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Data Science in Transportation by Mike Barlow

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Data Science in Transportation

Emerging Ecosystems and New Technology Stacks

With astonishing speed, we have entered the Age of Smart Transportation. It’s safe to say that within the next 10 years, the idea of manually operating a car, truck, ship, plane, or train will seem old-fashioned to most people.

Although there’s no question that some of us will prefer to continue hand-driving our automobiles, the overarching trend will be toward greater automation at every level of transportation, including shipping and logistics.

From a software-development perspective, there won’t be much difference between a moving vehicle and a smart phone. Your car will be a platform for apps providing a range of services, from entertainment and real-time navigation to collision avoidance and predictive maintenance.

For technology providers and developers, the Age of Smart Transportation will be the gift that keeps giving. Gartner predicts a quarter-billion connected vehicles on the road by 2020, roughly one in five vehicles worldwide. McKinsey estimates that connected cars will pump an additional $1.5 trillion into the economy by 2030, even as the number of vehicle unit sales diminishes due to falling demand.

Connected cars won’t merely collect data from external sources—they will also generate and transmit data to a variety of destinations, including remote databases, transportation infrastructure (e.g., roads, bridges, traffic signals, and highway lighting), and other vehicles. As Figure 1-1

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