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.

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Available from: Fred L. Mannering, Jul 25, 2014
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    • "Xie et al. (2012) compared the effectiveness of latent class logit models and MNL models in the assessment of driver injury severities in rural single-vehicle crashes and verified the applicability of them as an alternative of each other in traffic safety analyses. Other discrete choice models, such as mixed logit models, ordered Probit, Markov switching MNL, ordered logit, sequential logit and nested logit models, were also employed to analyze crash injury severities to address different methodological issues associated with the specific datasets (Abdel-Aty and Abdelwahab, 2004; Altwaijri et al., 2011; Anastasopoulos and Mannering, 2011; Das et al., 2008; Golob et al., 2008; Gray et al., 2008; Haleem and Abdel-Aty, 2010; Holdridge et al., 2005; Hu and Donnell, 2011; Malyshkina and Mannering, 2009, 2010; Moore et al., 2011; Park et al., 2010; Savolainen and Mannering, 2007; Wang and Abdel-Aty, 2008; Xie et al., 2007) Savolainen et al. (2011) provided a comprehensive review to summarize these statistical modeling approaches for crash injury severity analyses and shed light on their application restrictions with various data issues. However, most of these discrete choice models are established based on certain assumptions . "
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    ABSTRACT: Rear-end crash is one of the most common types of traffic crashes in the U.S. A good understanding of its characteristics and contributing factors is of practical importance. Previously, both multinomial Logit models and Bayesian network methods have been used in crash modeling and analysis, respectively, although each of them has its own application restrictions and limitations. In this study, a hybrid approach is developed to combine multinomial logit models and Bayesian network methods for comprehensively analyzing driver injury severities in rear-end crashes based on state-wide crash data collected in New Mexico from 2010 to 2011. A multinomial logit model is developed to investigate and identify significant contributing factors for rear-end crash driver injury severities classified into three categories: no injury, injury, and fatality. Then, the identified significant factors are utilized to establish a Bayesian network to explicitly formulate statistical associations between injury severity outcomes and explanatory attributes, including driver behavior, demographic features, vehicle factors, geometric and environmental characteristics, etc. The test results demonstrate that the proposed hybrid approach performs reasonably well. The Bayesian network reference analyses indicate that the factors including truck-involvement, inferior lighting conditions, windy weather conditions, the number of vehicles involved, etc. could significantly increase driver injury severities in rear-end crashes. The developed methodology and estimation results provide insights for developing effective countermeasures to reduce rear-end crash injury severities and improve traffic system safety performance. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Accident Analysis & Prevention 06/2015; 80. DOI:10.1016/j.aap.2015.03.036 · 1.87 Impact Factor
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    • "In the crash-severity field, Malyshkina and Mannering (2009) hypothesized the presence of two states of highway safety resulting from distinct combinations of driving-environment conditions, driver reactions, and other factors that not only vary across observations but also interact and change over time (resulting in roadway segments changing from one state to another over time, under specified probabilistic conditions). Their subsequent empirical analysis, using a Markov-switching multinomial logit model of crash severity, showed the presence of two states with the less-safe state (the state likely to result in the more severe crash injuries) being highly correlated with adverse climatic conditions relating to precipitation, low temperatures, snow, and visibility. "
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    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
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    • "Studies that have taken the unordered approach include binary logit and probit models (Al-Ghamdi, 2002; Winston et al., 2006; Lee and Abdel-Aty, 2008), multinomial logit (MNL) models (Shankar and Mannering, 1996; Khorashadi et al., 2005), nested MNL models (Shankar et al., 1996; Chang and Mannering, 1999; Lee and Mannering, 2002), mixed MNL models (Milton et al., 2008; Gkritza and Mannering, 2008), and markov switching MNL models (Malyshkina and Mannering, 2009). The nominal approach to the injury severity problem is more robust to under-reporting rates across injury severity levels of data in one category, e.g. "
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    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
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