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Transit Commuters Will Soon Thank IoT for a Safer Commute

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Transit agency turns to Adastra for expertise in crowd managing solution.  

Our Client’s Objectives: 

Our client is a Crown agency, managing rail and vehicle for a large metropolitan city. The company’s vision includes transit coverage expansion and higher frequency. Since its establishment, it has extended its network coverage by 70km and increased the number of train trips by 500%. 

Caring about the safety of its travellers, our client had a vision to provide a less stressful commute for public transit users across the city and was in search of a crowd management solution to make this happen. They were aware of the traffic flow coming in and out of the station and wanted to determine the volume of people standing on train service platforms by allocating enough security to manage the crowd of commuters on these platforms.  

  

Collaborating in Search for a Solution: 

Adastra had a well-established relationship with this transit agency and it was no surprise when they approached us with a request to provide our analytical expertise. Our mandate was to derive a solution, from the previously collected data. Initially, our client had implemented the use of cameras to observe the traffic flow on platforms, conducting a real-time video analysis, profiling and identifying commuters. Our experts’ keen observation immediately assessed and confirmed that the train platforms were large and poorly lit.  For this reason, images displayed various dark spots throughout the frames. In an ideal scenario, cameras would have had to be mounted at a height of 30ft to capture better accuracy, detecting children and people of shorter height. There was no room for wasted resources and no room for wasted time. The plan was quickly dissolved.  

A second option was to utilize thermal cameras for body heat signatures, however, weather conditions were often unforgiving and proved less than optimal, varying from minus 30 to plus 30. Upon testing this option, we quickly noticed the thermal imaging yielded inconsistent results. Commuters wore layers in winter, which proved difficult in precisely identifying the number of people. We were required to continuously change thermal profiles. In the end, the solution was a no go. 

We were down to our final option. This required our client to use a router and detect the number of people connected to Wi-Fi. The consensus was:  

  1. Our transit riders had smartphones
  2. Their smartphones were on and connected to Wi-Fi
  3. Their option to locate a new Wi-Fi signal was turned on and based on that signal strength we could determine its direction and length.

It seemed like a workable solution, until Adastra realized after detecting commuters, the chances of determining their precise location, whether they were on the same level and platform, were unlikely. We were unable to pinpoint their exact coordinates due to interrupted retail space. For instance, if there was a merchant in the same area, the cluster of people may have been in the vicinity of the merchant, not necessarily on the platform.  

  

Adastra’s Expert Advise: 

The third solution demonstrated potential and Adastra experts decided to work on a strategy to draw a map of the multi-levelled station, determining which direction the signals travelled.  Over two weeks, we collected enough data to implement the solution and tested it for over 48 hours. A roadblock hit when we discovered there was a delay in time and accuracy in the information gathered. We needed to reconstruct the trip of an individual from where and when they left to the arrival of their destination. This would help understand the flow influx and outflux of commuters at any time. Our experts recommended using an app to track the GPS coordinates of an individual’s travel between 5-10 minute intervals.   

  

How Adastra Helped: 

Adastra’s quick thinking assisted the transit agency in arriving at a decision point faster without wasting additional time and resources.  Through trial and error, diligence and persistence, we understood the problem, conducted a thorough analysis and provided a solution with a solid recommendation. All in just over a month. Following this process, our client considered our feedback and is now looking to implement the tracker in the next version of the app.  

 

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