Spatio temporal traffic pediction for a large-scale road network

In a country like Singapore, which is adapting the Smart Nation program, Intelligent Transport Systems (ITS) plays a key role. In order to improve the travel experience by proper planning and avoidance of traffic congestion, traffic prediction is the key. The potentials of deep neural networks fo...

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Main Author: Krishnamoorthy, Nardana
Other Authors: Justin Dauwels
Format: Theses and Dissertations
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10356/78903
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-789032023-07-04T15:22:15Z Spatio temporal traffic pediction for a large-scale road network Krishnamoorthy, Nardana Justin Dauwels School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering In a country like Singapore, which is adapting the Smart Nation program, Intelligent Transport Systems (ITS) plays a key role. In order to improve the travel experience by proper planning and avoidance of traffic congestion, traffic prediction is the key. The potentials of deep neural networks for real-life applications of various domains can be viewed in the recent years. Traffic speed prediction models that learn and adapt to the spatio-temporal correlations of the traffic network are always effective. This thesis deals with the problem of traffic speed prediction by developing a hybrid model that combines the known benefits of both CNN and LSTM neural networks. The traffic data collected from Land Transport Authority Singapore was utilized to predict the traffic speed of road segments in an express highway. Initially road segments in the considered Singapore road network were grouped into four clusters by means of Kmean clustering based on their average speed. The road segments belonging to one of these clusters were then tested. The architecture of the hybrid model was tailored according to the requirements of the test road network. The performance of the optimal hybrid model was then compared with LSTM and CNN models, which showed that the hybrid model performed better in terms of MAPE (Mean Absolute Percentage Error) and MSE (Mean Squared Error). By adapting to both the temporal and the spatial domain, the hybrid model gives better results. This proves the superior ability of the hybrid model is applicable even on the complex road networks. Master of Science (Computer Control and Automation) 2019-10-08T12:12:52Z 2019-10-08T12:12:52Z 2019 Thesis http://hdl.handle.net/10356/78903 en 66 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Krishnamoorthy, Nardana
Spatio temporal traffic pediction for a large-scale road network
description In a country like Singapore, which is adapting the Smart Nation program, Intelligent Transport Systems (ITS) plays a key role. In order to improve the travel experience by proper planning and avoidance of traffic congestion, traffic prediction is the key. The potentials of deep neural networks for real-life applications of various domains can be viewed in the recent years. Traffic speed prediction models that learn and adapt to the spatio-temporal correlations of the traffic network are always effective. This thesis deals with the problem of traffic speed prediction by developing a hybrid model that combines the known benefits of both CNN and LSTM neural networks. The traffic data collected from Land Transport Authority Singapore was utilized to predict the traffic speed of road segments in an express highway. Initially road segments in the considered Singapore road network were grouped into four clusters by means of Kmean clustering based on their average speed. The road segments belonging to one of these clusters were then tested. The architecture of the hybrid model was tailored according to the requirements of the test road network. The performance of the optimal hybrid model was then compared with LSTM and CNN models, which showed that the hybrid model performed better in terms of MAPE (Mean Absolute Percentage Error) and MSE (Mean Squared Error). By adapting to both the temporal and the spatial domain, the hybrid model gives better results. This proves the superior ability of the hybrid model is applicable even on the complex road networks.
author2 Justin Dauwels
author_facet Justin Dauwels
Krishnamoorthy, Nardana
format Theses and Dissertations
author Krishnamoorthy, Nardana
author_sort Krishnamoorthy, Nardana
title Spatio temporal traffic pediction for a large-scale road network
title_short Spatio temporal traffic pediction for a large-scale road network
title_full Spatio temporal traffic pediction for a large-scale road network
title_fullStr Spatio temporal traffic pediction for a large-scale road network
title_full_unstemmed Spatio temporal traffic pediction for a large-scale road network
title_sort spatio temporal traffic pediction for a large-scale road network
publishDate 2019
url http://hdl.handle.net/10356/78903
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