Motorway Traffic Risks Identification Model - MyTRIM Methodology and Application
Road traffic crashes are becoming increasing concerns in many countries. In Europe, many efforts have been devoted to improve road traffic safety yet the important target of halving the number of yearly road deaths in 2010 could not be achieved in many European countries. Among different road types, motorways are safe by design yet crashes if occur would be severe due to high speed practiced. If motorway traffic crash risk could be identified, lives could be saved and severity could be reduced. For this objective, the current thesis aims to establish a methodology for developing models capable of identifying real-time traffic crash risk on motorways. A real-time MotorwaY Traffic Risk Identification Model (MyTRIM) is developed for a study site on motorway A1 in Switzerland. MyTRIM is tested, validated with real data. Three types of historical data altogether available at the study site are used for developing MyTRIM. The data include individual vehicle traffic data collected from double loop traffic detectors, meteorological data from meteorological station located near the study site, and a crash database containing crashes recorded by the police. Based on crash time, pre-crash data representing traffic and meteorological conditions leading to crashes are extracted. Similarly, non-crash data representing traffic and meteorological conditions that are unrelated with crashes are also extracted. As crashes are rare events, a methodology for sampling non-crash data comparable with pre-crash data is developed using clustering – classification basis: non-crash data are clustered into groups; pre-crash data are classified into obtained groups; pre-crash and non-crash data within one group are similar and therefore, comparable. Each group is called a traffic regime. Under each traffic regime, a regime-based Risk Identification Model (RIM) is developed to differentiate pre-crash and non-crash data. Given a new datum, regime-based RIM must be able to classify the datum into pre-crash or non-crash. As a result of the model development, variables which are important for the differentiation are also identified. These important variables can be potential for implementing countermeasures to prevent the risk from ending up with a crash. MyTRIM is developed based on the outputs from regime-based RIM. MyTRIM memorizes the latest risk evolution to predict whether there is crash risk in the coming time interval. Regime-based RIM and MyTRIM are tested and validated using real data. Results show that regime-based RIM and MyTRIM perform with high accuracy. The output of MyTRIM can be useful for traffic operators as an input for actively managing the traffic. The developed methodology can be applied for any motorway traffic sections where similar data are available.
Keywords: motorway traffic safety ; accident prevention ; real-time risk identification ; individual vehicle data ; meteorological data ; imbalanced data sets ; rare events ; MyTRIM ; sécurité du trafic autoroutier ; prévention des accidents ; identification des risques en temps réel ; prédiction du risque d'accident ; données de véhicule individuel ; données météorologiques ; déséquilibre des classes ; événements rares ; MyTRIMThèse École polytechnique fédérale de Lausanne EPFL, n° 4998 (2011)
Programme doctoral Environnement
Faculté de l'environnement naturel, architectural et construit
Institut d'ingénierie civile
Laboratoire des voies de circulation
Record created on 2011-01-13, modified on 2016-08-09