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New data has emerged to explain why transportation network company (TNC) services like Uber and Lyft are adding to congestion in cities. Traffic analyst Bruce Schaller in his report, “The New Automobility: Lyft, Uber and the Future of American Cities,” found that “TNCs have added 5.7 billion miles of driving annually in the Boston, Chicago, Los Angeles, Miami, New York, Philadelphia, San Francisco, Seattle and Washington DC metro areas.”


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It is no wonder that cities are struggling with worsening traffic and pollution. New York City, for example, has stopped issuing ridehailing licenses after data showed that the services had added thousands of vehicles to city streets and slowed traffic speeds by 20 percent, and negatively impacted public transit utilization.


The New York City Economic Development Corporation told the New York Post, “While there are many factors that can be attributed to the decline in mass-transit ridership, our research indicates that app-based ride-hailing services [like Uber and Lyft] are transforming how New Yorkers are commuting to their destinations.”


The San Francisco County Transportation Authority found 170,000 ridehailing trips per day are responsible for more than half of the county’s growing traffic woes.



Inefficient Service

Schaller’s research found that peer-to-peer ridehailing vehicles add a whopping 2.8 new vehicle miles for each mile of personal driving they eliminate, and even their pooled services adds 2.6 new miles for every mile of personal driving reduced. Because most users are hailing rides instead of taking public transit, walking, or staying home, there is a significant net gain in vehicle miles on city streets. Miles are also added when drivers are searching for the next rider after dropping someone off.


It doesn’t have to be this way — especially for shared services. In a new white paper, On-Demand Mobility Service Success by Design, Bestmile explored three service scenarios in the city of Chicago, including a micro-transit service downtown, an airport shuttle service, and a citywide ridesharing service. The study used taxi data from the city as a representative demand sample to build a demand model and simulated how the shared services would perform against their unshared counterparts.



Move More People with Fewer Vehicles

The simulation used Bestmile’s Mobility Services Platform to optimize dispatching, routing, and ride-matching — the same platform that is used by service providers to manage mobility services on three continents. The platform uses advanced algorithms that get the right vehicle to the right traveler at the right time.


The study showed that a fleet of 60 shared micro-transit vehicles would have an average occupancy of 1.8 passengers. The shared rides would add just 40 seconds of waiting time and 1.9 minutes of ride time compared to a 60-vehicle taxi service. Over the course of a day, the shared fleet would travel 38 percent fewer kilometers (8700) than the taxi fleet (14,000).


The results were most dramatic for the airport shuttle service, which involves longer rides of 40 minutes on average. A 90-vehicle fleet of shared shuttles would have an average occupancy of 4.6 passengers with an added waiting time of just 2.1 minutes and an excess ride time of 11.3 minutes, and the shared vehicles traveled only about a third (17,500) of the total kilometers traveled by the taxi service (51,100).


Citywide ridesharing had similar results, with a fleet of 100 shared vehicles averaging an occupancy of more than 2 passengers, depending on the configuration, with a 30 second shorter waiting time and 3.5 to 6 minutes of excess ride times compared to an unshared taxi service, while traveling 30 percent fewer kilometers.


When the simulation was scaled up to sample the entire city’s 31,000 daily rides by 2,700 taxis, the results showed that just 200 vehicles could accommodate 90 percent of the demand. The ability to cut the number of vehicles required by a factor of 10 shows the true advantages of an optimized and coordinated service design.



Realizing the Promise

Today’s TNC services are not optimized for fleet efficiency and performance. That’s in part due to the TNCs’ peer-to-peer business models — they don’t own vehicles and don’t pay drivers unless they are carrying a passenger. Inefficiency isn’t expensive in this case.

Businesses that own the fleets and pay professional drivers, on the other hand, will be much more motivated to maximize vehicle utilization and efficiency.


Can shared, on-demand mobility services deliver on the promise of these services to reduce traffic and move more people with fewer vehicles? The numbers say “yes” — one shared vehicle can do the work or more than 10 taxis, but the services need to be optimized for efficiency.




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