Prediction of Queue Dissipation Time for Mixed Traffic Flows With Deep Learning
Queue dissipation has been extensively studied about traffic signalization, work zone operations, and ramp metering. Various methods for estimating the intersection’s queue length and dissipation time have been reported in the literature, including the use of car-following models with sim...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9743408/ |
| _version_ | 1797974429645406208 |
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| author | Hung-Hsun Chen Yi-Bing Lin I-Hau Yeh Hsun-Jung Cho Yi-Jung Wu |
| author_facet | Hung-Hsun Chen Yi-Bing Lin I-Hau Yeh Hsun-Jung Cho Yi-Jung Wu |
| author_sort | Hung-Hsun Chen |
| collection | DOAJ |
| description | Queue dissipation has been extensively studied about traffic signalization, work zone operations, and ramp metering. Various methods for estimating the intersection’s queue length and dissipation time have been reported in the literature, including the use of car-following models with simulation, vehicle trajectories from GPS, shock-wave theory, statistical estimation from traffic flow patterns, and artificial neural networks (ANN). However, most of such methods cannot account for the impacts of interactions between different vehicle types and their spatial distributions in the queue length on the initial discharge time and the resulting total dissipation duration. As such, this study presents a system, named TrafficTalk, that applies a deep learning-based method to reliably capture the queue characteristics of mixed traffic flows, and produce a robust estimate of the dissipating duration for the design of the optimal signal plan. The proposed TrafficTalk, featuring the effectiveness in transforming video-imaged traffic conditions into vehicle density maps, has proved its performance under extensive field evaluations. For instance, compared with the benchmark model, XGBoost in the literature, it has reduced the MAPE from 25.8% to 10.4%., and from 31.3% to 10.4% if the queue discharging stream comprises motorcycles. |
| first_indexed | 2024-04-11T04:19:50Z |
| format | Article |
| id | doaj.art-ad0bb0c657ab4a108fe78fa02fdef4f7 |
| institution | Directory Open Access Journal |
| issn | 2687-7813 |
| language | English |
| last_indexed | 2024-04-11T04:19:50Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj.art-ad0bb0c657ab4a108fe78fa02fdef4f72022-12-31T00:02:11ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132022-01-01326727710.1109/OJITS.2022.31625269743408Prediction of Queue Dissipation Time for Mixed Traffic Flows With Deep LearningHung-Hsun Chen0https://orcid.org/0000-0001-6437-9804Yi-Bing Lin1https://orcid.org/0000-0001-6841-4718I-Hau Yeh2Hsun-Jung Cho3Yi-Jung Wu4Program of Artificial Intelligence and Information Security, Fu Jen Catholic University, New Taipei City, TaiwanCollege of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, TaiwanElan Microelectronics Corporation, Hsinchu, TaiwanPiXORD Corporation, Hsinchu, TaiwanInstitute of Computational Intelligence, National Yang Ming Chiao Tung University, Hsinchu, TaiwanQueue dissipation has been extensively studied about traffic signalization, work zone operations, and ramp metering. Various methods for estimating the intersection’s queue length and dissipation time have been reported in the literature, including the use of car-following models with simulation, vehicle trajectories from GPS, shock-wave theory, statistical estimation from traffic flow patterns, and artificial neural networks (ANN). However, most of such methods cannot account for the impacts of interactions between different vehicle types and their spatial distributions in the queue length on the initial discharge time and the resulting total dissipation duration. As such, this study presents a system, named TrafficTalk, that applies a deep learning-based method to reliably capture the queue characteristics of mixed traffic flows, and produce a robust estimate of the dissipating duration for the design of the optimal signal plan. The proposed TrafficTalk, featuring the effectiveness in transforming video-imaged traffic conditions into vehicle density maps, has proved its performance under extensive field evaluations. For instance, compared with the benchmark model, XGBoost in the literature, it has reduced the MAPE from 25.8% to 10.4%., and from 31.3% to 10.4% if the queue discharging stream comprises motorcycles.https://ieeexplore.ieee.org/document/9743408/Deep learning (DL)traffic queue dissipation timetraffic queue patternmixed traffic flowsobject detectiontraffic signal countdown timer (TSCT) |
| spellingShingle | Hung-Hsun Chen Yi-Bing Lin I-Hau Yeh Hsun-Jung Cho Yi-Jung Wu Prediction of Queue Dissipation Time for Mixed Traffic Flows With Deep Learning IEEE Open Journal of Intelligent Transportation Systems Deep learning (DL) traffic queue dissipation time traffic queue pattern mixed traffic flows object detection traffic signal countdown timer (TSCT) |
| title | Prediction of Queue Dissipation Time for Mixed Traffic Flows With Deep Learning |
| title_full | Prediction of Queue Dissipation Time for Mixed Traffic Flows With Deep Learning |
| title_fullStr | Prediction of Queue Dissipation Time for Mixed Traffic Flows With Deep Learning |
| title_full_unstemmed | Prediction of Queue Dissipation Time for Mixed Traffic Flows With Deep Learning |
| title_short | Prediction of Queue Dissipation Time for Mixed Traffic Flows With Deep Learning |
| title_sort | prediction of queue dissipation time for mixed traffic flows with deep learning |
| topic | Deep learning (DL) traffic queue dissipation time traffic queue pattern mixed traffic flows object detection traffic signal countdown timer (TSCT) |
| url | https://ieeexplore.ieee.org/document/9743408/ |
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