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Intelligent Transportation Systems need methods to automatically monitor the road traffic, and especially track vehicles. Most research has concentrated on highways. Traffic in intersections is more variable, with multiple entrance and exit regions. This paper describes an extension to intersections of the feature-tracking algorithm described in [1].(More)
This paper advocates the use of multivariate Poisson-lognormal (MVPLN) regression to develop models for collision count data. The MVPLN approach presents an opportunity to incorporate the correlations across collision severity levels and their influence on safety analyses. The paper introduces a new multivariate hazardous location identification technique,(More)
This work aims at addressing the many problems that have hindered the development of vision-based systems for automated road safety analysis. The approach relies on traffic conflicts used as surrogates for collision data. Traffic conflicts are identified by computing the collision probability for any two road users in an interaction. A complete system is(More)
Recent research advocates the use of count models with random parameters as an alternative method for analyzing accident frequencies. In this paper a dataset composed of urban arterials in Vancouver, British Columbia, is considered where the 392 segments were clustered into 58 corridors. The main objective is to assess the corridor effects with alternate(More)
The importance of reducing the social and economic costs associated with traffic collisions can not be over-stated. The first goal of this research is to develop a method for automated road safety analysis using video sensors in order to address the problem of a dependency on the deteriorating collision data. The method will automate the extraction of(More)
This paper describes a traffic conflicts computer simulation model and graphic display for both T and 4-leg unsignalized intersections. The goal of the model is to study traffic conflicts as critical-event traffic situations and the effect of driver and traffic parameters on the occurrence of conflicts. The analysis extends conventional gap acceptance(More)
Accident data sets can include some unusual data points that are not typical of the rest of the data. The presence of these data points (usually termed outliers) can have a significant impact on the estimates of the parameters of safety performance functions (SPFs). Few studies have considered outliers analysis in the development of SPFs. In these studies,(More)
Several studies have investigated the relationship between field-measured conflicts and the conflicts obtained from micro-simulation models using the Surrogate Safety Assessment Model (SSAM). Results from recent studies have shown that while reasonable correlation between simulated and real traffic conflicts can be obtained especially after proper(More)
We introduce a graphical framework for multiple instance learning (MIL) based on Markov networks. This framework can be used to model the traditional MIL definition as well as more general MIL definitions. Different levels of ambiguity – the portion of positive instances in a bag – can be explored in weakly supervised data. To train these models, we propose(More)