Citation

Tiwari G. Int. J. Inj. Control Safe. Promot. 2023; 30(3): 325-326.

Copyright

(Copyright © 2023, Informa - Taylor and Francis Group)

DOI

10.1080/17457300.2023.2245654

PMID

37643463

Abstract

Digital technologies and Artificial Intelligence (AI) based solutions to enable safer mobility and transportation systems are being promoted in all countries. Policy makers and other stakeholders, specifically in low and middle income countries where the traffic fatalities continue to rise, are pinning their hopes on digital technologies to reduce traffic injuries and fatalities. The applications range from the use of AI-based Advance Driver Assistance Systems (ADAS), identification of black spots, and detection of violations of traffic laws and regulations, to promoting road safety awareness and driver training. Can we rely on the digital technology revolution alone to solve the growing burden of traffic injuries? This issue of the journal presents results from research studies addressing the varied dimensions of traffic and other injuries where sometimes, digital technologies assist in providing more reliable data. However, the continuation of sound scientific methods using many different sets of data can show the way forward for improving our understanding of new and emerging problems of traffic injuries.

Rahul Goel from India, reports on the population-level estimate of bicycle use and fatality risk in a data-poor setting. While the number of injuries per unit distance helps in the comparison of risks in different categories of vehicles, the author here suggests a new method to estimate the fatality risk per km for cyclists, motorcyclists, and car occupants. He uses the annual city-wide motorcycle distance, and the ratio of city-wide motorcycle volume to cycle volume counts. This is a case study of Delhi, India. The three-year annual average number of fatalities for cyclists is 52; for motorcyclists 541 and 52.6 for car occupants. Dividing these figures by the average person-kilometres travelled, we arrive at the average fatality risk of 20.8 per km for cyclists; 9.5 for motorcyclists; and 0.53 for car occupants.

Zhiyuan Sun et al. from China, have made a submission on the subject of vulnerable road users and motor vehicles. This study is based on police-reported crash data from Shenyang in China. Unlike the generally used clustering techniques to divide crashes, the authors, in a hybrid approach, use a latent class analysis which uses a probabilistic model...

Language: en