Improving reliability of road safety estimates based on high correlated accident counts

Risultato della ricerca: Article

8 Citazioni (Scopus)

Abstract

Calibrating a safety performance function (SPF) with many years of accident data creates a temporal correlation that traditional model calibration procedures cannot deal with. It is well known that generalized estimating equations (GEE) models are able to incorporate trends into accident data and thus overcome difficulties in accounting for correlation; the usual application of GEEs to safety analysis uses robust (or sandwich) estimates of regression coefficients under the independence hypothesis for the working correlation matrix. This practice is justified by the robustness of the GEE procedure against misspecification of the response correlation structure. Nevertheless, with this method, one has to renounce the entirety of the advantages of GEE estimates, and—especially when correlation within the subject is high—significant losses in efficiency and misleading conclusions in model interpretation can occur. In such a case, losses in efficiency of the estimates will be transferred to the reliability of the final safety estimation, for example, by the empirical Bayes method. On the basis of these considerations, the main idea of this study is that, in safety modeling, additional effort to obtain the true data correlation structure will result in better precision in the estimation of SPF parameters. An example to illustrate the methodological aspects of the proposed approach is included.
Lingua originaleEnglish
pagine (da-a)197-204
RivistaTransportation Research Record
Volume2019
Stato di pubblicazionePublished - 2007

Fingerprint

Accidents

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Mechanical Engineering

Cita questo

@article{7889c50c355641aeafffe76da37f9c54,
title = "Improving reliability of road safety estimates based on high correlated accident counts",
abstract = "Calibrating a safety performance function (SPF) with many years of accident data creates a temporal correlation that traditional model calibration procedures cannot deal with. It is well known that generalized estimating equations (GEE) models are able to incorporate trends into accident data and thus overcome difficulties in accounting for correlation; the usual application of GEEs to safety analysis uses robust (or sandwich) estimates of regression coefficients under the independence hypothesis for the working correlation matrix. This practice is justified by the robustness of the GEE procedure against misspecification of the response correlation structure. Nevertheless, with this method, one has to renounce the entirety of the advantages of GEE estimates, and—especially when correlation within the subject is high—significant losses in efficiency and misleading conclusions in model interpretation can occur. In such a case, losses in efficiency of the estimates will be transferred to the reliability of the final safety estimation, for example, by the empirical Bayes method. On the basis of these considerations, the main idea of this study is that, in safety modeling, additional effort to obtain the true data correlation structure will result in better precision in the estimation of SPF parameters. An example to illustrate the methodological aspects of the proposed approach is included.",
keywords = "road safety high correlated data reliability",
author = "Orazio Giuffre' and Roberta Marino and Anna Grana' and Tullio Giuffre'",
year = "2007",
language = "English",
volume = "2019",
pages = "197--204",
journal = "Transportation Research Record",
issn = "0361-1981",
publisher = "US National Research Council",

}

TY - JOUR

T1 - Improving reliability of road safety estimates based on high correlated accident counts

AU - Giuffre', Orazio

AU - Marino, Roberta

AU - Grana', Anna

AU - Giuffre', Tullio

PY - 2007

Y1 - 2007

N2 - Calibrating a safety performance function (SPF) with many years of accident data creates a temporal correlation that traditional model calibration procedures cannot deal with. It is well known that generalized estimating equations (GEE) models are able to incorporate trends into accident data and thus overcome difficulties in accounting for correlation; the usual application of GEEs to safety analysis uses robust (or sandwich) estimates of regression coefficients under the independence hypothesis for the working correlation matrix. This practice is justified by the robustness of the GEE procedure against misspecification of the response correlation structure. Nevertheless, with this method, one has to renounce the entirety of the advantages of GEE estimates, and—especially when correlation within the subject is high—significant losses in efficiency and misleading conclusions in model interpretation can occur. In such a case, losses in efficiency of the estimates will be transferred to the reliability of the final safety estimation, for example, by the empirical Bayes method. On the basis of these considerations, the main idea of this study is that, in safety modeling, additional effort to obtain the true data correlation structure will result in better precision in the estimation of SPF parameters. An example to illustrate the methodological aspects of the proposed approach is included.

AB - Calibrating a safety performance function (SPF) with many years of accident data creates a temporal correlation that traditional model calibration procedures cannot deal with. It is well known that generalized estimating equations (GEE) models are able to incorporate trends into accident data and thus overcome difficulties in accounting for correlation; the usual application of GEEs to safety analysis uses robust (or sandwich) estimates of regression coefficients under the independence hypothesis for the working correlation matrix. This practice is justified by the robustness of the GEE procedure against misspecification of the response correlation structure. Nevertheless, with this method, one has to renounce the entirety of the advantages of GEE estimates, and—especially when correlation within the subject is high—significant losses in efficiency and misleading conclusions in model interpretation can occur. In such a case, losses in efficiency of the estimates will be transferred to the reliability of the final safety estimation, for example, by the empirical Bayes method. On the basis of these considerations, the main idea of this study is that, in safety modeling, additional effort to obtain the true data correlation structure will result in better precision in the estimation of SPF parameters. An example to illustrate the methodological aspects of the proposed approach is included.

KW - road safety high correlated data reliability

UR - http://hdl.handle.net/10447/42291

M3 - Article

VL - 2019

SP - 197

EP - 204

JO - Transportation Research Record

JF - Transportation Research Record

SN - 0361-1981

ER -