Traffic Flow Forecasting using DeepAR

Joshi, Prajwal (2024) Traffic Flow Forecasting using DeepAR. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (742kB) | Preview

Abstract

Improved traffic volume forecasting techniques are required due to disruptions in to travel patterns caused by the COVID-19 pandemic. During pandemics, traditional approaches find it difficult to handle complicated dynamics. This work investigates the use of an RNN architecture called DeepAR to forecast traffic volume both during and after the epidemic. DeepAR is a good fit for simulating pandemic-induced traffic patterns because of its capacity to manage temporal dependencies and long-range interactions. DeepAR models were trained for various prediction horizons using historical data covering pre-pandemic, pandemic, and post-pandemic eras. The results show that DeepAR works better than conventional techniques at capturing pandemic-induced changes in traffic patterns. Its flexibility makes proactive traffic control techniques possible. The implications of these findings for traffic management after the pandemic are noteworthy. Transportation authorities can improve traffic flow, manage infrastructure, and lessen congestion by utilising DeepAR. Because of its adaptability, DeepAR is a useful tool for transportation systems to adjust to constantly shifting traffic patterns.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Yaqoob, Abid UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > Diseases > Outbreaks of disease > Epidemics > COVID-19 Pandemic, 2020- Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Motor Industry
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	09 May 2025 10:08
Last Modified:	09 May 2025 10:08
URI:	https://norma.ncirl.ie/id/eprint/7537

Actions (login required)

View Item