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Improving Eco-Friendly Routing Considering Detailed Mobility Profiles, Driving Behavior and Vehicle Type

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Databases Theory and Applications (ADC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13459))

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Abstract

Traditional vehicle routing algorithms aim to find the fastest or shortest route, whereas eco-friendly routing algorithms aim to find the route that minimizes vehicle fuel consumption or greenhouse gas (GHG) emissions. To accurately estimate fuel consumption and emissions along a route, a detailed mobility profile of the vehicle traveling on the route is needed including acceleration/deceleration and idling time. However, the existing techniques that aim to find eco-friendly routes make a simplistic assumption by assigning each road segment of the road network an average speed or average fuel consumption along the road, ignoring detailed mobility profiles and driving behavior (e.g., aggressive or moderate) altogether. This simplistic treatment leads to sub-optimal route choices because such representation fails to capture driving behaviors and detailed mobility profiles of the candidate routes resulting in poor quality estimates. Furthermore, many of the existing techniques employ a one-size-fits-all approach ignoring that different vehicles (e.g., truck vs car) and drivers exhibit different behaviors, thus, the most eco-friendly route may be significantly different for different types of vehicles and/or drivers. This paper addresses these limitations and presents an eco-routing algorithm that computes the most fuel economical route considering the detailed mobility profiles, driving behavior, and vehicle type. We conduct an extensive experimental study on a real road network considering different vehicles and driving behaviors and show that our algorithm generates routes that reduce fuel consumption by up to 35%.

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Correspondence to Ahmed Fahmin .

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Fahmin, A., Zhang, S., Cheema, M.A., Toosi, A.N., Rakha, H.A. (2022). Improving Eco-Friendly Routing Considering Detailed Mobility Profiles, Driving Behavior and Vehicle Type. In: Hua, W., Wang, H., Li, L. (eds) Databases Theory and Applications. ADC 2022. Lecture Notes in Computer Science, vol 13459. Springer, Cham. https://doi.org/10.1007/978-3-031-15512-3_10

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  • DOI: https://doi.org/10.1007/978-3-031-15512-3_10

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-15512-3

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