‎Evolving Fuzzy Systems in Taxi Demand Forecasting and Classification

Transactions on Fuzzy Sets and Systems

مقاله 8، دوره 2، شماره 1، مرداد 2023، صفحه 101-132   

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.30495/tfss.2023.1971760.1053

نویسندگان

Luis Fernando Linhares؛ Alisson Marques Silva^*

Graduate Program in Mathematical and Computational Modeling, ‎Federal Center of Technological Education of Minas Gerais, ‎Belo Horizonte‎, ‎MG‎, ‎Brazil.

چکیده

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.

کلیدواژه‌ها

Forecast‎؛ ‎Classification‎؛ ‎Fuzzy Systems‎؛ ‎Evolving Systems‎؛ ‎Taxi Demand‎

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