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Macroscopic Traffic States Estimation Based on Vehicle-to-Infrastructure (V2I) Connected Vehicle Data
Technical Paper
2017-01-2013
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The rapid development of connected vehicle technology provides a promising platform for traffic monitoring and traffic data collection. In the connected vehicle environment, the vehicles equipped with wireless communication devices can transmit vehicle safety messages to other connected vehicles and the Roadside Unit (RSU). The trajectory information in the safety message may provide potential usage for macroscopic traffic states estimation in the urban street network. Over the last few years, the applications of a macroscopic traffic states model, the Macroscopic Fundamental Diagram (MFD) has attracted increased attention. However, the detection of MFD remains a challenging task. This paper explores a potential method of measuring the macroscopic traffic states in terms of MFD based on Vehicle-to-Infrastructure (V2I) connected vehicle data. The methodology of generating MFDs is conducted and the potential characteristics of the macroscopic traffic states are explored. A simulation testbed based on real-world Sioux Falls network is established in VISSM. The wireless data transmissions between connected vehicles and RSUs are simulated by the Discrete Event Network Simulator (NS-3 Simulation). The simulation results illustrate the feasibility of monitoring macroscopic traffic states with the proposed method. The macroscopic traffic states under different radio signal loss models are compared, and the results indicate a significant influence of the wireless characteristics of radio propagation model on the observed traffic states. However, the observed MFD still retain key characteristics such as hysteresis loop direction, traffic breakdown and congestion recovery time.
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Citation
Xu, Z., "Macroscopic Traffic States Estimation Based on Vehicle-to-Infrastructure (V2I) Connected Vehicle Data," SAE Technical Paper 2017-01-2013, 2017, https://doi.org/10.4271/2017-01-2013.Data Sets - Support Documents
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