Estimation of lane-level travel time distributions under a connected environment

Abstract

Travel time distribution estimation is fundamentally important for the evaluation of travel time variability and reliability. For urban roads, signal delays are key components of travel time. They are stochastic and differ for vehicular movements due to different signal timings for through, left-turning, and right-turning vehicles. To better assist travelers in making trip decisions under connected environments, this study seeks to investigate lane-level travel time distributions for signalized arterial roads by specifically considering the impacts of both the link travel time and the vehicle movement-based signal delays at an intersection. A simulation testbed based on the VISSIM microscopic traffic simulator and a Java plugin is developed to mimic the traffic flow dynamics of a signalized arterial, El Camino Real, in Palo Alto, California, under a connected environment. The detailed vehicle trajectory data obtained from the simulation can be leveraged to obtain lane-level travel time information. Typical normal, lognormal, gamma, and Weibull distributions, as well as kernel density estimation (KDE), are adopted to calibrate the lane-level travel time distributions. The estimation results demonstrate that conventional distribution models are suitable for estimating the travel time distributions of only a few road segments, while KDE fully captures travel time reliability metrics such as the buffer time index, skewness, and width of the travel time distributions for all road segments. This result will help traffic managers and engineers carry out effective traffic management and control to optimize the operation of arterial roads.

Keywords:

• Connected environments
• kernel density estimation
• lane-level travel time distribution
• simulation
• travel time reliability

Acknowledgments

The authors are very grateful to the anonymous reviewers for their valuable suggestions and comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research is supported by the National Key Research and Development Program of China - Traffic Modeling, Surveillance and Control with Connected & Automated Vehicles (2017YFE9134700), Zhejiang Provincial Natural Science Foundation under Grant No. LGF20E080010, and Ningbo Transportation Technology Program No.202002, Natural Science Foundation of Ningbo (2017A610139).