Understanding the Effect of an E-Hailing App Subsidy War on Taxicab Operation Zones

Understanding taxicab operation behaviors under various management or market policies (i.e., subsidies) is critical to making informed operating decisions for e-hailing companies and for government surveillance. This paper investigates the change of taxicab operation zones in context of an e-hailing app subsidy war in China, which is an important perspective that reflects changes in taxicab behavior, such as how the operation zones of taxicabs under the e-hailing app subsidy war change and how this change affects their trip distance and cruising time. To investigate this issue, this paper utilizes three indexes to elucidate the change of taxicab operation zones, namely, the repetition ratio of operation zone pairs, the area, and the degree of dispersion in the spatial distribution. A case study using taxicab trajectories during all of the important periods of the e-hailing app subsidy war in Shenzhen, China, was conducted and produced several valuable findings; for example, with respect to taxicabs as a whole, the proportion of habitual operation zone pairs among operation zone pairs in neighboring periods is relatively stable under any subsidy policy, and changes in the operation zones have little effect on changes in the average daily trip distance and average daily cruising time. Four groups of taxicabs divided according to initial change patterns in the operation zones present different change patterns during the subsidy war. By comparing these changes before and after the subsidy war, this paper finds that the subsidy war influences the taxicabs in groups I and II, while it has little influence on the taxicabs in groups III and IV, although all groups were affected during the subsidy war. For the taxicab groups in the period with the highest subsidy, the average daily trip distance and average daily cruising time decreased, whereas, in other periods, they presented different patterns.

[1]  Liang Liu,et al.  Uncovering cabdrivers' behavior patterns from their digital traces , 2010, Comput. Environ. Urban Syst..

[2]  Constantinos Antoniou,et al.  Quantifying Demand Dynamics for Supporting Optimal Taxi Services Strategies , 2017 .

[3]  Antonio Lima,et al.  Personalized routing for multitudes in smart cities , 2015, EPJ Data Science.

[4]  Gang Chen,et al.  Mining Frequent Trajectory Patterns from GPS Tracks , 2010, 2010 International Conference on Computational Intelligence and Software Engineering.

[5]  S. Hackman,et al.  A computational analysis of R&D support programs , 2015 .

[6]  Ling Yin,et al.  Spatiotemporal model for assessing the stability of urban human convergence and divergence patterns , 2017, Int. J. Geogr. Inf. Sci..

[7]  S. Ukkusuri,et al.  Spatial variation of the urban taxi ridership using GPS data , 2015 .

[8]  Chaogui Kang,et al.  Understanding operation behaviors of taxicabs in cities by matrix factorization , 2016, Comput. Environ. Urban Syst..

[9]  Zuo Zhang,et al.  Revealing New York taxi drivers' operation patterns focusing on the revenue aspect , 2016, 2016 12th World Congress on Intelligent Control and Automation (WCICA).

[10]  B. Ripley Modelling Spatial Patterns , 1977 .

[11]  George R. Parker,et al.  Relationships between landcover proportion and indices of landscape spatial pattern , 1992, Landscape Ecology.

[12]  Cecilia Mascolo,et al.  Mining open datasets for transparency in taxi transport in metropolitan environments , 2015, EPJ Data Science.

[13]  Yu Liu,et al.  Pervasive location acquisition technologies: Opportunities and challenges for geospatial studies , 2012, Comput. Environ. Urban Syst..

[14]  Z. Fang,et al.  Understanding the Dynamics of the Pick-Up and Drop-Off Locations of Taxicabs in the Context of a Subsidy War among E-Hailing Apps , 2018 .

[15]  P. J. Clark,et al.  Distance to Nearest Neighbor as a Measure of Spatial Relationships in Populations , 1954 .

[16]  Christo Wilson,et al.  Peeking Beneath the Hood of Uber , 2015, Internet Measurement Conference.

[17]  Hai Yang,et al.  Pricing strategies for a taxi-hailing platform , 2016 .

[18]  Cecilia Mascolo,et al.  OpenStreetCab: Exploiting Taxi Mobility Patterns in New York City to Reduce Commuter Costs , 2015, ArXiv.

[19]  Xiaowei Hu,et al.  Exploring Urban Taxi Drivers’ Activity Distribution Based on GPS Data , 2014 .

[20]  Cecilia Mascolo,et al.  Developing and Deploying a Taxi Price Comparison Mobile App in the Wild: Insights and Challenges , 2017, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA).

[21]  M. Kandlikar,et al.  Taxi apps, regulation, and the market for taxi journeys , 2016 .

[22]  Gyung-Leen Park,et al.  Analysis of the Passenger Pick-Up Pattern for Taxi Location Recommendation , 2008, 2008 Fourth International Conference on Networked Computing and Advanced Information Management.

[23]  K. Pearson NOTES ON THE HISTORY OF CORRELATION , 1920 .

[24]  Fahui Wang,et al.  Urban land uses and traffic 'source-sink areas': Evidence from GPS-enabled taxi data in Shanghai , 2012 .

[25]  Yikai Ma,et al.  Evaluating the Influence of Taxi Subsidy Programs on Mitigating Difficulty Getting a Taxi in Basis of Taxi Empty-loaded Rate , 2017 .

[26]  Yuan Tian,et al.  Understanding intra-urban trip patterns from taxi trajectory data , 2012, Journal of Geographical Systems.

[27]  Yafeng Yin,et al.  Surge pricing and labor supply in the ride-sourcing market , 2018, Transportation Research Part B: Methodological.

[28]  Zuo-Jun Max Shen,et al.  Modeling taxi services with smartphone-based e-hailing applications , 2015 .

[29]  Knowledge spillovers and R&D subsidies to new, emerging technologies , 2015 .

[30]  Qingquan Li,et al.  Spatiotemporal analysis of critical transportation links based on time geographic concepts: a case study of critical bridges in Wuhan, China , 2012 .

[31]  Qingquan Li,et al.  Visualizing hot spot analysis result based on mashup , 2009, LBSN '09.

[32]  S. Ukkusuri,et al.  Taxi market equilibrium with third-party hailing service , 2017 .

[33]  M. Keith Chen,et al.  Dynamic Pricing in a Labor Market: Surge Pricing and Flexible Work on the Uber Platform , 2016, EC.

[34]  Li Li,et al.  Analysis of Taxi Drivers' Behaviors Within a Battle Between Two Taxi Apps , 2016, IEEE Transactions on Intelligent Transportation Systems.