TY - JOUR
T1 - Flexi-Sharing
T2 - A Flexible and Personalized Taxi-Sharing System
AU - Lyu, Yan
AU - Lee, Victor C.S.
AU - NG, Joseph K Y
AU - Lim, Brian Y.
AU - Liu, Kai
AU - Chen, Chao
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61872049 and Grant 61572088, and in part by the Frontier Interdisciplinary Research Funds for the Central Universities under Project 2018CDQYJSJ0034.
PY - 2019/10
Y1 - 2019/10
N2 - Taxi sharing is a promising approach to reducing energy consumptions, utilizing limited taxi resources efficiently while preserving the interest of individuals. The existing studies mostly fail to locate a pick-up/drop-off point for each individual passenger in scheduling the sharing route. Besides, they can hardly provide personalized services. To this end, we propose a new taxi-sharing system called Flexi-Sharing to provide flexible and personalized taxi sharing services. It considers the nearby alternative pick-up/drop-off locations and schedules a flexible sharing route with the maximum reduced travel distance by letting passengers walk a short distance. For a sharing request, Flexi-Sharing generates the sharing schedule consisting of a set of companions, the shortest sharing route and the best pick-up/drop-off locations by maximizing the satisfaction of involved passengers. Extensive experiments are conducted using a one-month taxi trajectory data collected in Chengdu, China. Experimental results show that Flexi-Sharing achieves 60% sharing rate and reduces 28 000 km travel distances per hour in the city, which are 15% higher and 16% longer than those of the method that delivers passengers at exact requested locations, respectively. Flexi-Sharing also promises a satisfactory system response time and provides superior sharing experiences.
AB - Taxi sharing is a promising approach to reducing energy consumptions, utilizing limited taxi resources efficiently while preserving the interest of individuals. The existing studies mostly fail to locate a pick-up/drop-off point for each individual passenger in scheduling the sharing route. Besides, they can hardly provide personalized services. To this end, we propose a new taxi-sharing system called Flexi-Sharing to provide flexible and personalized taxi sharing services. It considers the nearby alternative pick-up/drop-off locations and schedules a flexible sharing route with the maximum reduced travel distance by letting passengers walk a short distance. For a sharing request, Flexi-Sharing generates the sharing schedule consisting of a set of companions, the shortest sharing route and the best pick-up/drop-off locations by maximizing the satisfaction of involved passengers. Extensive experiments are conducted using a one-month taxi trajectory data collected in Chengdu, China. Experimental results show that Flexi-Sharing achieves 60% sharing rate and reduces 28 000 km travel distances per hour in the city, which are 15% higher and 16% longer than those of the method that delivers passengers at exact requested locations, respectively. Flexi-Sharing also promises a satisfactory system response time and provides superior sharing experiences.
KW - intelligent transportation systems
KW - location-based services
KW - Taxi sharing
KW - taxi trajectories
UR - http://www.scopus.com/inward/record.url?scp=85073873412&partnerID=8YFLogxK
U2 - 10.1109/TVT.2019.2932869
DO - 10.1109/TVT.2019.2932869
M3 - Journal article
AN - SCOPUS:85073873412
SN - 0018-9545
VL - 68
SP - 9399
EP - 9413
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 10
M1 - 8786246
ER -