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Is Uber equally accessible everywhere?

Researchers study factors that affect the accessibility of ride-sharing programs such as Uber in Philadelphia.

Image Credit: Priscilla Du Preez on Unsplash

Ride-sourcing platforms such as Uber or Lyft, which operate using mobile apps to connect riders with drivers, have gained extensive popularity due to their convenience, lower cost, and faster services in comparison with traditional taxi systems — 50%  cheaper than regular taxies.

Uber serves in more than 858 cities in 83 countries, serving approximately 75 million riders with 3 million drivers. Tom Goodwin the senior vice president of strategy and innovation at Havas Media once said, “Uber, the world’s largest taxi company, owns no vehicles. Something interesting is happening.”

In ride-sourcing platforms, drivers can flexibly choose where and when to provide ride services as they are independent contractors. This felexibility provides options for them in terms of times and locations where drivers are comfortable to serve. However, this makes manging availability of the drivers very challenging as they are independent contractors. Since ride-sourcing platforms are in huge demand, studying factors that affect its accessibility is critical because it would be helpful for ride-sourcing providers and authorities to understand where the accessibility to these types of services is low and try to improve it.

In a recent study published in WIREs Data Mining and Knowledge Discovery, Drs. Shokoohyar (Saint Joseph’s University), Sobhani (Utrecht University), and Nargesi (University of Texas at Arlington) investigated the determinants of Uber accessibility in the city of Philadelphia. The result of this study shows that Uber accessibility is not balanced in different neighborhoods. Uber surge pricing is the main reason behind this unbalanced network. Uber surge price algorithm dynamically prices its services to equilibrate demand and supply, i.e., it increases prices in neighborhoods where demand is high and decreases it otherwise, which motivates drivers to move to where demand is. Therefore Uber is more accessible (that is, the wait times are less) where there is demand. There are several factors impacting demand in the ride-sourcing platforms such crime rate, population density and walkability of the neighborhood.

The surprising result that may contradict ones intuition is that Uber is more accessible  in areas with a higher crime rate. Uber drivers may want to avoid providing service in areas with a high crime rate and discriminate against these areas. However, areas with higher crime incidents motivate residents to take Uber more often for the safety issues, which in turn leads to a higher demand. High demand in these areas leads to a higher surge price, which in turn attract more drivers and leads to lower waiting time. The result also shows that Uber is more accessible in denser areas, and that it is less accessible in neighborhood with more amenities within a walkable distance — accessing amenities in these neighborhoods is simpler for residents, which leads to a lower demand for Uber.

This study may help transportation planners, ride-sourcing providers, cities’ authorities, and policymakers to understand where the issues exist in rideshare accessibility and attempt to resolve them, at least (for now) in the city of Philadelphia. Investigation on ride-sourcing programs such as Uber would enhance the quality of these services and thus provide a comfortable, affordable, and accessible transportation mode for people.

If these types of services improve, the quality and affordability of other modes of transportation such as buses, trains, taxis may improve in order to be able to compete. Lastly, if some sort of communities’ voice-hearing approaches are conducted, such as online surveys and focus groups, they may help scholars to understand where the issues of such services exist and attempt to bold those barriers to the policy makers and authorities for resolving them.

Written by: Sina Shokoohyar

Reference: S. Shokoohyar, etl al. ‘On the Determinants of Uber Accessibility and Its Spatial Distribution: Evidence from Uber in Philadelphia‘, WIREs Data Mining and Knowledge Discovery (2020). DOI: 10.1002/widm.1362

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