Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow

A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series

“…Calculate a fuzzy-based similarity measure between and ( ): (13) Save the to (14) ← + ℎ ℎ (15) end while (16) return ℎ ℎ = max{ } (17)…”

“…The fuzzy approach makes it possible to handle incomplete data, vague, and imprecise circumstances [12], which provide a high uncertainty environment to make decision. This property enables modelling and short-term forecasting of traffic flow in urban arterial networks using multivariate traffic data [13,14]. Recent works to urban traffic flow prediction [15] and to lane-changes prediction [16] have been proposed with success.…”

How to Provide Accurate and Robust Traffic Forecasts Practically?

“…Specifically, traffic forecasting is essentially the prediction of these basic parameters based on dynamic road traffic time series data. For instance, most of literature foucs on traffc flow forecasting (Jiang & Adeli, 2004;Qiao et al, 2001;Abdulhai et al, 1999;Castillo et al, 2008;Chen & Chen, 2007;Dimitriou et al, 2008;Ding et al, 2002;Huang & Sadek, 2009;Ghosh et al, 2005Ghosh et al, , 2007Smith et al, 2002), travel time forecasting, and related analysis such as validation, optimization, etc. (Chan et al, 2003;Chang et al, 2010;Kwon, 2000;Kwon & Petty, 2005;Lam, 2008;Lam et al, 2002Lam et al, , 2008Lam & Chan, 2004;Lee et al 2009;Nath et al, 2010;Schadschneider et al, 2005;Tam & Lam, 2009;Tang & Lam, 2001;Yang et al, 2010).…”

“…Besides the above neural networks models, computational intelligence (CI) techniques that encompass fuzzy systems, machine learning and evolutionary computation have been successfully developed in the field of traffic forecasting. For instance, some literature applies Bayesian networks (Zhang et al , 2004;Castillo et al, 2008) and Bayesian inference based regression techniques (Khan, 2011;Tebaldi et al, 2002;Sun et al, 2005Sun et al, , 2006Zheng et al, 2006;Ghosh et al, 2007), some literature uses fuzzy systems or fuzzy NNs to predict the traffic states (Dimitriou et al, 2008;Quek et al, 2009). While others start to explore support vector regression (SVR) to model traffic characteristics and produce prediction of traffic states (Castro-Neto, 2009;Ding et al, 2002;Hong, 2011;Hong et al, 2011;Wu et al, 2004;Vanajakshi & Rilett, 2004).…”

Previsões Pontuais E Intervalares De Séries Temporais De Alta Frequência Com Sistema De Lógica Fuzzy

“…Os artigos [144,145] utilizam algoritmos genéticos para regular as funções de pertinência do modelo. O artigo [142], além de utilizar GA para regular as funções, de pertinência, utiliza o método de clusterização k-means para reduzir o ruído na entrada do modelo.…”