Business Intelligence In Real Life – Predictive Analytics For Public Transport Systems
Business Intelligence In Real Life – Predictive Analytics For Public Transport Systems
As Enrique Penalosa, the former mayor of Bogota once said “A developed country is not a place where the poor have cars; it’s where the rich use public transport”; speaks for all the public transport across the globe. The convenience and comfort of a good public transport puts its users in a very relaxed state of mind. Predicting the demand for public transport, route planning, optimizing routes and providing state of the art facilities to suit every pocket is a big job for a city. Public transportation is vital to the development of a city big or small. Whether a large network or a small fleet, the city can be on the move with BI tools. By incorporating Predictive Analytics, problems can be solved before they come up!! The big collection of statistical data can be put to use to predict probable possibilities in the future to identify risk areas and newer requirements. Predictive Analytics for Public Transport Systems Business Intelligence is put to test to predict and carry out many a things for any public transport system.
Predictive Analytics and Predictive Modeling can be implemented intelligently to meet the citizens’ expectations from the public transport systems. Creation of the transport system is only the beginning for any government. The dynamics of demand and supply are to be judiciously and constantly analyzed. The public transport’s online dashboards from the Business Intelligence systems should allow remote monitoring of safety cameras, heating, ventilation, air conditioning or humidity or to put it short HVAC equipments. The threshold alerts and equipment warnings should also be constantly checked to predict probable failures and attend to them beforehand. Predictive Modeling
Predictive modeling is part of predictive analytics used to determine the probable future outcome of an event. It builds scenarios and the probable solutions to the given case by analyzing large amounts of historical data. Patterns analysis can be generated by using the predictive models based on the frequency and timings of certain patterns in the usage and failures of the transport system. These patterns could be triggered by natural causes, seasonal causes or simply human behavior. Relying upon a historical model to make a realistic prediction is the key challenge for the predictive model. Accuracy is improved by the ability to develop reliable logics. To sum up, a competent Predictive Analysis integrates huge volumes of data from heterogeneous sources in order to give immediate answers to key questions of the public transportation system and to adapt that system to the ever changing demands and the needs of the day.