Real-time estimation of pedestrian volume at button-activated midblock crosswalks using traffic controller event-based data

Pedestrian volume is essential for optimizing midblock pedestrian signals as well as for quantifying pedestrian exposure in safety analyses. However, previous methods of pedestrian volume collection require either time-consuming ground-truth data collection, or the purchase and maintenance of costly sensors in a large-scale application. Therefore, this paper proposes a novel method for large-scale pedestrian estimation at midblock crosswalks using button-pushing and signal timing events. The pedestrian arrival is modeled as a Poisson process, and two submethods are developed to estimate pedestrian volume at one-stage and two-stage button-activated mid block crosswalks (BAMCs). To address the issue of missing signal cycles at two-stage BAMCs, all missing cycles that are identified by using the proposed paired signal cycle identification algorithm are accounted for and added by minimizing the error between estimation results of two stages. Eight days of the ground-truth pedestrian volume is manually collected from two study midblock crosswalks to evaluate the proposed methods. On average, 235 and 230 pedestrians per day were observed to cross the one-stage BAMC and two-stage BAMC, respectively. The average mean absolute error of estimated pedestrian volume using a one-hour interval is 2.27 and 1.78 ped/hour at two study locations, respectively. The evaluation results indicate that the proposed methods are promising for estimating pedestrian volume at midblock crosswalks using event based data. A further sensitivity analysis of changing the estimation interval shows that the one-hour interval pedestrian volume estimation is recommended as having the least error.

Automated summary

This paper proposes a novel method for estimating pedestrian volume at midblock crosswalks using button-pushing and signal timing events, and evaluation results indicate that the proposed methods are promising for estimating pedestrian volume with event based data.