Cohesive Ridesharing Group Queries in Geo-Social Networks

Abstract

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,-cohesive m-ridesharing group (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. INDEX TERMS Geo-social networks, query processing, ridesharing services, spatial databases.

Trust-Based Recommendation for Shared Mobility Systems Based on a Discrete Self-Adaptive Neighborhood Search Differential Evolution Algorithm

“…A decision model is obtained by combining the trust optimization problem and cost optimization problem. The concept of cohesive ridesharing in geo-social networks was studied in [12]. In Ref.…”

“…One approach to ensuring safety and trust is to exploit the social relation information about potential ridesharing participants from social media [6]. Therefore, several studies on ensuring safety/trust in ridesharing through social networks have been carried out in recent years [7][8][9][10][11][12]. The decision models to generate recommendations for the trust-based ridesharing problem are generally formulated as non-linear integer programming problems, which consist of non-linear constraints and discrete decision variables.…”

Nearest close friend query in road-social networks

“…multiple POIs. Shim et al [23] proposed the -cohesive m-ridesharing group ( mCRG) query, which retrieves a cohesive ridesharing group by considering spatial, social, and temporal information.…”

Query Processing of Geosocial Data in Location-Based Social Networks

“…With respect to the technological challenges, the results of the SLR reveal a need to explore new kinds of social and spatial data to include in the query processing for refining the results of the geosocial queries. For instance, Shim et al [39] suggested the use of the shortest route or the interest of riders to enhance the query ridesharing processing and to apply this kind of query also to environments with obstacles on the road and location uncertainty. Zhang et al [47] proposed the use of the historical information of each user in the group to automatically set the group preference and its weight in the social graph.…”

“…Finally, only one study [39] proposing geosocial group queries incorporates temporal constraints, in addition to spatial and social ones, to retrieve a cohesive ridesharing group.…”