Recommending taxi routes with an advance reservation – a multi-criteria route planner

ABSTRACT

In this paper, we propose a multi-criteria route recommendation framework that considers real-time spatial–temporal predictions and traffic network information, aiming to optimize a taxi driver’s profit when considering an advance reservation. Our framework consists of four components. First, we build a grid-based road network graph for modelling traffic network information during the search process. Next, we conduct two prediction modules that adopt advanced deep learning techniques to guide proper search directions in the final planning stage. One module, 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 J* (J-star) algorithm, which jointly considers pick-up probabilities, drop-off distribution, road network, distance, and time factors based on the attentive heuristic function. Compared with existing route planning methods, the experimental results on a real-world dataset have shown our proposed approach is more effective and robust. Moreover, our designed search scheme in J* can decrease the computing time and make the search process more efficient. To the best of our knowledge, this is the first work that focuses on designing a guiding route, which can increase the income of taxi drivers when they have an advance reservation.

KEYWORDS:

• Taxi service
• heuristic search
• spatial–temporal predictions
• multi-criteria searching
• route planning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support the findings of this study are available with the identifier link: https://figshare.com/s/51ccc4b28afcebb4514a

Additional information

Funding

This work was supported by Ministry of Science and Technology (MOST) of Taiwan [grant number 109-2636-E-006-025], [grant number 108-2636-E-006-013].