How a Smart City can Manage In-bus Congestion with a 4K Video
Traffic congestion increases the time required to commute. We all know this too well here in the bay area, like any urban citizens across the world. It inflicts increased operational costs on the urban transport system. Many forecasts suggest that this will only get worse in the years to come. This rise in congestion has pushed governing authorities to promote the usage of public transport vehicles instead of private ones. Few cities have also tried building dedicated roads for public vehicles and few others have tried to lure commuters by lowering the costs. Even after several measures, a most common issue arising with public transport is that they are overcrowded during peak hours. Many people for this sole reason abandon the public transport and use alternative ways to commute in peak hours. While many cities are aiming to be transformed as smart cities by the end of this decade, public transport congestion poses a genuine challenge to the smart infrastructure. Overcrowding of public buses is caused due to the negligence of public authorities in managing the load on every single bus. The load on each bus on the same route at a given time window varies since the bus which arrives first to the terminal predictably sees a greater load. Since passengers lack real-time information on bus schedules and their current load, they assume that the next bus arriving at the terminal has to be inevitably boarded. If they can get a real-time data about the crowd density in each bus on a real-time basis on their mobile app, they can plan their travel accordingly. Estimating the exact crowd density in each bus can be achieved by implementing people analytics coupled with high-resolution 4K camera input. That being said, it requires a set of cutting-edge technologies to make the accurate estimation. Following are the camera technologies which augment the efforts of reducing the in-bus congestion in a smart city ecosystem.