Towards Social-Aware Ridesharing Group Query Services

Abstract

With the deep penetration of smartphones and geo-locating devices, ridesharing is envisioned as a promising solution to transportation-related problems in metropolitan cities, such as traffic congestion and air pollution. Despite the potential to provide significant societal and environmental benefits, ridesharing has not so far been as popular as expected. Notable barriers include social discomfort and safety concerns when traveling with strangers. To overcome these barriers, in this project, a new type of Social-aware Ridesharing Group (SaRG) queries which retrieves a group of riders by taking into account their social connections and spatial proximities was proposed. While SaRG queries are of practical usefulness, it is proved that, however, the SaRG query problem is NP-hard. Thus, we design an efficient algorithm with a set of powerful pruning techniques to tackle this problem. We also present several incremental strategies to accelerate the search speed by reducing repeated computations. Moreover, we propose a novel index tailored to our problem to further speed up query processing. Experimental results on real datasets show that our proposed algorithms achieve desirable performance.

Existing System

With the rapid development of location-aware mobile devices, ubiquitous Internet access and social computing technologies, individuals’ location data and social data have been readily available from smart phones and mobile platforms. In the existing system, we study a new type of Geo-Social K-Cover Group (GSKCG) queries. It has a set of query points and a social network, retrieves a minimum user group in which each user is socially related to at least k other users and the users’ associated regions (e.g., familiar regions or service regions) can jointly cover all the query points. Albeit it’s practical usefulness, the GSKCG query problem is NP-hard. We consequently explore a set of effective pruning strategies to derive an efficient algorithm for finding the optimal solution

Proposed System

A new type of Social-aware Ridesharing Group (SaRG) queries to accommodate the real-world need have considering social comfort and trust in ridesharing. A SaRG query retrieves a ridesharing group where each rider’s trip is similar to that of the driver, and each member of the ridesharing group should be familiar with at least k other group members. An efficient algorithm named RSExplorer and a set of efficient pruning techniques to answer SaRG queries. In this design a novel index structure, Social- Info R-tree (SIR-tree), which integrates social information into R-tree, to further prune the search space and then propose the SIRBased algorithm that integrates the RSExplorer algorithm with the SIR-tree structure. A new type of Social-aware Ridesharing Group (SaRG) queries to accommodate the real-world need of considering social comfort and trust in ridesharing was formulated. A SaRG query retrieves a ridesharing group where each rider’s trip is similar to that of the driver, and each member of the ridesharing group should be familiar with at least k other group members. We also prove that the SaRG query problem is NP-hard. An efficient algorithm named RSExplorer and a set of efficient pruning techniques to answer SaRG queries was proposed. It also devise several incremental strategies by reducing repeated computations to speed up query processing. A novel index structure, Social- Info R-tree (SIR-tree), which integrates social information into R-tree, to further prune the search space and then propose the SIRBased algorithm that integrates the RSExplorer algorithm with the SIR-tree structure was also designed.

Architecture

Architecture Diagram