面向常发拥堵点的主动交通诱导方法

罗舒琳; 张存保; 张泰文; 曹雨

doi:10.3963/j.jssn.1674-4861.2021.05.009

面向常发拥堵点的主动交通诱导方法

doi: 10.3963/j.jssn.1674-4861.2021.05.009

武汉理工大学智能交通系统研究中心武汉 430063

基金项目:

国家重点研发计划项目 2018YFB1601000

详细信息

作者简介:
罗舒琳（1995—），硕士研究生.研究方向：交通管理与控制. E-mail: 964230482@qq.com

通讯作者:
张存保（1976—），博士，研究员. 研究方向：交通信息工程及控制、交通安全. E-mail: zhangcunbao@163.com

中图分类号: U491.4
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- 被引次数: 0
出版历程
- 收稿日期: 2021-05-08

Active Traffic Guidance Method for Recurrent Congestion Points

Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China

摘要

摘要:
基于动态用户均衡、系统最优分配的诱导方法，侧重路网需求的宏观预测和调节，难以准确辨识道路拥堵点的关联车流，制约了诱导效果。为精准调控致堵车流，有效缓解常发性拥堵，研究基于需求溯源的主动交通诱导方法。遵循靶向诱导的思路，分析车辆行驶轨迹和常发拥堵点的交通流关联性，运用卡尔曼滤波对关联车流进行短时预测，在此基础上，结合流量占比、路径饱和度等指标，对诱导目标车流进行优选。同时，从负荷均衡的角度出发，基于路段与路径交通流的时空关联更新路网交通状态，建立以饱和度均衡为目标的主动诱导优化模型。仿真结果表明：相比反应型诱导与基于路径偏好的主动型诱导，所提方法使常发拥堵点的车均延误、停车次数等下降30%~60%，路网车均延误、停车次数等下降10%~15%，模型收敛速度提高，交通效益提升，验证了该方法的有效性。
- 交通工程 /
- 常发性拥堵 /
- 拥堵溯源 /
- 关键车流 /
- 主动诱导
Abstract:
Traffic guidance methods based on dynamic user equilibrium and optimal system allocation focus on macro forecasting and adjustment of road network demands. They are difficult to accurately identify related traffic flow, which restricts the guidance effects. An active traffic guidance method based on traceable demand is proposed to control traffic flows and alleviate recurrent congestion. The study following the idea of targeted guidance, analyzes the correlation between vehicle trajectory and traffic flow at frequent congestion points, and uses Kalman filter to make short-term predictions of the associated traffic flow. Furthermore, it is preferable to optimize target traffic flows in combination with indicators such as traffic ratio and path saturation. Meanwhile, based on balanced traffic distribution, the spatio-temporal correlation between the road section and the path traffic flow is used to update the road network traffic state and establish an active induction optimization model with saturation equilibrium as the goal. The simulation results show that, compared with the active induction based on path preference, this method result in a reduction from 30% to 60% in the average delay and the number of stops of vehicles at frequent congestion points, and a reduction from 10% to 15% in the whole road network. The convergence speed and traffic benefit of the model are significantly improved, which verifies the effectiveness of the method in the work.
- traffic engineering /
- recurrent traffic congestion /
- congestion tracing /
- key traffic flow /
- active traffic guidance

HTML全文

图 1 基于需求溯源的主动交通诱导思路

Figure 1. Thoughtof active traffic guidance based on traceable demand

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图 2 拥堵点交通流溯源示意图

Figure 2. Schematic diagram of traffic flow traceability at congestion points

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图 3 无效路径示意图

Figure 3. Invalid path diagram

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图 4 路网结构示意图

Figure 4. Schematic diagram of road network structure

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图 5 交通运行状态对比图

Figure 5. Comparison diagram of traffic operation state

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图 6 诱导前后交通效益对比图

Figure 6. Comparison of traffic benefits before and after guidance

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图 7 主动型诱导运算效率对比

Figure 7. Comparison of active inducement operation efficiency

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表 1 路径交通流优先度输出值

Table 1. Traffic-guidance priority of traffic flow of paths

路径交通流	x^{k, t}	y^{k, t}	z^{k, t}	H^{k, t}
^f_{2-7-8-17-18-23}	0.43	0.34	0.76	0.54
^f_{6-14-15-16-17-18-19-20}	0.73	0.82	1.00	0.87
^f_{14-15-16-17-18-19-20}	0.92	1.00	0.00	0.00
^f_{15-16-17-18-19-20}	1.00	0.57	0.00	0.00
^f_{21-14-15-16-17-18-19-20}	0.71	0.65	0.23	0.50
^f_{22-16-17-18-19-20}	0.76	0.73	0.47	0.64
^f_{26-22-16-17-18-19-20}	0.64	0.81	0.62	0.68
$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $	$ \vdots $

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