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Technical Report

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Real world fuel usage, MOVES, fuel efficient route

Digital Object Identifier (DOI)


Many travelers use Google Maps to select the route for their trip and the Google recommendation can have a significant impact on traffic congestion. Google recently added a new route option: the most fuel-efficient route. In theory, the algorithm behind this route selection (Route E) examines the current travel conditions on the available routes and estimates typical fuel consumption based on those conditions. This should include acceleration/deceleration events as these change of speed events significantly impact fuel consumption and are a critical aspect of selecting the most fuel-efficient route, especially when comparing freeway general purpose lanes (GPLs) to Express Lanes/Managed Lanes (MLs). Initial testing of the Google Maps algorithm indicates it may not account for these speed changes.

This study examines if the new route guidance from Google Maps is accurately identifying the most fuel-efficient routes and tests the RouteE API models. Researchers examined typical travel conditions on GPLs and MLs on two Dallas freeways with MLs. Several vehicles equipped with on-board diagnostic (OBD) data loggers recorded key aspects of the vehicle operations while driving in real-world traffic conditions. These vehicles were driven on the Dallas freeways (both GPLs and MLs) during various traffic conditions, which allowed for detailed fuel consumption to be estimated based on the OBD data collected. The data collected from OBD devices were then compared with RouteE and MOVES for their accuracy on the fuel usage estimation.

RouteE and MOVES were both found to miss the actual fuel consumption by a significant amount and do not appear to accurately incorporate speed changes of vehicles in real-world situations. Thus, it was not surprising that Google would usually identify the GPLs as the most fuel- efficient route when comparing GPLs and MLs. Using the real-world fuel consumption data along with detailed speed profiles, researchers developed equations that could be used to estimate fuel consumption based on microscopic traffic data on vehicle speeds. Unlike Google, our real-world based fuel consumption equations, along with detailed Wejo traffic data, found the MLs to be more likely to be the most fuel- efficient, but this varied based on the exact traffic conditions. This was based on a small set of fuel consumption data and can only be used as proof of concept. It does show the importance of including speed changes in the analysis. There needs to be considerably more data collected in real-world conditions to further refine these models of fuel consumption and possibly incorporate these models into route recommendation algorithms. This research shows that this proof of concept model can prove helpful in estimating route-specific emissions when combined with either high-resolution data like that from Wejo or lower-resolution data like that from Google to provide a more accurate estimate of which route really is more fuel-efficient.

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Policy brief