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Title: Estimating traffic volumes for signalized intersections using connected vehicle data

Abstract

Recently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT). At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimate traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model in this paper vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilotmore » Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9–12%, based on benchmark data manually collected and data from loop detectors. Finally, considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future.« less

Authors:
 [1];  [2]
  1. Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Civil and Environmental Engineering
  2. Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Civil and Environmental Engineering. Transportation Research Inst.
Publication Date:
Research Org.:
Univ. of Michigan, Ann Arbor, MI (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
OSTI Identifier:
1433805
Alternate Identifier(s):
OSTI ID: 1416637
Grant/Contract Number:  
EE0007212
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Transportation Research Part C: Emerging Technologies
Additional Journal Information:
Journal Volume: 79; Journal ID: ISSN 0968-090X
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; connected vehicle; mobile data; GPS trajectory; traffic signal; vehicle-to-infrastructure communication; traffic volume estimation; Safety Pilot Model Deployment (SPMD) project

Citation Formats

Zheng, Jianfeng, and Liu, Henry X. Estimating traffic volumes for signalized intersections using connected vehicle data. United States: N. p., 2017. Web. doi:10.1016/j.trc.2017.03.007.
Zheng, Jianfeng, & Liu, Henry X. Estimating traffic volumes for signalized intersections using connected vehicle data. United States. doi:10.1016/j.trc.2017.03.007.
Zheng, Jianfeng, and Liu, Henry X. Mon . "Estimating traffic volumes for signalized intersections using connected vehicle data". United States. doi:10.1016/j.trc.2017.03.007. https://www.osti.gov/servlets/purl/1433805.
@article{osti_1433805,
title = {Estimating traffic volumes for signalized intersections using connected vehicle data},
author = {Zheng, Jianfeng and Liu, Henry X.},
abstractNote = {Recently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT). At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimate traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model in this paper vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilot Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9–12%, based on benchmark data manually collected and data from loop detectors. Finally, considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future.},
doi = {10.1016/j.trc.2017.03.007},
journal = {Transportation Research Part C: Emerging Technologies},
number = ,
volume = 79,
place = {United States},
year = {Mon Apr 17 00:00:00 EDT 2017},
month = {Mon Apr 17 00:00:00 EDT 2017}
}

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