交通运输系统工程与信息

交通运输系统工程与信息 ›› 2010, Vol. 10 ›› Issue (2): 49-56 .

• 智能交通系统与信息技术 • 上一篇    下一篇

交叉口右转机动车穿越决策BP仿真模型及灵敏度分析

李珊珊a,b;钱大琳*a,b;李年源a,b   

  1. 北京交通大学a.交通运输学院，b.城市交通复杂系统理论与技术教育部重点实验室，北京 100044
  • 收稿日期:2009-07-21 修回日期:2009-10-30 出版日期:2010-04-25 发布日期:2010-04-25
  • 通讯作者: 钱大琳
  • 作者简介:李珊珊（1984-），女，陕西省渭南人，博士生
  • 基金资助:

    国家自然科学基金项目（70672023）

BP Simulation Model and Sensitivity Analysis of Right-turn Vehicles’ Crossing Decisions at Signalized Intersection

LI Shan-shan a,b;QIAN Da-lin a,b;LI Nian-yuan a,b   

  1. a.School of Traffic and Transportation; b.MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China
  • Received:2009-07-21 Revised:2009-10-30 Online:2010-04-25 Published:2010-04-25
  • Contact: QIAN Da-lin

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被引次数

10

摘要： 机非相互穿越模型是信号交叉口混合交通微观仿真系统中反映机动车和自行车相互影响的核心模型. 为了描述交叉口处机动车穿越自行车流的决策行为，分析了两相位信号交叉口右转机动车穿越邻道直行自行车的微观行为，提出了基于BP神经网络的机动车穿越决策模型. 以北京2个交叉口调查数据为基础，对该模型验证并与Logistic模型比较，结果表明BP模型优于Logistic模型且具有较好的预测精度. 根据所建模型的映射关系计算出系统输出对输入参数的一阶灵敏度矩阵，灵敏度分析结果表明，自行车提供给机动车的穿越间隙是影响机动车穿越决策行为的决定性因素，且间隙在2.76s～2.96s变动时对机动车穿越决策行为影响最大.

关键词: 交通工程, 机非干扰, 穿越决策模型, BP神经网络, 间隙接受, 延时接受, 灵敏度分析

Abstract: Inter-crossing behavior model of motor vehicles and bicycles is the key part of micro-simulation for mixed traffic at signalized intersection. The microscopic behaviors of the motor vehicles passing through the bicycle flow at a two phased signalized intersection were analyzed to reproduce the passing behavior of motor vehicles. A BP neural network model was proposed to describe the motor vehicles’ passing decision. Based on the field data at two typical intersections in Beijing, the model was validated and compared with the Logistic model. The results indicated that the BP model was more effective than the Logistic model and had high prediction accuracy. First derivative sensitivity matrix of the BP model was established. The sensitivity analysis showed that the most important factor impacting on the motor vehicles’ passing decision-making behavior is the gap allowing motor vehicles to pass through. The passing decision-making behavior is the most sensitive to the gap when it lies between 2.76 s and 2.96 s.

Key words: traffic engineering, interference between motor vehicles and bicycles, passing decision-making model, BP neural network, gap acceptance, lag acceptance, sensitivity analysis

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