基于电警数据的交叉口短车道排队“溢出”识别与信号配时优化毕业论文

 2021-04-14 04:04

摘 要

交叉口是道路通行能力的瓶颈,在高峰时段常发生排队溢出,即车道排队车辆数量超过蓄车空间容量,阻碍临近车道车辆以致其不能按时通过交叉口,影响相邻车道的消散流率,降低交叉口运行效率,制约整条道路乃至路网的服务水平。排队溢出的有效识别可服务于交叉口交通状态估计和信号控制方案的优化。现有排队溢出检测多为根据线圈检测器获取的交通流数据,线圈数据易损坏且不易维修,导致精度较低。另一方面,电警卡口数据,又称车辆号牌识别数据(LPR),能够记录经过停车线断面处车辆号牌、通过时刻、车辆类别等信息,近年来在我国很多城市得到了广泛应用。

本研究提出了一种基于卡口电警数据的交叉口短车道排队溢出识别方法。该方法面向我国城市地面带有短车道的道路交叉口环境,基于交叉口卡口电警数据,采用交通流理论和数理统计方法,建立适用于不同信号配时场景的排队溢出识别方法。为验证方法的有效性,通过微观仿真软件对国内通常信号放行规则交叉口进行验证,结果表明本方法可较为准确识别出交叉口排队溢出现象,在直左同放情况下排队溢出识别精度为89%,先直后左放行规则下精度为96%,先直后直左放行规则下精度为95%,先左后直放行规则下精度为85%,先左后左直放行规则下精度为75%;同时,基于连云港市朝阳东路-通灌南路交叉口的电警卡口数据,实例验证算法对于排队溢出的识别率高达93%。

本研究的成果可以丰富我国城市道路交叉口排队溢出识别方法,服务于交叉口交通状态估计和信号控制方案的优化,提升交叉口运行效率。

关键词:信号控制交叉口;短车道;排队溢出;电警数据;临界点分析法

Abstract

Intersections are the bottleneck of road capacity. During rush hour, queue spillback often occurs. That is, the number of vehicles queuing in the lane exceeds the capacity of the storage space, which impedes vehicles in adjacent lanes so that they cannot pass through the intersection on time, affecting the dissipated flow rate of adjacent lanes and reducing The intersection operation efficiency limits the service level of the entire road and even the road network. The effective identification of queue spillback can serve the optimization of traffic state estimation and signal control schemes at intersections. The existing queue spillback detection is mostly traffic flow data acquired by the loop detector. The loop data is easily damaged and difficult to maintain, resulting in low accuracy. On the other hand, the license plate recognition data can record information such as vehicle number plates, passing moments, and vehicle types at the section of the parking line. It has been widely used in many cities in China in recent years.

This study proposes a method for identifying short-lane queue spillback at intersections based on license plate recognition data. This method is aimed at the intersection environment of China's urban roads with short lanes. Based on the license plate recognition data at the intersections, traffic flow theory and mathematical statistics methods are used to establish queue spillback recognition methods suitable for different signal timing scenarios. In order to verify the validity of the method, the microscopic simulation software was used to verify the regular signal release regular intersections in China. The results show that the method can more accurately identify the queuing spillback at intersections. Accuracy is 89% in the case of straight-left release rule and 96% in the first-straight-then-left release rule. In the case of first-straight-then-straight-left release rule the accurary is 95% and in the case of first-left-then-straight release rule the accurary is 85%. Accuracy is 75% in the case of first-left-then-straight-left release rule. At the same time, based on the license plate recognition data at the intersection of Chaoyang East Road and Tongchuan South Road in Lianyungang City, the recognition rate of the instance verification algorithm for queue spillback is as high as 93%.

The results of this study can enrich the queuing spillback identification method at the intersection of urban roads in China, serve the optimization of the traffic state estimation and signal control scheme at the intersection, and improve the intersection operation efficiency.

Key Words:signalized intersection; short lane; queue spillback; license plate recognition data; critical point analysis

目 录

第1章 绪论 1

1.1 研究背景 1

1.2 研究目的及意义 1

1.2.1 研究目的 1

1.2.2 研究意义 2

1.3 研究内容与技术路线 2

1.3.1 研究内容 2

1.3.2 技术路线 3

1.4 论文结构 4

第2章 文献综述 5

2.1 交叉口排队长度估计综述 5

2.2 交叉口短车道排队溢出识别综述 6

2.3 本章小结 7

第3章 排队溢出识别方法研究 8

3.1 研究对象 8

3.1.1 排队长度 8

3.1.2 排队溢出 8

3.2 电警数据特性分析 10

3.2.1 电警数据 10

3.2.2 电警数据特征 11

3.3 排队溢出识别方法 12

3.3.1 排队长度估计 12

3.3.2 排队溢出识别流程 15

3.4 改善措施 29

3.5 本章小结 30

第4章 仿真验证与评价 31

4.1 仿真建模 31

4.2 数据处理 34

4.3 结果分析 36

4.3.1 参数标定 36

4.3.2 结果分析 38

4.3.3 漏检验证 42

4.4 本章小结 44

第5章 实证分析 45

5.1 实证路口概况 45

5.2 参数标定 46

5.3 结果分析 46

5.4 本章小结 47

第6章 结论及展望 48

6.1 主要结论 48

6.2 主要创新点 49

6.3 研究展望 49

参考文献 50

绪论

1.1 研究背景

随着经济水平的提高,人民物质生活水平不断增强,我国机动车保有量也在不断增加。在很多城市,由于其交通规划年限早,其配套设施已远远不能满足交通需求,出现很多交通问题,而交叉口拥堵是最常见、也是最为复杂的交通拥堵现象。

交叉口拥堵现象的成因有很多,其最重要的一个便是交叉口通行能力不足,交叉口的通行能力不足会制约整条道路乃至路网的服务水平。在设有左转短车道的交叉口中,如果周期内排队车辆超过了其蓄车空间,便会阻碍其他车道的车辆以致不能按时通过交叉口,产生短车道排队溢出现象。排队溢出会影响相邻车道的消散流率,从而降低交叉口运行效率。

现有排队溢出检测方法多为根据线圈检测器获取的交通流数据,线圈数据易损坏且不易维修,导致精度较低。电警卡口数据又称车辆号牌识别数据(LPR)、AVI车辆号牌识别数据、车辆视频检测自动识别数据等[1],能够记录经过停车线断面处车辆号牌、通过时刻、车辆类别等信息。近年来电警卡口在我国很多城市得到了广泛应用,例如北京在2017年已经建成1958套电警系统;上海市目前已经累计建设超出3500套电警系统。相较于其他数据源(微波雷达、线圈、浮动车等),电警数据具有三个特征,①电警高清摄像机可以精确地记录信控交叉口每条车道车辆驶离信息。②记录的车辆号牌可以追踪车辆在上下游交叉口的运行过程,获得车辆在路段的行程时间,③电警高清摄像机可以监测停车线前每条车道,获得详细的基于车道的交通参数信息。同时电警数据具有识别率高、全天候运行、受环境影响小的特点,近年来,在学术界得到了很大关注。

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