Navigant Research Blog

Results In for San Francisco’s Parking Experiment

— October 1, 2014

Navigant Research’s Smart Parking Systems report examines technologies and policies that have the potential to reduce both congestion and greenhouse gas (GHG) emissions in cities.  San Francisco has been one of the cities at the forefront of parking innovation with its SFpark project.  The city’s assessment of the project, recently released, has significant lessons for cities considering similar solutions.

SFpark was an extensive smart parking trial run by the San Francisco Municipal Transportation Agency (SFMTA) and largely funded by the U.S. Department of Transportation, which provided 80% ($19.8 million) of the program’s total cost of $24.8 million.  The project encompassed approximately 6,000 metered on-street parking spaces (about one-quarter of the city’s total supply) and 12,250 parking spaces in 14 city operated garages (75% of the spaces managed by SFMTA).  Around 11,700 parking sensors were deployed, along with 300 repeaters and gateways.  The key strategic initiatives in SFpark included:

  • Real-time parking availability information to make it easier to find a parking space
  • Demand-responsive pricing to create parking availability
  • Longer time limits at parking meters to make parking more convenient
  • Meters that make it easy to pay by credit card and other forms of payment
  • Garage facility upgrades to make garages more convenient

How It Worked in Practice

According to the SFpark Pilot Project Evaluation, the amount of time that the target parking occupancy (60% to 80%) was achieved increased by 31% in pilot areas, compared to a 6% increase in control areas.  In so-called high payment (HP) compliance pilot areas (where people tend to pay the meter most of the time), achievement of the 60% to 80% target occupancy rate nearly doubled.

The amount of time that blocks were too full to find parking decreased 16% in pilot areas, while increasing 51% in control areas.  In HP zones, there was a 45% decrease.

During the trial, SFpark decreased rates on half of all blocks and increased rates on the other half, with average meter rates falling 4% from $2.69 an hour to $2.58 an hour during the pilot.  At garages, the average hourly rate fell from $3.45 to $3.03.

Meters First

SFpark maintained consistent parking availability while increasing utilization of SFpark garages.  Utilization of these facilities grew by 11%, far exceeding non-SFpark garages.

There was also an estimated reduction in GHG emissions of 30%, from 7 metric tons per day to 4.9 tons per day in the pilot areas.  Vehicle miles driven also decreased by 30% (compared to a 6% decrease in the control areas), and traffic volumes fell 8%.

Demand-responsive pricing and new technologies helped improve parking management and optimize the use of parking space, but simple tools also work.  The most basic improvement was seen from the simple deployment and enforcement of parking meters.  “One of the clearest findings of this evaluation is that parking meters are extremely effective at managing parking demand,” the study found.  This is not so surprising.  Parking meters – like electricity and water meters – are a basic tool for making visible the cost of a shared resource.  New technologies – whether parking sensors or smart meters – enable more sophisticated and dynamic forms of metering and billing, but the basic principle of payment for use has to be accepted first.

SFpark benefited not only from federal funding, but also from the authority of SFMTA over most aspects of the city’s transportation system.  This allows SFMTA to consider parking as part of its broader mobility targets and revenue projections.  Such an approach is likely to be a critical element of getting the best not only from new parking systems, but also from other innovations in urban mobility.

 

Autonomous Vehicles Will Work Best Within Limits

— October 1, 2014

About the only way your next car has much chance of driving itself is if you live in a gated community or on a college campus where it won’t have to deal with too many variables like other traffic.  Just as voice recognition systems work best with limited vocabularies, autonomous vehicles will probably be limited to such constrained environments for the foreseeable future.  That’s the conclusion from the recent ITS World Congress 2014 in Detroit.  Increasing levels of vehicle automation were a major topic of discussion during the annual conference on intelligent transportation systems.

Google has been pushing the idea that self-driving vehicles will hit the road within the next 5 years.  Google had no official presence at the conference, but a lot of companies that build cars, parts, and infrastructure systems were there, and no one that I spoke with was in agreement with Google’s timing projections.  The general consensus is that we won’t see widespread use of full operating range autonomous vehicles until closer to 2030.

Not Street-Ready

That’s not to say that no one believes in automated driving; quite the opposite.  It’s just that in engineering circles, there’s a rule of thumb known as the 90/10 rule.  That is, 90% of the technical challenge of a project takes about 10% of the time and effort.  The last 10% takes the other 90% of the time.  In the realm of self-driving cars, we have just begun that last 10% phase, where the basic hardware elements are all worked out but a lot of software decisions have yet to be made in order for autonomous systems to be truly robust.

Much of the on-road development by Google and other companies has been occurring in places like California and Nevada, where environmental factors like snow and even rain are a rarity.  In order for autonomous vehicles to be both commercially and legally viable, they’ll have to work reliably under any weather and road conditions.

General Motors (GM), Volkswagen, and other automakers have been working on autonomous technology much longer than Google, and they understand these limitations.  When GM rolled out a two-seat self-driving pod car known as the Electric Networked-Vehicle, or EN-V, at the 2010 Shanghai World Expo, program leader Dr. Chris Borroni-Bird acknowledged that, while this type of vehicle would eventually be an ideal way to deal with the congestion problems of megacities like New York, Shanghai, and Mumbai, the first feasible real-world applications were likely to be in restricted environments, such as campuses and gated communities.

Say Again

As powerful as computers have become, they still don’t deal with the nuances of the real world very well.  That’s why voice recognition systems still struggle to understand what should be simple natural language commands on a smartphone.  The most successful applications of the technology have been for tasks like medical transcription, with limited and specific word vocabularies and little ambient noise.  Similarly, automated vehicles function best in constrained spaces, such as buses over fixed routes or the aforementioned commuter pods.

Google hasn’t actually made any major breakthroughs in the technology that we know of.  It just jumped into field relatively recently, hiring many of the engineers and scientists that worked on the autonomous vehicles fielded by automakers in the DARPA Grand Challenge and Urban Challenge competitions of 2006 and 2007, and leveraging the cost declines of the required sensors.

Where Google has outdone the incumbents is getting the technology media to talk about their efforts – but that’s unlikely to put full-function self-driving cars into consumers’ hands any sooner.

 

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