The DOI matches its Crossref record.
Derivation and Validation of New Simulation-Based Surrogate Safety Measure
Abstract
Traffic safety evaluation is one of the most important processes in analyzing transportation systems performance. Traditional methods like statistical models and before-after comparisons have many drawbacks, such as limited time periods, sample size problems, and reporting errors. The advancement of traffic conflict techniques combined with microsimulation offers a potentially innovative way for conducting safety assessment of traffic systems even before safety improvements are implemented. In this paper, simulation-based safety studies are reviewed, and a modified simulation-based surrogate safety measure and a new simulation-based surrogate safety measure that can capture the probability of collisions, as well as the severity of these potential collisions, are proposed. Conceptual and computational logic of the proposed surrogate safety indicators are described in detail. These surrogate safety indices are initially proposed for link-based analysis and should not be used for other purposes, such as intersection safety assessment, without further enhancements, and the use of these indices should be limited to the analysis of linear conflicts. In addition, these link-based indices are extended to be able to conduct aggregate networkwide safety assessments. The proposed indices are validated by means of a well-calibrated traffic simulation model of a section of the New Jersey Turnpike and real accident data from the same section. Preliminary results indicate a strong relationship between the proposed surrogate safety measures and real accident data. Further research is needed to investigate these new surrogate safety indices under different locations and traffic conditions.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Mahmud, S. M. S., Ferreira, L., Hoque, M. S., & Tavassoli, A. (2017) · IATSS Research · doi:10.1016/j.iatssr.2017.02.001 · Cited by 459
Pauwels, A., Pourmohammad-Zia, N., & Schulte, F. (2022) · Sustainability · doi:10.3390/su142013486 · Cited by 8
Metadata
- Authors
- Ozbay, K., Yang, H., Bartin, B., & Mudigonda, S.
- Venue
- Transportation Research Record: Journal of the Transportation Research Board
- Publisher
- SAGE Publications
- Published
- 2008-01
- Volume
- 2083
- Issue
- 1
- Pages
- 105-113
- ISSN
- 0361-1981 (ISSN-L)
- 2169-4052
- OpenAlex
- W2154373748
- Cited by
- 384
- References
- 0
- FWCI
- 8.53
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Derivation and Validation of New Simulation-Based Surrogate Safety Measure. https://app.cochat.ai/papers/10.3141/2083-12
- The paper
- Ozbay, K., Yang, H., Bartin, B., & Mudigonda, S. (2008). Derivation and Validation of New Simulation-Based Surrogate Safety Measure. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.3141/2083-12
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.