Traffic recovery time estimation under different flow regimes in traffic simulation

查看全文 作　　者：Mansoureh [1]Jeihani;Petronella [1]James;Anthony [1]A.Saka;Anam [1]Ardeshiri 机构地区：[1]Department of Transportation and Urban Infrastructure Studies,Morgan State University 出　　处：《Journal of Traffic and Transportation Engineering(English Edition)》索引2015年第2卷第5期,共10页 基　　金：the Maryland State Highway Administration Office of Traffic and Safety,National Transportation Center at Morgan State University,and the Federal Highway Administration through the Dwight Eisenhower Fellowship Program(Grant Number:MD-09-SP708B4L) 摘　　要：Incident occurrence and recovery are critical to the smooth and efficient operations of freeways.Although many studies have been performed on incident detection,clearance,and management,travelers and traffic managers are unable to accurately predict the length of time required for full traffic recovery after an incident occurs.This is because there are no practical studies available to estimate post-incident recovery time.This paper estimates post-incident traffic recovery time along an urban freeway using traffic simulation and compares the simulation results with Shockwave theory calculations.The simulation model is calibrated and validated using a freeway segment in Baltimore,MD.The model explores different flow regimes(traffic intensity) and incident duration for different incident severity,and their effects on recovery time.A total of 726 simulations are completed using VISSIM software.Finally,the impact of congestion and incident delay on the highway network is quantified by a regression formula to predict traffic recovery time.The developed regression model predicts post-incident traffic recovery time based on traffic intensity,incident duration,and incident severity(ratio of lanes closure).In addition,three regression models are developed for different flow regimes of near-capacity,moderate,and low-traffic intensity.The model is validated by collected field data on two different urban freeways. 关 键 词：交通仿真 时间估计 低流量 事件检测 城市快速路 恢复时间 模型预测 高速公路