Predicting traffic incident duration using deep learning model with real-time data

Predicting traffic incident duration using deep learning model with real-time data

Traffic accidents have a negative impact on traffic. The prediction of incident clearance time helps to reduce its impact by diverting traffic flow, assisting traffic management organizations in making decisions about appropriate responses and resource allocation and identifying critical factors tha...

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Bibliographic Details
Main Author: Zhang, Ruilin
Other Authors: Zhu Feng
Format: Final Year Project
Language:English
Published: 2019
Subjects:
DRNTU::Engineering::Civil engineering
Online Access:http://hdl.handle.net/10356/77626
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-776262023-03-03T17:11:19Z Predicting traffic incident duration using deep learning model with real-time data Zhang, Ruilin Zhu Feng School of Civil and Environmental Engineering DRNTU::Engineering::Civil engineering Traffic accidents have a negative impact on traffic. The prediction of incident clearance time helps to reduce its impact by diverting traffic flow, assisting traffic management organizations in making decisions about appropriate responses and resource allocation and identifying critical factors that influence the length of duration. Previous researches utilized more detailed information that may be confidential to the public. Therefore, this study aimed to predict duration with transparent real-time data from LTA Datamall, MSS and OpenStreetMap. This study used a deep learning model to predict the incident duration. Various tests were carried out to optimize the neural network and to achieve the possible highest accuracy. The result of this research was comparable to previous researches in terms of MAE and MAPE, improvement in accuracy was be observed. This research also pointed out the directions for future research. Bachelor of Engineering (Civil) 2019-06-03T07:28:37Z 2019-06-03T07:28:37Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77626 en Nanyang Technological University 49 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 DRNTU::Engineering::Civil engineering
spellingShingle DRNTU::Engineering::Civil engineering
Zhang, Ruilin
Predicting traffic incident duration using deep learning model with real-time data
description Traffic accidents have a negative impact on traffic. The prediction of incident clearance time helps to reduce its impact by diverting traffic flow, assisting traffic management organizations in making decisions about appropriate responses and resource allocation and identifying critical factors that influence the length of duration. Previous researches utilized more detailed information that may be confidential to the public. Therefore, this study aimed to predict duration with transparent real-time data from LTA Datamall, MSS and OpenStreetMap. This study used a deep learning model to predict the incident duration. Various tests were carried out to optimize the neural network and to achieve the possible highest accuracy. The result of this research was comparable to previous researches in terms of MAE and MAPE, improvement in accuracy was be observed. This research also pointed out the directions for future research.
author2 Zhu Feng
author_facet Zhu Feng
Zhang, Ruilin
format Final Year Project
author Zhang, Ruilin
author_sort Zhang, Ruilin
title Predicting traffic incident duration using deep learning model with real-time data
title_short Predicting traffic incident duration using deep learning model with real-time data
title_full Predicting traffic incident duration using deep learning model with real-time data
title_fullStr Predicting traffic incident duration using deep learning model with real-time data
title_full_unstemmed Predicting traffic incident duration using deep learning model with real-time data
title_sort predicting traffic incident duration using deep learning model with real-time data
publishDate 2019
url http://hdl.handle.net/10356/77626
_version_ 1759854521792593920

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