01476782

Component

This paper provides insight to lane change maneuver data by employing a real-time vehicle re-identification and classification system capable of producing individual vehicle matches and classes based on inductive signatures during congested and uncongested conditions. Vehicle re-identification results for a 0.66 mile multilane freeway segment are compared to manually matched vehicle pairs from video data. Examination of lane change probabilities show that re-identification is capable of reproducing lane change maneuvers with minimal error (root mean square error = 0.0162 and correlation coefficient= 0.927). Differences in lane change probability by level-of-service (LOS), vehicle class, and segment type are also examined. Results show that there is variability in lane change probability by LOS and vehicle class. Although other studies have quantified lane change behavior using vehicle re-identification, none has been successful in obtaining measures during congestion and for separate vehicle classes. Not only would the information gathered from this research be useful in calibrating microsimulation models but also could be used as the basis of real-time traffic calming strategies designed to reduce lane changing at the onset of congestion. In addition to an evaluation of merging behavior using re-identification, improvements to the current re-identification methodology based on lane changing to increase correct classification rates are proposed.

This paper was sponsored by TRB committee ABJ35 Highway Traffic Monitoring.

01470560

13-3911

English

15p

2013

Transportation Research Board 92nd Annual Meeting

Digital/other

Figures; References; Tables

Automatic vehicle classification; Behavior; High occupancy vehicle lanes; Lane changing; Level of service; Merging traffic; Traffic data; Traffic flow

Interstate 405

Data and Information Technology; Highways; Operations and Traffic Management; I70: Traffic and Transport; I73: Traffic Control

Transportation Research Board Annual Meeting 2013 Paper #13-3911

TRIS, TRB, ATRI

Feb 5 2013 12:45PM