Real-Time Traffic Forecast System for the Accident-Prone Large-Scale Transportation Network in the Seoul Metropolitan Area
This study proposes a reliable and efficient real-time forecasting platform for use in an accident-prone large-scale transportation network. We showed the method could be applied to various roadway sections without any loss of performance or efficiency. Due to its robustness, efficiency, and versati...
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| Published in | KSCE journal of civil engineering Vol. 27; no. 7; pp. 3085 - 3096 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Seoul
Korean Society of Civil Engineers
01.07.2023
Springer Nature B.V 대한토목학회 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1226-7988 1976-3808 |
| DOI | 10.1007/s12205-023-0349-9 |
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| Abstract | This study proposes a reliable and efficient real-time forecasting platform for use in an accident-prone large-scale transportation network. We showed the method could be applied to various roadway sections without any loss of performance or efficiency. Due to its robustness, efficiency, and versatility, the method could be implemented in the Seoul Metropolitan Area to provide traffic authorities and road users with future traffic information even under accident conditions. This is the major contribution of this research and contrasts with state-of-the-art techniques proposed by prior studies, which rely heavily on parameter tuning with large historical datasets and produce only site-specific forecasts with limited prediction horizons under recurrent traffic conditions. The proposed method makes no assumptions about the physical structure of the transportation network and can be applied to different roads under different traffic conditions without time constraints. |
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| AbstractList | This study proposes a reliable and efficient real-time forecasting platform for use in an accident-prone large-scale transportation network. We showed the method could be applied to various roadway sections without any loss of performance or efficiency. Due to its robustness, efficiency, and versatility, the method could be implemented in the Seoul Metropolitan Area to provide traffic authorities and road users with future traffic information even under accident conditions. This is the major contribution of this research and contrasts with state-of-the-art techniques proposed by prior studies, which rely heavily on parameter tuning with large historical datasets and produce only site-specific forecasts with limited prediction horizons under recurrent traffic conditions. The proposed method makes no assumptions about the physical structure of the transportation network and can be applied to different roads under different traffic conditions without time constraints. This study proposes a reliable and efficient real-time forecasting platform for use in an accidentpronelarge-scale transportation network. We showed the method could be applied to variousroadway sections without any loss of performance or efficiency. Due to its robustness,efficiency, and versatility, the method could be implemented in the Seoul Metropolitan Areato provide traffic authorities and road users with future traffic information even under accidentconditions. This is the major contribution of this research and contrasts with state-of-the-arttechniques proposed by prior studies, which rely heavily on parameter tuning with largehistorical datasets and produce only site-specific forecasts with limited prediction horizonsunder recurrent traffic conditions. The proposed method makes no assumptions about thephysical structure of the transportation network and can be applied to different roads underdifferent traffic conditions without time constraints. KCI Citation Count: 0 |
| Author | Park, Minju Kim, Youngho Lee, Chungwon Ka, Dongju |
| Author_xml | – sequence: 1 givenname: Youngho orcidid: 0000-0002-3345-6907 surname: Kim fullname: Kim, Youngho organization: Dept. of Mobility Transformation, Korea Transport Institute – sequence: 2 givenname: Minju orcidid: 0000-0001-5982-650X surname: Park fullname: Park, Minju organization: Dept. of Big Data Application, Hannam University – sequence: 3 givenname: Dongju orcidid: 0000-0002-7446-0558 surname: Ka fullname: Ka, Dongju organization: Dept. of Civil and Environmental Engineering, Seoul National University – sequence: 4 givenname: Chungwon orcidid: 0000-0002-2845-5002 surname: Lee fullname: Lee, Chungwon email: chungwon@snu.ac.kr organization: Dept. of Civil and Environmental Engineering, Seoul National University |
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| Cites_doi | 10.3390/s17071501 10.1016/S0968-090X(02)00009-8 10.1108/EC-12-2021-0719 10.1109/TITS.2014.2311123 10.1016/j.trc.2014.01.005 10.3141/1855-04 10.1016/j.trc.2021.103466 10.3390/s19102229 10.1109/COMST.2014.2339817 10.3141/1768-19 10.1016/j.sbspro.2013.08.076 10.1080/15472450903287781 10.1016/0191-2615(84)90002-X 10.1111/j.1467-8667.2009.00620.x 10.1016/0191-2615(93)90038-C 10.1109/TITS.2019.2935152 10.1016/j.trc.2012.08.004 10.1061/(ASCE)0733-947X(2002)128:6(490) 10.1109/TITS.2012.2203122 10.1080/15472450.2013.824762 10.1016/0191-2615(94)90002-7 10.1109/TITS.2011.2174051 10.1111/j.1467-9892.1987.tb00435.x 10.1061/(ASCE)0733-947X(2003)129:6(664) 10.1016/S0010-4655(99)00366-5 10.1049/cp:20020221 10.3141/2482-17 |
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| Keywords | Non-recurrent traffic congestion Real-time traffic prediction Large-scale transportation network Prediction horizon |
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| SubjectTerms | Accident conditions Accuracy Algorithms Civil Engineering Data processing Driving conditions Engineering Environmental engineering Forecasting Geotechnical Engineering & Applied Earth Sciences Industrial Pollution Prevention Kalman filters Machine learning Metropolitan areas Real time Roads & highways Traffic accidents & safety Traffic congestion Traffic information Transportation Engineering Transportation networks Transportation planning 토목공학 |
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| Title | Real-Time Traffic Forecast System for the Accident-Prone Large-Scale Transportation Network in the Seoul Metropolitan Area |
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