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.

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The objective of this study is to estimate the real time travel times on urban networks that are partially covered by moving sensors. The study proposes two machine learning approaches; the random forest (RF) model and the multi-layer feed forward neural network (MFFN) to estimate travel times on urban networks which are partially covered by moving sensors. A MFFN network with three hidden layers was developed and trained using the back-propagation learning algorithm, and the neural weights were optimized using the Levenberg–Marquardt optimization technique. A case study of an urban network with 100 links is considered in this study. The performance of the proposed models was compared to a statistical model, which uses the empirical Bayes (EB) method and the spatial correlation between travel times. The models’ performances were evaluated using data generated from VISSIM microsimulation model. Results show that the machine learning algorithms, e.g., RF and ANN, achieve average improvements of about 4.1% and 2.9% compared with the statistical approach. The RF, MFFN, and the statistical approach models correctly predict real time travel times with estimation accuracies reaching 90.7%, 89.5%, and 86.6% respectively. Moreover, results show that at low moving sensor penetration rate, the RF and MFFN achieve higher estimation accuracy compared with the statistical approach. At probe penetration rate of 1%, the RF, MFFN, and the statistical approach models correctly predict real time travel times with estimation accuracy of 85.6%, 84.4%, and 80.9% respectively. Furthermore, the study investigated the impact of the probe penetration rate on real time neighbor links coverage. Results show that at probe penetration rates of 1%, 3%, and 5%, the models cover the estimation of real time travel times on 73.8%, 94.8%, and 97.2% of the estimation intervals.

This study aims to model pedestrian temporal violation behavior at signalized crosswalks. Video data of pedestrian crossing behavior were collected from three locations in China and were used to investigate the effect of several factors on pedestrian temporal violation behavior. The temporal violation behavior was analyzed using the relationship between pedestrian waiting duration and their endurance probabilities. A fully parametric duration model with Weibull distribution was used to model the temporal violation behavior, and the cluster-specific heterogeneity among the three study sites was accounted for using random intercepts. Six variables were identified to significantly affect the violation behavior: pedestrian gender and phone distraction status, location type, pedestrian volume, day of the week, and time of the day. The results show that pedestrians are likely to disobey traffic regulations when there are longer waiting durations. Male pedestrians have a higher violation tendency than females. Pedestrians distracted by their phones have longer waiting durations than undistracted pedestrians. Signalized road segment crosswalks are associated with higher temporal violation propensity than signalized intersection crosswalks. Pedestrians are more likely to commit violations at higher pedestrian densities. Weekdays are associated with shorter waiting durations and higher violation tendency than weekends. Pedestrians are more likely to violate traffic regulations in the morning than at midday and in the evening. These findings give insights into the pedestrian crossing behavior to better accommodate pedestrians and improve safety.

This study investigates the microscopic interaction behaviour between cyclists and pedestrians in shared space environments. Video data was collected at the Robson Square shared space in downtown Vancouver, British Columbia. Trajectories of cyclists and pedestrians involved in 208 interactions (416 trajectories) were extracted using computer vision algorithms. The extracted trajectories were used to define different indicators for the analysis. The indicators included the speed and acceleration profiles and the longitudinal and lateral distances between road users during different phases of the interactions. The study also investigated the collision avoidance mechanisms employed by road users to avoid collisions with other shared space users. The collision avoidance mechanisms included changing the walking–cycling speed and changing the movement direction. The results showed that the collision avoidance mechanisms depend on the shared space density and the space available for road users. The study identified a set of parameters that can be used to calibrate microscopic cyclist–pedestrian modeling platforms to represent the behaviour of pedestrians and cyclists in shared space environments.

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