Using microscopic video data measures for driver behavior analysis during adverse winter weather: opportunities and challenges

ABSTRACT: This paper presents a driver behavior analysis using microscopic video data measures including vehicle speed, lane-changing ratio, and time to collision. An analytical framework was developed to evaluate the effect of adverse winter weather conditions on highway driving behavior based on automated (computer) and manual methods. The research was conducted through two case studies. The first case study was conducted to evaluate the feasibility of applying an automated approach to extracting driver behavior data based on 15 video recordings obtained in the winter 2013 at three different locations on the Don Valley Parkway in Toronto, Canada. A comparison was made between the automated approach and manual approach, and issues in collecting data using the automated approach under winter conditions were identified. The second case study was based on high quality data collected in the winter 2014, at a location on Highway 25 in Montreal, Canada. The results demonstrate the effectiveness of the automated analytical framework in analyzing driver behavior, as well as evaluating the impact of adverse winter weather conditions on driver behavior. This approach could be applied to evaluate winter maintenance strategies and crash risk on highways during adverse winter weather conditions

Automatic traffic data collection under varying lighting and temperature conditions in multimodal environments: thermal versus visible spectrum video-based systems

Vision-based monitoring systems using visible spectrum (regular) video cameras can complement or substitute conventional sensors and provide rich positional and classification data. Although new camera technologies, including thermal video sensors, may improve the performance of digital video-based sensors, their performance under various conditions has rarely been evaluated at multimodal facilities. The purpose of this research is to integrate existing computer vision methods for automated data collection and evaluate the detection, classification, and speed measurement performance of thermal video sensors under varying lighting and temperature conditions. Thermal and regular video data was collected simultaneously under different conditions across multiple sites. Although the regular video sensor narrowly outperformed the thermal sensor during daytime, the performance of the thermal sensor is significantly better for low visibility and shadow conditions, particularly for pedestrians and cyclists. Retraining the algorithm on thermal data yielded an improvement in the global accuracy of 48%. Thermal speed measurements were consistently more accurate than for the regular video at daytime and nighttime. Thermal video is insensitive to lighting interference and pavement temperature, solves issues associated with visible light cameras for traffic data collection, and offers other benefits such as privacy, insensitivity to glare, storage space, and lower processing requirements

A video-based methodology for extracting microscopic data and evaluating safety countermeasures at intersections using surrogate safety indicators

Pedestrians and cyclists are amongst the most vulnerable road users as their accidents involving motor vehicles result in high injury and fatality rates for these two modes. Data collection for non-motorized road users remains a challenge and automated data collection methods are far more advanced for motorized traffic. To improve cyclist safety and promote urban cycling, cities have been building bicycle infrastructure, such as cycle tracks and bicycle boxes. These facilities have been built and expanded but due to the lack of appropriate data and problems with automated cyclist data collection, very little in-depth research has been carried out to investigate the safety impacts of these infrastructures. The majority of non-motorized safety studies are based on traditional methods which use observed accident and injury data. An important shortcoming of this approach is the need to wait for accidents to occur over several years. An alternative to traditional safety analysis is surrogate safety methods which can provide statistically sufficient data in a shorter time period. However, to perform surrogate safety studies, microscopic data from road users is needed. To address the shortcomings of the current literature and to improve the microscopic data collection tools for non-motorized road users, this thesis presents an automated methodology to classify road users in traffic videos – this methodology is complementary to existing object-tracking tools. The methodology is tested and validated using a large dataset from signalized intersections with high mixed traffic in Montreal, Canada. Road users are classified into three main categories: pedestrian, cyclist, and motor vehicle, with an overall accuracy of over 95 %. The proposed methodology is capable not only of counting the movements of the different road users (generating exposure measures), but also provides microscopic data separately for each road user type for safety analysis. As a result, performing automated surrogate safety studies becomes possible for facilities with mixed motorized and non-motorized traffic. As part of this thesis, the relationship between the surrogate safety measure used in this research, post encroachment time, and the historical accident data has been investigated and shows promising correlation. Using several hours of video recorded from a sample of signalized intersections in Montreal, and analyzed using the proposed techniques, the safety effects of two types of bicycle infrastructure, cycle tracks and bicycle boxes, have been investigated. The results show that based on the interactions between cyclists and turning vehicles, having a cycle track on the right side of the road is safer than not having a cycle track or than having a cycle track on the left side of the road. Also the study on the safety of bicycle boxes at intersections reveals that this type of bicycle facility is associated with a significant reduction in the severity of interactions (increase in post encroachment time) between cyclists and vehicles.Les piétons et les cyclistes sont parmi les usagers les plus vulnérables de la route puisque leurs accidents avec des véhicules motorisés entraînent des taux élevés de blessures et de décès pour ces usagers. La collecte de données pour les usagers de la route non-motorisés demeure un défi et les méthodes de collecte de données automatiques sont beaucoup plus avancés pour les usagers motorisés. Pour améliorer la sécurité des cyclistes et promouvoir le cyclisme urbain, les villes construisent des infrastructures pour les cyclistes comme des pistes cyclables et des sas vélo (« bicycle box »). Ces aménagements se sont répandus, mais en raison du manque de données et des problèmes avec la collecte de données automatique pour les cyclistes, peu d'études approfondies ont été réalisées pour mesurer les impacts sur la sécurité de ces infrastructures. La majorité des études de la sécurité des usagers non-motorisés repose sur des analyses traditionnelles des données d'accidents et de blessures. Une lacune importante de cette approche traditionnelle est la nécessité d'attendre que des accidents se produisent pendant plusieurs années pour effectuer les évaluations des traitements de sécurité. Parmi les alternatives aux méthodes d'analyse de la sécurité traditionnelles, se développent les méthodes substituts de sécurité qui peuvent fournir des données statistiquement suffisantes pendant une période de temps plus courte. Cependant, pour réaliser des études substituts de sécurité, des données microscopiques sur les usagers de la route sont nécessaires. Pour combler les lacunes de la littérature et améliorer la collecte de données microscopiques pour les usagers de la route non motorisés, cette thèse présente une méthode automatique (qui complète un système de suivi vidéo développé précédemment) pour classifier les usagers de la route dans des vidéos de circulation. Les usagers de la route sont classifiés en trois catégories principales: les piétons, les cyclistes et les véhicules motorisés, avec une précision globale de plus de 95 %. En plus de la capacité de cette méthode à compter les différents mouvements des usagers de la route (constituant des mesures d'exposition), cette méthode fournit des données microscopiques de trajectoire pour chaque type d'usager. En conséquence, la réalisation d'études substituts de sécurité automatiques, y compris des études impliquant des usagers non-motorisés, devient possible. Dans le cadre de cette thèse, la relation entre la mesure substitut de sécurité utilisée dans cette recherche, soit le temps post-empiètement, et des données historiques d'accidents a été étudiée et montre une forte corrélation. À l'aide de plusieurs heures de vidéo enregistrées dans un ensemble d'intersections à Montréal, analysées avec les techniques proposées, les effets de deux types d'infrastructures cyclables, à savoir les pistes cyclables et les sas vélo, ont été étudiés. Les résultats montrent que pour les interactions entre les cyclistes et les véhicules qui tournent, une piste cyclable sur le côté droit de la route est plus sûre que l'absence de piste cyclable ou qu'une piste cyclable sur le côté gauche de la route. De plus, l'étude de la sécurité des sas vélo aux intersections révèle que ce type d'installation est associé à une réduction significative de la sévérité des interactions (augmentation du temps post empiétement) entre les cyclistes et les véhicules

Towards a comprehensive safety evaluation of cycling infrastructure including objective and subjective measures

Cycling infrastructure has been implemented worldwide to promote bicycle use and to minimize injury risk. A comprehensive evaluation of such infrastructure is required to assess its success. In terms of safety, assessments ideally focus on both objective and subjective parameters. This study explores the application of a combined objective-subjective safety assessment approach in a pre-post analysis of a left-turning bicycle box in Zurich, Switzerland. A computer-based video technology was used to objectively measure passing distance between bicycles turning left and continuing motor vehicles passing on the right. In an in-situ survey perceived safety while crossing the intersection and a photo-based assessment of the intersection were collected as indicators of subjective safety. Median passing distance between bicycles and motor vehicles did not significantly change after the implementation of the bicycle box, but the shortest distances were increased. Perceived safety while crossing the intersection was significantly higher after marking the bicycle box, which is consistent with safety expectations expressed based on photos with and without left-turning box. Gender and general perception of traffic safety within the city are significant determinants of expected and perceived intersection safety. Women expect greater safety gains from the left-turning box (photo based), but its effect on perceived safety when actually crossing the intersection does not differ between genders. While the applied video technology is not yet practice-ready, it shows great potential to complement cycling safety evaluations, in combination with self-reported perceived safety indicators

Automatic Traffic Data Collection under Varying Lighting and Temperature Conditions in Multimodal Environments: Thermal versus Visible Spectrum Video-Based Systems

Vision-based monitoring systems using visible spectrum (regular) video cameras can complement or substitute conventional sensors and provide rich positional and classification data. Although new camera technologies, including thermal video sensors, may improve the performance of digital video-based sensors, their performance under various conditions has rarely been evaluated at multimodal facilities. The purpose of this research is to integrate existing computer vision methods for automated data collection and evaluate the detection, classification, and speed measurement performance of thermal video sensors under varying lighting and temperature conditions. Thermal and regular video data was collected simultaneously under different conditions across multiple sites. Although the regular video sensor narrowly outperformed the thermal sensor during daytime, the performance of the thermal sensor is significantly better for low visibility and shadow conditions, particularly for pedestrians and cyclists. Retraining the algorithm on thermal data yielded an improvement in the global accuracy of 48%. Thermal speed measurements were consistently more accurate than for the regular video at daytime and nighttime. Thermal video is insensitive to lighting interference and pavement temperature, solves issues associated with visible light cameras for traffic data collection, and offers other benefits such as privacy, insensitivity to glare, storage space, and lower processing requirements