本文選題：城市道路路段　切入點(diǎn)：不良駕駛行為　出處：《哈爾濱工業(yè)大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：根據(jù)城市道路歷史交通事故統(tǒng)計(jì)數(shù)據(jù)顯示,在誘發(fā)或造成交通事故的諸多因素中人的因素占主導(dǎo)地位,基于歷史事故數(shù)據(jù)的城市道路安全研究本身屬于一種“事后補(bǔ)救型”,而基于非事故數(shù)據(jù)的“防患未然型”逐步引起交通安全領(lǐng)域的重視,從常見的交通流以及交通沖突有效識(shí)別城市道路的不良駕駛行為信息,是評(píng)價(jià)城市道路不良駕駛安全水平的重要途徑之一。本文分析了城市道路路段的交通流基本特性,將交通流狀態(tài)劃分為暢行流、穩(wěn)定流以及強(qiáng)制流三種狀態(tài),與其對(duì)應(yīng)則有自由駕駛行為、跟馳駕駛行為以及換道駕駛行為三種駕駛行為�；诖�,對(duì)城市道路路段不良駕駛行為進(jìn)行界定,主要有超速不良駕駛行為、壓線不良駕駛行為、違章掉頭不良駕駛行為、未保持安全車距不良駕駛行為、頻繁換道不良駕駛行為五種。分別對(duì)晴天、雨天情境下城市道路路段交通流進(jìn)行視頻數(shù)據(jù)采集,使用視頻處理軟件獲取車輛的運(yùn)動(dòng)軌跡數(shù)據(jù),提取每一輛車的運(yùn)動(dòng)參數(shù)并分析長(zhǎng)江路研究區(qū)段的交通流特性及駕駛特性,具體分析晴天、雨天不同情境下的交通量及其組成、車輛速度、車輛加速度、車頭時(shí)距、沖突時(shí)距、距右側(cè)車道線距離以及距攝像頭距離每一項(xiàng)特性。其中,本文構(gòu)建的沖突度量指標(biāo)“沖突時(shí)距”是隨著速度、加速度、天氣等變化而變化的,這彌補(bǔ)了在以往研究中速度、方向或加減速度不變假設(shè)條件的缺陷。本文采用隱馬爾可夫模型從左至右模型結(jié)構(gòu),輸入觀測(cè)序列速度、加速度、距右側(cè)車道線距離、沖突時(shí)距以及距攝像頭距離五項(xiàng)觀測(cè)序列信息,分別構(gòu)建晴雨天場(chǎng)景下,正常駕駛行為、超速不良駕駛行為、壓線不良駕駛行為、違章掉頭不良駕駛行為、未保持安全車距不良駕駛行為以及頻繁換道不良駕駛行為六種狀態(tài)識(shí)別模型,并驗(yàn)證模型識(shí)別精度高達(dá)86%以上。最后,針對(duì)本文每一項(xiàng)不良駕駛行為提出了改善懲罰措施,其效果可采用HMM模型進(jìn)行評(píng)價(jià)。
[Abstract]:According to the statistics of urban road traffic accidents, human factors play a dominant role in inducing or causing traffic accidents. The study of urban road safety based on historical accident data belongs to a kind of "remedial type" after the event, while the "precautionary type" based on non-accident data gradually attracts the attention of the traffic safety field. It is one of the important ways to evaluate the safety level of bad driving of urban roads from the common traffic flow and the information of traffic conflict to identify the bad driving behavior of urban roads. This paper analyzes the basic characteristics of traffic flow in urban road sections. The traffic flow state is divided into three states: smooth flow, steady flow and forced flow, corresponding to which there are three driving behaviors: free driving behavior, following driving behavior and changing traffic driving behavior. To define the bad driving behavior of urban road sections, there are mainly bad driving behavior in speeding, bad driving behavior in line pressing, bad driving behavior in illegal turn around, bad driving behavior without keeping safe car distance, There are five kinds of bad driving behavior in changing roads frequently. We collect video data of traffic flow on urban road sections in sunny and rainy days, and use video processing software to obtain the moving track data of vehicles. The motion parameters of each vehicle are extracted and the traffic flow characteristics and driving characteristics of the study section of Changjiang Road are analyzed. The traffic volume and its composition, vehicle speed, vehicle acceleration, headway time distance, conflict time distance in sunny and rainy days are analyzed in detail. The distance from the right lane to the right lane and the distance from the camera. Among them, the conflict measure "conflict time distance" constructed in this paper changes with the speed, acceleration, weather and so on, which makes up for the speed in previous studies. In this paper, the structure of hidden Markov model from left to right is used to input the velocity, acceleration and distance from the right driveway of the observation sequence. According to the five observation sequence information of conflict time distance and distance from camera, normal driving behavior, speeding bad driving behavior, line pressing bad driving behavior, illegal turning around bad driving behavior were constructed in rainy and sunny weather respectively. There are six state recognition models of bad driving behavior without keeping safe distance and frequent bad driving behavior of changing lanes, and the recognition accuracy of the model is as high as 86%. Finally, for each bad driving behavior in this paper, some measures are put forward to improve the punishment. Its effect can be evaluated by HMM model.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：U492.8
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本文編號(hào)：1605505