Development of network-scale traffic state estimation method considering the uncertainty of road condition caused by natural disaster

2023 Fiscal Year Final Research Report

• Project/Area Number: 21K20440
• Research Category: Grant-in-Aid for Research Activity Start-up
• Allocation Type: Multi-year Fund
• Review Section: 0303:Civil engineering, social systems engineering, safety engineering, disaster prevention engineering, and related fields
• Research Institution: Hokkaido University
• Principal Investigator: Tani Ryuichi 北海道大学, 工学研究院, 助教 (80908426)
• Project Period (FY): 2021-08-30 – 2024-03-31
• Keywords: 冬期道路管理 / 交通状態推定 / 均衡配分

Outline of Final Research Achievements

This study developed a methodology to estimate the driving performance of a road network considering the fluctuation of the driving performance of a road network depending on the change in the road conditions caused by natural disaster. Firstly, focusing on the fluctuation of the traffic flow characteristics depending on the winter road conditions, we proposed a method to estimate a stochastic link capacity depending on the snow width. The model was verified by using the data observed in Sapporo city. Secondary, assuming the usage of the network-scaled traffic data observed incompletely and spatiotemporally, we developed a method to estimate network-scaled traffic states as a multivariate random variable. The proposed model is formulated as a maximum likelihood problem constrained to the equilibrium model to overcome the data shortage.

Free Research Field

交通計画

Academic Significance and Societal Importance of the Research Achievements

本研究の意義は，天候や気候条件といった道路環境の違いを考慮して，道路の走行性能指標の確率的な特徴を明らかにする手法を開発した点と，道路利用者の経路選択行動を考慮した上で，道路ネットワークレベルの交通状態を推定する手法を開発した点である．前者について，道路環境の違いによって道路リンクの交通容量の確率的特性が変化することが定性的に説明されてきたがこの変化を定量的に説明する手法を開発した．後者について，地方のように高密度・高頻度な交通観測が実現できない地域では，観測データから交通状態を統計的に推定するのが難しい．そこで，観測機会の不足を均衡配分モデルの利用によって補う最尤推定モデルを開発した．