Understanding the Factors Influencing Pedestrian Walking Speed over Elevated Facilities using Tree-Based Ensembles and Shapley Additive Explanations
Abstract Accurate estimation of factors affecting pedestrian walking speed is of paramount importance for efficient operation and management of at-grade and grade-separated infrastructures (such as foot over bridges or skywalks). Understanding such factors helps in planning for better circulation of pedestrians within confined elevated passageways as well as evacuation preparedness during emergencies. The walking speed on elevated infrastructure generally depends on the microscopic factors (demographics characteristics), macroscopic factors (average flow and density), and geometric factors (obstruction, land use type, length, connectivity, and effective width). The wide variability of these factors and their impact on walking speed makes the speed prediction modeling complex. Therefore, accuracy of such models depends on accurate field data collection, identification of pertinent variables, and implementation of appropriate modeling approaches. With the increase in computational capabilities, tree-based ensembles have gained immense popularity due to their high prediction accuracy in comparison to traditional regression models. The tree-based ensembles provide better interpretable results without a huge data requirement and are able to capture the complex non-linear relationships. These properties make tree-based ensemble models better candidates for modeling pedestrian walking speed, however, exploration on the tree-based ensemble in pedestrian related research is limited. In the current study, an attempt is made to model and compare seven tree-based models (including ensembles) to suggest the best modeling approach to identify the dominating factors and accurate prediction of pedestrian walking speeds over elevated walkways. The result of the present study showed that Gradient Boosted Trees (MAE 9.27) and Light Gradient Boosted Trees (MAE: 9.96) were best in predicting walking speed over the skywalk and foot over bridge facilities, as these boosting based methods improved the weak trees (on the basis of accuracy) sequentially. The variable importance of final models was estimated using SHapley Additive exPlanations (SHAP) which revealed that walking speed was dependent on the average flow, average density, and length of the facility. Moreover, other features such as gender, age, height, and width of the facility also play a significant role in determining the pedestrian walking speeds. The identification of important variables not only provides better insight on factors that affect walking speed over elevated facilities but also provides a valuable source of information to researchers, planners, and policymakers for better designing, operation, and management of the elevated pedestrian infrastructures.
