Advanced Eco-Driving Strategies for Drivers and Automated vehicles Traveling within Intersection Vicinities
Project Title
Advanced Eco-Driving Strategies for Drivers and Automated vehicles Traveling within Intersection Vicinities
University
Texas Southern University
Principal Investigator
Peijia Tang, Mehdi Azimi, Fengxiang Qiao, Lei Yu
Texas Southern University
PI Contact Information
Department of Transportation Studies
Texas Southern University
3100 Cleburne Avenue
Houston, TX 77004
Funding Sources and Amounts Provided
—
Total Project Cost
—
Agency ID or Contract Number
DTRT12GUTC17/KLK900-SB-002
Start Date
1/1/12
End Date
1/31/16
Description of Research Project
Vehicle emissions occupy a considerable share of emission inventories in the United States. One of the approaches taken to minimize vehicle emissions is eco-driving. Supported by advanced ITS technologies, it is available to provide the real-time eco-driving advice/suggestions to drivers and to automated vehicles. In order to examine the most effective eco-driving advising strategies for drivers, and evaluate potential emission mitigations of eco-driving for automated vehicles. Real-time eco-driving models for drivers and for automated vehicles were developed respectively. The eco-driving model for drivers was programmed in a high-fidelity driving simulator. Different eco- driving advising strategies with regard to the types (audio vs. visual) and the frequencies of the suggestions were designed and tested by thirty-one driver participants. On the other hand, the eco-driving model for automated vehicles was applied in a VISSIM simulation platform under different traffic conditions. The automated vehicles in the simulation environment could adjust their driving behaviors second-by-second according to the eco-driving model. Finally, the MOVES’ method was used to estimate vehicle emissions for both driving simulator tests and VISSIM simulations. It is found that all of the eco-driving scenarios for drivers are effective in emission reducing. The audio eco-driving strategy with a 10-second interval is the most effective strategy to reduce emissions. However, eco-driving scenario spent more travel time. Meanwhile, real-time eco-driving suggestions for automated vehicles saved 20% CO2. However, vehicle emissions are dependent on traffic condition.
Implementation of Research Outcomes
—
Impacts and Benefits of the Project
- —
Papers
- —
Web Links
Final Report: TranLIVE_TSU-16-02_Advanced Eco-driving Strategies for Drivers and Automated Vehicles
Keywords
- Eco-driving
- Vehicle Emissions
- Automated Vehicles
- Traffic Simulation
- Driving Simulator
- VISSIM
- MOVES