International Conference on Transportation and Development 2019
Congested Traffic Impact Mechanism on Vehicle Classification over Dual-Loop Detection
Publication: International Conference on Transportation and Development 2019: Innovation and Sustainability in Smart Mobility and Smart Cities
ABSTRACT
This paper presents a study to reveal the mechanism of congestion impact on length-based vehicle classification using dual-loop data. Both ground-truth vehicle trajectory and simultaneous loop event data are used to characterize the impact of congested traffic on vehicle classification. Eight scenarios are synthesized to define the vehicles’ stopping locations over two single loops. Under the synchronized traffic, acceleration or deceleration is considered in the new developed vehicle classification under synchronized traffic model. The error of vehicle classification is reduced from 33.5% to 6.7%, compared to the existing applied model. Under the stop-and-go traffic condition, a stop-on-both-loops-only (SBL) was developed to simplify the complexity of congested traffic situation, resulting in the error from 235% to 17.1%. To identify traffic congestion states using the dual-loop data, an innovative method for identifying the traffic phases has been proposed based relationship of speed and occupancy, and then successfully tested with the experimental data.
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ACKNOWLEDGEMENT
The research presented in this paper was partially supported by Ohio Transportation Consortium (OTC). The authors would like to thank Dr. Benjiamin Coifman at the Ohio State University for his assistance in providing the event dual-loop data.
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Published In
International Conference on Transportation and Development 2019: Innovation and Sustainability in Smart Mobility and Smart Cities
Pages: 54 - 65
Editor: David A. Noyce, Ph.D., University of Wisconsin–Madison
ISBN (Online): 978-0-7844-8258-2
Copyright
© 2019 American Society of Civil Engineers.
History
Published online: Aug 28, 2019
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