Ridesharing-Inspired Trip Recommendations

The objective of this paper is to determine how ridesharing can help lowering the travel cost of a user who already has a preplanned trip. This problem is formulated as the Ridesharing-Inspired Trip Recommendation Query (RSTR). In the first phase of the proposed method, the trip of the query initializer is matched with other users. In the second phase, a heuristic-based algorithm is employed to generate a new trip recommendation. Experimental results showed that the proposed solution is comparable to the optimal solution and performs much better in run-time efficiency and scalability.

[1]  Xiaokui Xiao,et al.  Keyword-aware Optimal Route Search , 2012, Proc. VLDB Endow..

[2]  Mario A. Nascimento,et al.  A Mixed Breadth-Depth First Search Strategy for Sequenced Group Trip Planning Queries , 2015, 2015 16th IEEE International Conference on Mobile Data Management.

[3]  Elham Ahmadi,et al.  Group Trip Planning Queries in Spatial Databases , 2017 .

[4]  Panos Kalnis,et al.  Collective Travel Planning in Spatial Networks , 2016, IEEE Transactions on Knowledge and Data Engineering.

[5]  Paolo Bolzoni,et al.  Efficient itinerary planning with category constraints , 2014, SIGSPATIAL/GIS.

[6]  Lei Chen,et al.  Utility-Aware Ridesharing on Road Networks , 2017, SIGMOD Conference.

[7]  Yu Zheng,et al.  T-share: A large-scale dynamic taxi ridesharing service , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

[8]  Shengchao Qin,et al.  Optimal Route Search with the Coverage of Users' Preferences , 2015, IJCAI.

[9]  Kyriakos Mouratidis,et al.  Group nearest neighbor queries , 2004, Proceedings. 20th International Conference on Data Engineering.

[10]  Jiajie Xu,et al.  On personalized and sequenced route planning , 2015, World Wide Web.

[11]  Cyrus Shahabi,et al.  The optimal sequenced route query , 2008, The VLDB Journal.