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...

Full description

Bibliographic Details
Main Authors: Weiwei Jiang, Lin Zhang
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8303226/
id doaj-c3055368fe4b4ca1b54bb41ed991594e
record_format Article
spelling 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/
work_keys_str_mv AT weiweijiang theimpactofthetransportationnetworkcompaniesonthetaxiindustryevidencefrombeijingx2019sgpstaxitrajectorydata
AT linzhang theimpactofthetransportationnetworkcompaniesonthetaxiindustryevidencefrombeijingx2019sgpstaxitrajectorydata
AT weiweijiang impactofthetransportationnetworkcompaniesonthetaxiindustryevidencefrombeijingx2019sgpstaxitrajectorydata
AT linzhang impactofthetransportationnetworkcompaniesonthetaxiindustryevidencefrombeijingx2019sgpstaxitrajectorydata
_version_ 1724194289945149440