Volume Estimation using Traffic Signal Event-Based Data from Video-Based Sensors

Research output: Contribution to journalArticle

Abstract

Traffic volume data is one of the most critical variables for signal retiming. However, collecting traffic volume manually can be time-consuming and costly. In recent years, video-based sensor systems have been applied on signalized intersections for signal timing control. The detectors in video-based sensors generate large amounts of real-time high-resolution event-based data, including signal status and detection status data. The vehicle arrivals for each detection event is a stochastic process and has a relationship with the signal status and the detection duration (time occupancy). Therefore, a modified dynamic hidden Markov model (DHMM) is proposed to estimate vehicular volume by modeling the vehicle arrivals using event-based data collected at signalized intersections. The concept of an additional hidden state is introduced to make the vehicular volume finite by grouping volumes that have only a small probability of occurring into one hidden state. Additionally, a linear regression model is built to estimate the vehicular volume when the output of the DHMM is an additional hidden state. The resulting mean absolute percentage errors of the 15-min estimated volume are 14.1%, 10.3%, and 10.5%, respectively, at three study locations in Tucson, Arizona.

Original languageEnglish (US)
JournalTransportation Research Record
DOIs
StatePublished - Jan 1 2019

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Traffic signals
Hidden Markov models
Sensors
Random processes
Linear regression
Detectors

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering

Cite this

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title = "Volume Estimation using Traffic Signal Event-Based Data from Video-Based Sensors",
abstract = "Traffic volume data is one of the most critical variables for signal retiming. However, collecting traffic volume manually can be time-consuming and costly. In recent years, video-based sensor systems have been applied on signalized intersections for signal timing control. The detectors in video-based sensors generate large amounts of real-time high-resolution event-based data, including signal status and detection status data. The vehicle arrivals for each detection event is a stochastic process and has a relationship with the signal status and the detection duration (time occupancy). Therefore, a modified dynamic hidden Markov model (DHMM) is proposed to estimate vehicular volume by modeling the vehicle arrivals using event-based data collected at signalized intersections. The concept of an additional hidden state is introduced to make the vehicular volume finite by grouping volumes that have only a small probability of occurring into one hidden state. Additionally, a linear regression model is built to estimate the vehicular volume when the output of the DHMM is an additional hidden state. The resulting mean absolute percentage errors of the 15-min estimated volume are 14.1{\%}, 10.3{\%}, and 10.5{\%}, respectively, at three study locations in Tucson, Arizona.",
author = "Xiaofeng Li and Yao-jan Wu and Yi-Chang Chiu",
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N2 - Traffic volume data is one of the most critical variables for signal retiming. However, collecting traffic volume manually can be time-consuming and costly. In recent years, video-based sensor systems have been applied on signalized intersections for signal timing control. The detectors in video-based sensors generate large amounts of real-time high-resolution event-based data, including signal status and detection status data. The vehicle arrivals for each detection event is a stochastic process and has a relationship with the signal status and the detection duration (time occupancy). Therefore, a modified dynamic hidden Markov model (DHMM) is proposed to estimate vehicular volume by modeling the vehicle arrivals using event-based data collected at signalized intersections. The concept of an additional hidden state is introduced to make the vehicular volume finite by grouping volumes that have only a small probability of occurring into one hidden state. Additionally, a linear regression model is built to estimate the vehicular volume when the output of the DHMM is an additional hidden state. The resulting mean absolute percentage errors of the 15-min estimated volume are 14.1%, 10.3%, and 10.5%, respectively, at three study locations in Tucson, Arizona.

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