‎Evolving Fuzzy Systems in Taxi Demand Forecasting and Classification

This work presents an approach to taxi demand forecasting and classification‎. ‎The proposed approach uses historical data from taxi rides and meteorological data‎. ‎The Kruskal-Wallis variable ranking method is used to identify the most relevant variables‎. ‎The selected variables are used as input to an evolving fuzzy system to perform the prediction‎. ‎Once the forecast is made‎, ‎the demand results are classified by value ranges‎. ‎Those ranges are also identified by colors that compose a heatmap‎, ‎displayed at each time interval‎. ‎In this work‎, ‎to perform the prediction‎, ‎four evolving systems are evaluated‎: ‎Autonomous Learning Multi-Model (ALMMo); evolving Multivariable Gaussian Fuzzy Modeling System (eMG); evolving Fuzzy with Multivariable Gaussian Participatory Learning and Recursive Maximum Correntropy eFCE and; evolving Neo-Fuzzy Neuron (eNFN)‎. ‎Computational experiments were carried out to evaluate the evolving systems in predicting Pick-Up and Drop-Off‎, ‎at intervals of 15 and 30 minutes‎, ‎for 86 zones in New York‎, ‎covering the period from 01/01/2018 to 31‎/ ‎10/2018‎. ‎The results obtained by the evolving systems are compared with each other and state of the art‎. ‎Among the evolving models‎, ‎ALMMo presented the best results compared to the state of the art and other evolving models‎. ‎Performance obtained by the evolving models suggests that the proposed approach is promising an alternative to forecasting and classifying passenger demand‎.