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Transportation Troubleshooting: Solving a Decades-Old Problem: The Commute

Vikrant Vaze on November 29, 2021 - in Articles, Column

It’s no secret that commuter travel choices change traffic patterns and times. On-demand service providers such as Uber and Lyft have been very successful at adapting their service offerings and price points to travelers’ needs and preferences. In the process, they sometimes generate trips that would otherwise not have been made; at other times they incentivize travelers to switch from traditional trains and buses to on-demand services; and in some cases they increase road traffic.

But what if we flipped the script? What if scheduled transportation providers such as buses and trains were to adapt their service offerings to commuter needs, on-demand alternatives and traffic patterns to benefit the city as a whole? What if scheduled transportation service providers used commuter and traffic patterns to identify the types of commuting options that are best for each area based on rider preferences?

New research says it could save millions of dollars daily. The work, published in the INFORMS journal Transportation Science, finds that a redistribution of public-transit resources to better align available transportation options with what passengers want will result in strong societal benefits, including financial savings.

How Can It Work?

So how exactly can scheduled providers of buses and trains leverage the presence of on-demand ride-hailing instead of trying to fend off on-demand services? By scaling back scheduled travel at times and on routes better served by on-demand services, scheduled service can be expanded elsewhere. This can increase operator profits and consumer welfare by millions of dollars daily, making on-demand drivers happy with the extra fare revenues, and allowing scheduled providers to add frequency and increase fleet where needed.

Some people may ask, “what’s the catch?” But our work says it really can be a win-win-win, simultaneously making commuters and service providers happy while benefiting the system as a whole.

There has been speculation in popular media that rideshare services such as Uber and Lyft could make scheduled services such as trains and buses obsolete. But what we find is quite the opposite. We see synergy between these competing providers with complementary strengths and weaknesses. Travelers prefer different modes of transportation at different times for different trip purposes and destinations. Our work provides a solution so all these modes can successfully coexist and thrive.

Systemwide Cost Reduction

Using New York City as a case study, our model redistributes transit resources based on commuter preferences. From this approach, we found we could optimize transit schedules to consistently lead to a 0.4- to 3-percent systemwide cost reduction. This amounts to rush-hour savings of millions of dollars per day, while simultaneously reducing costs to passengers and transportation service providers.

When designing schedules, public-transit agencies should explicitly consider the impact of commuter-choice factors such as travel convenience, price, travel time and traffic congestion. The opposite also is true: commuters’ choices in turn change traffic patterns and travel times.

Realistically looking at areas where scheduled transit can cut down and let on-demand operators take on a larger proportion of trips benefits a diverse group of stakeholders. It allows for a more-thoughtful reconfiguration, which leads to schedules that are better for passengers, transportation operators and the city as a whole—a rare win-win-win.


Vikrant Vaze is a professor at Thayer School of Engineering at Dartmouth College, whose research focuses on data-driven optimization for design, planning and operations of large-scale, multistakeholder systems; email: Vikrant.S.Vaze@dartmouth.edu.

About Vikrant Vaze

Vikrant Vaze is a professor at Thayer School of Engineering at Dartmouth College, whose research focuses on data-driven optimization for design, planning and operations of large-scale, multistakeholder systems; email: Vikrant.S.Vaze@dartmouth.edu.

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