January 8, 2017
Texas A&M’s 2015 Urban Mobility Scorecard reports: “The [US traffic congestion] problem is very large. In 2014, congestion caused urban Americans to travel an extra 6.9 billion hours and purchase an extra 3.1 billion gallons of fuel for a congestion cost of $160 billion. Trucks account for $28 billion (17 percent) of that cost, much more than their 7 percent of traffic.”
My home city of San Francisco and its surrounding communities constitute the California “Bay Area.” We have a major traffic problem. Bizjournals.com of January 3, 2017, reports that traffic congestion shot up 84% across the last decade in the Bay Area. 86,200 vehicle hours lost daily due to delays in 2005 rose to 158,300 daily hours lost in 2015. Much of this congestion comes from traffic flowing from suburbs to the San Francisco city business district and back in the evening. We are a city of small physical size, dynamic economy, surrounded on three sides by water, making us a delightful place to live, but a very hard place to create expanded transportation.
Traffic congestion is a major threat to our Bay Area economy. If we can’t fix our cost of housing and our traffic, we will inevitably lose business to other cities. We are the third most congested city now: “Washington, D.C. tops the list of gridlock-plagued cities, with 82 hours of delay per commuter, followed by Los Angeles (80 hours), San Francisco (78 hours), New York (74 hours), and San Jose (67 hours).”
In San Francisco, an estimated 45,000 Uber and Lyft cars have added significantly to traffic congestion and are a major source of concern to residents and transportation officials. So, Uber and Left are exacerbating the problem just now.
But maybe advancing technology is bringing solutions, particularly with “ride sharing” offerings: A new study released by MIT, with New York City as a prototype, uses an algorithm which results in findings that ride sharing services, such as UberPool and LyftLine could eventually reduce the number of vehicles on the streets of Manhattan by 75% without increasing travel time for riders.
“Led by Professor Daniela Rus of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), researchers developed an algorithm that found that 3,000 four-passenger cars could serve 98 percent of taxi demand in New York City, with an average wait-time of only 2.7 minutes. “Instead of transporting people one at a time, drivers could transport two to four people at once, results in fewer trips, in less time, to make the same amount of money,” says Rus. “A system like this could allow drivers to work shorter shifts, while also creating less traffic, cleaner air and shorter, less stressful commutes.”
“To our knowledge, this is the first time that scientists have been able to experimentally quantify the trade-off between fleet size, capacity, waiting time, travel delay, and operational costs for a range of vehicles, from taxis to vans and shuttles,” says Rus. “What’s more, the system is particularly suited to autonomous cars, since it can continuously reroute vehicles based on real-time requests.”
Will a similar algorithm apply to San Francisco, which is different from Manhattan? I imagine yes.
And, what about the implicit assumption that we will all be happy to share rides? That’s a big one. What if I want a private time to talk with a colleague or a romantic ride with only a special partner? What if I am not motivated by savings I can get by sharing, just want privacy and speed? This brings to mind “the fallacy of composition,” which arises when one infers that something is true of the whole from the fact that it is true of some part of the whole. In this context, it means that I can get to my destination faster if I go alone, but if everyone else who’s traveling at the same time makes the same choice, then none of us will get to the destination as fast as we would if we all took ride sharing.
I’m not so sure the majority of travelers will use this indisputable logic to decide. Maybe most will have a bias to assume others will take ride sharing and I’ll be among the few who benefit in speed by riding alone. We will need a major change in our attitude toward community–a growing realization that we are all in this together, together to protect the environment, together to reduce the cost and pollution and time and $ lost to traveling. Only if we can see a collective benefit which indeed transfers to a personal benefit, can we enjoy those savings and those benefits MIT illustrates with its algorithm.
But the vision is provocative. Already, I feel I will try harder to choose UberPool, with this in mind. It’s comforting to remember that I’m not only saving money, but contributing to lesser traffic congestion for others. I’m using pool about 60% of the time now, and will try to increase that percentage. Maybe this will override my sense of feeling cheap or burdening Uber drivers.
This is a utopian vision for commuters and for the environment. However, there is also bad news in this prospect of improvement. First, the 3,000 imagined pool drivers in New York can make as much as before on less time worked. But, what about the reminder of 14,000 taxi drivers–what about that 11,000 who lose jobs? And what of the estimated 35,000 Uber drivers working either full time or part time in New York? Furthermore, note the MIT comment above, “well suited to autonomous cars.” Just what if self driving vehicles do take over, as many predict, over the next 20 years? My post of March, 2016, estimates around 5 million US drivers could lose their jobs.
Should we, or indeed, can we, slow or halt technology to protect workers? I doubt we can, although some will certainly try. A better solution is in government taking bold steps to re-train, invest in innovation, and during all the while providing support to those displaced. Isn’t there an implicit obligation to society (through Government as our collective agent) and to the likes of Travis Kalanick, the CEO and founder of Uber, to contribute from this windfall to assist those caught up in the maelstrom of economic and technological advancement?