Implementing deep learning techniques for network-scale traffic forecasting

Ammourah, Rami Ahmad Mohammad

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https://hdl.handle.net/2142/101724 Copy

Description

Title

Implementing deep learning techniques for network-scale traffic forecasting

Author(s)

Ammourah, Rami Ahmad Mohammad

Issue Date

2018-07-19

Director of Research (if dissertation) or Advisor (if thesis)

• Ouyang, Yanfeng
• Sowers, Richard

Department of Study

Civil & Environmental Eng

Discipline

Civil Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Deep Learning
• LSTM
• CNN
• Traffic Forecasting
• Traffic Prediction
• Long Short Term Memory Networks
• Convolutional Neural Networks
• Network-Scale Traffic Prediction

Abstract

In the past few years, Deep learning has re-emerged as a powerful tool to solve complex problems and create prediction models that can outperform a lot of the existing state-of-the-art methods. This is primarily due to two main reasons; the rise of big data, where huge amounts of information has become readily available to the public, as well as the recent technological advancements in computer processing powers which has enabled researchers to take advantage of these large volumes of data. One of the major fields which requires dealing with and understanding extensive amounts of data is transportation. In the United States alone, 220 billion vehicle trips have taken place in 2017 [1]. This creates the need for researchers who can work with such huge data to build models and infer beneficial knowledge which can contribute to improving transportation networks and the overall travel experience. In this thesis, we study the use of several machine learning and deep learning techniques to predict travel times on a road network. The two main methods proposed to tackle the problem are Convolutional Neural Networks and Long-Short Term Memory Networks. The location of interest of this thesis is the city of New York. The New York City Taxi and Limousine Commission provides the origin and destination pairs, along with the travel times and other formation, for each taxi trip between the years of 2010 and 2013. A more refined representation of the data was obtained from B. Donovan and D. Work [2], where travel time estimates for each hour of the day is provided along every road in the city.

Graduation Semester

2018-08

Type of Resource

text

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http://hdl.handle.net/2142/101724

Copyright and License Information

Copyright 2018 Rami Ammourah

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