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An Approach of Traffic Flow Prediction Using ARIMA Model with Fuzzy Wavelet Transform

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  • Fri, 10/05/2018 - 1:00pm - 3:00pm

An Approach of Traffic Flow Prediction Using ARIMA Model with Fuzzy Wavelet Transform

MSc Thesis Defense by:

Sukruti Vaghasia

Date:  Friday, October 5, 2013
Time:  1:00 pm – 3:00 pm
Location: 3105, Lambton Tower

Abstract: It is essential for intelligent transportation systems to be capable of producing accurate forecast of traffic flow in both short and long terms. However, the counting datasets of traffic volume are non-stationary time series, which are integrally noisy. As a result, the accuracy of traffic prediction carried out on such unrefined data is reduced by the arbitrary components. Prior study shows that Box-Jenkins’ Autoregressive Integrated Moving Average (ARIMA) models convey demands noise free dataset for model construction. Therefore, this study proposes to overcome the noise issue by using a hybrid approach that combines the ARIMA model with fuzzy wavelet transform. In this approach, fuzzy rules are developed to categorize traffic datasets according to influencing factors, such as time of a day, season of a year, and weather conditions. As the input of linear data series for ARIMA model needs to be converted into linear time series for traffic flow prediction, discrete wavelet transform is applied to help separating the non-linear and liner part of the time series along with de-noising time series traffic data. The sharp variation points are used to detect the regular or irregular trend in time series for long term traffic prediction.

Thesis Committee:
Internal Reader: Dr. Jianguo Lu       
External Reader: Dr. Gokul Bhandari           
Advisor:  Dr. Xiaobu Yuan
Chair:  Dr. Luis Rueda  

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