Evaluation of Advanced Vehicle and Communication Technologies through Traffic Microsimulation (Project D)

Evaluation of VISSIM revealed that internal modeling of CAV has several limitations. For external modeling, two VISSIM interfaces are useful. The Component Object Model (COM)Application Programming Interface (API) is the superior approach for fetching data and modeling connectivity, whereas the External Driver Model (EDM) is a better tool for lateral and longitudinal control. Utilizing both the COM API and EDM overcomes the disadvantages of both, creating a more robust platform for CAV modeling. Based on this, a comprehensive simulation extension was developed to represent CAVs in VISSIM. CAVs were modeled and an isolated signalized intersection was simulated. The trajectory data from VISSIM were leveraged to estimate energy, fuel consumption, and greenhouse gas emissions using the Motor Vehicle Emission Simulator (MOVES) method. The results show that CAVs in the traffic stream result in net improvement in traffic operational measures (travel time and speed). CAV, the combination of the two technologies (i.e., autonomy and connectivity) yields better performance than each (CV and AV) on their own. However, emissions did not follow the same trend. While increasing AV penetration rates resulted in emissions reductions, increasing CV and CAV penetration rates resulted in higher emissions. A deeper analysis into the root cause for these trends showed that while the CV logic chosen for testing in the VISSIM simulation environment seeks to maximize the likelihood of vehicle arrival-on-green, the algorithm likely results in increased variations in second-by-second accelerations, leading to overall higher emissions. The results are based on a small and relatively simple network, and operations may be different for larger and more complex networks. In addition, the AV, CV, and CAV findings are limited to the connectivity and autonomy algorithms tested in this project. A more complex network with varying vehicle movement algorithms would allow for a more robust analysis.


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Filing Info

  • Accession Number: 01669527
  • Record Type: Research project
  • Source Agency: Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE)
  • Contract Numbers: 69A3551747104
  • Files: UTC, RIP
  • Created Date: May 21 2018 3:09PM