Application of dynamic artificial neural network to road collision prediction modelling
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Faghihi, Seyed Farhad
issued: 2018-10-02T15:47:56Z
issued: 2018
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Many sets of road traffic data are a collection of ordered measures over the course of time. The key characteristic of time series data is the autocorrelation inside the observed points. Time series analysis is a technique to find the patterns inside time series data, while considering the autocorrelation. The existing time series statistical models in the literature suffer from several assumptions such as stationarity, normality, or homoscedasticity. In this research, a two-stage methodology of Discrete Wavelet Transform (DWT) coupled with Dynamic Artificial Neural Network (DANN) was proposed for predicting a given collision time series. The DANN model enabled the mapping of nonlinear, nonstationary relationship between the future values of collision series and its previous values or, moreover, the previous values of other time series. The DWT facilitated the learning process for limited, noisy observations by decomposing a time series into multiple subseries, which represented specific features of data. The relative performance of the proposed methodology was compared to the conventional AutoRegressive Integrated Moving Average (ARIMA) model for road safety studies. The variables used to build and validate the models were monthly time series data recorded from January 2003 to June 2016 in the City of Kelowna, BC. 54,000 DANN models were trained in total to determine the optimum network configurations for the subseries. The findings of this research showed that the combined DWT and DANN methodology outperformed the ARIMA models in terms of several indices. In addition, the proposed methodology was applied to evaluate the safety phases implemented on Springfield Road, Kelowna at various times. The results demonstrated that the methodology was also capable of using for the intervention analysis, where the traditional time series modelling approaches cannot be executed. The comprehensive study of this thesis is expected to have important implications for both theory a…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/67301
note: APPLICATION OF DYNAMIC ARTIFICIAL NEURAL NETWORK TO ROAD COLLISION PREDICTION MODELLING by Seyed Farhad Faghihi B.A.Sc., In Civil Engineering, Azad University, 2008 M.A.Sc., In Transportation Engineering, Azad University, 2012 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) September 2018 © Farhad Faghihi, 2018 The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: APPLICATION OF DYNAMIC ARTIFICIAL NEURAL NETWORK TO ROAD COLLISION PREDICTION MODELLING submitted by Seyed Farhad Faghihi in partial fulfillment of the requirements of the degree of Doctor of Philosophy . Dr. Gordon Lovegrove, School of Engineering Supervisor Dr. Abbas Milani, School of Engineering Supervisory Committee Member Dr. Ahmed Idris, School of Engineering Supervisory Committee Member Dr. Mohammad H. Zarifi, School of Engineering University Examiner Dr. Lina Kattan, School of Engineering, University of Calgary External Examiner ii Abstract Many sets of road traffic data are a collection of ordered measures over the course of time. The key characteristic of time series data is the autocorrelation inside the observed points. Time series analysis is a technique to find the patterns inside time series data, while considering the autocorrelation. The existing time series statistical models in the literature suffer from several assumptions such as stationarity, normality, or homoscedasticity. In this research, a two-stage methodology of Discrete Wavelet Transform (DWT) coupled with Dynamic Artificial Neural Network (DANN) was proposed for predicting a given collision time series. The DANN model enabled the mapping of nonlinear, nonstationary relationship between the future values of collision series and its previous values or, moreover, the previous values…
has type: Thesis/Dissertation
date issued: 2018-11
is shown at: 10.14288/1.0372348
language: eng
degree discipline: Civil Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/67301