Dynamic ECO-driving for arterial corridors

There are a variety of strategies that are now being considered to reduce fuel consumption and carbon dioxide (CO2) emissions from the transportation sector. One strategy that is gaining interest worldwide is known as “eco-driving”. Eco-driving typically consists of changing a person's driving behavior based on general (static) advice to the driver, such as accelerating slowly, driving smoothly, reducing high speeds, etc. Taking this one-step further, it is possible to provide realtime advice to drivers based on changing traffic and infrastructure conditions for even greater fuel and emission savings. This concept of dynamic eco-driving takes advantage of real-time traffic sensing and infrastructure information, which can then be communicated to a vehicle with a goal of reducing fuel consumption and emissions. In this paper, we consider dynamic eco-driving in an arterial corridor with traffic signals, where signal phase and timing information of a traffic light is provided to the vehicle. The vehicle can then adjust its velocity while traveling through a signalized corridor with the goal of minimizing fuel consumption and emissions. A dynamic eco-driving velocity planning algorithm has been developed and is described herein. This algorithm has then been tested in simulation, showing initial fuel economy and CO2 improvements of around 12%.

[1]  M. Barth,et al.  Recent Validation Efforts for a Comprehensive Modal Emissions Model , 2001 .

[2]  Kanok Boriboonsomsin,et al.  Real-World Carbon Dioxide Impacts of Traffic Congestion , 2008 .

[3]  Matthew G. Karlaftis,et al.  PARAMETERS RELATED TO MODELLING INTELLIGENT SPEED ADAPTATION SYSTEMS WITH THE EMPLOYMENT OF A MICROSCOPIC TRAFFIC MODEL , 2008 .

[4]  Olavi H Koskinen.  Improving Vehicle Fuel Economy and Reducing Emissions by Driving Technique , 2008 .

[5]  Matthew Barth,et al.  Modal Emissions Model for Heavy-Duty Diesel Vehicles , 2004 .

[6]  Stefan Myhrberg.  Saving Fuel and Environment with Intelligent Speed Adaptation , 2008 .

[7]  Kanok Boriboonsomsin,et al.  Energy and emissions impacts of a freeway-based dynamic eco-driving system , 2009 .

[8]  Kanok Boriboonsomsin,et al.  Traffic Energy and Emission Reductions at Signalized Intersections: A Study of the Benefits of Advanced Driver Information , 2009 .

[9]  Matthew Barth,et al.  Measuring and Modeling Emissions from Extremely Low-Emitting Vehicles , 2006 .

[10]  Hitoshi Kanoh,et al.  Wide-area Traffic Signal Control Using Predicted Traffic Based on Real-time Information , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[11]  Guoqiang Li,et al.  Signal timing of intersections using integrated optimization of traffic quality, emissions and fuel consumption: a note , 2004 .

[12]  Matthew Barth,et al.  Development of a Comprehensive Modal Emissions Model , 2000 .

[13]  G. Boy,et al.  ON-BOARD SYSTEM DESIGN TO OPTIMISE ENERGY MANAGEMENT , 2006 .

[14]  Vincent Marchau,et al.  In-Car Speed Assistance to Improve Speed Management , 2008 .

[15]  Toshio Yoshii,et al.  A Study of the Signal Control for the Minimization of CO2 Emission , 2005 .

[16]  Matthew Barth,et al.  STATISTICAL ANALYSIS AND MODEL VALIDATION OF AUTOMOBILE EMISSIONS , 2000 .

[17]  Takao Onoda.  IEA policies--G8 recommendations and an afterwards , 2009 .

[18]  Kanok Boriboonsomsin,et al.  Impacts of freeway high-occupancy vehicle lane configuration on vehicle emissions , 2008 .

[19]  Kai Zhang,et al.  Optimizing Traffic Control to Reduce Fuel Consumption and Vehicular Emissions , 2009 .

[20]  Guillaume Saint Pierre,et al.  Impact of Intelligent Speed Adaptation Systems on Fuel Consumption and Driver Behaviour , 2008 .

[21]  Kyongsu Yi,et al.  Nonlinear brake control for vehicle CW/CA systems , 2001 .

[22]  J. Paez,et al.  Evaluation of in-vehicle dynamic speed assistance in Spain: algorithm and driver behaviour , 2008 .