April 24, 2019

Using AI to help make London Roadworks Safer, Smarter and More Inclusive

Louise by Louise

SAM is proud to partner with TFL to make London’s roads smarter, safer and more inclusive during roadworks, as part of their inaugural Roadlabs programme hosted by Plexal.

TFL, in partnership with major utilities companies and London Councils, is looking for innovative solutions to address roadworks-related issues experienced by cyclists, motorists and pedestrians alike.

From our conception in the news industry, SAM has expanded based on the realisation that other industries have the same needs as journalists. SAM provides actionable intelligence in the form of real-time alerts for clients ranging from airlines, schools, first responders and others. Just as social media is a powerful force for breaking news it can be an extremely fast and informative data source to help power smart cities.

We were excited to take on TFL’s challenge to make roadworks more efficient and safer for the public and workers on-site using our proprietary AI engine. We consulted with senior stakeholders within TFL to learn their specific needs and pain points, as well as to understand and utilise the transport body’s extensive data and technical experience. In these collaborative sessions we defined several new AI event detection models bespoke to road network disruptions and safety concerns the TFL team wished to be alerted to, which we went on to develop within the Roadlabs programme.

SAM’s suggested solution to the challenge of making London roads, smarter, safer and more inclusive during roadworks was twofold. We sought to use social media data to firstly alert TFL to potential issues as they emerge, and secondly to provide situational awareness in the form of images and videos from the ground.

 

Safer

One of our initial learnings from this programme was that for TFL, the concept of safety covers multiple layers of awareness. From the more obvious threats posed by potential terror attacks, to the danger posed by an out-of-service traffic light, or the risk posed by potholes to cyclists, there is a myriad of events to take into consideration when attempting to address safety. The same solution prevails for any of the above incidences in that awareness is key. The faster TFL knows about an event, the faster it can begin to address it.

 

Smarter

In addition to alerting TFL to potential issues as they emerge, SAM’s social media-based data sources also provide a layer of enhanced situational awareness as more information comes to light on the matter. Pictures of potholes or road deterioration are regularly shared on social media by exasperated commuters allowing TFL to assess and prioritise responses. The same is true of faulty traffic lights, burst water mains or station overcrowding. SAM’s solution can provide the context required by TFL in order to formulate the appropriate responses to a reported incident.

 

Inclusive

Another crucial learning from this programme was that often it is London’s most vulnerable road users who are most affected by roadworks issues that emerge. For wheelchair users, or anyone with mobility impairments or visual or aural impediments, any change to their route can result in disrupted, delayed or abandoned journeys. SAM’s detection and alerting of difficulties posted on social media by one person may enable TFL to respond in time to prevent further people sharing the same negative experience.

 

Solution

SAM uses AI to harness one of the most powerful data streams in the world, social media, to detect crisis events from potholes to station overcrowding within minutes of posting and flag concerns to the appropriate teams within TFL. The SAM AI engine works in real-time corroborating events to ensure accuracy of alerts while classifying what type of issue is being reported and where these events are located. The end result is a system that brings a time advantage when time matters most while lessening the workload required for manually monitoring tools like TweetDeck. SAM will supplement TFL’s existing data sources enabling a faster response time with a quicker visual assessments of what is happening on the ground.

 

Conclusion

SAM is hugely excited to partner with TFL and Plexal on the London Roadlabs programme to help make a real difference to the safety and efficiency of roadworks in London. This programme has allowed us to test out the concept of building smart monitoring streams within SAM, designed to cater to specific client requirements on a very micro event level (we’re literally spotting potholes) in additional to our global event detection stream.

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