详情

原文传递 Estimating cycle-level real-time traffic movements at signalized intersections

题名：	Estimating cycle-level real-time traffic movements at signalized intersections
正文语种：	eng
作者：	Nada Mahmoud;Mohamed Abdel-Aty;Qing Cai;Jinghui Yuan
作者单位：	Department of Civil Environmental and Construction Engineering University of Central Florida (UCF);Department of Civil Environmental and Construction Engineering University of Central Florida (UCF);Department of Civil Environmental and Construction Engineering University of Central Florida (UCF);Department of Civil Environmental and Construction Engineering University of Central Florida (UCF)
关键词：	Gradient boosting;machine learning;non-parametric models;parametric models;turning movement estimation
摘要：	Abstract Real-time traffic movements at intersections is vital for transportation and traffic engineering. It helps in providing intersection traffic data and optimizing signal control plans. This study attempts to extend the data coverage by developing algorithms to estimate through and left-turn movements in real-time at signalized intersections. This study is the first attempt to estimate short-term traffic movement counts at signalized intersections at the cycle-level for signal control. Real-time data were collected from 19 intersections along two main corridors in Orange county, Florida. A framework was proposed to identify different variables considering the characteristics of the cycle-level traffic movement estimation. To develop generic algorithms, a proposed approach was utilized to generalize the traffic movement estimation at the corridor level. Signal timing and movement counts at the upstream and downstream intersections were utilized to estimate through and left-turn movements at target intersections. An extensive comparison study was carried out based on the processed data. The experimental results show that the proposed Gradient Boosting model outperformed the baseline models with Mean Absolute Percentage Error 9.53% and 4.6% for the through and left-turn movements, respectively. In addition, the transferability of the developed models for the abnormal traffic conditions was validated and the estimated model proved that it could instantaneously capture the traffic change due to an accident. It is expected that the proposed method could reduce the sensor cost and extend the movement data coverage, which could help in developing efficient signal control plans at signalized intersections.
出版年：	2022
期刊名称：	Journal of Intelligent Transportation Systems
卷：	26
期：	1/6
页码：	405-424