Data Collection and Prediction of Urban Transport Flow using Neural Networks

Abstract

Smart cities can use artificial neural networks to provide more accurate information about public transportation schedules, and thus help the population plan their day to day activities. In this context, this paper describes the essential steps for the acquisition and processing of data, and the creation of a neural network model capable of predicting possible delays or advances on bus lines in the city of Curitiba, Paraná. The neural network considers traffic data, climate, time and history of a public transport line. The article details all phases of collection and treatment, as well as how information is inserted into the network and what are the obtained results.