United States Department of Transportation
i
Smartphone-based solutions to monitor and reduce fuel consumption and CO2 footprint : final report.
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2016-06-01
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[PDF-798.03 KB]
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TRIS Online Accession Number:1644747
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Abstract:Smartphones equipped with GPS and several low-energy sensors (e.g., gyroscope, compass, and accelerometer) can provide a medium to collect probe data. As smartphone users navigate the transportation networks, their travel modes and trajectories can be inferred to estimate fuel consumption and CO2 footprint. The specific goals of the proposed research are: (i) Develop new algorithms to estimate the mode of travel (walking, biking, train, car, bus, etc.) and operating mode of a vehicle (e.g., idling) based on low-energy sensors available within smartphones; (2) Evaluate the effectiveness of FC and CO2 estimation from probe vehicles at various market penetration levels; and (3) Develop shortest paths algorithms for finding eco-friendly routes. To achieve these goals, various methodologies are developed and tested with both simulation and field data. For example, machine learning algorithms (e.g., support vector machines) are developed to predict the travel mode and to detect whether a vehicle has stopped. The results show that the travel mode can be detected accurately, about 94% on average, when considering all five travel modes within the sample data. Using the accelerometer data only, the results show that the models can accurately detect the times at which the vehicle stops and moves during its journey. To predict the impacts of probe vehicle market penetration on estimating fuel consumption, simulation data are created for an intersection. Lastly, the shortest path (SP) algorithm for static networks are modified so that the algorithm can find the SP for minimizing both the travel time and fuel consumption for a given network. Together, the models and algorithms developed in this study can be integrated to support various mobility and environmental applications.
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