(Photo by Carlos Felipe Ramírez Mesa on Unsplash)
NEW YORK — New York City officials are doing everything possible to reduce traffic congestion in one of the busiest cities in the world — including trying to stick drivers with a daily toll just to go to work. Previously, they added a ride-hailing surcharge to taxis and ride-share trips, but a new study finds these fees failed to stop traffic jams. In fact, the measure mainly hurt lower-income communities that already had limited transit alternatives. So, will “congestion pricing” be more of the same?
New York recently reworked the MTA’s controversial congestion pricing plan with the hope of charging drivers $9 a day in 2025. The 2019 ride-sharing surcharge is separate from the upcoming congestion pricing plan, but its findings offer valuable insights into how pricing policies impact travel behavior and equity. The results analyzing the success rate for the ride-sharing surcharges were recently published in Transportation Research Part A.
“Indeed, the research reveals how pricing policies can disproportionately affect different communities and emphasizes that accessible transit alternatives play a crucial role in shaping how such policies impact travel behavior,” says lead study author Daniel Vignon, an assistant professor of Civil and Urban Engineering at NYU Tandon School of Engineering and member of C2SMARTER, a U.S. Department of Transportation Tier 1 University Transportation Center, in a media release.
Overall, the ride-hailing fee — between $2.50 and $2.75 — failed to clear up Manhattan traffic. Although Lyft had a 17% drop in trips, Uber saw a 9% decrease, and yellow taxis saw an 8% decrease because of the surcharge. One reason the charge made little of a difference in the overall congestion is that they are not the ones making up most of the traffic.
“We were not necessarily surprised by the findings,” explains Vignon. “The City claims that Uber, Lyft and taxis increase congestion, but we would say that they are not the major contributors,” noting that research from other cities has also found ride-hailing services don’t significantly contribute to traffic congestion. “In general, most cities experience a reduction in travel speed between 2% to 8% following the entry of Uber/Lyft.”
The ride-hailing surcharge affected areas differently depending on other factors. Areas with limited transit alternatives, such as no subways or Citi Bikes, saw only a slight 1.6% decrease in rides. In contrast, neighborhoods with both options experienced a 7.4% decrease, highlighting the importance of accessible alternatives in reducing ride-hailing demand.
Higher-income areas, which often have better transit options, showed varying impacts on ride-hailing usage, with Lyft experiencing notable reductions in demand compared to taxis and Uber. Meanwhile, lower-income areas saw a steep drop in using these transit options, even when no other alternatives were available.
“When policymakers plan for any type of congestion pricing, it’s critical they account for the alternative transportation options available at a granular level. A policy that works well in one neighborhood may impose a very high cost in areas where people live with far fewer resources and choices,” Vignon explains.
The researchers examined over 300,000 ride-hailing records from NYC’s Taxi and Limousine Commission and nearly one million traffic speed measurements captured from Uber Movement. Additionally, they looked at data taken from Citi Bike, subway locations, household income statistics, and weather patterns. They used a statistical method to compare ride-hailing activity in different city areas, analyzing patterns inside and outside the surcharge zone and ride-hailing activity before and after the policy was enacted.
“It seems that this policy resulted in a net welfare loss for the city, at least in the shorter term, when considering all factors, such as abandoned rides and the decrease in driver revenues,” Vignon concludes. “In the longer term, to determine whether the policy is a net positive, one would have to account for how the collected fees are spent.”
Paper Summary
Methodology
This study examined the effects of a surcharge policy for ride-hailing services in New York City, using a method called Difference-in-Differences (DiD). This approach compares trends in areas affected by the surcharge to similar areas that were not. Researchers used data from sources like the NYC Taxi and Limousine Commission and Uber Movement, which provide details about trips, traffic speeds, and ride-hailing activity.
They categorized trips based on their interaction with the “congestion zone,” which includes areas of Manhattan below 96th Street. By comparing changes in ride-hailing demand and traffic patterns before and after the policy in these zones, they estimated the policy’s direct effects. To ensure fairness, the analysis also considered weather, time of day, and availability of alternatives like subways or bike-sharing programs.
Key Results
The study found that NYC’s congestion fees led to an 11% drop in ride-hailing trips overall. Lyft trips decreased the most (17%), followed by Uber (9%) and yellow cabs (8%). Short trips under one mile were hit hardest, likely because passengers shifted to walking, biking, or taking the subway. Interestingly, while demand for ride-hailing dropped, traffic congestion did not improve significantly. Traffic speeds increased by less than 1%, suggesting other vehicles on the road may have filled the gap left by fewer ride-hailing cars.
Study Limitations
First, the study primarily used public data from ride-hailing companies and traffic databases, which might not capture the full picture. For instance, the study couldn’t directly measure how drivers adjusted their behavior or earnings due to the surcharge.
Additionally, while the study showed a reduction in ride-hailing trips, the lack of data on congestion outside the analyzed zones could mean the policy’s broader effects went unnoticed. Lastly, the study only focused on short-term impacts, so longer-term trends, like changes in public transit use or environmental effects, remain unknown.
Discussion & Takeaways
The surcharge policy succeeded in reducing ride-hailing trips but failed to noticeably alleviate traffic congestion. This might be because ride-hailing vehicles account for only a fraction of city traffic. The study also highlighted equity issues: the policy affected lower-income riders more, as they are often more price-sensitive and rely on ride-hailing for essential trips.
Policymakers should consider these findings when designing congestion strategies. Measures like improving public transit access in low-income areas or coupling surcharges with broader traffic management policies might lead to better outcomes. The increase in bike-share use following the surcharge is a positive side effect, hinting at the potential benefits of investing in sustainable transit options.
Funding & Disclosures
The study was conducted by researchers from New York University and funded through institutional resources. The authors declared no financial conflicts of interest that could influence the findings. Data sources included publicly available databases from the NYC Taxi and Limousine Commission, Uber Movement, and Citi Bike.
