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India’s Tryst With Mobility Revolution & Urban Planning Using AI

India’s Tryst With Mobility Revolution & Urban Planning Using AI

Sameer Balaganur

In today’s world, people living in cities want to live in an environment where there is less pollution, more space, a drop in accidents, accurate information about traffic and public transportation. All this takes a certain degree of smart planning. That is where the term Mobility planning comes in. Mobility Plan covers the strategies to satisfy the mobility needs of a city. And when machine learning is integrated with mobility planning, it has the potential to transform an entire town. Now with the advent of taxi service apps, bike-sharing and mainly electric vehicles, urban planning has become more complex and the integration of machine learning more critical.

How India is using AI with Mobility

The concept of autonomous cars is growing old as the days go by even if the autonomous vehicles being fully functional is pretty far. Below are some applications of AI that India is using beyond autonomous cars for the mobility revolution:

  1. Intelligent Transport Systems: Intelligent traffic management system makes use of sensors, CCTV cameras, automatic plate recognition cameras, signalised pedestrian crossings, speed detection cameras contribute to the effective traffic management systems. And with the help of AI real-time dynamic decisions on traffic flows such as lane monitoring, access to exits, toll pricing, allocating right of way to public transport vehicles, enforcing traffic regulations through smart ticketing etc. can be made. Accident heat maps could be generated using accident data and driver behaviour at specific locations on the road network.
  2. Autonomous Trucking: Autonomous trucks can help transform the way we move the goods today. AI can help in road safety and give the driver rest from the long hours of driving. With AI, optimal road-space utilisation which will help improve road infrastructure capacity.
  3. Travel flow optimisation: With access to traffic data at the network level, AI can help make smart predictions for public transport journeys by optimising total journey time, including access time, waiting time and travel time. When one considers factors such as accessibility to the nearest mode of travel, the most convenient access path based on local conditions and one’s preferences paths, AI can predict these accurately.
  4. Railways and AI: according to official statistics, more than 500 trains were involved in accidents between the years 2012-2017, 53% of them were due to derailment. Govt. of India has decided to use AI to undertake remote condition monitoring. The Govt. is using non-intrusive sensors for monitoring signals, track circuits, axle counters.
  5. Community-Based Parking: AI will be needed to mediate the complex vehicle grid interaction (VGI) as well as charging optimisation. Parking guiding systems help drivers to find vacant parking spots while they are using the road and are near their destination. The guidance systems mainly help if the car is stuck in traffic, and the AI informs the driver well in advance about the empty parking spots.

Use Case

The San Francisco, California based StreetLight Data plans to harness the data being generated (like from sensors or mobile phones) continuously around us to improve urban planning. When it comes to StreetLight Data, almost any device that transmits information about the location is taken into StreetLight data’s service and of course, it anonymises the information it receives via partnerships.

StreetLight Data makes use of the information it has stored for random traffic insights for cars, buses, bikes and even pedestrians. What machine learning helps with here is to figure out the urban planning of the city. The company’s machine learning helps identify where the data is coming from. Once the information is acquired for a particular area, it helps to think about the investments in infrastructure along with the kind of infrastructure needed to improve circulation.

The data collected can help in urban planning when there are limited financial resources available. On being asked what can one do when there is a limited pool of funds, for example: to build cycling lanes, Laura Schewel, CEO and co-founder of StreetLight Data, said, “Well, it depends on what you want. If what you want is to facilitate bike safety for low-income people, then certain places are your top priority: the places where low-income people take long bike trips. And we can show you where that’s true. But if your priority is to get as many vehicles off the road as possible, then let’s find the places where most people are driving two miles per trip, because that’s silly.”

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 Another company named Airsageis integrating a comprehensive panel of more than 120 million devices from GPS and other location-based data sources into its core products and insights platform.


With companies like StreetLight Data moving towards completely integrating AI and machine learning in urban planning, its potential in India looks great. India is rapidly moving towards augmenting artificial intelligence in every aspect possible. Because of India’s urban planning still being weak in some locations, one can only wait and watch how the Mobility revolution changes India’s landscape.

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