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Estimation of Connected Vehicle Penetration on US Roads in Indiana, Ohio, and Pennsylvania

Journal of Transportation Technologies 2021年第4期11卷 597-610页

作者： Margaret Hunter Jijo K. Mathew Howell Li Darcy M. Bullock Purdue University West Lafayette USA

Connected vehicle data is an important assessment tool for agencies to evaluate the performance of freeways and arterials, provided there is sufficient penetration to provide statistically robust performance measures. A common concern by agencies interested in using crowd sourced probe data is the penetration rate across different types of roads, different hours of the day, and different regions. This paper describes and demonstrates a methodology that uses data from state highway performance monitoring systems in Indiana, Ohio and Pennsylvania. The study analyzes 54 locations over the 3 states for select Wednesdays and Saturdays in 2020 and 2021. Overall, across all locations and dates, the median penetration was approximately 4.5%. The median penetration for August 2020 for Indiana, Ohio, and Pennsylvania was 4.6%, 4.3%, and 4.0%, respectively. The median penetration for those same states in August 2020 on interstates and non-interstates was 3.9% and 4.6%, respectively. Additionally, the study conducted a longitudinal evaluation of Indiana penetration for selected months between January 2020 and June 2021. Indiana penetration increased modestly between December 2020 and June 2021, perhaps due to the post-COVID rebound of passenger vehicle traffic. This paper concludes by recommending that the techniques described in this paper be scaled to other states so that traffic engineers can make informed decisions on the use and limitations of connected vehicle data for various use cases.

关键词： Connected Vehicle Trajectory Data Penetration traffic counts Big Data

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Simultaneous traffic Count and Ramp Flow Estimation for Multilane Freeways Based on Markov Models

Simultaneous Traffic Count and Ramp Flow Estimation for Mult...

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第25届中国控制与决策会议

作者： Yong Ma Liguo Zhang Lu Zhang School of Electronic and Control Engineering Beijing University of Technology Beijing traffic Management Bureau

ISBN: (纸本)9781467355339

As a distributed parameter system, traffic flow model of freeway traffic is determined by the traffic state on-road and boundary flows form on-ramp or off-ramp sections. The existing studies for traffic estimation mainly focus on the traffic parameter, namely density （or vehicles） of mainline traffic. In this paper, Kalman filtering for simultaneous traffic counts and off-ramp flow estimation is proposed with the linearization of the speed density observation equation. The state-space model is formulated by using a Markov chain to describe the vehicles’ lane-change movements. Numerical studies are carried out to investigate the performance of the developed approach.

关键词： traffic counts Off-ramp flow Kalman filtering Simultaneous input and state estimation Markov model

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