Paper Info
Title
Top-<inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="li-ieq1-2937031.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></alternatives></inline-formula> Vehicle Matching in Social Ridesharing: A Price-Aware Approach
Abstract
In the past few years ridesharing has largely reshaped the transportation marketplace. It is envisioned as a promising solution to transportation-related problems in metropolitan cities, such as traffic congestion and air pollution. In the current ridesharing research, social ridesharing, which makes use of social relations among drivers and riders to address safety issues, and dynamic pricing are two active directions with important business implications. Simultaneously optimizing social cohesion and revenue is vital to a commercial ridesharing platform's sustainable development, which, however, has not been previously studied. In this paper, we first present a new pricing scheme that better incentivizes drivers and riders to participate in ridesharing, and then propose a novel type of Price-aware Top-k Matching (PTkM) queries which retrieve the top-k vehicles for a rider's request by taking into account both social relations and revenue. We design an efficient algorithm with a set of powerful pruning techniques to tackle this problem. Moreover, we propose a novel index tailored to our problem to further speed up query processing. Extensive experimental results on real datasets show that our proposed algorithms achieve desirable performance for real-world deployment.
Year
DOI
Venue
2021
10.1109/TKDE.2019.2937031
IEEE Transactions on Knowledge and Data Engineering
Keywords
DocType
Volume
Price revenue,social ridesharing,location-based services,query processing,social acquaintance
Journal
33
Issue
ISSN
Citations 
3
1041-4347
2
PageRank 
References 
Authors
0.37
18
8
Name
Order
Citations
PageRank
Yafei Li161.81
Ji Wan220.37
Rui Chen3124749.96
Jianliang Xu42743168.17
Xiaoyi Fu5142.89
Hongyan Gu620.37
Pei Lv78013.94
Mingliang Xu814012.95