TITLE:
Real-Time Traffic State and Boundary Flux Estimation with Distributed Speed Detecting Networks
AUTHORS:
Yichi Zhang, Heng Deng
KEYWORDS:
Traffic State, Boundary Flux, Estimation, Extended Kalman Filtering, Distributed Speed Detecting Networks
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.12 No.4,
August
1,
2022
ABSTRACT: The rapid development of 5G mobile communication and portable traffic
detection technologies enhances highway transportation systems in detail and at
a vehicle level. Besides the advantage of no disturbance to the regular traffic operation, these ubiquitous sensing
technologies have the potential for unprecedented data collection at any
temporal and spatial position. While as a typical distributed parameter system,
the freeway traffic dynamics are determined by the current system states and
the boundary traffic demand-supply. Using the three-step extended Kalman filtering,
this paper simultaneously estimates the real-time traffic state and the
boundary flux of freeway traffic with the distributed speed detector networks
organized at any location of interest. In order to assess the effectiveness of
the proposed approach, a freeway segment from Interstate 80 East (I-80E) in
Alameda, Emeryville, and Northern California is selected. Experimental results show that the proposed method
has the potential of using only speed detecting data to monitor the state of
urban freeway transportation systems without access to the traditional
measurement data, such as the boundary flows.