LDFeRR: A Fuel-efficient Route Recommendation Approach for Long-distance Driving Based on Historical Trajectories
Min Liu, Zhaohui Peng, Xiaohui Yu, Senzhang Wang, Qiao Song · Society for Industrial and Applied Mathematics eBooks · 2021
Fuel-efficient route recommendation has been increasingly valuable for both energy conservation and environmental protection. Most existing methods analyze fuel consumption factors from the short-distance trajectories. However, due to the differences in the road network structure and route composition between long-distance and short-distance trajectories, directly using these methods to recommend fuel-efficient routes for long-distance drivers is less effective. In addition, previous works usually adopt heuristic algorithms due to efficiency, but empirically set the heuristic functions, which makes it difficult to integrate various influencing factors appropriately. In this paper, we propose a novel fuel-efficient route recommendation model for long-distance driving, LDFeRR. We first identify the potential factors that affect fuel consumption over long distances based on historical trajectories. To fully exploit these factors to estimate fuel consumption and further provide reliable recommendations, we propose to integrate deep learning methods with heuristic algorithm. Specially, we use a multi-layer perceptron (MLP) to predict the fuel consumption of a single road segment. We also employ an attention-based bidirectional gated recurrent unit (Att-BiGRU) to estimate the fuel consumption between two locations connected by routes. In this way, the two cost functions of the classic heuristic algorithm, i.e., A* algorithm, are automatically learned. Extensive experiments on the large real-world dataset demonstrate the effectiveness of our proposed model.