Selected models for correlated traffic accident count data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Accident counts are correlated in many ways that induced by unexplained heterogeneity. The sources of the unexplained of heterogeneity were identified as spatial, temporal and categorical. Multiple models were introduced to cater one or two of these factors. However, to date, there is no single model incorporates these three factors together. Hence, we review three selected models for correlated count data; the Generalized ARMA (GLARMA) model, model with lagged observation and seemingly unrelated negative binomial model (SUNB). The aim of this paper is to conduct an initial study to assess the stability of the selected models. Through simulation study, the strength and the weakness of these models are also identified to evaluate their potential for application to correlated accident count data. Based on the simulation results, the GLARMA model is found to give inconsistent estimates and need to have an adequate sample of data to provide a significant result. Models with lagged observation provide satisfactory results since the temporal structure is inserted as fixed effects. Meanwhile, the SUNB model needs to have a fixed variance component to ensure the stability of the parameter estimates. Overall, the simulation study conducted reveals that model with fixed components structure provide more stable estimates compared to models with random effects.

Original languageEnglish
Title of host publicationAdvances in Industrial and Applied Mathematics: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences, SKSM 2015
PublisherAmerican Institute of Physics Inc.
Volume1750
ISBN (Electronic)9780735414075
DOIs
Publication statusPublished - 21 Jun 2016
Event23rd Malaysian National Symposium of Mathematical Sciences: Advances in Industrial and Applied Mathematics, SKSM 2015 - Johor Bahru, Malaysia
Duration: 24 Nov 201526 Nov 2015

Other

Other23rd Malaysian National Symposium of Mathematical Sciences: Advances in Industrial and Applied Mathematics, SKSM 2015
CountryMalaysia
CityJohor Bahru
Period24/11/1526/11/15

Fingerprint

accidents
traffic
autoregressive moving average
estimates
simulation

Keywords

  • Count data
  • statistical model
  • traffic accident

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Zamzuri, Z. H. (2016). Selected models for correlated traffic accident count data. In Advances in Industrial and Applied Mathematics: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences, SKSM 2015 (Vol. 1750). [060024] American Institute of Physics Inc.. https://doi.org/10.1063/1.4954629

Selected models for correlated traffic accident count data. / Zamzuri, Zamira Hasanah.

Advances in Industrial and Applied Mathematics: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences, SKSM 2015. Vol. 1750 American Institute of Physics Inc., 2016. 060024.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zamzuri, ZH 2016, Selected models for correlated traffic accident count data. in Advances in Industrial and Applied Mathematics: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences, SKSM 2015. vol. 1750, 060024, American Institute of Physics Inc., 23rd Malaysian National Symposium of Mathematical Sciences: Advances in Industrial and Applied Mathematics, SKSM 2015, Johor Bahru, Malaysia, 24/11/15. https://doi.org/10.1063/1.4954629
Zamzuri ZH. Selected models for correlated traffic accident count data. In Advances in Industrial and Applied Mathematics: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences, SKSM 2015. Vol. 1750. American Institute of Physics Inc. 2016. 060024 https://doi.org/10.1063/1.4954629
Zamzuri, Zamira Hasanah. / Selected models for correlated traffic accident count data. Advances in Industrial and Applied Mathematics: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences, SKSM 2015. Vol. 1750 American Institute of Physics Inc., 2016.
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