TY - GEN
T1 - Revealing New York taxi drivers' operation patterns focusing on the revenue aspect
AU - Dong, Yongqi
AU - Zhang, Zuo
AU - Fu, Rui
AU - Xie, Na
PY - 2016
Y1 - 2016
N2 - The records generated by taxicabs which are equipped with GPS devices is of vital importance for studying human mobility behavior, however we are focusing on taxi drivers' operation patterns in this paper. We identify a group of valuable characteristics, which are simple but effective, through large scale drivers' behavior in a complex metropolis environment. Based on the daily operations of 31,000 taxi drivers in New York City which covers over 14 million of trip records during Jan 1 to Jan 31 in 2013, we classify drivers into top, ordinary and low income groups according to monthly working load, daily income and daily ranking. Then, we apply big data analysis and visualization methods to compare the different characteristics among top, ordinary and low income drivers in selecting of working time, working area as well as driving routes. The results verify that top drivers do have special operation tactics to help themselves serve more passengers, travel faster thus make more money per unit time. This research provides new possibilities for fully utilizing the information obtained from urban taxicab data for estimating human behavior, which is not only very useful for individual taxicab driver but also to those policy-makers in city authorities.
AB - The records generated by taxicabs which are equipped with GPS devices is of vital importance for studying human mobility behavior, however we are focusing on taxi drivers' operation patterns in this paper. We identify a group of valuable characteristics, which are simple but effective, through large scale drivers' behavior in a complex metropolis environment. Based on the daily operations of 31,000 taxi drivers in New York City which covers over 14 million of trip records during Jan 1 to Jan 31 in 2013, we classify drivers into top, ordinary and low income groups according to monthly working load, daily income and daily ranking. Then, we apply big data analysis and visualization methods to compare the different characteristics among top, ordinary and low income drivers in selecting of working time, working area as well as driving routes. The results verify that top drivers do have special operation tactics to help themselves serve more passengers, travel faster thus make more money per unit time. This research provides new possibilities for fully utilizing the information obtained from urban taxicab data for estimating human behavior, which is not only very useful for individual taxicab driver but also to those policy-makers in city authorities.
UR - http://www.scopus.com/inward/record.url?scp=84991736880&partnerID=8YFLogxK
U2 - 10.1109/WCICA.2016.7578771
DO - 10.1109/WCICA.2016.7578771
M3 - Conference contribution
AN - SCOPUS:84991736880
T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
SP - 1052
EP - 1057
BT - Proceedings of the 2016 12th World Congress on Intelligent Control and Automation, WCICA 2016
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 12th World Congress on Intelligent Control and Automation, WCICA 2016
Y2 - 12 June 2016 through 15 June 2016
ER -