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- Publisher Website: 10.1016/j.aap.2021.106237
- Scopus: eid_2-s2.0-85107696547
- PMID: 34119817
- WOS: WOS:000692084500021
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Article: On random-parameter count models for out-of-sample crash prediction: Accounting for the variances of random-parameter distributions
Title | On random-parameter count models for out-of-sample crash prediction: Accounting for the variances of random-parameter distributions |
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Authors | |
Keywords | Random parameters Crash frequency Predictive performance Cross validation Numerical experiment |
Issue Date | 2021 |
Publisher | Elsevier Ltd. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/336/description#description |
Citation | Accident Analysis & Prevention, 2021, v. 159, p. article no. 106237 How to Cite? |
Abstract | One challenge faced by the random-parameter count models for crash prediction is the unavailability of unique coefficients for out-of-sample observations. The means of the random-parameter distributions are typically used without explicit consideration of the variances. In this study, by virtue of the Taylor series expansion, we proposed a straightforward yet analytic solution to include both the means and variances of random parameters for unbiased prediction. We then theoretically quantified the systematic bias arising from the omission of the variances of random parameters. Our numerical experiment further demonstrated that simply using the means of random parameters to predict the number of crashes for out-of-sample observations is fundamentally incorrect, which necessarily results in the underprediction of crash counts. Given the widespread use and ongoing prevalence of the random-parameter approach in crash analysis, special caution should be taken to avoid this silent pitfall when applying it for predictive purposes. |
Description | Hybrid open access |
Persistent Identifier | http://hdl.handle.net/10722/300559 |
ISSN | 2023 Impact Factor: 5.7 2023 SCImago Journal Rankings: 1.897 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Xu, P | - |
dc.contributor.author | Zhou, H | - |
dc.contributor.author | Wong, SC | - |
dc.date.accessioned | 2021-06-18T14:53:44Z | - |
dc.date.available | 2021-06-18T14:53:44Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Accident Analysis & Prevention, 2021, v. 159, p. article no. 106237 | - |
dc.identifier.issn | 0001-4575 | - |
dc.identifier.uri | http://hdl.handle.net/10722/300559 | - |
dc.description | Hybrid open access | - |
dc.description.abstract | One challenge faced by the random-parameter count models for crash prediction is the unavailability of unique coefficients for out-of-sample observations. The means of the random-parameter distributions are typically used without explicit consideration of the variances. In this study, by virtue of the Taylor series expansion, we proposed a straightforward yet analytic solution to include both the means and variances of random parameters for unbiased prediction. We then theoretically quantified the systematic bias arising from the omission of the variances of random parameters. Our numerical experiment further demonstrated that simply using the means of random parameters to predict the number of crashes for out-of-sample observations is fundamentally incorrect, which necessarily results in the underprediction of crash counts. Given the widespread use and ongoing prevalence of the random-parameter approach in crash analysis, special caution should be taken to avoid this silent pitfall when applying it for predictive purposes. | - |
dc.language | eng | - |
dc.publisher | Elsevier Ltd. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/336/description#description | - |
dc.relation.ispartof | Accident Analysis & Prevention | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Random parameters | - |
dc.subject | Crash frequency | - |
dc.subject | Predictive performance | - |
dc.subject | Cross validation | - |
dc.subject | Numerical experiment | - |
dc.title | On random-parameter count models for out-of-sample crash prediction: Accounting for the variances of random-parameter distributions | - |
dc.type | Article | - |
dc.identifier.email | Xu, P: pengxu@hku.hk | - |
dc.identifier.email | Wong, SC: hhecwsc@hku.hk | - |
dc.identifier.authority | Wong, SC=rp00191 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1016/j.aap.2021.106237 | - |
dc.identifier.pmid | 34119817 | - |
dc.identifier.scopus | eid_2-s2.0-85107696547 | - |
dc.identifier.hkuros | 322855 | - |
dc.identifier.volume | 159 | - |
dc.identifier.spage | article no. 106237 | - |
dc.identifier.epage | article no. 106237 | - |
dc.identifier.isi | WOS:000692084500021 | - |
dc.publisher.place | United Kingdom | - |