Article

Markov switching multinomial logit model: An application to accident-injury severities

School of Civil Engineering, 550 Stadium Mall Drive, Purdue University, West Lafayette, IN 47907, United States
Accident Analysis & Prevention (Impact Factor: 1.87). 07/2009; 41(4):829-838. DOI: 10.1016/j.aap.2009.04.006
Source: PubMed

ABSTRACT In this study, two-state Markov switching multinomial logit models are proposed for statistical modeling of accident-injury severities. These models assume Markov switching over time between two unobserved states of roadway safety as a means of accounting for potential unobserved heterogeneity. The states are distinct in the sense that in different states accident-severity outcomes are generated by separate multinomial logit processes. To demonstrate the applicability of the approach, two-state Markov switching multinomial logit models are estimated for severity outcomes of accidents occurring on Indiana roads over a four-year time period. Bayesian inference methods and Markov Chain Monte Carlo (MCMC) simulations are used for model estimation. The estimated Markov switching models result in a superior statistical fit relative to the standard (single-state) multinomial logit models for a number of roadway classes and accident types. It is found that the more frequent state of roadway safety is correlated with better weather conditions and that the less frequent state is correlated with adverse weather conditions.

Download full-text

Full-text

Available from: Fred L. Mannering, Jul 25, 2014
0 Followers
 · 
275 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Time-constant assumptions in discrete-response heterogeneity models can often be violated. To address this, a time-varying heterogeneity approach to model unobserved heterogeneity in ordered response data is considered. A Markov switching random parameters structure (which accounts for heterogeneity across observations) is proposed to accommodate both time-varying and time-constant (cross-sectional) unobserved heterogeneity in an ordered discrete-response probability model. A data augmented Markov Chain Monte Carlo algorithm for non-linear model estimation is developed to facilitate model estimation. The performance of the cross-sectional heterogeneity model and time-varying heterogeneity model are examined with vehicle crash-injury severity data. The time-varying heterogeneity model (Markov switching random parameters ordered probit) is found to provide the best overall model fit. Two roadway safety states are shown to exist and roadway segments transition between these two states according to Markov transition probabilities. The results demonstrate considerable promise for Markov switching models in a wide variety of applications.
    Transportation Research Part B Methodological 09/2014; 67:109–128. DOI:10.1016/j.trb.2014.04.007 · 2.94 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Objective: The severity of traffic-related injuries has been studied by many researchers in recent decades. However, the evaluation of many factors is still in dispute and, until this point, few studies have taken into account pavement management factors as points of interest. The objective of this article is to evaluate the combined influences of pavement management factors and traditional traffic engineering factors on the injury severity of 2-vehicle crashes. Methods: This study examines 2-vehicle rear-end, sideswipe, and angle collisions that occurred on Tennessee state routes from 2004 to 2008. Both the traditional ordered probit (OP) model and Bayesian ordered probit (BOP) model with weak informative prior were fitted for each collision type. The performances of these models were evaluated based on the parameter estimates and deviances. Results: The results indicated that pavement management factors played identical roles in all 3 collision types. Pavement serviceability produces significant positive effects on the severity of injuries. The pavement distress index (PDI), rutting depth (RD), and rutting depth difference between right and left wheels (RD_df) were not significant in any of these 3 collision types. The effects of traffic engineering factors varied across collision types, except that a few were consistently significant in all 3 collision types, such as annual average daily traffic (AADT), rural-urban location, speed limit, peaking hour, and light condition. Conclusions: The findings of this study indicated that improved pavement quality does not necessarily lessen the severity of injuries when a 2-vehicle crash occurs. The effects of traffic engineering factors are not universal but vary by the type of crash. The study also found that the BOP model with a weak informative prior can be used as an alternative but was not superior to the traditional OP model in terms of overall performance.
    Traffic Injury Prevention 07/2013; 14(5):544-553. DOI:10.1080/15389588.2012.731547 · 1.29 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper describes an accident occurrence model for the risk analysis of industrial facilities. To better understand the characteristics of industrial accident data, the proposed accident occurrence model is based on a chemical reaction. The model introduces a defensive barrier, which corresponds to the activation energy in a chemical reaction, to prevent an accident. Furthermore, the uncertainty factor in the defensive barrier is mathematically derived as a gamma distribution. The analytical results for the proposed accident occurrence model indicate a Pareto type II distribution, which is the same result found by using a risk curve. Therefore, the analytical model validates the effectiveness of analyzing industrial risk with a riskcurve.
    Reliability Engineering [?] System Safety 06/2013; 114:71–74. DOI:10.1016/j.ress.2013.01.004 · 2.05 Impact Factor