In underground transport, accidents and terrorist acts can happen at any time, and these problems worry users because the solutions for their prevention and mitigation are insufficient: checkpoints, video surveillance cameras, fire extinguishers, emergency levers, etc.
We're going to develop a network of sensors for the monitoring and control of the security of the underground transport system. The nodes will send the data captured by the sensors to a centralized station for storage, analysis and troubleshooting.
- Let us suppose the scenario of a tunnel in which two trains collide and break some gas and fuel pipes that cross the network and cause a fire inside. Sensors are mounted in this tunnel that transmit information to the central control station: temperature, smoke, fire, vibration, damage caused, number of trains, etc.
- Each train involved is shown as its own sensor network.
- This solution uses artificial intelligence systems with Neural Networks and Machine Learning, as well as IoT (Internet of Things) platforms such as AWS, ThingSpeak, Ubidots, etc.
- Communication and response time will obviously be immediate. Traffic will be diverted to other tunnels and the tunnel's emergency systems do their part until human reinforcements arrive.
- The emergency trains also have control systems with updated information on the situation inside the tunnel, and introduce robots to detect and track the different levels of danger that may exist. For example, if there are victims.
- The firefighters would be provided with several sensors that indicate their health status, as well as their position in the tunnel, and their helmets are provided with a camera and screens that report on each key situation to make the best decisions.