A Decision Framework to Recommend Cruising Locations for Taxi Drivers under the Constraint of Booking Information

As the demand for taxi reservation services has increased, increasing the income of taxi drivers with advanced services has attracted attention. In this article, we propose a path decision framework that considers real-time spatial-temporal predictions and traffic network information. The goal is to optimize a taxi driver's profit when considering a reservation. Our framework contains four components. First, we build a grid-based road network graph for modeling traffic network information for speeding up the search process. Next, we conduct two prediction modules that adopt advanced deep learning techniques to guide proper search directions for recommending cruising locations. One module of the taxi demand prediction is used to estimate the pick-up probabilities of passengers in the city. Another one is destination prediction, which can predict the distribution of drop-off probabilities and capture the flow of potential passengers. Finally, we propose the H* (Heuristic-star) algorithm, which jointly considers pick-up probabilities, drop-off distribution, road network, distance, and time factors based on the attentive heuristic function to dynamically recommend next cruising locations. Compared with existing route planning methods, the experimental results on a real-world dataset have shown that our proposed approach is more effective and robust. Moreover, our designed search scheme in H* can decrease the computing time and allow the search process to be more efficient. To the best of our knowledge, this is the first work that focuses on guiding a route, which can increase the income of taxi drivers under the constraint of booking information.