The project will conduct a realistic assessment using a case study of a medium-sized metropolitan area of the likely environmental benefits of an intelligent transportation systems (ITS) strategy that involves providing route guidance to travelers based on the lowest fuel consumption route. At the present time, motorists typically choose their routes based on minimizing their perceived total travel time or generalized cost. Almost all routing algorithms commonly used within the transportation industry, such as user equilibrium (UE) and system optimal (SO) assignments, are based on the assumption of drivers selecting the shortest path (i.e., least travel time) between a given origin-destination (OD) pair. Moreover, the basis for routing in virtually all of the current global positioning system (GPS) 2 navigation devices, the use of which has skyrocketed in the United States in recent years, is still the minimum travel time, subject to perhaps some driver preference parameters (e.g., prefer highway, avoid toll roads, etc.).

Until recently, the transportation profession lacked the necessary tools to determine whether the fastest route between an origin-destination (OD) pair were, in fact, the optimal one from an environmental (i.e., energy consumption and emissions) standpoint. The dramatic scientific advances in state-of-the-art microscopic traffic simulation models and emissions models have made it possible to accurately determine the impact of route choice on regional energy consumption and emissions. Moreover, with advances in Intelligent Transportation Systems (ITS), the opportunity now exists to research the feasibility and likely benefits of routing strategies that explicitly consider the criteria of minimum energy consumption and emissions in recommending a route for a driver. This is especially true, given the most recent United States Department of Transportation (USDOT) connected vehicle research, which envisions a networked environment supporting vehicle-to-vehicle and vehicle-to-infrastructure communications.

The proposed research will be conducted using a Transportation Analysis Simulation System (TRANSIMS) model of the Greater Buffalo-Niagara region. The model was originally developed by the John A. Volpe National Transportation Systems Center, in a TRANSIMS test deployment study funded by the Federal Highway Administration (FHWA). The model is currently being refined and expanded in another study funded by FHWA, and being conducted by the University at Buffalo (UB) and the State University of New York.  To use TRANSIMS for the development of environmentally based routes and for evaluating the benefits of environmentally based routing, the model will be linked to the Multiscale Motor Vehicle Emissions Simulator Model (MOVES2010), which was recently released by the Environmental Protection Agency's (EPA's) Office of Transportation and Air Quality. MOVES2010 is designed to allow for conducting environmental analyses based on second-by-second dynamic vehicle information, which will be provided by the TRANSIMS model (EPA, 2009; EPA, 2010).The research will also assess the impact of several factors on the likely benefits of environmentally based route guidance.

These factors will include: (1) the market penetration for users of such routing strategies; (2) the additional benefits from providing real-time information about accidents and other traffic disturbances, information that should be readily available from a connected vehicle system; (3) the additional benefits from customizing the optimal environmental routes for the vehicle type (e.g., light-duty vehicle versus a truck). The study will also evaluate the degree to which the optimal routes derived based on the objective of minimizing fuel consumption are different from those derived based on minimizing the total travel time, as well as the reductions in emissions resulting from environmental-based routing.

Goals 

The key project objective is to provide fuel efficient route guidance to drivers using connected vehicle technologies that can significantly improve environmental performance in terms of emissions, greenhouse gas (GHG), and energy consumption.

• Operations

• FY 2002-2022 / Operations / Automation and Connectivity