Investigation into Idling Reduction Technology Using Intelligent Traffic Signal Controller Algorithms, 10-R8105
Steven W. Dellenback, Ph.D., PMP
Mark A. Workman
Inclusive Dates: 09/28/09 09/30/11
Background — Many industrialized countries are enacting (or considering) greenhouse gas (GHG) reduction legislation. Within the United States Senate, this legislation is called the "American Clean Energy and Security Act of 2009." Currently, transportation is the second largest source of GHG emissions within the U.S. (second to electric generation). As a result, public and private transportation entities must prepare for cap-and-trade or similar regulations to reduce GHG emissions. As part of these preparations, the transportation community must investigate steps towards reducing transportation-related GHG emissions. One opportunity to lessen GHG emissions within the transportation arena is to reduce idling time at signalized intersections. Anytime a vehicle slows, stops, idles and accelerates in relation to a signalized intersection, time is wasted, fuel is consumed and excess GHGs are emitted from the vehicle. By eliminating unnecessary speed variances and idling time generated by vehicles interacting with the traffic signaling system, the transportation community can reduce GHG emissions, improve fuel efficiencies and reduce traffic-related congestion and delays.
Approach — The objective of this research effort was to evaluate both actuated and non-actuated signal controller algorithms and their effect on GHG emissions. These evaluations are carried out through state-of-the-art commercial and SwRI-proprietary microscopic computer simulations. These simulations evaluate different signal control types and configurations under different traffic flow volumes. The simulation results provide comparative analysis for traffic signal algorithm effectiveness towards GHG reductions.
Accomplishments — Through sophisticated software simulation, a signalized traffic signal system was evaluated under real-world traffic signal controller types, vehicle sensing technologies and diverse traffic flow conditions. The traffic signal simulations generated individual driving profiles for each vehicle within the system. Each driving profile was evaluated for its corresponding GHG emissions. The outcome of this research demonstrates that effective traffic signal design and implementation involve many tradeoffs. A traffic signal algorithm may implement a strategy that increases positive traits such as traffic travel time and throughput, but it may also have a negative effect on queue wait times and/or GHG emissions. Before this research, such comparative analysis of GHG emissions relating to traffic signal systems was not possible. This research provides an approach to manage traffic based on emissions as opposed to the traditional method of optimizing on speed and occupancy.