Implementing deep learning techniques for network-scale traffic forecasting

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

Evaluation of social impact of traffic noice in Amman, Jordan

Few road traffic studies were conducted in Jordan, but the issue is drawing an increasing attention due to its growing magnitude and various impacts as a result of the high increase in vehicular traffic. This study further investigates the issue with the aim of providing an understanding of its social impact on residents of Amman, the capital of Jordan. Traffic noise levels were measured at selected locations along urban arterials and a social survey was performed to examine the reactions and attitudes of the neighboring residents towards these levels of traffic noise. The survey included social characteristics of individuals, and their attitudes towards traffic noise, and how it impacted their daily activities. A predesigned questionnaire was used for this purpose which included questions to evaluate the awareness of respondents of the problem and its environmental and health impacts. The financial impact that residents perceive of noise and the need for attenuation measures were also addressed. The results of the study also revealed that the impact of traffic noise on people can cause annoyance while performing daily activities were 24% of respondents reported that they get annoyed by traffic while working, 49% while resting, 34% while talking to others, 31% while talking on the phone, 39% while reading, 38% while watching TV and 53% of respondents get annoyed while sleeping. The respondents have also pointed out the following effects of noise: twist in mood (53%), headache (36%), and difficulty in concentration (40%). About 57% of respondents think traffic noise reduces the value of their properties and a total of 31% are willing to sell their house at reduced cost. About 59% of respondents consider attenuation measures necessary, and in order to reduce the noise, about 54% of respondents were willing to pay for attenuation measures which reflects the public awareness of the issue magnitude. Keywords: traffic noise, noise level, survey, social impacts

Implementing deep learning techniques for network-scale traffic forecasting

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.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste