首页|APPROACHES FOR FREEWAY WORK ZONE CAPACITY ESTIMATION: INCORPORATING PROBE VEHICLE DATA
APPROACHES FOR FREEWAY WORK ZONE CAPACITY ESTIMATION: INCORPORATING PROBE VEHICLE DATA
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
This study presents an Artificial Neural Network (ANN) model for estimating work zone capacity by taking into consideration various factors affecting the capacity， such as approaching traffic volume， average work zone speed， and travel time。 To overcome the deficiencies of the existing parametric model-based approaches， probe vehicle data are incorporated into the work zone capacity estimation procedure。 Exploiting a VISSIM-based hypothetical work zone environment on a freeway， the accuracy of estimated capacity produced by the ANN model with and without probe vehicle data is examined。 Simulation results show that the accuracy of estimated capacity incorporating probe-based travel time is improved up to 5%。
Work zoneCapacityArtificial neural networkProbe vehicle data
Bo Du、Joyoung Lee、Branislav Dimitrijevic、Kitae Kim、Steven Chien
展开 >
Interdisciplinary Program in Transportation New Jersey Institute of Technology, Newark, NJ 07102
Department of Civil and Environmental Engineering New Jersey Institute of Technology, Newark, NJ 07102
World congress on intelligent transport systems;ITS America annual meeting
NETL
