This report presents a participatory sensing service, called Fueoogle, that maps vehicular fuel consumption on city streets, allowing drivers to find the most fuel-efficient routes for their vehicles between arbitrary end-points. The service exploits measurements of vehicular sensors, available via the OBD-II interface that gives access to most gauges and engine instrumentation. The OBD-II sensors are standardized in all vehicles produced in the US since 1996, constituting some of the largest ``sensor deployments" to date. Using fuel-related measurements contributed by participating vehicles, we develop a route planner that maps normalized fuel-efficiency of city streets, enabling vehicles to compute minimum fuel routes from one point to another. Street congestion, elevation variability, average speed, and average distance between stops (e.g., stop signs) lead to changes in the amount of fuel consumed making fuel-efficient routes potentially different from shortest or fastest routes, and a function of vehicle type. Our experimental study answers two questions related to the viability of the new service. First, how much fuel can it save? Second, can it survive conditions of sparse deployment? The main challenge under such conditions is to generalize from relatively sparse measurements on a subset of streets to estimates of measurements of an entire city. Through extensive
experimental data collection and evaluation, conducted over the duration of a month across several different cars and drivers, we show that significant savings can be achieved by choosing the right route. We also provide extensive results pertaining to the accuracy of models that are used for prediction of fuel consumption values.unpublishednot peer reviewe
