Advances in estimating pedestrian measures through artificial intelligence: From data sources, computer vision, video analytics to the prediction of crash frequency

Abstract

Data are essential for planning walkable cities that are comfortable, convenient and safe to pedestrians. Yet, in contrast to massive vehicular traffic data, data on pedestrian traffic have not been systematically collected by municipal governments. Nowadays, geospatial big data provide rich information related to human activities and, hence, can capture street scenes in an innovative way. Using bus dashcam videos (on 244.36 km of roads covered by 33 bus routes in Hong Kong) and deep learning methods (Fast R-CNN and Deepsort), this study proposes a new method for estimating pedestrian volume from this data source. In comparison, we generate two alternative measures from household travel surveys and Google Street View images. The estimates are validated by manual counts at selected locations on a main road. Using five different modelling approaches (including three variants of negative binomial and two variants of random forest models), the pedestrian volume estimates are used for predicting pedestrian-vehicle crashes. The results show that pedestrian volumes calculated from bus dashcam videos consistently show comparable, if not better, performance in explaining crash frequency. In the future, different data sources should be used to supplement each other so that a more complete picture of pedestrian flows at the city level can be obtained.