A random thresholds random parameters hierarchical ordered probit analysis of highway accident injury-severities

Fountas, Grigorios; Anastasopoulos, Panagiotis Ch.

doi:10.1016/j.amar.2017.03.002

Cited by 131 publications

(59 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, Halton draws provides a better simulation performance than random draws due to the dramatic speed gains with no degradation. The normal distribution was considered as the distribution for random parameters among other distributions including lognormal, triangular, and uniform distribution since previous studies have shown that normal distribution almost always outperforms other distributions [13,18,38].…”

Section: Empirical Results and Discussionmentioning

confidence: 99%

A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions

Shao

Chen

et al. 2020

IJERPH

View full text Add to dashboard Cite

Social and economic burdens caused by truck-involved rear-end collisions are of great concern to public health and the environment. However, few efforts focused on identifying the difference of impacting factors on injury severity between car-strike-truck and truck-strike-car in rear-end collisions. In light of the above, this study focuses on illustrating the impact of variables associated with injury severity in truck-related rear-end crashes. To this end, truck involved rear-end crashes between 2006 and 2015 in the U.S. were obtained. Three random parameters ordered probit models were developed: two separate models for the car-strike-truck crashes and the truck-strike-car crashes, respectively, and one for the combined dataset. The likelihood ratio test was conducted to evaluate the significance of the difference between the models. The results show that there is a significant difference between car-strike-truck and truck-strike-car crashes in terms of contributing factors towards injury severity. In addition, indicators reflecting male, truck, starting or stopped in the road before a crash, and other vehicles stopped in lane show a mixed impact on injury severity. Corresponding implications were discussed according to the findings to reduce the possibility of severe injury in truck-involved rear-end collisions.

show abstract

Section: Empirical Results and Discussionmentioning

confidence: 99%

A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions

Shao

Chen

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…Neglecting these factors hampers forecast accuracy, and hence transferability. Of particular interest to this paper, a separate validation data set [18] is fundamental to model development.…”

Section: Data Descriptionmentioning

confidence: 99%

“…The strength of discrete choice models is that they explain variation in behavior based on a wealth of individual characteristics [60] (income, gender, urban environment), making it feasible, at least in theory, to transfer such models to other locations [2,17,18] (although some "updating" [36] of the model parameters using local data will be advantageous) [32]. By contrast a model linked to a particular set of transportation analysis zones from whence the data were calibrated will likely not be transferable as no two locations will have the same zonal configuration [2].…”

Section: Model Transferabilitymentioning

confidence: 99%

“…Thus, for any model, one must ask how does it perform with a different data set not used to calibrate the model? Hence, the authors determined the accuracy of the revised model, defined as the difference between model forecasts and observations, by using the validation data set (which had never been used for calibration); this approach provides the magnitude of error [18]. For example, consider the 1,954 travelers not used to develop the revised model.…”

Section: Model Transferabilitymentioning

confidence: 99%

“…The second bullet is of interest because while much literature [13][14][15][16] has focused on modeling decisions, less frequently have models been validated for transferability to similar regions [2,17,18]. (This paper assesses the model's accuracy using a different data set than that used to build the model, thereby helping to provide guidance on transferability elsewhere.)…”

mentioning

confidence: 99%

See 2 more Smart Citations

Using New Mode Choice Model Nesting Structures to Address Emerging Policy Questions: A Case Study of the Pittsburgh Central Business District

et al. 2017

View full text Add to dashboard Cite

Abstract:As transportation activities affect a region's environmental quality, knowing why individuals prefer certain modes can help a region make judicious transportation investments. Using a nested logit model, this paper studies the behavior of commuters to downtown Pittsburgh who use auto, bus, light rail, walking, and biking. Although statistical measures influence the selection of a nesting structure, another criterion for model selection is the policy questions such models inform. Hence this paper demonstrates how an alternative model structure allows planners to consider new policy questions. For example, how might a change in parking fee affect greenhouse gas emission (GHGs)? The proposed model showed that a 5%, 10% and 15% increase in parking cost reduces GHGs by 7.3%, 9% and 13.2%, respectively, through increasing carpoolers' mode share. Because the proposed model forecasts mode choices of certain groups of travelers with higher accuracy (compared to an older model that did not consider the model selection criteria presented here), the proposed model strengthens policymakers' ability to consider environmental impacts of interest to the region (in this case, GHGs). The paper does not suggest that one nesting structure is always preferable; rather the nesting structure must be chosen with the policy considerations in mind.

show abstract

How Can a State-of-the-Art Prediction Technique for Random Parameter Models Improve the Construction Work Environment in Ontarian Highways?

Nahidi

Tighe

2022

Lecture Notes in Civil Engineering

View full text Add to dashboard Cite

A random thresholds random parameters hierarchical ordered probit analysis of highway accident injury-severities

Cited by 131 publications

References 53 publications

A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions

A Random Parameters Ordered Probit Analysis of Injury Severity in Truck Involved Rear-End Collisions

Using New Mode Choice Model Nesting Structures to Address Emerging Policy Questions: A Case Study of the Pittsburgh Central Business District

How Can a State-of-the-Art Prediction Technique for Random Parameter Models Improve the Construction Work Environment in Ontarian Highways?

Contact Info

Product

Resources

About