A Framework for Real-time Traffic Trajectory Tracking, Speed Estimation, and Driver Behavior Calibration at Urban Intersections Using Virtual Traffic Lanes

TL;DR

This paper presents a real-time framework using VT-Lane for traffic trajectory tracking, speed estimation, and driver behavior calibration at urban intersections, demonstrating high accuracy and potential for large-scale traffic modeling improvements.

Contribution

The study introduces a method to accurately estimate vehicle speeds and calibrate driver behavior models using VT-Lane derived trajectories from traffic videos.

Findings

Speed estimation error of 0.19 m/sec (2%)

Instantaneous speed errors of 0.21 and 0.86 m/sec under different conditions

Calibrated driver behavior model with 0.45 m/sec average error

Abstract

In a previous study, we presented VT-Lane, a three-step framework for real-time vehicle detection, tracking, and turn movement classification at urban intersections. In this study, we present a case study incorporating the highly accurate trajectories and movement classification obtained via VT-Lane for the purpose of speed estimation and driver behavior calibration for traffic at urban intersections. First, we use a highly instrumented vehicle to verify the estimated speeds obtained from video inference. The results of the speed validation show that our method can estimate the average travel speed of detected vehicles in real-time with an error of 0.19 m/sec, which is equivalent to 2% of the average observed travel speeds in the intersection of the study. Instantaneous speeds (at the resolution of 30 Hz) were found to be estimated with an average error of 0.21 m/sec and 0.86 m/sec respectively for free-flowing and congested traffic conditions. We then use the estimated speeds to calibrate the parameters of a driver behavior model for the vehicles in the area of study. The results show that the calibrated model replicates the driving behavior with an average error of 0.45 m/sec, indicating the high potential for using this framework for automated, large-scale calibration of car-following models from roadside traffic video data, which can lead to substantial improvements in traffic modeling via microscopic simulation.