TY - JOUR
T1 - Route Recommendations for Intelligent Transportation Services
AU - Ge, Yong
AU - Li, Huayu
AU - Tuzhilin, Alexander
N1 - Funding Information:
This research was partially supported by the National Science Foundation (NSF) via grant numbers 1814771 and 1700263.
Publisher Copyright:
© 1989-2012 IEEE.
PY - 2021/3/1
Y1 - 2021/3/1
N2 - 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.
AB - 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.
KW - GPS data
KW - Recommender systems
KW - intelligent transportation
KW - mobile recommendations
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U2 - 10.1109/TKDE.2019.2937864
DO - 10.1109/TKDE.2019.2937864
M3 - Article
AN - SCOPUS:85100559523
VL - 33
SP - 1169
EP - 1182
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
SN - 1041-4347
IS - 3
M1 - 8815882
ER -