Given the recent developments in video analysis techniques, the application of these methods to the field of transportation has gained popularity. The collection of video data from roads, highways and intersections can provide detailed insight on traffic flow, road user behaviour and safety. Analysing the detailed information from the videos, would have previously required manual observers to perform traffic counts, logging of vehicle trajectories and safety critical events. More recently automated methods using computer vision techniques can potentially provide these results. The process of identifying different objects in a two-dimensional image space has been steadily improving over the years. However, they are not yet at a stage where all objects can be identified, and correctly classified.
In this study, the first objective is to evaluate whether the automated traffic analysis software are accurate enough to perform the analysis and produces results that are sufficiently reliable. Five companies were identified, and a basic comparison was conducted by providing each company sample video data that was collected in collaboration with The Hague municipality at intersections where infrastructural changes were implemented. The company with the highest performance and relevant results was selected to perform a full before-after analysis on the two redesigned intersections.
The second objective focuses on the infrastructural changes made to the intersections and the evaluation of whether or not safety was improved due to these changes. The intersection improvements are part of a project of improving road safety in the city of The Hague, where considerable attention has been paid to improving cycling safety. The two intersections are located in central The Hague. Infrastructural upgrades include implementing new dedicated cycling facilities on approaches where they did not previously exist, providing a left turning space for cyclists away from the centre of the intersection where it was previously located. Changing the traffic light green phase to provide a dedicated left turn phase for vehicles, implementing small islands to keep a distance between cars and cyclists, etc. Video data was collected for seven days from the before and after redesign, and automated methods were applied to evaluate the safety changes at the intersections using the surrogate safety measure Post-Encroachment Time (PET).
Results regarding the first objective indicate that the existing automated traffic analysis software still needs some manual input and checks to improve accuracy. Combining the manual and automated techniques relays more reliable results when evaluating car detection and tracking, whereas cyclist detection is still not fully satisfactory. There is still work needed to be done in the field of improving these automated traffic analysis tools.
Once the most reliable traffic analysis tool was applied to the set of video data, the safety results from PET (between 0 and 2 seconds) and risk (number of conflicts over exposure) indicators show that the infrastructural and traffic light phase changes were effective in improving safety at these intersections.
