The Impact of the Transportation Network Companies on the Taxi Industry: Evidence from Beijing’s GPS Taxi Trajectory Data
To gain insight into how transportation network companies, such as Uber and Didi, impact the taxi industry, we conduct a multi-period analysis of taxi drivers' behaviors, based on GPS trajectory data collected from three time periods in Beijing, i.e., November 2012, November 2014, and November...
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doaj-c3055368fe4b4ca1b54bb41ed991594e2021-03-29T20:42:16ZengIEEEIEEE Access2169-35362018-01-016124381245010.1109/ACCESS.2018.28101408303226The Impact of the Transportation Network Companies on the Taxi Industry: Evidence from Beijing’s GPS Taxi Trajectory DataWeiwei Jiang0https://orcid.org/0000-0003-2224-6178Lin Zhang1Department of Electronic Engineering, Tsinghua University, Beijing, ChinaShenzhen Engineering Laboratory for Data Science and Information Technology, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, ChinaTo gain insight into how transportation network companies, such as Uber and Didi, impact the taxi industry, we conduct a multi-period analysis of taxi drivers' behaviors, based on GPS trajectory data collected from three time periods in Beijing, i.e., November 2012, November 2014, and November 2015. We extract both passenger-delivery and passenger-searching trip information from GPS trajectories and compare the spatial, temporal, densification, and poolability properties of taxi trips in different time periods. Our results reveal that the taxi industry was adversely influenced by the competition between transportation network companies; as compared with that of 2012, the average passenger-delivery trip number per day per taxi dropped by 18.08% and the average daily profit per taxi dropped by 19.29% in the year 2015, respectively. We also compare passenger-searching strategies, passenger-delivery strategies, and service area preferences between taxi drivers with top and bottom efficiency in different time periods. We find that compared with drivers with lower efficiency, drivers with high efficiency tend to search locally, have a higher delivery speed, and serve more often within the inner part of Beijing.https://ieeexplore.ieee.org/document/8303226/Transportationhuman factorsperformance analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Weiwei Jiang Lin Zhang |
spellingShingle |
Weiwei Jiang Lin Zhang The Impact of the Transportation Network Companies on the Taxi Industry: Evidence from Beijing’s GPS Taxi Trajectory Data IEEE Access Transportation human factors performance analysis |
author_facet |
Weiwei Jiang Lin Zhang |
author_sort |
Weiwei Jiang |
title |
The Impact of the Transportation Network Companies on the Taxi Industry: Evidence from Beijing’s GPS Taxi Trajectory Data |
title_short |
The Impact of the Transportation Network Companies on the Taxi Industry: Evidence from Beijing’s GPS Taxi Trajectory Data |
title_full |
The Impact of the Transportation Network Companies on the Taxi Industry: Evidence from Beijing’s GPS Taxi Trajectory Data |
title_fullStr |
The Impact of the Transportation Network Companies on the Taxi Industry: Evidence from Beijing’s GPS Taxi Trajectory Data |
title_full_unstemmed |
The Impact of the Transportation Network Companies on the Taxi Industry: Evidence from Beijing’s GPS Taxi Trajectory Data |
title_sort |
impact of the transportation network companies on the taxi industry: evidence from beijing’s gps taxi trajectory data |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2018-01-01 |
description |
To gain insight into how transportation network companies, such as Uber and Didi, impact the taxi industry, we conduct a multi-period analysis of taxi drivers' behaviors, based on GPS trajectory data collected from three time periods in Beijing, i.e., November 2012, November 2014, and November 2015. We extract both passenger-delivery and passenger-searching trip information from GPS trajectories and compare the spatial, temporal, densification, and poolability properties of taxi trips in different time periods. Our results reveal that the taxi industry was adversely influenced by the competition between transportation network companies; as compared with that of 2012, the average passenger-delivery trip number per day per taxi dropped by 18.08% and the average daily profit per taxi dropped by 19.29% in the year 2015, respectively. We also compare passenger-searching strategies, passenger-delivery strategies, and service area preferences between taxi drivers with top and bottom efficiency in different time periods. We find that compared with drivers with lower efficiency, drivers with high efficiency tend to search locally, have a higher delivery speed, and serve more often within the inner part of Beijing. |
topic |
Transportation human factors performance analysis |
url |
https://ieeexplore.ieee.org/document/8303226/ |
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