TY - GEN
T1 - Online recommendation system for autonomous and human-driven ride-hailing taxi services
AU - Wan, Xiangpeng
AU - Ghazzai, Hakim
AU - Massoud, Yehia
N1 - Generated from Scopus record by KAUST IRTS on 2022-09-13
PY - 2019/12/1
Y1 - 2019/12/1
N2 - With the rapid development of autonomous vehicle technology, modern taxi services will potentially witness an important revolution, where some regular cabs will be substituted by self-driving taxis. In this study, we propose a hybrid taxi recommendation system where both autonomous and human-driven ride-hailing vehicles are guided in order to meet the needs of taxi customers as well as the expectation of human drivers. Customers desire to wait the minimum time before finding a taxi, while drivers aim to maximize their profits by speeding up their customer hunting. Autonomous taxis are used to back-up human-driven taxis in areas where customers' demand levels are low. Three major components compose our system: a taxi demand predictor, taxi-to-region assigner, and taxi routing optimizer. Our simulation model is applied on the borough of Manhattan, New York City (NYC), and is validated with realistic data. The selected results show that significant performance gains in terms of number of pickups, customer waiting time, and vacant traveled distance of human-drivers can be achieved compared to those of the traditional human-based taxi system.
AB - With the rapid development of autonomous vehicle technology, modern taxi services will potentially witness an important revolution, where some regular cabs will be substituted by self-driving taxis. In this study, we propose a hybrid taxi recommendation system where both autonomous and human-driven ride-hailing vehicles are guided in order to meet the needs of taxi customers as well as the expectation of human drivers. Customers desire to wait the minimum time before finding a taxi, while drivers aim to maximize their profits by speeding up their customer hunting. Autonomous taxis are used to back-up human-driven taxis in areas where customers' demand levels are low. Three major components compose our system: a taxi demand predictor, taxi-to-region assigner, and taxi routing optimizer. Our simulation model is applied on the borough of Manhattan, New York City (NYC), and is validated with realistic data. The selected results show that significant performance gains in terms of number of pickups, customer waiting time, and vacant traveled distance of human-drivers can be achieved compared to those of the traditional human-based taxi system.
UR - https://ieeexplore.ieee.org/document/9021725/
UR - http://www.scopus.com/inward/record.url?scp=85082131028&partnerID=8YFLogxK
U2 - 10.1109/ICM48031.2019.9021725
DO - 10.1109/ICM48031.2019.9021725
M3 - Conference contribution
SN - 9781728140582
SP - 351
EP - 354
BT - Proceedings of the International Conference on Microelectronics, ICM
PB - Institute of Electrical and Electronics Engineers Inc.
ER -