The coming of self-driving cars will redefine the urban landscape, in the biggest change since the creation of the Dwight Eisenhower interstate highway system.
Interstates took motorists and money from roads like Route 66 and shuttled them through high-speed lanes with occasional exits. Motorists services sprung up around these interchanges.
Much of the impact from the interstates affected rural and suburban areas.
Self-driving cars will have their most profound effects on urban cores, where density provides the greatest value. (In rural areas, individual ownership of vehicles is likely to continue at a greater rate than in cities.)
It’s important to note upfront that these changes will not happen overnight or all at once. These will take decades to take effect and timing will vary by city.
Developing the best outcomes will also require close cooperation between cities and the private sector.
Parking is the bane of an urban dweller’s existence. In dense urban cores like San Francisco and New York, reserved parking spaces can cost $350 a month or more. Parking meters have strict time limits, costs and fines for violations. “Free” street parking can require circling for 30 minutes or more.
Parking is needed because most cars sit idle for much of their existence; cars in use, by definition, aren’t parked.
With fleets of self-driving cars, users will be able to order cars on demand. Instead of owning a car that is idle more than 90% of the time, riders will be able to push a button and have cars come to them. As soon as a ride is complete, the car can be repurposed for the next rider.
This should lead to a decline in the demand for on-street parking, parking lots and parking garages. Demand for parking enforcement officers will also plummet.
This reduced demand will also affect city revenues. In San Francisco, parking is a $130 million business between parking fees and violations. Parking fines alone are projected to be $87 million in FY2017. (Of course, collection, maintenance and enforcement expenses will also drop.)
Space freed up from commercial and public parking lots can be reallocated to higher value activities like retail and residential, potentially increasing the city’s sales and income tax base.
This can also have positive environmental effects. Heat-absorbing asphalt can be replaced with green roofs.
It’s estimated that more than a third of traffic in urban cores can be attributed to motorists searching for parking. Much of that traffic can be eliminated by substitution of on-demand self-driving cars.
Self-driving cars will also reduce traffic congestion in two other ways: not blocking the box and fewer accidents.
During rush hours in San Francisco, it can take 30-45 minutes to travel 1 mile on a street like Bush St., which funnels traffic to the Bay Bridge. Some of that congestion can be attributed to vehicles that block the box and inducing gridlock. As self-driving cars take to the road, they can be programmed to avoid blocking intersections, smoothing the flow of traffic.
Accidents are a major source of traffic congestion as they reduce traffic flow capacity. The rubbernecking effect exacerbates these problems. Self-driving cars should dramatically reduce the number of accidents. When there are accidents, time spent on roadside investigations (which increase the rubbernecking effect) can be reduced based on access to data collected by vehicles.
With less space designated for parking, space can be more efficiently utilized. Homes no longer need to allocate 1/3 of their space to fitting a car.
Instead of lots scattered around the city, parking can be relegated to staging areas. For example, commuter buses in San Francisco currently park during the middle of the day outside of the core. They come back into the city during the afternoon rush to take suburbanites home.
Beyond simply autonomy, the sensors in self-driving vehicles can be used to collect and transmit data in real time that can help to improve infrastructure for all motorists.
Potholes can create traffic hazards and cause wear and tear on cars. Pothole repair often relies on motorists to report potholes. With self-driving cars, pothole locations can be detected and sent (along with pictures) to a city’s public works department. Instead of prioritizing repairs to potholes near the most vocal residents, they can be prioritized based on severity and degree of impact to the most motorists.
Signals are timed based on historical traffic patterns, not actual traffic conditions. Weather, special events and detours affect traffic patterns and can create suboptimal traffic flows based on signal timing. Data from sensors on connected cars can be used to optimize signals.
Sensors in connected cars can be used for other purposes that benefit society. For example, data from cameras can be used to identify suspects in Amber alerts or find dangerous suspects in emergency situations.
A massive change of this sort doesn’t come without a lot of challenges, some technological, others societal.
- Security — We’ve seen botnets take down Web sites used by tens of millions of people with little accountability. If sensors from connected vehicles are being sent into traffic management centers, the data has to be encrypted and validated.
- Privacy — All of the data transmitted by self-driving and other connected cars can be very personal. Steps need to be taken to anonymize and securely store the data.
- NIMBYism — Anything that involves reallocation of shared resources, such as parking spaces will inevitably face backlash from residents looking to protect their own interests, even if there is a long term public benefit.