Abstract

The acceleration profile of leading vehicles at intersections is critical for emission estimation and microlevel queue simulation. Data obtained from experiments using a high-resolution driving simulator can deliver useful insights into microscale acceleration behaviors at signalized intersections. Acceleration data of the leading vehicles in queues are collected by the simulator. The observed accelerations are found to be stochastic. The acceleration characteristics are also significantly diversified among participants. Hence, a Markov chain is implemented to simulate the acceleration behaviors. The acceleration data are classified into varied operation states. And the Markov chain reconstructs the acceleration profiles of leading vehicles and reproduces the randomness of acceleration behaviors. Among numerous candidate profiles, a speed profile is selected by a proposed criterion that represents the typical acceleration behaviors at signalized intersections.

抽象的

交叉路口领先车辆的加速度曲线对于排放估算和微队列仿真至关重要。使用高分辨率驾驶模拟器从实验中获得的数据可以提供有用的见解，以了解信号交叉口的微尺度加速行为。模拟器中收集队列中领先车辆的加速度数据。发现观察到的加速度是随机的。参与者的加速特性也明显不同。因此，实现了马尔可夫链来模拟加速行为。加速度数据被分类为变化的操作状态。马尔可夫链重构了领先车辆的加速度曲线，并再现了加速度行为的随机性。在众多候选人资料中，