Xhare-a-Ride: A Search Optimized Dynamic Ride Sharing System with Approximation Guarantee
Ref: CISTER-TR-181122 Publication Date: 19 to 22, Apr, 2017
Xhare-a-Ride: A Search Optimized Dynamic Ride Sharing System with Approximation Guarantee
Ref: CISTER-TR-181122 Publication Date: 19 to 22, Apr, 2017Abstract:
Ride sharing is a sustainable, environmentallyfriendly
mode of commute that is gaining in popularity. Though
there are well-established commercial service providers, there are
not many platforms for facilitating peer-to-peer ride sharing,
especially in a dynamic scenario, integrated with multi-modal
trip planners. Such systems would need to be highly searchoptimized
for retrieval of multiple potential ride matches in real
time, especially because multi-modal trip planners have a high
look-to-book ratio. At the same time, validity of the matches need
to be ensured, even in a dynamic setting, while addressing quality
considerations and constraints such as maximum detour incurred
by rides, walking distance for commuters, and time windows of
requests. We describe Xhare-a-Ride (XAR) system, a platform for
dynamic peer-to-peer ride sharing, that is scalable, efficient, and
highly search-optimized for retrieving multiple potential matches
for every ride request, while handling quality considerations.
We propose a hierarchical discretization of the geographical
region using grids, landmarks and clusters with theoretical
guarantees, along with an efficient in-memory indexing of rides
for maintaining spatio-temporal validity within a specified error
tolerance. This helps eliminate shortest path computation in realtime
during search, thus making XAR search-optimized, hence,
suitable for integration with a multi-modal trip planner. We
discuss modes of integrating XAR with such a trip planner
for building an integrated system. Finally, we evaluate XAR
thoroughly on ride share request data generated from the NY taxi
trip data set on three fronts: (i) empirical performance against
the theoretical guarantees as well as trade-off of performance
with system parameters; (ii) benchmark XAR against a state-ofthe-art
ride share system, showing a significant improvement in
the search efficiency; and finally, (iii) the efficacy of combining
ride sharing with public transportation.
Document:
33rd International Conference on Data Engineering 2017 (ICDE 2018), pp 1117-1128.
San Diego, CA, U.S.A..
ISBN: 978-1-5090-6543-1.
ISSN: 2375-026X.
Record Date: 14, Nov, 2018