TaxiGuider: Pick-Up Service Recommendation via Multiple Spatial-Temporal Trajectories

Vehicle GPS devices provide abundant trajectory data that can be exploited to generate helpful pick-up service recommendations for taxi drivers. However, existing trajectory clustering approaches struggle to perform well on trajectory data with different distribution characteristics (e.g., dense in downtown and discrete in suburbs) simultaneously. Additionally, current prediction models mainly focus on subsection prediction but fail to produce accurate multisection predictions. To this end, we propose a recommendation framework, namely TaxiGuider, that can generate accurate pick-up cluster recommendations for taxi drivers. First, historical pick-up points are extracted from the entire vehicle trajectory data after preprocessing. Then, a graph Laplacian-based multiple spatial-temporal clustering approach is presented to generate clusters that can effectively match the distribution of trajectory data. Furthermore, a pick-up frequency prediction model that employs a multi-head attention mechanism is proposed to produce accurate multisection predictions that can help taxi drivers make comprehensive considerations for their next destination. Finally, top $N$ clusters with the highest predicted pick-up frequency are recommended to the target taxis according to their request. Experimental results on real-world datasets suggest that TaxiGuider outperforms state-of-the-art approaches in terms of both subsection and multisection predictions. Moreover, it produces pick-up cluster recommendations with superior prediction and classification accuracy simultaneously.