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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Series: | Journal of Advanced Transportation |
Online Access: | http://dx.doi.org/10.1155/2020/8857502 |
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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 |
work_keys_str_mv |
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