Abstract Fuel consumption of vehicles at signalized intersections is directly impacted by congestion. Accurately characterizing fuel consumption at signalized intersections gives additional insights into how signal timing strategies and intelligent transportation systems impact traffic. Cost effective sensing technologies in vehicles have introduced new ways to collect vehicular trajectories ubiquitously, which can help characterize fuel consumption at intersections at scale. This study investigates fuel consumption prediction with a Long Short-Term Memory (LSTM) neural network using onboard diagnostic (OBD) and crowdsourced probe vehicle trajectory data. The samples represent both free flow and stopped traffic as well as different driving conditions like acceleration, deceleration, and idling. The results of the LSTM model indicate that total fuel consumption at an intersection is related to traffic operation parameters, i.e., delay. Accurately predicting fuel consumption will pave the way for improvement of fuel economy.
Time-Series fuel consumption prediction assessing delay impacts on energy using vehicular trajectory
2023-02-24
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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