RESEARCH ARTICLE


The Effect of Ramp Proximity, Weather, and Time-of-Day on Freeway Accident Frequency: A Case Study on I-75 and I-24 in Hamilton County, TN



Eric M. Laflamme1, *, Peter Way2, Jeremiah Roland2, Mina Sartipi2
1 Department of Mathematics, Plymouth State University, Plymouth, NH 03264, United States
2 College of Engineering and Computer Science, University of Tennessee at Chattanooga, Chattanooga, TN 37403, United States


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Creative Commons License
© 2022 Laflamme et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Mathematics, Plymouth State University, Plymouth, NH 03264, United States; Tel: +01 603 724 5336; E-mail Address: emlaflamme@plymouth.edu


Abstract

Background:

We present a case study to quantify the dangers of freeway ramps by comparing the observed accident counts from ramp locations to those from adjacent mainline locations. Few works make this direct comparison. Additionally, time-of-day and weather information is considered to collect a deeper understanding of the nature of freeway accidents near ramps. Real-world data collected from freeways in Hamilton County, TN, are considered as an application and give interesting results.

Methods:

First, we precisely define ramp influence areas or areas within close proximity to ramp locations where traffic is suspected to be affected by the ramp structure/geometry. Then, we introduce a theoretically justified Negative Binomial regression model to approximate the relationship between accident counts (response), presence of ramp influence areas, and additional weather and time-of-day designations. Our model also considers selected interaction terms, route designation, and multiple random components that are aimed at explaining unmeasured sources of variation.

Results:

Based on the interpretation of our fitted statistical model, we find that being in an influence/ramp area (compared to being in mainline traffic), on average, results in a 4-fold increase in accident frequency. Moreover, we find that during clear conditions, rush hour conditions increase the accident frequency substantially, while during rainy conditions, this increase is much less stark. During non-rush hour conditions, rain decreases the accident frequency substantially, and during rush hours, this decrease is intensified. Model diagnostics and a validation procedure further justify the assumed model form and lend credence to our results.

Conclusion:

While we do not make any claim of transferability of our results, they provide a proof-of-concept that accident frequency is attributable to multiple factors, among which is proximity to ramps. Furthermore, our procedure and statistical model allow us to directly quantify how these factors, most notably ramp traffic, effect accident frequency. These results illuminate potential safety risks. Subsequent work considering more diverse roadways could provide the evidence needed for policy changes and/or remedial measures.

Keywords: Accident frequency, Freeway ramps, Generalized linear mixed model, Negative Binomial regression, Rush hour, Weather.