Advocates of autonomous mobility are looking forward to the day when zero-emission, shared autonomous vehicles deliver services that dramatically reduce urban congestion and pollution. But as the mass deployment of autonomous vehicles seems farther and farther off, it is important to point out that we don’t have to wait for autonomy to realize many of the efficiencies that shared mobility can provide.
Trip optimization technology that can make shared transport work, whether it’s a computer or a human driving. One proof point can be found in an effort to make Boston’s public school buses more efficient. What can the humble yellow school bus teach us about efficient urban transport? Plenty.
In 2019, Boston’s public school district held a contest to find a solution that could better move its 25,000 students to more than 200 schools across 20 zip codes. Parents are allowed to select their children’s schools, so the transportation patterns are irregular. Further complicating matters is that schools have different start times. Student transit was costing the district $2,000 per student, 10 percent of its budget.
Researchers at MIT’s Operations Research Center rose to the challenge. They developed an algorithm capable of ingesting all ride requirements simultaneously, instead of optimizing service for each school independently. When treating each school independently, each bus was assigned to a specific school, sometimes bypassing stops for students attending nearby schools. The bus fleet service the entire network of schools. By optimizing the entire network, some buses dropped students off at multiple schools, treating schools as stops rather than as destinations. In short, the algorithm enabled the district to eliminate 50 buses, more than a million miles of driving, and 20,000 pounds of CO2 emissions per day. Student ride times and walking distances were not impacted. And the district now has $5 million per year in savings to invest in education programs.
Sharing the Future
Many have argued, including me, that the future of urban transport is electric, autonomous, and shared. I still believe this to be true. But the real key to traffic and pollution reduction comes from sharing. We don’t have to wait for the widespread adoption of electric and/or autonomous vehicles to achieve significant efficiency gains that remove cars and their emissions from city streets.
Multiple studies have found that shared autonomous vehicles can dramatically improve traffic.
- An International Transport Forum (ITF) study of traffic in Lisbonfound that shared self-driving cars combined with the existing public transport system could reduce traffic by 90 percent with a maximum wait time of five minutes.
- A University of Texas simulation of traffic in Austinfound that one shared autonomous vehicle could do the work of nine private autos with extra ride times of two to five minutes.
- A study of traffic in Osloby the PTV Group found that shared autonomous vehicles could meet the city’s rush hour needs with just seven percent of today’s vehicles, and combining the services with public transport would reduce trip time by an average of 11 minutes.
Swap autonomous vehicles that operate on a computer with human drivers receiving instructions through a mobile app, and you can achieve the same results, albeit today with fossil fuel-powered cars. But reducing the number of vehicles on streets by as much as 90 percent would enable proportionate emissions reductions.
Critical to the success of these models is the integration of services with public transport. The flip side of the equation would be shared services used in place of public transport, which would likely worsen congestion. Shared transport integrated with public mass transit would have to be mandated by cities rather than deployed as a free-for-all, like when peer-to-peer ridehailing services were introduced. Unregulated and unintegrated, these services have dramatically worsened traffic in most large cities.
Accenture analysts recently released a report calling for cities to take the lead in creating coordinated, “orchestrated” mobility ecosystems. Limiting shared services to routes that connect people with mass transit would be one way to deploy human-driven services now and to prepare for driverless service in the future. Services and schedules can be linked at the backend, and operators can, for example, automatically send more shared vehicles to a train station when the train has more passengers than usual, or tell the shared vehicles to wait for a train that is running late.
Orchestrating large fleets and processing large volumes of ride requests simultaneously and in real time, and dispatching vehicles and pooling ride requests in the most efficient manner possible are all extremely complex. The complexity grows as most urban environments are continually changing. Traffic, weather, special events, construction, and other conditions impact trip times and wait times.
Managing urban congestion and mobility comes down to the matter of managing space. Cities are characterized by defined and restricted residential, commercial, and transportation spaces. Private autos are the most inefficient use of transportation space, and mass transit represents the most efficient use of transportation space. Getting more people out of private cars, and into shared feeder routes to and from mass transit modes is the most promising way to reduce auto traffic. Computer models show that it can be done, and we don’t need autonomous vehicles to realize the benefits of shared mobility. With our climate crisis growing more acute by the day, we can and should start implementing shared mobility now.
Source: Meeting of the Minds
This article is culled from daily press coverage from around the world. It is posted on the Urban Gateway by way of keeping all users informed about matters of interest. The opinion expressed in this article is that of the author and in no way reflects the opinion of UN-Habitat.