Examination of driver injury severity in freeway single-vehicle crashes using a mixed logit model with heterogeneity-in-means

Hou, Qinzhong; Huo, Xiaoyan; Leng, Jinsong; Cheng, Yuxing

doi:10.1016/j.physa.2019.121760

Cited by 49 publications

(21 citation statements)

References 27 publications

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“…On the other hand, senior drivers are significantly more likely to be involved in high injury severity crashes, which can be easily explained by their physical frailty as well as reduced alertness and slower response with age. These observations are also in line with the findings of previous research ( 49 ).…”

Section: Models and Resultssupporting

confidence: 93%

Incorporating Demographic Proportions into Crash Count Models by Quasi-Induced Exposure Method

Sharmin

Ivan

Zhao

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Quasi-induced exposure (QIE) is an effective technique for estimating the exposure of a specific driving or vehicle population when real exposure data are not available. Typically crash prediction models are carried out at the site level, that is, segment or intersection. Driving population characteristics are generally not available at this level, however, and thus are omitted from count models. Because of the sparsity of traffic crashes, estimating driving population distributions at the site level using crash data at individual sites is challenging. This study proposes a technique to obtain demographic proportions to incorporate in the count models as an exposure at each site by aggregating similar adjacent sites until significant demographic proportions are obtained. Information on driver gender, age, and vehicle type are obtained by QIE using five years (2010–2014) of crash data; and road inventories are obtained for 1,264 urban four-lane divided highway segments in California. Count models including only site level factors were compared with models including both crash level and site level factors. The latter outperformed the former in relation to mean prediction bias and mean absolute deviation statistics on holdout sample predictions. Results indicate that teen drivers are more crash prone in total and in fatal plus injury severity crashes. For senior drivers, crash risk increases with the increase in severity level. The presence of vehicles other than passenger cars and trucks reduces total and property damage only crash counts. Female drivers are associated with higher total and fatal plus injury crash counts.

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Section: Models and Resultssupporting

confidence: 93%

Incorporating Demographic Proportions into Crash Count Models by Quasi-Induced Exposure Method

Sharmin

Ivan

Zhao

et al. 2020

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…e parameter estimates are also computed using the log likelihood estimate (see (32)). e developed model can achieve better goodness of fit of the model and contribute to in-depth analysis of risk factors that affect the severity of occupant injuries [80].…”

Section: Research Methods Reviewmentioning

confidence: 99%

Research on Highway Roadside Safety

Cheng

Pei

et al. 2021

Journal of Advanced Transportation

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Frequent and serious traffic accidents have become a focal issue because they hinder the sustainable development of society. In China, roadside accidents account for 40% of fatalities resulting from traffic accidents. Roadside safety has become an important issue of traffic management departments worldwide, and performing research on roadside safety contributes to improving the level of road safety and reducing the number of traffic accidents and fatalities. By systematically sorting a large number of relevant studies, this paper analyzed the current development trends of roadside safety in terms of three aspects (i.e., the year of publication, the country of publication, and the source of publication) and then summarized the research status, existing gaps, and future development directions of roadside safety in terms of three aspects: the frequency of roadside accidents, the severity of roadside accidents, and the practice of roadside safety design. This paper reviewed the different prediction methods and evaluation models for the frequency and severity of roadside accidents. According to the number of times mentioned in the literature, the first five significant risk factors that cause frequent roadside accidents are small-radius curves, heavy traffic, objects adjacent to the lane (such as poles and trees), narrow lanes, and narrow shoulders, and the first five significant risk factors that cause fatal roadside accidents are driver age ≤25 or ≥65, alcohol, speeding, failure to use seat belts, and heavy trucks. Future research on the frequency and severity of roadside accidents should focus on quantitatively analyzing the probability of roadside accidents and occupant injury risk and developing methods for identifying roadside accident blackspots. For roadside safety design, roadside clear zones and safety slopes should be precisely quantified based on a cost-benefit analysis in future studies.

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“…Many previous studies emphasized the importance of taking the unobserved heterogeneity into consideration while analyzing crash-related factors. Furthermore, random parameters logit (RPL) models [ 16 , 17 , 18 , 19 ], random parameters logit models with heterogeneities in the means [ 20 , 21 ], and random parameters logit models with heterogeneities in the means and variances [ 5 , 22 , 23 ] have all been successfully utilized in the investigation of crash injury-severity. Hence, to account for the unobserved heterogeneity, the random parameters logit model with heterogeneities in the means and variances was used in this study.…”

Section: Methodsmentioning

confidence: 99%

Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes

Song

et al. 2021

IJERPH

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Multi-vehicle (MV) crashes, which can lead to great damages to society, have always been a serious issue for traffic safety. A further understanding of crash severity can help transportation engineers identify the critical reasons and find effective countermeasures to improve transportation safety. However, studies involving methods of machine learning to predict the possibility of injury-severity of MV crashes are rarely seen. Besides that, previous studies have rarely taken temporal stability into consideration in MV crashes. To bridge these knowledge gaps, two kinds of models: random parameters logit model (RPL), with heterogeneities in the means and variances, and Random Forest (RF) were employed in this research to identify the critical contributing factors and to predict the possibility of MV injury-severity. Three-year (2016–2018) MV data from Washington, United States, extracted from the Highway Safety Information System (HSIS), were applied for crash injury-severity analysis. In addition, a series of likelihood ratio tests were conducted for temporal stability between different years. Four indicators were employed to measure the prediction performance of the selected models, and four categories of crash-related characteristics were specifically investigated based on the RPL model. The results showed that the machine learning-based models performed better than the statistical models did when taking the overall accuracy as an evaluation indicator. However, the statistical models had a better prediction performance than the machine learning models had considering crash costs. Temporal instabilities were present between 2016 and 2017 MV data. The effect of significant factors was elaborated based on the RPL model with heterogeneities in the means and variances.

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Examination of driver injury severity in freeway single-vehicle crashes using a mixed logit model with heterogeneity-in-means

Cited by 49 publications

References 27 publications

Incorporating Demographic Proportions into Crash Count Models by Quasi-Induced Exposure Method

Incorporating Demographic Proportions into Crash Count Models by Quasi-Induced Exposure Method

Research on Highway Roadside Safety

Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes

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