Researchers at the University of Birmingham in the UK have developed a new mobile app to crowd source data on Rail Tracks Health, via passengers’ smartphones. The data so collected, when studied can make smoother safer ride for passengers.
The mobile App uses the accelerometers in present day smartphones to record the vibrations during a Train journey. The data thus recorded by the smartphones is transferred to some server through internet connectivity. Because the researchers pitch the app as one able to detect rail track faults such as welding joint bumps, not properly aliened rail tracks or crack in tracks, it means that the App will also be transferring location data using GPS.
According to the researchers, the study is the first to use artificial neural networks (ANN) to map data gathered from smartphones in order to evaluate ride quality. What does that mean? … In simple words, the data will be processed the way human brain processes it. The use of ANN is the same as we using “Crowd Source” in place of data gathered from people.
What does this mean? … It still means the researchers will be studying the data gathered from the smartphones of traveler willing to use the mobile app. Because the mobile app will require access to user’s location, connectivity and switching the mobile app on; hence greatest challenge for the researchers in using such a technology is to persuade passengers themselves adopt the app. To do so the rail companies might make it easier for passengers to use the app by linking it to Wi-Fi access on their trains (Free WiFi Access).
What kind of inferences and conclusions are derived from that data will depend on the research on that data. The sophistication of the artificial neural networks used to to map data will determine that.
Thus over all, rather than the fancy word such as “artificial neural networks” , the key to this study is a simple data collecting mobile App. The whole idea of making this distinction clear is to make the reader aware that the main pivot of the news must not be the ANN, but the gathering of data via a mobile app for a Government or a university or a Company. And making such a mobile app is not a big deal. The moment we start seeing it from the ANN perspective, then the mobile itself becomes a kind of big deal. Going by the level of technology used in the present day smartphones such as accelerometer, gps, connectivity options etc. creating an app to collect data is neither cost intensive nor a big achievement.
Going further, the app data if studied well will help in finding faults on rail tracks. It’s possible to find even those cracks which are not visible to a human fault finder. The app can also act as a data gathering tool for other discomforts a traveler faces in a train journey, such as stench or temperature variations. For this all the user has to do is answer a feedback question and submit it to the remote server.
To conclude, the basic objective of the article is to make the reader aware that not everything marketed as high tech, is that high tech. If costs Governments, institutions and Companies just pennies. The real value comes from the willingness of the user to give data and the sophistication of the technology being used to derive insights, inferences and results from that data (which needs liberal spending on such researches). Although launching a mobile app and providing liberal spending on researching the data is not a big task for Governments.
As a rail passenger, you should be focusing on improvement in rail tracks, which for India means lesser accidents.