Login | Register

Predicting Short-Term Traffic Congestion on Urban Motorway Networks


Predicting Short-Term Traffic Congestion on Urban Motorway Networks

Adetiloye, Taiwo Olubunmi ORCID: https://orcid.org/0000-0002-0172-7477 (2018) Predicting Short-Term Traffic Congestion on Urban Motorway Networks. PhD thesis, Concordia University.

[thumbnail of Adetiloye_PhD_F2018.pdf]
Text (application/pdf)
Adetiloye_PhD_F2018.pdf - Accepted Version
Available under License Spectrum Terms of Access.


Traffic congestion is a widely occurring phenomenon caused by increased use of vehicles on roads resulting in slower speeds, longer delays, and increased vehicular queueing in traffic. Every year, over a thousand hours are spent in traffic congestion leading to great cost and time losses. In this thesis, we propose a multimodal data fusion framework for predicting traffic congestion on urban motorway networks. It comprises of three main approaches. The first approach predicts traffic congestion on urban motorway networks using data mining techniques. Two categories of models are considered namely neural networks, and random forest classifiers. The neural network models include the back propagation neural network and deep belief network. The second approach predicts traffic congestion using social media data. Twitter traffic delay tweets are analyzed using sentiment analysis and cluster classification for traffic flow prediction.

Lastly, we propose a data fusion framework as the third approach. It comprises of two main techniques. The homogeneous data fusion technique fuses data of same types (quantitative or numeric) estimated using machine learning algorithms. The heterogeneous data fusion technique fuses the quantitative data obtained from the homogeneous data fusion model and the qualitative or categorical data (i.e. traffic tweet information) from twitter data source using Mamdani fuzzy rule inferencing systems.

The proposed work has strong practical applicability and can be used by traffic planners and decision makers in traffic congestion monitoring, prediction and route generation under disruption.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (PhD)
Authors:Adetiloye, Taiwo Olubunmi
Institution:Concordia University
Degree Name:Ph. D.
Program:Information and Systems Engineering
Date:June 2018
Thesis Supervisor(s):Anjali, Awasthi
Keywords:traffic, congestion, prediction, urban, city, transportation, machine learning, deep belief network, neural network, random forest, sentiment analysis, data fusion, transportation, motorway
ID Code:985249
Deposited On:11 Jun 2019 16:47
Last Modified:11 Jun 2020 00:00
Related URLs:
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top