Modelling Motorway Accidents using Negative Binomial Regression
スポンサーリンク
概要
- 論文の詳細を見る
This paper investigates motorway safety by developing accident prediction models that link accident frequencies to their non-behavioural contributing factors, including traffic conditions, geometric and operational characteristics of road, and weather conditions. The study used a sample of accidents occurred from 2004 through 2010 on a 74 km long section of Auckland motorway. A number of accident prediction models were developed and assessed for their predictive ability using negative binomial regression models under three categories: first for the whole of the motorway, second for rural and urban motorway segments separately and third for motorway segments without ramp, with on-ramp and with off-ramp separately. The results uncovered the safety impacts of different non-behavioural contributing factors, in which segment length, AADT per lane and the number of lanes always have the most profound effects on accident frequency. The validation tools were applied to examine the ability of models to predict accidents.
- Eastern Asia Society for Transportation Studiesの論文
著者
-
RANJITKAR Prakash
Department of Civil and Environmental Engineering, University of Auckland
-
CHENGYE Pan
Department of Civil & Environmental Engineering, University of Auckland
-
RANJITKAR Prakash
Department of Civil & Environmental Engineering, University of Auckland
関連論文
- Investigating Drivers Response under Car-Following Situations
- Modelling Motorway Accidents using Negative Binomial Regression
- Approximation and Short-Term Prediction of Bus Dwell Time using AVL Data
- Delay Estimation at Signalized Intersections with Variable Queue Discharge Rate