DATA FUSION AND FEATURE COMPOSITION APPROACH TO SEQUENTIAL ACCIDENT DURATION FORECASTING

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概要

• 論文の詳細を見る

• This study creates an adaptive data fusion procedure to represent the sequential forecast of accident duration. This procedure includes two Artificial Neural Network-based models. Model A is used to forecast the duration time at the instant of accident notification while Model B provides multi-period updates of duration time after the moment of accident notification. These two models together provide a sequential forecast of accident duration from the accident notification to the accident site clearance. With these two models, the estimated duration time can be provided by plugging in relevant traffic data as soon as an accident is being notified. Through the feature composition approach, the number of inputs can be decreased while the relevant traffic characteristics are preserved. This study shows very promising practical applicability of the proposed models in the Intelligent Transportation Systems context.

• Eastern Asia Society for Transportation Studiesの論文

著者

• LEE Ying

  Department of Hospitality Management, Ming Dao University

• WEI Chien-Hung

  Department of Transportation and Communication Management Science National Cheng Kung University

関連論文

• MULTI-DEMSIONAL INTERGRATED TRANSPORT POLICIES OPTIMIZATION BASED ON GENETIC ALGORITHM AND REVISION REPORT
• APPLYING DATA FUSION TECHNIQUES TO TRAVELER INFORMATION SERVICES IN HIGHWAY NETWORK
• DESIGN OF DYNAMIC NEURAL NETWORKS TO FORECAST SHORT-TERM RAILWAY PASSENGER DEMAND
• DATA FUSION AND FEATURE COMPOSITION APPROACH TO SEQUENTIAL ACCIDENT DURATION FORECASTING

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