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Benchmarking regions using a heteroskedastic grouped random parameters model with heterogeneity in mean and variance: applications to grade crossing safety analysis

Benchmarking regions using a heteroskedastic grouped random parameters model with heterogeneity in mean and variance: applications to grade crossing safety analysis
Benchmarking regions using a heteroskedastic grouped random parameters model with heterogeneity in mean and variance: applications to grade crossing safety analysis

Comparing regions while adjusting for differences in characteristics of sites located in those regions is valuable since it identifies possible inter-regional dissimilarities in crash risk propensities according to specific safety performance measures (e.g., crash frequency of a specific type). This paper describes a framework to benchmark different regions (neighborhoods, provinces, etc.) in terms of a selected safety performance measure. To avoid issues relating to aggregated (macro-level) data, we use disaggregate (micro-level) data to draw inferences at a macro/region-level, which is often needed for developing large-scale transportation safety and planning programs and policies. To overcome unobserved heterogeneity, we employ a multilevel Bayesian heteroskedastic Poisson lognormal model with grouped random parameters allowing heterogeneity in both mean and variance parameters. The proposed approach is illustrated through a comprehensive study of highway railway grade crossings across Canada. The results indicate that the proposed model addresses unobserved heterogeneity more efficiently and provides more insight compared to conventional random parameters models. For example, we found that as traffic exposure increases, grade crossing safety deteriorates at a higher rate in the Canadian Prairies than in the other regions. Our benchmarking framework is also affected by different model specifications. The results indicate the need for further in-depth investigations, which could help to identify possible reasons for inter-region differences in terms of specific safety indicators. This study provides valuable guidelines to Canadian transportation authorities, revealing important underlying crash mechanisms at highway railway grade crossings in Canada.

2213-6657
33-48
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9
Fu, Liping
239058dc-3019-46af-9488-0bde99e6904a
Thakali, Lalita
7fd52523-5652-4103-bb17-ccfd74a50adc
Joseph, Lawrence
495a60cb-4dff-4d23-b2d3-2ac9c0802dd2
Heydari, Shahram
0d12a583-a4e8-4888-9e51-a50d312be1e9
Fu, Liping
239058dc-3019-46af-9488-0bde99e6904a
Thakali, Lalita
7fd52523-5652-4103-bb17-ccfd74a50adc
Joseph, Lawrence
495a60cb-4dff-4d23-b2d3-2ac9c0802dd2

Heydari, Shahram, Fu, Liping, Thakali, Lalita and Joseph, Lawrence (2018) Benchmarking regions using a heteroskedastic grouped random parameters model with heterogeneity in mean and variance: applications to grade crossing safety analysis. Analytic Methods in Accident Research, 19, 33-48. (doi:10.1016/j.amar.2018.06.003).

Record type: Article

Abstract

Comparing regions while adjusting for differences in characteristics of sites located in those regions is valuable since it identifies possible inter-regional dissimilarities in crash risk propensities according to specific safety performance measures (e.g., crash frequency of a specific type). This paper describes a framework to benchmark different regions (neighborhoods, provinces, etc.) in terms of a selected safety performance measure. To avoid issues relating to aggregated (macro-level) data, we use disaggregate (micro-level) data to draw inferences at a macro/region-level, which is often needed for developing large-scale transportation safety and planning programs and policies. To overcome unobserved heterogeneity, we employ a multilevel Bayesian heteroskedastic Poisson lognormal model with grouped random parameters allowing heterogeneity in both mean and variance parameters. The proposed approach is illustrated through a comprehensive study of highway railway grade crossings across Canada. The results indicate that the proposed model addresses unobserved heterogeneity more efficiently and provides more insight compared to conventional random parameters models. For example, we found that as traffic exposure increases, grade crossing safety deteriorates at a higher rate in the Canadian Prairies than in the other regions. Our benchmarking framework is also affected by different model specifications. The results indicate the need for further in-depth investigations, which could help to identify possible reasons for inter-region differences in terms of specific safety indicators. This study provides valuable guidelines to Canadian transportation authorities, revealing important underlying crash mechanisms at highway railway grade crossings in Canada.

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Benchmarking Shahram R1 final 2 - Accepted Manuscript
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Accepted/In Press date: 18 June 2018
e-pub ahead of print date: 23 June 2018
Published date: 1 September 2018

Identifiers

Local EPrints ID: 425319
URI: http://eprints.soton.ac.uk/id/eprint/425319
ISSN: 2213-6657
PURE UUID: 7a64d581-4b19-450c-87f8-204df916b49f

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Date deposited: 12 Oct 2018 16:30
Last modified: 07 Oct 2020 06:37

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Contributors

Author: Shahram Heydari
Author: Liping Fu
Author: Lalita Thakali
Author: Lawrence Joseph

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