Citation

Karaer A, Kaczmarek W, Mank E, Ghorbanzadeh M, Koloushani M, Dulebenets MA, Moses R, Sando T, Ozguven EE. Data Sci. Transp. 2023; 5(2): e7.

Copyright

(Copyright © 2023, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s42421-023-00070-1

PMID

unavailable

Abstract

Pedestrian fatalities have been rising sharply in the U.S. over the last decade. To reverse this trend, several pedestrian safety countermeasures are developed by the authorities, particularly for the uncontrolled crosswalks where the majority of those fatalities occurred. Although geocoded and categorized crosswalk inventories are essential to determine where to implement the most appropriate countermeasure, building such an inventory database may take years with manual observations. Thus, this study proposes a framework using Aerial Imagery and Artificial Intelligence (AI2) to map all crosswalks on Florida public roadways according to their control strategy. A transfer learning approach with anchor box modifications is applied to the You-Only-Look-Once (YOLOv2) object detection algorithm. Crosswalks with various pavement markings and width/height ratios are identified by running YOLOv2 crosswalk detector on statewide high-resolution aerial images. Additionally, Geographic Information System-GIS techniques are used to reduce the input data, establish a geocoded connection for each detected crosswalk, and conduct a sensitivity analysis to identify signal, midblock, and driveway crosswalks. According to the performance evaluations conducted with a manually generated ground truth dataset, the proposed approach can inventory the crosswalks with 85.9% recall and 88.7% precision, while accuracy is significantly higher for signal crosswalks (94%) and major road crossings (96.6%) regardless of zebra or non-zebra pavement markings. The results revealed that there are 861 midblock, 30,784 signal, and 29,307 driveway crosswalks on Florida State Roads. The proposed framework and findings of this study can contribute to the wider use of machine learning in transport policy.

Language: en

Keywords

Aerial image; Countermeasure; Crosswalk; GIS; Midblock; YOLO