Back to Search
Start Over
Route Recommendations for Intelligent Transportation Services.
- Source :
-
IEEE Transactions on Knowledge & Data Engineering . Mar2021, Vol. 33 Issue 3, p1169-1182. 14p. - Publication Year :
- 2021
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *BUSINESS success
*TRANSPORTATION
*GLOBAL Positioning System
Subjects
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 33
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 148595938
- Full Text :
- https://doi.org/10.1109/TKDE.2019.2937864