Investigation of pedestrian jaywalking behaviour at mid-block locations using artificial neural networks

This research presents a framework based on artificial neural networks (ANNs) to predict jaywalker's trajectory while crossing the road. In this process, different causal and conditional variables related to jaywalking, such as, gender, direction of crossing, walking or running, cell phone use, roadway lane number, etc. are taken as input variables. The dataset comprises of 2504 samples which is collected under non-lane based heterogeneous traffic conditions. Through testing predictive performance of several ANN architectures based on correlation coefficient and mean square error, the best ANN architecture to predict jaywalker's movement is determined. The optimal ANN architecture comprises of 9 input nodes, 10 hidden nodes and 1 output node. This study also determines the microscopic variables, i.e., speed, flow and density, associated with jaywalking. The results indicate that the average speed of male jaywalkers is higher than female jaywalkers. The density is found to be higher in the last lanes of the crossing paths. In addition, this research develops jaywalker trajectories in order to understand their position shifting strategies. Findings suggest that `median to sidewalk' movement trajectories tend to move closer to the foot-over bridge, whereas, the `sidewalk to median' movement trajectories tend to move further away from foot-over bridge. The outcome of this research is expected to assist driver assistance technology as well as Connected and Autonomous Vehicle (CAV) technologies by allowing vehicles to safely navigate through both clustered and individual jaywalkers.

Automated summary

This research utilizes artificial neural networks to predict jaywalker trajectories, determining the best model through testing various ANN architectures, and uncovering movement patterns and position shifting strategies of jaywalkers.