For over a decade, researchers at the University of California Riverside have been actively researching and working towards performing energy/environmental assessments for various intelligent transportation systems (ITS) programs. The proposed research would build upon existing work through developing and improving data collection methods, developing new data fusion techniques to improve estimates, and applying appropriate models for ITS environmental/energy assessments.
The key project objectives are listed below:
(1) Intelligent Transportation Systems (ITS) applications and operating strategies using connected vehicle technologies can significantly improve environmental performance in terms of emissions, greenhouse gas (GHG), and energy consumption and research can improve the understanding of the relationship between vehicle performance and emissions.
(2) Intelligent Transportation Systems applications and operating strategies can be modeled using an improved Comprehensive Modal Emissions Model (CMEM) model, Motor Vehicle Emission Simulator (MOVES), and a new model, Simulation of Urban MObility (SUMO).
(3) An ecosignal application can improve environmental performance.
(4) Real-time data from vehicles can enable improved environmental data.
(5) Other ITS applications can potentially improve environmental performance.
The research report recognized the challenges to quantify properly the potential environmental and energy impacts of intelligent transportation systems (ITS) applications, highlighting the lack of pertinent data and suggested better ways of collecting data to support quantitative evaluation. A large part of that involves modeling, and the support from Applications for the Environment: Real-Time Information Synthesis (AERIS) has gone to improve data curves used in mesoscale modeling. Focus has been placed on developing new data curves for hybrid electric vehicles, which will play a major role in future vehicle fleets. In addition, one of the key challenges has been in estimating ITS impacts on arterial corridors, and a new methodology has been developed allowing for better energy/emission assessments compared to today’s standard techniques. This has resulted in an academic paper, showing results that this new method is typically within 10 percent of the true values, compared to the standard approach, which falls within 40 percent of the actual values.Ongoing research in the transportation and emissions modeling interface was also examined. The Urban Congestion Report (UCR) team has been working in this area for some time, and have developed a variety of techniques working with the Comprehensive Modal Emissions Model (CMEM) as well as the Motor Vehicle Emission Simulator (MOVES). A summary of the current efforts in this area is provided. In addition, a summary is given of the latest ITS-based transportation models such as Simulation of Urban MObility (SUMO), and how a similar energy/emissions estimation process can be applied.Finally, an ecosignal application has been advanced using these tools, showing that specific velocity planning algorithms can result in a 10 percent to 15 percent fuel economy improvement over a standard baseline case without the velocity planning. This work has also been highlighted in a recent set of academic papers.An innovative approach has also been developed to provide in situ real-time environmental information estimates from vehicles. This system is called the Mobile Energy/Emissions Telematics System (MEETS), and has the potential to interact directly with the transportation infrastructure (e.g., traffic signals, ramp meters, etc.) for reducing energy and emissions. The system has been validated using the University of California Riverside’s College of Engineering ecofriendly intelligent transportation system (ECO-ITS) testbed vehicle, showing very promising results.Lastly, the report outlines several of the general categories of ITS applications that have the potential for energy and emissions reductions.