Automated diagnoses of safety problems at collision prone intersections using computer vision techniques

• Main Author: Mahiban, Aaron
• Language: English
• Published: University of British Columbia 2013
• Online Access: http://hdl.handle.net/2429/43884

Road safety studies attempt to develop solutions to deficiencies by identifying causes and prescribing remedies. Most often, traffic safety engineers use collision information to detect potential problem locations and to provide assessments of treatments. The main short-coming of this technique is that it analyzes past information to determine whether a problem exists. This reactive approach requires an expert tasked to improve safety having to stand by and wait for collisions to occur. Many experts have recognized the need for a more proactive approach in order to reduce the analysis period and provide timely safety improvements. One particularly promising alternative is the use of traffic conflicts as surrogates to actual collisions. Conflict data collection offers many benefits to that of collisions, including their relative frequency, and marginal social cost. Traffic conflict studies can be deployed in any location, need little planning, and do not require a vigilant database maintenance. However, since trained human reviewers are required there are significant costs associated with in-situ conflict observation studies. Furthermore, traffic conflict studies also rely on human judgement, which introduces subjectivity into results. The goal, therefore, is to find a way to harness data-rich traffic conflicts that is both efficient and fundamentally objective. This thesis presents the novel use of an automated traffic conflict detection tool to diagnose safety issues at intersections with known safety deficiencies. Two intersections were analyzed to determine which movement types were over-represented. Once the most dangerous movements were identified, characteristics of the road user, environment, and conflicts themselves were analyzed to provide an educated recommendation for safety improvement. When the treatments had been implemented for some time, additional data was collected and similarly analyzed to determine whether it had achieved the intended goal. The outcomes of this research provide evidence that objective and surrogate safety indicators can effectively be used to identify safety problems at intersections. In addition, the rich data collected using the automated traffic conflict technique can be mined to understand the mechanisms leading to and resulting in offending conflicts. This information can help traffic safety experts make informed decisions for focused countermeasure implementation.