Digitizing Traffic Management for Smart Cities

From electric scooters and bikes to ride-hailing and car-sharing, the way humans move around cities is markedly different than it was 100 years ago. Over the next decade, the proliferation of connected, autonomous, shared and electric (CASE) vehicles will usher in a new era of mobility and further change the way we move people and goods. However, transportation infrastructure and management systems have been slow to adapt, creating a bottleneck in mobility innovation.

Every city has a traffic policy regarding how vehicles and actors (pedestrians, cyclists, etc.) are organized and prioritized, and use traffic lights to implement the policy. Current traffic management systems operate traffic lights on fixed time frames and in-road sensors to extend or shorten green lights when needed. This system was created in an era dominated by passenger vehicles and designed to move as many vehicles, not necessarily people, as possible.

By 2030, 1 billion more people are expected to live in cities. Today, 55% of the world’s population lives in urban areas and this percentage is expected to rise to 68% by 2050. Additionally, freight volumes are expected to grow by 40% during this time period. Uncoordinated deployment of new mobility services has overwhelmed aging infrastructure which was designed only for private vehicles and public transit. Although CASE mobility promises to provide a transportation system which is safe, efficient and emissions-free, the path to get there is currently unclear and will require simultaneous innovation in infrastructure and management.

Next gen mobility will require next gen infrastructure and management

Digitization and advancements in enabling technologies, including artificial intelligence, IoT sensors, computer vision and computing technology has allowed for the development of new traffic management tools for cities. Traditional in-road sensors at traffic lights collect limited data on traffic (mainly, the presence of a vehicle) and can only be used to shorten or extend green lights. This system operates on fixed time frames and can’t adapt to dynamic traffic demand. IoT sensors and machine learning technology in cameras deployed at intersections collect granular data on vehicle, pedestrian and cyclist movement and traffic volume at intersections. This data can then be used for multiple applications:

• Remote monitoring and real-time traffic flow data.
• Building complex models of a city’s transportation system to help city planner’s make informed, data-driven decisions for infrastructure development and investment.
• Leverage AI and cloud computing to adjust traffic light patterns dynamically and in real time.
• Provide the building blocks for connected vehicles and intelligent traffic systems (ITS).In recent years, early stage startups leveraging these technology advances designed to create value for cities have received significant investment. Since 2017, urban mobility and mobility service startups have received over $44 billion in venture funding across 380 deals, while seed investments have increased from $26 million in 2017 to $101 million in 2019, nearly half of which occurred in October 2019 alone. In addition, automotive OEMs are developing mobility services and solutions for cities to establish their place in the new service-based value chain of the next generation of mobility, where mobility services may overtake vehicle ownership.

Click here to read the full article by By Cassidy Shell, originally published in CleanTech.com