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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Bibliographic Details
Published inKSCE journal of civil engineering Vol. 27; no. 7; pp. 3085 - 3096
Main Authors Kim, Youngho, Park, Minju, Ka, Dongju, Lee, Chungwon
Format Journal Article
LanguageEnglish
Published Seoul Korean Society of Civil Engineers 01.07.2023
Springer Nature B.V
대한토목학회
Subjects
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
Non-recurrent traffic congestion
Real-time traffic prediction
Large-scale transportation network
Prediction horizon
Online AccessGet full text
ISSN1226-7988
1976-3808
DOI10.1007/s12205-023-0349-9

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Summary: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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ISSN:1226-7988
1976-3808
DOI:10.1007/s12205-023-0349-9