A Piecewise Switched Linear Approach for Traffic Flow Modeling

Traffic modeling is a key step in several intelligent transportation systems（ITS） applications. This paper regards the traffic modeling through the enhancement of the cell transmission model. It considers the traffic flow as a hybrid dynamic system and proposes a piecewise switched linear traffic model. The latter allows an accurate modeling of the traffic flow in a given section by considering its geometry. On the other hand, the piecewise switched linear traffic model handles more than one congestion wave and has the advantage to be modular. The measurements at upstream and downstream boundaries are also used in this model in order to decouple the traffic flow dynamics of successive road portions. Finally, real magnetic sensor data, provided by the performance measurement system on a portion of the Californian SR60-E highway are used to validate the proposed model.

快速路入口匝道瓶颈宏观交通流模型

基于入口匝道汇入方式与基本图形态,提出了一种调整型元胞传输模型;增加了入口匝道状态变量以追踪入口匝道交通状态,定义了新的入口匝道汇入规则;将双通行能力基本图引入到调整型元胞传输模型中,以适应不同交通状态下通行能力的变化;将单纯形法与遗传算法相结合,提出了混合多目标参数优化方法;建立了3个仿真场景,评价调整型元胞传输模型与混合多目标参数优化方法的效果。仿真结果表明:在预测入口匝道上游主线拥堵发生与结束时间方面,与经典元胞传输模型相比,调整型元胞传输模型将时间预测准确性分别提升了22.3、10.8 min;在模拟入口匝道汇入段主线拥堵传播与消散方面,调整型元胞传输模型模拟结果更加符合实际的传播与消散规律;在模拟试验路段早发性失效交通特性方面,调整型元胞传输模型对于拥堵前最大流量与拥堵后消散流量的拟合误差在4%以内,小于经典元胞传输模型;在模型仿真精度方面,调整型元胞传输模型各项评价指标均优于经典元胞传输模型,前者的仿真速度误差为10.42 km·h-1,较后者降低了25.4%;与传统的遗传算法相比,混合多目标参数优化方法的总计算次数更少,参数标定过程总耗时缩短了29.3%。