Amid criticism over traffic jams, the Bengaluru Traffic Police is aiming at data-driven traffic management to handle the Outer Ring Road (ORR), according to Joint Commissioner of Police (JCP), Traffic MN Anucheth.
Information ranging from data from aggregators to the number of cars parked in the lot as of 2 pm each day is used to examine whether the area will see traffic congestion beyond the baseline model.
“In that situation, we take decisions like stopping heavy goods vehicles from plying on that stretch or prohibiting separate exit of vehicles. Each tech park has its own individual exit plan…With data sources, in the next four to five months, we can also get involved in predictive mapping,” he said.
Edited excerpts from the interview:
You are using AI and drones to collect data on traffic congestion in Bengaluru. How does this work?
In Bengaluru, we are aiming for data-driven traffic management. We have large sources of data in the city and we are also collaborating with other sources. We get data from multiple sources such as our 8,000 Safe City cameras, 4,000 strong force on the ground, which gives us real-time updates on road conditions, including waterlogging, vehicle derailments, fallen trees or accidents. We also collect data from map-based sources, mobility aggregators, and drones. We get a bird’s eye view over the corridors. The idea is to ensure that we move towards congestion management with the resources available. We cannot change roads, this is a long-term plan, so this will help us get real-time solutions. This will also lead to a new signaling system and will increase speed in the city as well as reduce time at traffic junctions. We are also trying to get AI-based crowd alerts. The idea is to find problems before people tell us.
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You have a major choke point called the Outer Ring Road…
For the Outer Ring Road, we are collecting data from aggregators. From ORRCA we get information about booking taxis or trains in advance. We get accurate data of how many cars are parked in the lot park by 2pm every day. This information is crowdsourced. If we see that there is a baseline model beyond which traffic congestion is occurring, then we take decisions like banning heavy goods vehicles on that stretch or having separate exits for vehicles. Every tech park has its own exit plan, we recommend them to use it based on our AI-based information. Along with data sources, in the next four to five months, we can also get into predictive mapping.
Traffic plans are changed daily based on updates. What patterns are you seeing?
We are collecting data on vehicular flow and traffic congestion in Bengaluru. Some corridors such as the Outer Ring Road corridor between Silk Broad to Marathahalli, Tin Factory see IT congestion. We want to understand traffic flow in the morning and evening and what contributes to congestion. We get data from corridor level to the street, where problems are arising and work on solving the issues.
How many drones have been deployed? What happens to the data received from them?
We have 10 drones, and their flight time is 1.5 to 2 hours, twice a day – morning and evening. They fly for about four hours a day and we have identified about 20 important junctions in the city. We get a bird’s eye view, and the views give us a lot of information from important junctions like Hebbal, Sarakki, Marathahalli, Iblur, Silk Board etc. During busy times, we are able to detect the problem and take immediate measures.
Are you saying that in case of unprecedented traffic jam for hours, drones can fly, locate the problem area and send live view to solve the problem?
That’s the end game. Probably, in the next six months, we will have a station where the drone flies automatically at a particular time, does the analysis and the AI on the back end will give us information about what has happened, so that we can physically intervene. . ,
Are you able to detect and resolve problems using these images? Any example?
Technology is a force multiplier and enhancer. It is no replacement for human work, but it really helps us in quick detection and taking action. For example, the Hebbal flyover was unusually jammed at 2 am on January 15. We found that it was an accident which was not reported. We were able to immediately send an ambulance and take the person to the hospital with the help of AI. It is not just about overcrowding, but also about emergency services. On 17 January, there was congestion on NICE Road at 5.15 am, which was again unusual. We found that it happened because a herd of elephants was crossing the road. So drones with visuals help us find the problem and cause for better intervention.
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