Prévention et sécurité routières

thumb|Limites de vitesses dans différentes zones, affichant une limite « recommandée » de pour l'autoroute (Allemagne). thumb|Les véhicules connaissant une panne ou une autre urgence peuvent s'arrêter dans la bande d'arrêt d'urgence. La prévention routière, ou sécurité routière, est l'ensemble des mesures mises en place pour empêcher les usagers de la route d'être tués ou gravement blessés dans les accidents de la route (prévention des risques), ou en atténuer les conséquences (prévision). Les usagers de la route sont les piétons, les cyclistes, les automobilistes et leurs passagers, les chauffeurs de poids-lourd, et les passagers des transports publics routiers (principalement les autocars, autobus et tramways) La prévention routière est un domaine étudié depuis plus de 75 ans. Contexte Les accidents de la route sont l'un des problèmes de santé publique et de prévention des blessures les plus importants au monde. Le problème est d'autant plus grave que les victime

Motorway Traffic Risks Identification Model - MyTRIM Methodology and Application

Minh Hai Pham

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.

EPFL2011

Sensitivity of road safety indicators in normal and crash cases

Edward Chung, Minh Hai Pham

Road traffic safety is receiving increasing attention. Different real-time safety indicators relying on traffic sensor data can be found in the literature. Some are based on the posterior analysis of crash and traffic data; while others are based on real time traffic risk assessment using microscopic parameters (e.g., short time headways). Other non-traffic related parameters have an impact on safety too, such as day/night, state of the road (dry/wet), etc. Yet, in most studies, not all parameters are available or considered. In this paper, individual traffic, road weather and crash data are considered on a Swiss canton (Vaud). All data sources are available for at least 4 years (2002-2005) on more than 200 km of motorways. This comprehensive data enables the authors to study 1) the adaptability to Vaud canton of different safety indicators from the literature, and 2) the sensitivity of the safety indicators on Vaud canton data, both under normal situations and crash situations and specially the rear-end crashes.

2008

Motorway Traffic Risks Identification Model - MyTRIM Methodology and Application

Minh Hai Pham

EPFL2011

Prévention et sécurité routières

Anticipative Traffic Safety by Using Microsimulation

Motorway Traffic Risks Identification Model - MyTRIM Methodology and Application

Sensitivity of road safety indicators in normal and crash cases

Anticipative Traffic Safety by Using Microsimulation

Motorway Traffic Risks Identification Model - MyTRIM Methodology and Application

Sensitivity of road safety indicators in normal and crash cases