Real-Time Incident Detection and Capacity Estimation Using Loop Detector Data

Given the fact that the existing literature lacks the real-time estimation of road capacity and incident location using data from inductance loop detectors, a data-driven framework is proposed in this study for real-time incident detection, as well as road capacity and incident location estimation....

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Main Authors: Syed Muzammil Abbas Rizvi, Afzal Ahmed, Yongjun Shen
Format: Article
Language:English
Published: Hindawi-Wiley 2020-01-01
Series:Journal of Advanced Transportation
Online Access:http://dx.doi.org/10.1155/2020/8857502
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spelling doaj-fa16df0a8ae84ea2b35dafbe3141f1552020-11-25T03:44:58ZengHindawi-WileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88575028857502Real-Time Incident Detection and Capacity Estimation Using Loop Detector DataSyed Muzammil Abbas Rizvi0Afzal Ahmed1Yongjun Shen2Southeast University, Nanjing, ChinaNED University of Engineering and Technology, Karachi, PakistanSoutheast University, Nanjing, ChinaGiven the fact that the existing literature lacks the real-time estimation of road capacity and incident location using data from inductance loop detectors, a data-driven framework is proposed in this study for real-time incident detection, as well as road capacity and incident location estimation. The proposed algorithm for incident detection is developed based on the variation in traffic flow parameters acquired from inductance loop detectors. Threshold values of speed and occupancy are determined for incident detection based on the PeMS database. The detection of the incident is followed by the real-time road capacity and incident location estimation using a Cell Transmission Model (CTM) based approach. The data of several incidents were downloaded from PeMS and used for the development of the proposed framework presented in this study. Results show that the developed framework detects the incident and estimates the reduced capacity accurately. The location of the incident is estimated with an overall accuracy of 92.5%. The performance of the proposed framework can be further improved by incorporating the effect of the on-ramps, off-ramps, and high-occupancy lanes, as well as by modeling each lane separately.http://dx.doi.org/10.1155/2020/8857502
collection DOAJ
language English
format Article
sources DOAJ
author Syed Muzammil Abbas Rizvi
Afzal Ahmed
Yongjun Shen
spellingShingle Syed Muzammil Abbas Rizvi
Afzal Ahmed
Yongjun Shen
Real-Time Incident Detection and Capacity Estimation Using Loop Detector Data
Journal of Advanced Transportation
author_facet Syed Muzammil Abbas Rizvi
Afzal Ahmed
Yongjun Shen
author_sort Syed Muzammil Abbas Rizvi
title Real-Time Incident Detection and Capacity Estimation Using Loop Detector Data
title_short Real-Time Incident Detection and Capacity Estimation Using Loop Detector Data
title_full Real-Time Incident Detection and Capacity Estimation Using Loop Detector Data
title_fullStr Real-Time Incident Detection and Capacity Estimation Using Loop Detector Data
title_full_unstemmed Real-Time Incident Detection and Capacity Estimation Using Loop Detector Data
title_sort real-time incident detection and capacity estimation using loop detector data
publisher Hindawi-Wiley
series Journal of Advanced Transportation
issn 0197-6729
2042-3195
publishDate 2020-01-01
description Given the fact that the existing literature lacks the real-time estimation of road capacity and incident location using data from inductance loop detectors, a data-driven framework is proposed in this study for real-time incident detection, as well as road capacity and incident location estimation. The proposed algorithm for incident detection is developed based on the variation in traffic flow parameters acquired from inductance loop detectors. Threshold values of speed and occupancy are determined for incident detection based on the PeMS database. The detection of the incident is followed by the real-time road capacity and incident location estimation using a Cell Transmission Model (CTM) based approach. The data of several incidents were downloaded from PeMS and used for the development of the proposed framework presented in this study. Results show that the developed framework detects the incident and estimates the reduced capacity accurately. The location of the incident is estimated with an overall accuracy of 92.5%. The performance of the proposed framework can be further improved by incorporating the effect of the on-ramps, off-ramps, and high-occupancy lanes, as well as by modeling each lane separately.
url http://dx.doi.org/10.1155/2020/8857502
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AT afzalahmed realtimeincidentdetectionandcapacityestimationusingloopdetectordata
AT yongjunshen realtimeincidentdetectionandcapacityestimationusingloopdetectordata
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