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.
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 net- work 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.
出处
《交通运输系统工程与信息》
EI
CSCD
2010年第2期49-56,共8页
Journal of Transportation Systems Engineering and Information Technology
基金
国家自然科学基金项目(70672023)
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
traffic engineering
interference between motor vehicles and bicycles
passing deeision-making model
BP neural network
gap acceptance
lag acceptance
sensitivity analysis
