Cohesive Ridesharing Group Queries in Geo-Social Networks

Ridesharing has gained much attention as a solution for mitigating societal, environmental, and economic problems. For example, commuters can reduce traffic jams by sharing their rides with others. Notwithstanding many advantages, the proliferation of ridesharing also brings some crucial issues. One of them is to rideshare with strangers. It makes someone feel uncomfortable or untrustworthy. Another complication is the high-latency of ridesharing group search because users may want to receive the result of their requests in a short time. Despite continuous efforts of academia and industry, the issues still remain. In this paper, for resolving the obstacles, we define a new problem,
$\ell $
-cohesive

$m$
-ridesharing group
(
$\ell m$
-CRG
) query, which retrieves a cohesive ridesharing group by considering spatial, social, and temporal information. The problem is based on the three underlying assumptions: people tend to rideshare with socially connected friends, people are willing to walk but not too much, and optimization of finding good groups is essential for both drivers and passengers. In our ridesharing framework, queries are processed by efficiently taking geo-social network data into account. For this purpose, we propose an efficient method for processing the queries using a new concept,
exact

$n$
-friend set
, with its efficient update. Moreover, we further improve our method by utilizing
inverted timetable
(
ITT
), which grasps crucial time information. Specifically, we devise
time-constrained and incremental personalized-proximity search
(
TIPS
). Finally, the performance of the proposed method is evaluated by extensive experiments on several data sets.