A traffic conflict is usually composed of a chain of events in which at least one of the involved road users performs some sort of evasive action to avoid a collision. An evasive action usually involves powerful braking, which leads to sudden, negative change in acceleration (deceleration). The temporal dynamics (variation over time) of the acceleration of a vehicle is represented by the jerk profile. More formally, jerk is the derivative of the acceleration. In the case of an evasive action by braking, the jerk profile is characterized by strong, negative values. This study examined two issues in the quest to understand the benefits of evasive action analysis. The first issue was whether jerk profiles can be used to identify critical traffic events (conflicts). The second issue addressed the validity of the assumption that the deceleration profile is inadequate as a stand-alone measure for conflict identification. Automated video analysis was used to collect traffic data and analysis was applied on two data sets with distinct traffic patterns. The study revealed a significant difference between the jerk behavior of the groups of drivers involved in conflictive and normal traffic interactions. It also showed instances in which automated jerk evaluation was successful in finding conflicts undetected by conventional conflict indicators. The same could not be demonstrated for the road users’ deceleration behavior. These findings support earlier studies on the shortcomings of the use of deceleration data for conflict identification.

The objective of the study is to assess the effect of the use of cell phones while walking at urban crosswalks. The methodology uses recent findings in health science concerning the relationship between tempo-spatial characteristics of gait and the cognitive abilities of pedestrians. Gait measures are shown to be affected by the complexity of the task (e.g., talking and texting) performed during walking. This study focuses on the effect of distraction states, distraction types (visual such as texting/reading and auditory such as talking/listening), and pedestrian-vehicle interactions on the gait parameters of pedestrians at crosswalks. Experiments are performed on a video data set near a college campus in the city of Kamloops, British Columbia. The analysis relies on automated video-based data collection using a computer vision technique. The benefits of such an automated system include the ability to capture the natural movement of pedestrians and minimizing the risk of disturbing their behavior. Results show that pedestrians distracted by texting/reading (visually) or talking/listening (auditory) while walking tend to reduce and control their walking speed by adjusting their step length or step frequency, respectively. Pedestrians distracted by texting/reading (visually) have significantly lower step length and are less stable in walking. Distracted pedestrians involved in interactions with approaching vehicles tend to reduce and control their walking speeds by adjusting their step frequencies. This research can find applications in pedestrian facility design, modeling and calibrating pedestrian simulations, and pedestrian safety intervention programs and legislative actions.