Despite similar population densities, levels of urbanization, climates, and levels of economic development, traffic accidents across the province of Québec (and the rest of Canada) are twice as high as in Sweden, as measured by traffic accident frequency and severity. Some of this disparity may be explained by differences in road design, but some of this disparity is hypothesized to also be attributed to latent behavioural factors present in the general population. The objective of this research is to investigate these latent differences in road user behaviour and experience that may explain differences in accident history beyond any road safety effects derived from road design and traffic composition. To that aim, a number of roundabouts in Québec and Sweden are selected on the basis of similarity in design, for cross-sectional comparison. Analysis of behaviour and resulting safety is performed proactively using video data, automated video analysis for road user trajectory extraction and surrogate measures of safety. Surrogate measures of safety of interest for this study include speed and time-to-collision, based on motion prediction with empirical motion patterns. Accident records available at the sample of roundabouts studied are found to be consistent with national averages of each country respectively (twice as high and severe in Québec as in Sweden). After controlling for various geometric design features, land use, construction year, traffic exposure, and traffic patterns, an overall tendency of lower speeds and fewer serious conflicts (as measured by time-to-collision) are found at the Swedish roundabouts. These results would suggest that some important latent regional factors—possibly related to driver education, culture or traffic safety enforcement—are at play at the microscopic level.
Implementation of roundabouts has been relatively new in North America, and especially so in Québec. As the original design of the roundabout originates from Europe, where a greater emphasis is placed on yielding behaviour and unsiganlized priority rules in intersection design, some degree of uncertainty remains regarding suitability of implementation of certain design features of the roundabout in a North American driving context. This research aims to investigate the safety effects of various geometric design features, land uses, and traffic conditions on road safety for roundabouts in Québec. In order to achieve this, video data is collected at a large number of roundabouts across the major population centres of the province of Québec. The video data is analyzed automatically using computer vision to extract road user trajectories at various merging zones among the roundabouts sampled. Several dozen potential geometry, land use, and traffic factors are identified at each of these merging zones and 35 merging zones are instrumented and annotated in this way. Safety at each of these merging zone is quantified using surrogate safety methods, a proactive approach to road safety which makes use of road user trajectories to model potential collision courses from ordinary road user behaviour. Basic surrogate safety measures used in this work include driving speed and yielding post-encroachment time, but the more sophisticated time-to-collision measure, modelled using motion-pattern motion-prediction, is also included in this analysis. Smaller roundabout aprons are found to be associated with higher speeds. Higher speed limits, are also associated with higher observed speeds, though only at a fraction of the posted increase. Irregular design of the merging zone, as well as presence of driveways on or immediately next to the merging zone is found to be associated with more serious conflicts (as measured by time-to-collision). Additionally, lane configuration and roundabout size is found to be less significant on the relevant safety factors than expected. Overall, geometric design and land use factors are found to be correlated with traffic conditions, which in turn are also found to be correlated with surrogate safety measures, suggesting some degree of interplay between all of these.
Despite the extensive studies on the performance of video sensors and computer vision algorithms, calibration of these systems is usually done by trial and error using small datasets and incomplete metrics such as brute detection rates. There is a widespread lack of systematic calibration of tracking parameters in the literature. This study proposes an improvement in automatic traffic data collection through the optimization of tracking parameters using a genetic algorithm by comparing tracked road user trajectories to manually annotated ground truth data with Multiple Object Tracking Accuracy and Multiple Object Tracking Precision as primary measures of performance. The optimization procedure is first performed on training data and then validated by applying the resulting parameters on non-training data. A number of problematic tracking and visibility conditions are tested using five different camera views selected based on differences in weather conditions, camera resolution, camera angle, tracking distance, and camera site properties. The transferability of the optimized parameters is verified by evaluating the performance of the optimization across these data samples. Results indicate that there are significant improvements to be made in the parametrization. Winter weather conditions require a specialized and distinct set of parameters to reach an acceptable level of performance, while higher resolution cameras have a lower sensitivity to the optimization process and perform well with most sets of parameters. Average spot speeds are found to be insensitive to MOTA while traffic counts are strongly affected.
The age of Big Data is here and many industries have already started embracing it. The transportation industry stands much to gain from large-scale data analysis due to the complexity and pervasiveness of transportation in daily life, which promises smarter roads and a better understanding of our transportation needs and environment. But this inertia is also one of the greatest challenges to big data adoption initiatives. Transitionary technologies may, however, provide the answer to kick-start this migration today. This paper presents, in detail, a practical framework for implementation of an automated, high-resolution, video-based traffic-analysis system, particularly geared towards traffic flow modelling, behavioural studies, and road safety analysis. This system collects large amounts of microscopic traffic flow data from ordinary video cameras and provides the tools for studying basic traffic flow measures as well as more advanced, pro-active safety measures. This paper demonstrates the process step-by-step illustrated with examples and applies it to a case study of a large set of roundabout data. In addition to providing a rich set of behavioural data, the analysis suggests a relationship between flow ratio and safety, between lane arrangement and safety, and is inconclusive about the relationship between approach distance and safety.