INTRODUCTION Traditional methods of road safety analysis rely on direct road accident observations, data sources which are rare and expensive to collect and which also carry the social cost of placing citizens at risk of unknown danger. Surrogate safety analysis is a growing discipline in the field of road safety analysis that promises a more pro-active approach to road safety diagnosis. This methodology uses non-crash traffic events and measures thereof as predictors of collision probability and severity as they are significantly more frequent, cheaper to collect, and have no social impact. Time-to-collision (TTC) is an example of an indicator that indicates collision probability primarily: the smaller the TTC, the less likely drivers have time to perceive and react before a collision, and thus the higher the probability of a collision outcome. Relative positions and velocities between road users or between a user and obstacles can be characterised by a collision course and the corresponding TTC. Meanwhile, driving speed (absolute speed) is an example of an indicator that measures primarily collision severity. The higher the travelling speed, the more stored kinetic energy is dissipated during a collision impact . Similarly, large speed differentials between road users or with stationary obstacles may also contribute to collision severity, though the TTC depends on relative distance as well. Driving speed is used extensively in stopping-sight distance models , some even suggesting that drivers modulate their emergency braking in response to travel speed . Others content that there is little empirical evidence of a relationship between speed and collision probability . Many surrogate safety methods have been used in the literature, especially recently with the renewal of automated data collection methods, but consistency in the definitions of traffic events and indicators, in their interpretation, and in the transferability of results is still lacking. While a wide diversity of models demonstrates that research in the field is thriving, there remains a need of comparison of the methods and even a methodology for comparison in order to make surrogate safety practical for practitioners. For example, time-to-collision measures collision course events, but the definition of a collision course lacks rigour in the literature. Also lacking is some systematic validation of the different techniques. Some early attempts have been made with the Swedish Traffic Conflict Technique  using trained observers, though more recent attempts across different methodologies, preferably automated and objectively-defined measures, are still needed. Ideally, this would be done with respect to crash data and crash-based safety diagnosis. The second best method is to compare the characteristics of all the methods and their results on the same data set, but public benchmark data is also very limited despite recent efforts . The objectives of this paper are to review the definition and interpretation of one of the most ubiquitous and least context-sensitive surrogate safety indicators, namely time-to-collision, for surrogate safety analysis using i) consistent, recent, and, most importantly, objective definitions of surrogate safety indicators, ii) a very large data set across numerous sites, and iii) the latest developments in automated analysis. This work examines the use of various motion prediction methods, constant velocity, normal adaptation and observed motion patterns, for the TTC safety indicator (for its properties of transferability), and space and time aggregation methods for continuous surrogate safety indicators. This represents an application of surrogate safety analysis to one of the largest data sets to date.

Due to the complexity and pervasiveness of transportation in daily life, the use and combination of larger data sets and data streams promises smarter roads and a better understanding of our transportation needs and environment. For this purpose, ITS systems are steadily being rolled out, providing a wealth of information, and transitionary technologies, such as computer vision applied to low-cost surveillance or consumer cameras, are already leading the way. This paper presents, in detail, a practical framework for implementation of an automated, high-resolution, video-based traffic-analysis system, particularly geared towards researchers for behavioural studies and road safety analysis, or practitioners for traffic flow model validation. This system collects large amounts of microscopic traffic flow data from ordinary traffic using CCTV and consumer-grade video cameras and provides the tools for conducting basic traffic flow analyses as well as more advanced, pro-active safety and behaviour studies. This paper demonstrates the process step-by-step, illustrated with examples, and applies the methodology to a case study of a large and detailed study of roundabouts (nearly 80,000 motor vehicles tracked up to 30 times per second driving through a roundabout). In addition to providing a rich set of behavioural data about Time-to-Collision and gap times at nearly 40 roundabout weaving zones, some data validation is performed using the standard Measure of Tracking Accuracy with results in the 85-95% range.

INTRODUCTION Roundabouts are a relatively new design for intersection traffic management in North America. With great promises from abroad in terms of safety, as well as capacity—roundabouts are a staple of European road design—roundabouts have only recently proliferated in parts of North America, including the province of Québec. However, questions still remain regarding the feasibility of introducing the roundabout to regions where driving culture and road design philosophy differ and where drivers are not habituated to their use. This aspect of road user behaviour integration is crucial for their implementation, for roundabouts manage traffic conflicts passively. In roundabouts, road user interactions and driving conflicts are handled entirely by way of driving etiquette between road users: lane merging, right-of-way, yielding behaviour, and eye contact in the case of vulnerable road users are all at play for successful passage negotiation at a roundabout. This is in contrast with typical North American intersections managed by computer-controlled traffic-light controllers (or on occasion police officers) and traffic circles of all kinds which are also signalized. And while roundabouts share much in common with 4 and 2-way stops, they are frequently used for high-capacity, even high-speed, intersections where 4 and 2-way stops would normally not be justified. Resistance to adoption in some areas is still important, notably on the part of vulnerable road users such as pedestrians and cyclists but also by some drivers too. While a number of European studies cite reductions in accident probability and accident severity, particularly for the Netherlands , Denmark , and Sweden , research on roundabouts in North America is still limited, and even fewer attempts at microscopic behaviour analysis exist anywhere in the world. The latter is important because it provides insight over the inner mechanics of driving behaviour which might be key to tailoring roundabout design for regional adoption and implementation efforts. Fortunately, more systematic and data-rich analysis techniques are being made available today. This paper proposes the application of a novel, video-based, semi-automated trajectory analysis approach for large-scale microscopic behavioural analysis of 20 of 100 available roundabouts in Québec, investigating 37 different roundabout weaving zones. The objectives of this paper are to explore the impact of Québec roundabout design characteristics, their geometry and built environment on driver behaviour and safety through microscopic, video-based trajectory analysis. Driver behaviour is characterized by merging speed and time-to-collision , a maturing indicator of surrogate safety and behaviour analysis in the field of transportation safety. In addition, this work represents one of the largest applications of surrogate safety analysis to date.

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.

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.

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.