Citation

Wang Y, Liu D, Luo J. Int. J. Environ. Res. Public Health 2022; 19(21): e14054.

Copyright

(Copyright © 2022, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/ijerph192114054

PMID

36360941

Abstract

In the prioritized vehicle traffic environment, motorized transportation has been obtaining more spatial and economic resources, posing potential threats to the travel quality and life safety of non-motorized transportation participants. It is becoming urgent to improve the safety situation of non-motorized transportation participants. Most previous studies have focused on the psychological aspects of pedestrians and cyclists exposed to the actual road environment rather than quantifying the objective safety hazards, which has led to a non-rigorous evaluation of their basic safety situation. An integrated processing approach is proposed to comprehensively and objectively evaluate the overall safety level of non-motorized transportation participants on each road segment. Our main contributions include (1) the universal approach is established to automatically identify hazard scenarios related to non-motorized transportation and their direct causing factors from street view images based on multiple deep learning models; (2) a seed points spreading algorithm is designed to convert semantic images into target detection results with detail contour, which breaks the functional limitation of these two types of methods to a certain extent; (3) The safety situation of non-motorized transportation on various road sections in Gulou District, Nanjing, China has been evaluated and based on this, a series of suggestions have been put forward to guide the better adaptation among multiple transportation participants.

Language: en

Keywords

hazard scenarios; multiple deep learning; non-motorized transportation; seed points spreading + PSPNet algorithm; street view images