# OpenStreetCab: Exploiting Taxi Mobility Patterns in New York City to Reduce Commuter Costs

**Authors:** Vsevolod Salnikov, Renaud Lambiotte, Anastasios Noulas, Cecilia Mascolo  
**arXiv:** [1503.03021](https://arxiv.org/abs/1503.03021)  
**Date:** 2015-03-10  
**Summary page:** https://www.summarizepaper.com/en/paper/1503.03021v1/

## Key points

- Study titled "OpenStreetCab: Exploiting Taxi Mobility Patterns in New York City to Reduce Commuter Costs" by Vsevolod Salnikov, Renaud Lambiotte, Anastasios Noulas, and Cecilia Mascolo
- Focus on Uber as a global alternative taxi operator disrupting traditional transportation industry
- Interest from policy makers, economists, citizens, and scientists in integrating sharing economy services into urban ecosystems
- Utilization of publicly available dataset on yellow taxi mobility in NYC for comparison with Uber
- Identification of instances where Uber X is more expensive than traditional yellow taxis for similar journeys
- Proposal of a new mobile application to compare taxi prices within the city to empower travelers and reduce commuter costs
- Highlighting how leveraging large datasets can enhance urban services by promoting transparency and optimizing commuter experiences

## Summary

In their study titled "OpenStreetCab: Exploiting Taxi Mobility Patterns in New York City to Reduce Commuter Costs," authors Vsevolod Salnikov, Renaud Lambiotte, Anastasios Noulas, and Cecilia Mascolo delve into the impact of Uber as a global alternative taxi operator. The researchers highlight the widespread interest surrounding Uber's disruptive presence in the traditional transportation industry, prompting discussions among policy makers, economists, citizens, and scientists on integrating sharing economy services into urban ecosystems. Utilizing a publicly available longitudinal dataset detailing yellow taxi mobility in New York City, the study offers a unique opportunity to compare the iconic yellow taxi with its modern competitor, Uber. By analyzing movement patterns and fare information from taxi customers, the researchers identify instances where Uber X – the cheapest version of Uber's service – proves to be more expensive than traditional yellow taxis for similar journeys. Furthermore, they showcase how Uber's economic model effectively capitalizes on well-established human movement patterns. Taking their analysis a step further, the authors propose a new mobile application designed to compare taxi prices within the city. This tool aims to empower travelers by facilitating informed decisions when choosing between different taxi services, ultimately leading to reduced commuter costs. The study serves as a testament to how leveraging large datasets can enhance urban services for consumers by promoting transparency in economic sectors that may lack up-to-date regulations. Overall, "OpenStreetCab" sheds new light on the evolving landscape of urban transportation and underscores the potential benefits of harnessing data-driven insights to optimize commuter experiences and drive efficiency within metropolitan areas.