scholarly journals Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Bowen Dong ◽  
Xiaoxiang Ma ◽  
Feng Chen ◽  
Suren Chen
Keyword(s):  
Logit Model ◽  
Road Traffic ◽  
Unobserved Heterogeneity ◽  
Mixed Logit ◽  
Weather Condition ◽  
Geometric Feature ◽  
Mixed Logit Model ◽  
Contributing Factors ◽  
Accident Probability ◽  
Single Vehicle

Road traffic accidents are believed to be associated with not only road geometric feature and traffic characteristic, but also weather condition. To address these safety issues, it is of paramount importance to understand how these factors affect the occurrences of the crashes. Existing studies have suggested that the mechanisms of single-vehicle (SV) accidents and multivehicle (MV) accidents can be very different. Few studies were conducted to examine the difference of SV and MV accident probability by addressing unobserved heterogeneity at the same time. To investigate the different contributing factors on SV and MV, a mixed logit model is employed using disaggregated data with the response variable categorized as no accidents, SV accidents, and MV accidents. The results indicate that, in addition to speed gap, length of segment, and wet road surfaces which are significant for both SV and MV accidents, most of other variables are significant only for MV accidents. Traffic, road, and surface characteristics are main influence factors of SV and MV accident possibility. Hourly traffic volume, inside shoulder width, and wet road surface are found to produce statistically significant random parameters. Their effects on the possibility of SV and MV accident vary across different road segments.

Modeling head-on crash severity on NCDOT freeways: a mixed logit model approach

2019 ◽  
Vol 46 (4) ◽  
pp. 322-328 ◽  
Author(s):  
Pengfei Liu ◽  
Wei (David) Fan
Keyword(s):  
Logit Model ◽  
Injury Severity ◽  
Safety Information ◽  
Mixed Logit ◽  
Weather Condition ◽  
Mixed Logit Model ◽  
Young Drivers ◽  
Adverse Weather Condition ◽  
Adverse Weather ◽  
Model Approach

This study employs a mixed logit model approach to evaluate contributing factors that significantly affect the severity of head-on crashes. The head-on crash data are collected from Highway Safety Information System (HSIS) from 2005 to 2013 in North Carolina. The effects that vehicle, driver, roadway, and environmental characteristics have on the injury severity of head-on crashes are examined. The results of this research demonstrate that adverse weather, young drivers, rural roadways, and pickups are found to be better modeled as random-parameters at specific injury severity levels, while others should remain fixed. Also, the model results indicate that driving under the influence of alcohol or drugs, grade or curve roadway configuration, old drivers, high speed limit, motorcycles will increase the injury severity of head-on crashes. Adverse weather condition, two-way divided road, traffic control, young drivers, and pickups will decrease the injury severity of head-on crashes.

Improved Support Vector Machine Models for Work Zone Crash Injury Severity Prediction and Analysis

2019 ◽  
Vol 2673 (11) ◽  
pp. 680-692 ◽  
Author(s):  
Seyedmirsajad Mokhtarimousavi ◽  
Jason C. Anderson ◽  
Atorod Azizinamini ◽  
Mohammed Hadi
Keyword(s):  
Support Vector Machine ◽  
Logit Model ◽  
Harmony Search ◽  
Mixed Logit ◽  
Work Zone ◽  
Support Vector ◽  
Mixed Logit Model ◽  
Contributing Factors ◽  
Svm Model ◽  
Whale Optimization

Work zones are a high priority issue in the field of road transportation because of their impacts on traffic safety. A better understanding of work zone crashes can help to identify the contributing factors and countermeasures to enhance roadway safety. This study investigates the prediction of work zone crash severity and the contributing factors by employing a parametric approach using the mixed logit modeling framework and a non-parametric machine learning approach using the support vector machine (SVM). The mixed logit model belongs to the class of random parameter models in which the effects of flexible variables across different observations are identified, that is, data heterogeneity is taken into account. The performance of the SVM model is enhanced by applying three metaheuristic algorithms: particle swarm optimization (PSO), harmony search (HS), and the whale optimization algorithm (WOA). Empirical findings indicate that SVM provides higher prediction accuracy and outperforms the mixed logit model. Estimation results reveal key factors that increase the likelihood of severe work zone crashes. Furthermore, the analysis illustrates the ability of the three metaheuristics to enhance the SVM and the superiority of the harmony search algorithm in improving the performance of the SVM model.

scholarly journals Analysis of Factors Contributing to the Severity of Large Truck Crashes

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1191
Author(s):  
Jinhong Li ◽  
Jinli Liu ◽  
Pengfei Liu ◽  
Yi Qi
Keyword(s):  
Logit Model ◽  
Prediction Accuracy ◽  
Mixed Logit ◽  
Driving Under The Influence ◽  
Mixed Logit Model ◽  
Contributing Factors ◽  
Baseline Model ◽  
Adaptive Boosting ◽  
Crash Data ◽  
Significant Factors

Crashes that involved large trucks often result in immense human, economic, and social losses. To prevent and mitigate severe large truck crashes, factors contributing to the severity of these crashes need to be identified before appropriate countermeasures can be explored. In this research, we applied three tree-based machine learning (ML) techniques, i.e., random forest (RF), gradient boost decision tree (GBDT), and adaptive boosting (AdaBoost), to analyze the factors contributing to the severity of large truck crashes. Besides, a mixed logit model was developed as a baseline model to compare with the factors identified by the ML models. The analysis was performed based on the crash data collected from the Texas Crash Records Information System (CRIS) from 2011 to 2015. The results of this research demonstrated that the GBDT model outperforms other ML methods in terms of its prediction accuracy and its capability in identifying more contributing factors that were also identified by the mixed logit model as significant factors. Besides, the GBDT method can effectively identify both categorical and numerical factors, and the directions and magnitudes of the impacts of the factors identified by the GBDT model are all reasonable and explainable. Among the identified factors, driving under the influence of drugs, alcohol, and fatigue are the most important factors contributing to the severity of large truck crashes. In addition, the exists of curbs and medians and lanes and shoulders with sufficient width can prevent severe large truck crashes.

Predicting Firm Financial Distress: A Mixed Logit Model

2004 ◽  
Vol 79 (4) ◽  
pp. 1011-1038 ◽  
Author(s):  
Stewart Jones ◽  
David A. Hensher
Keyword(s):  
Financial Distress ◽  
Logit Model ◽  
Unobserved Heterogeneity ◽  
Mixed Logit ◽  
Probit Analysis ◽  
Mixed Logit Model ◽  
Multiple Discriminant Analysis ◽  
Accounting Research ◽  
Financial Distress Prediction ◽  
Distress Prediction

Over the past three decades the literature on financial distress prediction has largely been confined to simple multiple discriminant analysis, binary logistic or probit analysis, or rudimentary multinomial logit models (MNL). There has been a conspicuous absence of modeling innovation in this literature as well as a failure to keep abreast of important methodological developments emerging in other fields of the social sciences. In particular, there has been no recognition of major advances in discrete choice modeling over the last 15 years, which has increasingly relaxed behaviorally questionable assumptions associated with the independently and identically distributed errors (IID) condition and allowed for observed and unobserved heterogeneity. In contrast to standard logit, the mixed logit model fulfils this purpose and provides a superior framework for explanation and prediction. We explain the theoretical and econometric underpinnings of mixed logit and demonstrate its empirical usefulness in the context of a specific but topical area of accounting research: financial distress prediction. Comparisons of model-fits and out-of-sample forecasts indicate that mixed logit outperforms standard logit by significant margins. While mixed logit has valuable applications in financial distress research, its potential usefulness in other areas of accounting research should not be overlooked.

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