Abstract
The accumulated large amount of mobility data and the ability to track moving people or objects have enabled us to develop advanced mobile recommendations, which are essential to recommend a sequence of locations to an individual user on the move. In this paper, we study a particular case of mobile recommendations, route recommendations to drivers, by utilizing vehicle GPS data. Specifically, we formulate a new Route Recommendation with Relaxed Assumptions (RR-RA) problem, the goal of which is to recommend a sequence of locations to a driver based on his current location in order to maximize his business success. To make our recommendation practical and scalable for real practice, we need to produce recommendation results in a timely fashion once a request emerges. Therefore, we propose an efficient algorithm to efficiently generate recommendations. Furthermore, we identify and address a destination-oriented route recommendation (DORR) problem. Without solving DORR problem, RR-RA alone does not work well in practice because drivers may encounter the destination constraint on a daily basis. We develop a dedicated and efficient algorithm for solving DORR problem. The package of solutions for both RR-RA and DORR problems provide a comprehensive approach for route recommendations to drivers. We evaluate our methods using both real-world GPS data and synthetic data, and demonstrate the effectiveness and efficiency of proposed methods with different evaluation metrics.
Original language | English (US) |
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Article number | 8815882 |
Pages (from-to) | 1169-1182 |
Number of pages | 14 |
Journal | IEEE Transactions on Knowledge and Data Engineering |
Volume | 33 |
Issue number | 3 |
DOIs | |
State | Published - Mar 1 2021 |
Externally published | Yes |
Keywords
- GPS data
- Recommender systems
- intelligent transportation
- mobile recommendations
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Computational Theory and Mathematics