Developing and Deploying a Taxi Price Comparison Mobile App in the Wild: Insights and Challenges

As modern transportation systems become more complex, there is need for mobile applications that allow travelers to navigate efficiently in cities. In taxi transport the recent proliferation of Uber has introduced new norms including a flexible pricing scheme where journey costs can change rapidly depending on passenger demand and driver supply. To make informed choices on the most appropriate provider for their journeys, travelers need access to knowledge about provider pricing in real time. To this end, we developed OpenStreetCab a mobile application that offers advice on taxi transport comparing provider prices. We describe its development and deployment in two cities, London and New York, and analyse thousands of user journey queries to compare the price patterns of Uber against major local taxi providers. We have observed large heterogeneity across the taxi transport markets in the two cities. This motivated us to perform a price validation and measurement experiment on the ground comparing Uber and Black Cabs in London. The experimental results reveal interesting insights: not only they confirm feedback on pricing and service quality received by professional driver users, but also they reveal the tradeoffs between prices and journey times between taxi providers. With respect to journey times in particular, we show how experienced taxi drivers, in the majority of the cases, are able to navigate faster to a destination compared to drivers who rely on modern navigation systems. We provide evidence that this advantage becomes stronger in the centre of a city where urban density is high.

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

[2]  Xing Xie,et al.  Urban computing with taxicabs , 2011, UbiComp '11.

[3]  Yu Zheng,et al.  Computing with Spatial Trajectories , 2011, Computing with Spatial Trajectories.

[4]  Mason A. Porter,et al.  Lost in transportation: Information measures and cognitive limits in multilayer navigation , 2016, Science Advances.

[5]  Licia Capra,et al.  Mining mobility data to minimise travellers' spending on public transport , 2011, KDD.

[6]  Richard S. J. Frackowiak,et al.  Navigation-related structural change in the hippocampi of taxi drivers. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

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

[8]  Isaac Skog,et al.  In-Car Positioning and Navigation Technologies—A Survey , 2009, IEEE Transactions on Intelligent Transportation Systems.

[9]  Bruno Agard,et al.  MINING PUBLIC TRANSPORT USER BEHAVIOUR FROM SMART CARD DATA , 2006 .

[10]  Rafael E. Banchs,et al.  Article in Press Pervasive and Mobile Computing ( ) – Pervasive and Mobile Computing Urban Cycles and Mobility Patterns: Exploring and Predicting Trends in a Bicycle-based Public Transport System , 2022 .

[11]  Michael Batty,et al.  Mining bicycle sharing data for generating insights into sustainable transport systems , 2014 .

[12]  Winston J. Craig MIRAGE in the MARKETPLACE , 2005 .

[13]  Paolo Santi,et al.  Supporting Information for Quantifying the Benefits of Vehicle Pooling with Shareability Networks Data Set and Pre-processing , 2022 .

[14]  Antonio Lima,et al.  Understanding individual routing behaviour , 2016, Journal of The Royal Society Interface.

[15]  Xing Xie,et al.  T-drive: driving directions based on taxi trajectories , 2010, GIS '10.

[16]  Gerd Kortuem,et al.  Mining temporal patterns of transport behaviour for predicting future transport usage , 2013, UbiComp.