TY - GEN
T1 - Top-k taxi recommendation in realtime social-aware ridesharing services
AU - Fu, Xiaoyi
AU - Huang, Jinbin
AU - Lu, Hua
AU - Xu, Jianliang
AU - Li, Yafei
N1 - Funding Information:
Acknowledgments. This work is supported by Hong Kong RGC grants 12200114, 12201615, 12244916 and NSFC grant 61602420.
PY - 2017/7/22
Y1 - 2017/7/22
N2 - Ridesharing has been becoming increasingly popular in urban areas worldwide for its low cost and environment friendliness. In this paper, we introduce social-awareness into realtime ridesharing services. In particular, upon receiving a user’s trip request, the service ranks feasible taxis in a way that integrates detour in time and passengers’ cohesion in social distance. We propose a new system framework to support such a social-aware taxi-sharing service. It provides two methods for selecting candidate taxis for a given trip request. The grid-based method quickly goes through available taxis and returns a relatively larger candidate set, whereas the edge-based method takes more time to obtain a smaller candidate set. Furthermore, we design techniques to speed up taxi route scheduling for a given trip request. We propose travel-time based bounds to rule out unqualified cases quickly, as well as algorithms to find feasible cases efficiently. We evaluate our proposals using a real taxi dataset from New York City. Experimental results demonstrate the efficiency and scalability of the proposed taxi recommendation solution in real-time social-aware ridesharing services.
AB - Ridesharing has been becoming increasingly popular in urban areas worldwide for its low cost and environment friendliness. In this paper, we introduce social-awareness into realtime ridesharing services. In particular, upon receiving a user’s trip request, the service ranks feasible taxis in a way that integrates detour in time and passengers’ cohesion in social distance. We propose a new system framework to support such a social-aware taxi-sharing service. It provides two methods for selecting candidate taxis for a given trip request. The grid-based method quickly goes through available taxis and returns a relatively larger candidate set, whereas the edge-based method takes more time to obtain a smaller candidate set. Furthermore, we design techniques to speed up taxi route scheduling for a given trip request. We propose travel-time based bounds to rule out unqualified cases quickly, as well as algorithms to find feasible cases efficiently. We evaluate our proposals using a real taxi dataset from New York City. Experimental results demonstrate the efficiency and scalability of the proposed taxi recommendation solution in real-time social-aware ridesharing services.
UR - https://www.scopus.com/pages/publications/85028451296
U2 - 10.1007/978-3-319-64367-0_12
DO - 10.1007/978-3-319-64367-0_12
M3 - Conference proceeding
AN - SCOPUS:85028451296
SN - 9783319643663
T3 - Lecture Notes in Computer Science
SP - 221
EP - 241
BT - Advances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Arlington, VA, USA, August 21 – 23, 2017, Proceedings
A2 - Gertz, Michael
A2 - Renz, Matthias
A2 - Zhou, Xiaofang
A2 - Hoel, Erik
A2 - Ku, Wei-Shinn
A2 - Voisard, Agnes
A2 - Zhang, Chengyang
A2 - Chen, Haiquan
A2 - Tang, Liang
A2 - Huang, Yan
A2 - Lu, Chang-Tien
A2 - Ravada, Siva
PB - Springer Verlag
T2 - 15th International Symposium on Spatial and Temporal Databases, SSTD 2017
Y2 - 21 August 2017 through 23 August 2017
ER -