A fatal point concept and a low-sensitivity quantitative measure for traffic safety analytics
Shan Suthaharan

TL;DR
This paper introduces a novel traffic safety measure based on fatal points within clusters, reducing variability effects and improving reliability in crash data analysis.
Contribution
It proposes a fatal point concept and a low-sensitivity quantitative measure that are less affected by cluster variability in fatal crash data.
Findings
The measure is less sensitive to clustering variability.
It effectively identifies high-risk segments with stable safety assessments.
Empirical results show improved robustness over traditional crash frequency.
Abstract
The variability of the clusters generated by clustering techniques in the domain of latitude and longitude variables of fatal crash data are significantly unpredictable. This unpredictability, caused by the randomness of fatal crash incidents, reduces the accuracy of crash frequency (i.e., counts of fatal crashes per cluster) which is used to measure traffic safety in practice. In this paper, a quantitative measure of traffic safety that is not significantly affected by the aforementioned variability is proposed. It introduces a fatal point -- a segment with the highest frequency of fatality -- concept based on cluster characteristics and detects them by imposing rounding errors to the hundredth decimal place of the longitude. The frequencies of the cluster and the cluster's fatal point are combined to construct a low-sensitive quantitative measure of traffic safety for the cluster. The…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTraffic and Road Safety · Traffic Prediction and Management Techniques · Anomaly Detection Techniques and Applications
Methodsk-Means Clustering
