TY - GEN
T1 - Price-and-Time-Aware Dynamic Ridesharing
AU - Chen, Lu
AU - Zhong, Qilu
AU - Xiao, Xiaokui
AU - Gao, Yunjun
AU - Jin, Pengfei
AU - Jensen, Christian Søndergaard
PY - 2018/10/24
Y1 - 2018/10/24
N2 - Ridesharing refers to a transportation scenario where travellers with similar itineraries and time schedules share a vehicle for a trip and split the travel cost, which may include fuel, tolls, and parking fees. Ridesharing is popular among travellers because it can reduce their travel costs, and it also holds the potential to reduce travel time, congestion, air pollution, and overall fuel consumption. However, existing ridesharing systems often offer each traveller only one choice that aims to minimize system-wide vehicle travel distance or time. We propose a solution that offers more options. Specifically, we do this by considering both pick-up time and price, so that travellers are able to choose the vehicle that matches their preferences best. In order to identify quickly vehicles that satisfy incoming ridesharing requests, we propose two efficient matching algorithms that follow the single-side and dual-side search paradigms, respectively. To further accelerate the matching, indexes on the road network and vehicles are developed, based on which several pruning heuristics are designed. Extensive experiments on a large Shanghai taxi dataset offer insights into the performance of our proposed techniques and compare with a baseline that extends the state-of-The art method. © 2018 IEEE.
AB - Ridesharing refers to a transportation scenario where travellers with similar itineraries and time schedules share a vehicle for a trip and split the travel cost, which may include fuel, tolls, and parking fees. Ridesharing is popular among travellers because it can reduce their travel costs, and it also holds the potential to reduce travel time, congestion, air pollution, and overall fuel consumption. However, existing ridesharing systems often offer each traveller only one choice that aims to minimize system-wide vehicle travel distance or time. We propose a solution that offers more options. Specifically, we do this by considering both pick-up time and price, so that travellers are able to choose the vehicle that matches their preferences best. In order to identify quickly vehicles that satisfy incoming ridesharing requests, we propose two efficient matching algorithms that follow the single-side and dual-side search paradigms, respectively. To further accelerate the matching, indexes on the road network and vehicles are developed, based on which several pruning heuristics are designed. Extensive experiments on a large Shanghai taxi dataset offer insights into the performance of our proposed techniques and compare with a baseline that extends the state-of-The art method. © 2018 IEEE.
KW - Price aware
KW - Query Processing
KW - Ridesharing
KW - Spatial Database
KW - Time aware
UR - http://www.scopus.com/inward/record.url?scp=85057134136&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2018.00099
DO - 10.1109/ICDE.2018.00099
M3 - Article in proceeding
SN - 978-1-5386-5521-4
T3 - Proceedings of the International Conference on Data Engineering
SP - 1061
EP - 1072
BT - Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
PB - IEEE
T2 - 34th IEEE International Conference on Data Engineering, ICDE 2018
Y2 - 16 April 2018 through 19 April 2018
ER -