Automated Assessment of Public Transit Bus Stops Using Computer Vision MethodsSource: Journal of Computing in Civil Engineering:;2023:;Volume ( 037 ):;issue: 005::page 04023021-1Author:Seyed Masoud Shameli
,
Brad Loroff
,
Robert Jancev
,
Shahrzad Borjian
,
Ehsan Rezazadeh Azar
DOI: 10.1061/JCCEE5.CPENG-5300Publisher: ASCE
Abstract: Transit bus systems are an essential part of public transportation systems and they improve the accessibility of the communities and economical activities. On-street assets, namely bus stops, are key elements of public bus transit systems, but their condition assessment mostly depends on manual efforts that are labor-intensive and expensive. This research proposes a novel system for detection and assessment of the public transit bus stops using captured videos by on-board cameras. This system utilizes deep convolutional neural networks (DCNNs) to detect bus stop assets and semantically segment pixels in the corresponding video frames. Then, the results of these two modules are further processed to assess the connectivity of the bus stop mobility pads and accessibility level of the bus stops on snowy days. The experimental results showed promising performance of the proposed methods, which have potentials for practical applications.
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contributor author | Seyed Masoud Shameli | |
contributor author | Brad Loroff | |
contributor author | Robert Jancev | |
contributor author | Shahrzad Borjian | |
contributor author | Ehsan Rezazadeh Azar | |
date accessioned | 2023-11-27T23:10:54Z | |
date available | 2023-11-27T23:10:54Z | |
date issued | 6/23/2023 12:00:00 AM | |
date issued | 2023-06-23 | |
identifier other | JCCEE5.CPENG-5300.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4293361 | |
description abstract | Transit bus systems are an essential part of public transportation systems and they improve the accessibility of the communities and economical activities. On-street assets, namely bus stops, are key elements of public bus transit systems, but their condition assessment mostly depends on manual efforts that are labor-intensive and expensive. This research proposes a novel system for detection and assessment of the public transit bus stops using captured videos by on-board cameras. This system utilizes deep convolutional neural networks (DCNNs) to detect bus stop assets and semantically segment pixels in the corresponding video frames. Then, the results of these two modules are further processed to assess the connectivity of the bus stop mobility pads and accessibility level of the bus stops on snowy days. The experimental results showed promising performance of the proposed methods, which have potentials for practical applications. | |
publisher | ASCE | |
title | Automated Assessment of Public Transit Bus Stops Using Computer Vision Methods | |
type | Journal Article | |
journal volume | 37 | |
journal issue | 5 | |
journal title | Journal of Computing in Civil Engineering | |
identifier doi | 10.1061/JCCEE5.CPENG-5300 | |
journal fristpage | 04023021-1 | |
journal lastpage | 04023021-17 | |
page | 17 | |
tree | Journal of Computing in Civil Engineering:;2023:;Volume ( 037 ):;issue: 005 | |
contenttype | Fulltext |