Ali Kassim

Investigation of the Effect of Super Sharrows on Cyclist and Vehicle Behavior

This study examined the potential effect of special paintings of shared lane markings (super sharrows) on a number of operational and safety performance parameters for cyclists and motor vehicles. These performance parameters were used to assess pretreatment and posttreatment behavior when cyclists and motor vehicles were near one another. The performance parameters were (a) rate of lane change maneuvers performed by vehicles in the presence as well as the absence of cyclists and (b) lateral spacing between cyclists, vehicles, and curb edge. In general, the main objectives of this treatment were (a) providing cyclists with comfort by allowing them to ride in the middle of the travel lane and (b) promoting safe passing by motor vehicles. The effect of the super sharrows on cyclists and motor vehicles was analyzed with statistical analysis by comparing pretreatment and posttreatment conditions. The key findings are as follow: (a) super sharrows had an effect on motor vehicle lane change maneuvers, represented by an increase in the percentage of motor vehicles that changed from the right lane (location of super sharrows) to the left lane with the presence of a cyclist on the right lane; (b) the number of motor vehicles that changed from right lane to left lane and back to right lane in both full and partial encroachment into the left lane decreased; (c) the number of the motor vehicle lane change maneuvers from left to right lane decreased; and (d) cyclists were found to be riding farther from the right curb with the presence of the super sharrows.

Operational Evaluation of Central Sharrows and Dooring Zone Treatment on Road User Behavior in Ottawa, Canada

The purpose of this study is to determine how new pavement markings (central sharrows and indication of “dooring zone”) influence cyclist and motorist interactions and positioning, especially with respect to the distance cyclists travel from the parking edge line. The markings are designed to discourage cyclists from riding in the dooring-zone area and to ride in the center of the travel lane to avoid serious safety hazards. A number of safety performance parameters were used to assess whether safer conditions existed after the new treatment was installed. Safe motorist behavior was defined as: (a) motor vehicle following a cyclist with or without visible oncoming traffic in the opposing lane, and (b) motor vehicle passing a cyclist at a safe lateral distance. Safe cyclist behavior was defined as: cyclist riding in the lane position indicated by the central sharrows and outside of the dooring-zone area. The findings indicate that the central sharrows and dooring-zone markings created more favorable conditions for cyclist safety and for motorist compliance. Cyclists rode further from the parking edge line and closer to the sharrows that were marked in the center of the travel lane, and motorists were shown to be less likely to pass and more likely to follow cyclists.

Modeling cyclists speed at signalized intersections: Case study from Ottawa, Canada

The study of cyclist behavior for developing realistic and reliable behavioral models is attracting research focus. Achieving a detailed understanding of cyclist behavior is a cornerstone in building micro-simulation models and ultimately creating a more sustainable transportation system. Cyclist behavior is especially important at traffic intersections due to the exposure to turning and crossing vehicle movements. This study focuses on cyclist speed modelling at traffic intersections in urban areas. Data collection was conducted at three different traffic intersections in the downtown area in Ottawa, Canada. Video monitoring covered cyclists, vehicles, and pedestrian movements. Cyclists approached these intersections through physically segregated bike lanes. Cyclist speed was measured based on metric measurements at the intersections and temporal measurements from the video data. The variables associated with cyclist speed that were examined are: pedestrian crossing movements, adjacent vehicle traffic, traffic signal indication, type of right-turn lane, potential conflicts with turning vehicles, and occurrence of traffic violations. Multivariate regression analysis was conducted to link cyclist speed and the explanatory variables. The resulting R2 values were 0.43 for predicting cyclist crossing speed and was 0.56 for predicting the change in cyclist speed while approaching the intersection. A number of statistically significant associations were observed and documented. Overall, considering the data collection effort, sample size, and the predictive power of this regression model, it can be concluded that the developed models are of potential practical use in predicting cyclist crossing speed.