基于聚类分析的城市交通TOD优化控制方法

姚佼; 徐洁琼; 韩印

基于聚类分析的城市交通TOD优化控制方法

上海理工大学管理学院, 上海 200093

基金项目:

国家自然科学基金项目 60974093

上海高校一流学科建设计划项目 S1201YLXK

上海高校青年教师培养资助计划项目 slg12009

详细信息

作者简介:
姚佼(1982-), 男, 山西运城人, 上海理工大学讲师, 工学博士, 从事智能交通控制研究

中图分类号: U491.54
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- HTML全文浏览量: 127
- PDF下载量: 822
- 被引次数: 0
出版历程
- 收稿日期: 2014-07-13
- 刊出日期: 2014-12-25

TOD optimal control method of urban traffic based on clustering analysis

School of Business, University of Shanghai for Science and Technology, Shanghai 200093, China

More Information

Author Bio:
YAO-Jiao (1982-), male, lecturer, PhD, +86-21-65710430, yaojiao@126.com

摘要

摘要: 为了降低环形线圈车辆检测器故障率, 基于指数平滑异同移动平均线法对缺失历史数据进行修补, 运用Ward最小方差法对历史交通流量数据进行聚类分析, 以改进立方群准则作为聚类终止条件, 确定TOD多方案控制的最优方案数和最佳切换时刻, 利用交通信号配时优化软件Synchro对TOD优化控制方法进行仿真验证。验证结果表明: 优化控制方法能够提供更精细的TOD控制方案, 更能体现对实际交通需求波动的响应, 优化后控制方案的车均延误减少率平均为11.90%, 其中早高峰前时段的车均延误减少率为20.27%, 晚低峰、晚高峰和早高峰的车均延误减少率分别为12.99%、8.07%、6.25%。
- 智能交通系统 /
- 城市交通 /
- 聚类分析 /
- 立方群准则 /
- TOD优化控制方法 /
- 车均延误
Abstract: To overcome the high failure rate of circular loop vehicle detector in real road, the loss historical data were mended by using moving average convergence divergence method.The historical traffic flow data were clustered by using Ward least square method.A clustering terminal condition was proposed to determine the optimal control plan number and switch time of TOD multi-schedule control based on the modified cubic clustering criterion.TOD optimal control method was simulated and verified by using signal timing optimization software Synchro.Verification result indicates that TOD optimal control method can provide more detailed TOD control plan, which can also respond the fluctuation of real traffic demand.The average decrement rate of each vehicle delay based on the optimal control method is 11.9%, in which the decrement rate in pre-morning peak period is 20.27%, and the values in evening low peak period, evening peak period and morning peak period are 12.99%, 8.07% and 6.25%, respectively.
- intelligent transportation system /
- urban traffic /
- clustering analysis /
- cubic clustering criterion /
- TOD optimal control method /
- average vehicle delay

HTML全文

图 1 车道功能和相位设置

Figure 1. Lane function and phase configuration

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图 2 运行时段对比

Figure 2. Comparison of operation periods

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图 3 实际交通流量

Figure 3. Actual traffic flow

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图 4 车均延误对比曲线

Figure 4. Comparison curves of average each vehicle delay

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图 5 车均延误减少率

Figure 5. Decrement rates of average each vehicle delay

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表 1 现状控制方案的运行时段

Table 1. Operation periods of current control plans

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表 2 信号配时方案

Table 2. Signal timing plans

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表 3 历史交通流量数据的聚类过程Fig.3 Clustering procedure of historical traffic flow data

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表 4 聚类后控制方案的运行时段

Table 4. Operation periods of control plans after clustering

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表 5 聚类前后控制方案的车均延误

Table 5. Average each vehicle delays of control plans before and after clustering

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