Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158059
Title: Time-series AI models for traffic congestion prediction
Authors: Low, Carina Su Yun
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Low, C. S. Y. (2021). Time-series AI models for traffic congestion prediction. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158059
Project: A1132-211
Abstract: In recent years, Artificial Intelligence (AI) has gained much popularity in the real world due to its capability to outperform the human mind. With the ever-evolving technologies, AI plays a vital role in society. In fact, it has become a constant necessity in our daily lives and it is projected to continually grow exponentially in the upcoming years. In Intelligent Transporation Systems (ITS), traffic prediction has been a leading topic of interest amongst researchers in specialized fields. In this paper, we will explore the art of deep learning and examine the feasibility of using Time-Series AI models for predicting future traffic flow using historical data in large-scale roadway networks. The goal of this research is to achieve higher traffic precision to minimize traffic congestion.
URI: https://hdl.handle.net/10356/158059
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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