Could pedestrian crashes and their severity be estimated without using actual crash data?

Devising countermeasures for improving pedestrian safety, especially targeted measures for severe and non-severe crashes is crucial for road authorities. However, such efforts predominantly rely on police-reported crash data, facing obvious and ethical issues and hindering proactive safety management. While computer vision techniques offer high-resolution trajectory data of road users, the fundamental research question is, ‘Could we estimate pedestrian crashes and their severity without actually using crash data?’ To answer this question, researchers at Queensland University of Technology, Australia, collected large video data of pedestrian movements at signalized intersections in Brisbane, Queensland, Australia.

 

They published their study in Communications in Transportation Research.

 

“We develop a hybrid model for estimating pedestrian crash frequency by severity levels to investigate the determinants of pedestrian crashes. Using machine learning, extreme vehicle-pedestrian interactions are identified and modelled through extreme value theory considering the severe and non-severe nature of a crash”, says Fizza Hussain, a researcher at the School of Civil and Environmental Engineering, Queensland University Technology.

 

Estimating pedestrian crashes with severity

In this study, the research team observed exceptional performance of the developed model in estimating pedestrian crash frequency by severity levels. For instance, the 5-year observed mean severe and non-crashes were 2 and 29, respectively, and the corresponding predictions by the best-fitted model were 2.91 and 30.91, respectively.

“In the past, we needed to rely on crash statistics from 3 to 5 years to understand the crash risk level of a transport facility. The finding of this study provides us evidence that we can now accurately predict crash risks of transport facilities just by observing the traffic movement for a week or so.” Prof Shimul (Md Mazharul) Haque, a Professor of Transport Safety, says.

 

Increasingly, road authorities are interested in predicting crash frequency by severity levels to devise tailored countermeasures. For instance, at signalized intersections, the proposed modelling results will provide insights into crash occurrences along with severity, facilitating road authorities to prioritize their actions according to severity level.

 

Role of machine learning in estimating pedestrian crash frequency by severity

Machine learning has been gaining prominence, and its usage in estimating crash frequencies from traffic conflicts is rather scant. This research demonstrates that when using machine learning to identify risky pedestrian interactions, the performance of crash risk prediction models increases by about 3 times compared to using conventional (non-machine learning methods).

 

“These results suggest the superiority of applying machine learning in estimating pedestrian crash frequency by severity levels. We hope this research could lay a strong foundation for future applications of machine learning in such vital scenarios of pedestrian safety and developing countermeasures”, says Yuefeng Li, a Professor in Computer Science.

 

The above research is published in Communications in Transportation Research (COMMTR), which is a fully open access journal co-published by Tsinghua University Press and Elsevier. COMMTR publishes peer-reviewed high-quality research representing important advances of significance to emerging transport systems. COMMTR is also among the first transportation journals to make the Replication Package mandatory to facilitate researchers, practitioners, and the general public in understanding and advancing existing knowledge. At its discretion, Tsinghua University Press will pay the open access fee for all published papers from 2021 to 2025.

 

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About Communications in Transportation Research

Communications in Transportation Research was launched in 2021, with academic support provided by Tsinghua University and China Intelligent Transportation Systems Association. The Editors-in-Chief are Professor Xiaobo Qu, a member of the Academia Europaea from Tsinghua University and Professor Shuai’an Wang from Hong Kong Polytechnic University. The journal mainly publishes high-quality, original research and review articles that are of significant importance to emerging transportation systems, aiming to become an international platform and window for showcasing and exchanging innovative achievements in transportation and related fields, to promote the exchange and development of transportation research between China and the international academic community. It has been indexed in ESCI, Ei Compendex, Scopus, DOAJ, TRID and other databases. In 2022, it was selected as a high-starting-point new journal project of the “China Science and Technology Journal Excellence Action Plan”. This year, it received the first impact factor of 12.5.  The 2023 IF is 12.5, ranking in the Top1 (1/57, Q1) among all journals in  "TRANSPORTATION" category. At its discretion, Tsinghua University Press will pay the open access fee for all published papers from 2024 to 2025.

About Tsinghua University Press

Established in 1980, as a department of Tsinghua University, Tsinghua University Press (TUP) is a leading comprehensive higher education and professional publisher in China. TUP publishes 59 journals and 41 of them are in English. There are 16 journals indexed by SCIE/ESCI. Three of them have the highest impact factor in its field. In 2022, TUP launched SciOpen. As a publishing platform of TUP, SciOpen provides free access to an online collection of journals across diverse academic disciplines and serves to meet the research needs of scientific communities. SciOpen provides end-to-end services across manuscript submission, peer review, content hosting, analytics, identity management, and expert advice to ensure each journal’s development.

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