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Mining open datasets for transparency in taxi transport in metropolitan environments
Anastasios Noulas1,2, Vsevolod Salnikov3, Renaud Lambiotte3
11Computer Laboratory, University of Cambridge, 15 JJ Tomson Avenue, Cambridge, CB3 0FD UK.
EPJ Data Science
|May 2, 2020
Summary
Uber
Area of Science:
- Urban transportation economics
- Data science applications in public transit
Background:
- Ride-sharing services like Uber introduce dynamic pricing strategies.
- Understanding the economic impact of these new technologies on urban commuters is crucial.
Purpose of the Study:
- To compare Uber's pricing against traditional taxi services (Yellow Cab) in New York City.
- To develop a tool for commuters to identify cost-effective taxi options.
- To analyze the impact of surge pricing on overall taxi fares.
Main Methods:
- Comparative price analysis between Uber and Yellow Cab.
- Development of the OpenStreetCab smartphone application for real-time taxi cost comparison.
- Utilizing open datasets (Yellow Cab, Foursquare) for surge pricing prediction.
Main Results:
- Uber's surge pricing leads to higher average fares, particularly for short trips.
- The OpenStreetCab app demonstrated potential user savings of approximately $6 per journey.
- Accurate prediction of surge pricing using external data sources is feasible.
Conclusions:
- Dynamic surge pricing significantly impacts urban taxi costs.
- Open data and mobile applications can empower commuters to make informed, cost-saving decisions.
- Predictive modeling of surge pricing can enhance transparency in ride-sharing services.
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