Macroscopic Traffic States Estimation Based on Vehicle-to-Infrastructure (V2I) Connected Vehicle Data

Paper #:
  • 2017-01-2013

Published:
  • 2017-09-23
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Abstract:
The rapid development of connected vehicle technology provides a promising platform for traffic monitoring and traffic data collection. In 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 traffic states estimation. Over the last few years, the applications of a macroscopic traffic flow model, the Macroscopic Fundamental Diagram (MFD) has attracted increased attention. However, the detection of MFD remains a challenging task even with probe vehicle data from mobile GPS, Bluetooth, and cellphone sensors. This paper explores a potential method of observing the MFD through Vehicle-to-Infrastructure (V2I) connected vehicle data. In Connected Vehicle Technologies, vehicles equipped with Dedicated Short Range Communication (DSRC) On-Board Unit (OBU) can transmit vehicle safety messages including its location and vehicle states to a Roadside Unit (RSU). When aggregated together, the vehicle trajectory information can be used to generate MFD within the communication range of RSU. In this study, we investigate the methodology to generate MFDs through V2I communications in VANETs and explore the potential characteristics of MFD through combined traffic and VANET simulation. A simulation test bed based on real-world Sioux Falls network is established in VISSM. The wireless data transmissions between OBUs and RSUs are simulated by the NS-3 (Discrete Event Network Simulator-3) simulation platform. The simulation results illustrate the feasibility of monitoring urban street MFD in V2I communication. The MFDs 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 MFDs. However, the observed MFD still retain key characteristics such as hysteresis loop direction and traffic breakdown and recovery time.
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