Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial Model
Inclement weather affects traffic safety in various ways. Crashes on rainy days not only cause fatalities and injuries but also significantly increase travel time. Accurately predicting crash risk under inclement weather conditions is helpful and informative to both roadway agencies and roadway user...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8828939 |
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| author | Ying Chen Zhongxiang Huang |
| author_facet | Ying Chen Zhongxiang Huang |
| author_sort | Ying Chen |
| collection | DOAJ |
| description | Inclement weather affects traffic safety in various ways. Crashes on rainy days not only cause fatalities and injuries but also significantly increase travel time. Accurately predicting crash risk under inclement weather conditions is helpful and informative to both roadway agencies and roadway users. Safety researchers have proposed various analytic methods to predict crashes. However, most of them require complete roadway inventory, traffic, and crash data. Data incompleteness is a challenge in many developing countries. It is common that safety researchers only have access to data on sites where a crash has occurred (i.e., zero-truncated data). The conventional crash models are not applicable to zero-truncated safety data. This paper proposes a finite-mixture zero-truncated negative binomial (FMZTNB) model structure. The model is applied to three-year wet-road crash data on 395 divided roadway segments (total 586 km), and the parameters are estimated using the Markov chain Monte Carlo (MCMC) method. Comparison indicates that the proposed FMZTNB model has better fitting performance and is more accurate in predicting the number of wet-road crashes. The model is capable of capturing the heterogeneity within the sample crash data. In addition, lane width showed mixed effects in different components on wet-road crashes, which are not observed in conventional modeling approaches. Practitioners are encouraged to consider the finite-mixture zero-truncated modeling approach when complete safety dataset is not available. |
| format | Article |
| id | doaj-art-0fb9a6e728074110bd6d8adabf841def |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-0fb9a6e728074110bd6d8adabf841def2025-02-03T01:01:26ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88289398828939Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial ModelYing Chen0Zhongxiang Huang1School of Traffic and Transportation Engineering, School of Architecture, Changsha University of Science & Technology, Changsha 410114, ChinaSchool of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaInclement weather affects traffic safety in various ways. Crashes on rainy days not only cause fatalities and injuries but also significantly increase travel time. Accurately predicting crash risk under inclement weather conditions is helpful and informative to both roadway agencies and roadway users. Safety researchers have proposed various analytic methods to predict crashes. However, most of them require complete roadway inventory, traffic, and crash data. Data incompleteness is a challenge in many developing countries. It is common that safety researchers only have access to data on sites where a crash has occurred (i.e., zero-truncated data). The conventional crash models are not applicable to zero-truncated safety data. This paper proposes a finite-mixture zero-truncated negative binomial (FMZTNB) model structure. The model is applied to three-year wet-road crash data on 395 divided roadway segments (total 586 km), and the parameters are estimated using the Markov chain Monte Carlo (MCMC) method. Comparison indicates that the proposed FMZTNB model has better fitting performance and is more accurate in predicting the number of wet-road crashes. The model is capable of capturing the heterogeneity within the sample crash data. In addition, lane width showed mixed effects in different components on wet-road crashes, which are not observed in conventional modeling approaches. Practitioners are encouraged to consider the finite-mixture zero-truncated modeling approach when complete safety dataset is not available.http://dx.doi.org/10.1155/2020/8828939 |
| spellingShingle | Ying Chen Zhongxiang Huang Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial Model Journal of Advanced Transportation |
| title | Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial Model |
| title_full | Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial Model |
| title_fullStr | Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial Model |
| title_full_unstemmed | Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial Model |
| title_short | Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial Model |
| title_sort | predicting wet road crashes using the finite mixture zero truncated negative binomial model |
| url | http://dx.doi.org/10.1155/2020/8828939 |
| work_keys_str_mv | AT yingchen predictingwetroadcrashesusingthefinitemixturezerotruncatednegativebinomialmodel AT zhongxianghuang predictingwetroadcrashesusingthefinitemixturezerotruncatednegativebinomialmodel |