A novel spatiotemporal framework for efficient traffic prediction and visualization

This thesis proposes an efficient traffic prediction framework to estimate congestion at intersections depending on neighboring road links. The framework encompasses three major components, data extraction, Bayesian Linear Regression-based traffic prediction model, and an interactive map-based traffic simulator to visualize the results. To collect traffic data, we have developed an open-source web-based data scraper tool to extract and export publicly available traffic data from the Google Maps web interface. We also developed a Bayesian Linear Regression-based traffic prediction model to estimate traffic congestion that leverages Bayesian inference to facilitate model interpretability and quantify model uncertainty. The experiments show that Bayesian linear regression modeling can be trained on small data observations to quantify model uncertainty and predict traffic congestion without sacrificing interpretability and accuracy compared to the frequentist approach. We have also developed a web-based traffic simulator to simulate linear regression-based traffic prediction models and visualize the results on interactive maps.

Name thesis:degree_name

Master of Applied Science (MASc)

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Mostafi, Sifatul

Advisor dc:contributor.advisor

• Elgazzar, Khalid

• Traffic data extraction
• Bayesian linear regression
• Traffic simulator

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1405

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1405