How Smart Transportation will Transform Cities

Learner Experience, Learning, Learning Design

Transportation is likely to be one of the first domains in which the general public will be asked to trust the reliability and safety of an AI system for a critical task. Autonomous transportation will soon be commonplace and, as most people’s first experience with physically embodied AI systems, will strongly influence the public’s perception of AI. Once the physical hardware is made sufficiently safe and robust, its introduction to daily life may happen so suddenly as to surprise the public, which will require time to adjust. As cars will become better drivers than people, city-dwellers will own fewer cars, live further from work, and spend time differently, leading to an entirely new urban organization. Further, in the typical North American city in 2030, changes won’t be limited to cars and trucks, but are likely to include flying vehicles and personal robots, and will raise social, ethical and policy issues. A few key technologies have already catalyzed the widespread adoption of AI in transportation. Compared to 2000, the scale and diversity of data about personal and population-level transportation available today—enabled by the adoption of smartphones and decreased costs and improved accuracies for variety of sensors—is astounding. Without the availability of this data and connectivity, applications such as real-time sensing and prediction of traffic, route calculations, peer-to-peer ridesharing and self-driving cars would not be possible.

Artificial Intelligence and Life in 2030; inaugural report of Stanford’s AI100 initiative

If you need a lift in Pittsburgh or Singapore, you can catch a ride in a self-driving car. The cars have technicians riding along during the pilot phase to placate passengers and politicians and give the systems a chance to learn more about the winding roads and weather conditions. The trials came a few years faster than most experts predicted.

“Over the next five years, autonomous vehicles will move into the marketplace and usher in a new transportation era,” said Darrell West, Director, Center for Technology Innovation at Brookings. “Connected vehicles are likely to improve highway safety, alleviate traffic congestion, and reduce air pollution. However, to do that, designers must overcome obstacles such as poor infrastructure, bad weather, inadequate spectrum, hacking threats, and public acceptance.”

“People are used to thinking about vehicles from a transportation standpoint, but increasingly they have become large mobile devices with tremendous processing power,” added West.

“Automated cars will improve very rapidly with more data, so from a safety perspective I really don’t have much doubt that they will be much much better drivers,” said Silicon Valley software engineer Gerald Huff. “They won’t ever be perfect, we won’t eliminate to zero all fatalities and injuries, but the rate will be far far lower than with human drivers,” and “will definitely save lives and avoid terrible injuries with AI doing most of the driving.”

Huff also notes that once there’s a critical mass of automated driving on a freeway we should see fewer accidents, even more optimization of vehicular spacing on the roads and less traffic.

Smart vehicles offer big opportunity–and massive dislocation. Millions of car and truck drivers in the US stand to lose their jobs over the next decade. But it may happen faster than expected,  look at how quickly Uber eclipsed taxis in Brooklyn last year.

But it’s not all bad news, the World Economic Forum estimates that the digital transformation of the automotive industry will generate $67 billion in value for that sector and $3.1 trillion in societal benefits. That includes improvements from autonomous vehicles, connected travelers, and the transportation enterprise ecosystem as a whole.

The dislocation of truck and car drivers may be the exception in transportation. Compared to other sectors, the McKinsey heat map below highlights the wide variation in how automation could play out, both in individual sectors and for different types of activities within them. Other than data analysis, most aspects of transportation rate relatively low on machines eating jobs because of a relatively high level of unpredictability.

Public Transportation

IBM is betting their business on Cognitive Computing, what one manager called “AI on steroids.” Cognitive systems understand the context of communications, whether spoken, written, video, or images. They learn and improve over time.

The IBM transportation practice includes rail and freight logistics, airlines and airports, hotel and travel services. Current deployments result in incremental improvements in travel times, fewer delays, and better.

Keith Dierkx directs IBM’s rail practice and describes a near future when you’ll ride a self-aware autonomous railcar where the tracks, locomotives, even shipped goods all communicate with each other to improve service–an end-to-end ecosystem where smart machines learn and adapt to meet rising expectation of the traveling.

This is all good news if there is a train station near you. My community won’t get light rail for another 20 years. We could use a little collective intelligence when it comes to public infrastructure investment.

Travel & Tourism

GPS is one of the coolest developments of the last two decades–and an example of a free public infrastructure that improves life for everyone with a smart device.

Sick of carting the kids to practice? Parents in Los Angeles can ship them off with HopSkipDrive, an Uber for kids.

It will soon be easy and affordable to use Personal Tour Guides powered by IBM Watson that get to know your travel and tourism preferences over time.

Airline on time rates should keep getting better as AI improves maintenance and reduces delays. When things don’t work, KLM offers AI-boosted customer service in multiple languages.

Airlines want to know you better to own more of your travel. WayBlazer is a new travel insights platform that uses natural language and unstructured data processing capabilities to make it easier to plan travel.

Connie is a AI-driven hotel concierge assisting guests at selected Hilton hotels.

Machine Learning tools are beginning to reduce traffic (and can’t get to Seattle soon enough).

If the traffic in your community doesn’t improve, it may be possible in a few decades for you and a hundred friends to move to Mars on Elon’s big rocket.

Implications of Smart Transportation

Speaking of Musk, his concerns about the dangers of artificial intelligence have been well publicized. The SpaceX and Tesla founder wants to avoid a future in which we’re all crushed under the heel of a sentient and angry computer overlord.

A Musk-funded report from the Future of Life Institute issued a warning about weapons of war being developed to operate autonomously–drone that hover over targets, weapons that recognize and target images. Humans currently control the observe, orient, decide, and act cycle (OODA) but in other areas like high-frequency trading, decision making has moved to machines. Will machines soon be fighting our wars?

When and how will we turn over driving planes, trains, and automobiles to smart machines?

The AI100 study (quoted above) is the opening salvo of a 100 year conversation. It identified eight domains where AI is already having or is projected to have the greatest impact. In this series, #AskAboutAI, we’re working our way through these categories attempting to identify questions that teachers and parents should be discussing with young people–the questions that will impact their lives and livelihoods.

Education Developments

  • We should see cheaper more flexible pupil transportation with a shift to smaller autonomous buses. Expect a wave of bus levies in 10 years.
  • Even with a fleet of autonomous school buses, there will still be interest in utilization (i.e., number of cycles a vehicle makes each morning) so it’s not likely that you’ll see all secondary schools starting after 9am (except where districts contract out transportation).
  • Uber-for-kids and self-driving cars are likely to increase educational choice–at least for families that can foot the bill.
  • Cheap flexible transportation should make it easier for high school students to access career centers and work-based learning opportunities.
  • Some communities will invest in affordable housing clustered around public transportation hubs leading to small urban villages with a concentration of students requiring schools (a new school or microschool opportunity).
  • Fewer young drivers, less need for driver’s education (and fewer youth fatalities).
  • More educational travel aided by augmented reality and smart tour guides.

Ethical Questions

  • What protections should be afforded to people whose skills are rendered obsolete?
  • Who should reap the gains of efficiencies enabled by AI technologies?
  • Who is responsible when a self-driven car crashes?
  • AI algorithms could make less biased decisions than a typical person (e.g., taxis are less likely to pick up African Americans). Will we teach algorithms our biases?
  • Accurate predictive models of individuals’ movements, their preferences, and their goals are likely to emerge with the greater availability of data. Do you think it’s creepy when Siri knows where you’re going before you tell her? Do you want several tech companies to track all of your movements? How much control should you have over who tracks what?

Policy Recommendations

Progress will be lumpy, it will occur fastest where communities are discussing opportunities and making infrastructure investments. Progress will be threatened by public backlash from system mistakes–one driverless car wreck gets 100 times the coverage of the rampage of a drunk driver.

The Stanford AI100 report outlined several transportation relevant public policy recommendations:

  • Build understanding of AI;
  • Add more technical expertise in AI at all levels of government;
  • Support more investment in interdisciplinary studies of AI; and
  • Balancing innovation and regulating for safety and respect human rights.

AI-aided transportation has the potential to benefit most people on earth in the near future with better, faster, cheaper, more informed travel and transport. Extending the benefits to everyone while protecting individual privacy makes this a good time to #AskAboutAI.

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Photo credits: IBM





Tom Vander Ark

Tom Vander Ark

Tom Vander Ark is author of Smart Parents, Smart Cities and Getting Smart. He is co-founder of Getting Smart and Learn Capital and serves on the boards of 4.0 Schools, eduInnovation, Digital Learning Institute, Imagination Foundation, Charter Board Partners and Bloomboard. Follow Tom on Twitter, @tvanderark.