布情況,提出了基于樣本量比率、交通流速度變化和速度差三個識別指標的橋下積水交通擁堵點段識別方法,并實現(xiàn)了基于ArcGIS的擁堵點段程序化應(yīng)用識別。然后,在綜合考慮積水當日交通流特性、橋下積水對道路通行能力的折減、通行能力與交通流量比、快速路出入口等影響因素的基礎(chǔ)上,并結(jié)合邏輯判斷和實際經(jīng)驗,初步建立了橋下積水交通擁堵的蔓延和消散速度模型。同時,基于VISSIM仿真數(shù)據(jù)和積水當日的浮動車數(shù)據(jù),對模型參數(shù)進行了初步標定。
[Abstract]:In recent years, traffic jams caused by bad weather, such as rain and snow, have occurred frequently, which not only led to large-scale traffic congestion in the road network, but also caused serious economic and property losses. How to deal with traffic jams under bridges caused by bad weather has become a new problem for Beijing and other cities. At present, scholars and experts have studied many aspects of regular traffic jams. However, there are relatively few studies on accidental traffic congestion caused by heavy rain weather using floating vehicle data. In this context, this paper takes Beijing as an example. A multi-angle floating vehicle data analysis is carried out for the waterlogged road under the key bridge, and the evolution law of the traffic congestion under the bridge is summarized. On the basis of this, a method of identifying the traffic jams under the bridge is proposed based on three indexes. The speed model of spreading and dissipating traffic congestion under the bridge is constructed. First of all, starting with the definition of traffic congestion and traffic congestion characteristics, the paper discusses the speed division of traffic grade and the similarities and differences between regular traffic congestion and accidental congestion. Based on the research of automatic detection of traffic events and identification of traffic congestion points at home and abroad, the adaptability of automatic detection methods of traffic events at home and abroad to traffic congestion identification under bridges is analyzed. At the same time, the research on the impact of severe weather on traffic is summarized. Based on this, the research goal, train of thought and technical route are defined. Secondly, by comparing the characteristics of traffic flow data between the normal situation and the day of water accumulation, it is found that the traffic congestion under the bridge will lead to the significantly missing sample size of floating vehicle, and the concept of sample size ratio of floating vehicle data is put forward. At the same time, the concept of sample size ratio of floating vehicle data is put forward. This paper compares and analyzes the similarities and differences of the sample size ratio between traffic congestion and traffic accidents, traffic control, and traffic control. The results show that the missing sample ratio of the West third Ring Expressway in Beijing is nearly 80%, and the missing sample ratio of the West fourth Ring Road Expressway is as high as 97%. Based on the calculation and analysis of the spread and dissipation speed of traffic congestion under the bridge and the vehicle mileage of the congested section, the relationship between the depth of waterlogging under the bridge and the spread of congestion is summarized, and the influence range of congestion space is summarized. Finally, according to the spatial influence range of the traffic congestion under the bridge, the change of speed and the spatial and temporal distribution of the congested section, the sample size ratio is put forward. The identification method of traffic congestion section under bridge with three identification indexes of traffic flow velocity variation and velocity difference is presented, and the program application recognition of congestion point segment based on ArcGIS is realized. Then, considering the traffic flow characteristics of the same day, the traffic flow characteristics are considered synthetically. On the basis of the reduction of traffic capacity under bridge, the ratio of capacity to traffic flow, the entrance and exit of expressway, and the combination of logic judgment and practical experience, At the same time, based on the VISSIM simulation data and floating vehicle data, the parameters of the model are preliminarily calibrated.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U491.265

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