Evaluating alternate discrete choice frameworks for modeling crash injury severity




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НазваниеEvaluating alternate discrete choice frameworks for modeling crash injury severity
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EVALUATING ALTERNATE DISCRETE CHOICE FRAMEWORKS FOR MODELING CRASH INJURY SEVERITY


Shamsunnahar Yasmin

Doctoral Student

Department of Civil Engineering & Applied Mechanics

McGill University

Suite 483, 817 Sherbrooke St. W.

Montréal, Québec, H3A 2K6

Canada

Ph: 514 398 6823, Fax: 514 398 7361

Email:shamsunnahar.yasmin@mail.mcgill.ca


Naveen Eluru*

Assistant Professor

Department of Civil Engineering & Applied Mechanics

McGill University

Suite 483, 817 Sherbrooke St. W.

Montréal, Québec, H3A 2K6

Canada

Ph: 514 398 6823, Fax: 514 398 7361

Email:naveen.eluru@mcgill.ca


August 2012


* Corresponding author


ABSTRACT


This paper focuses on the relevance of alternate discrete choice frameworks for modeling driver injury severity. The study empirically compares the ordered response and unordered response models in the context of driver injury severity in traffic crashes. The alternative modeling approaches considered for the comparison exercise include: for the ordered response framework- ordered logit (OL), generalized ordered logit (GOL) and for the unordered response framework - multinomial logit (MNL), nested logit (NL) and ordered generalized extreme value logit (OGEV) model. A host of comparison metrics are computed to evaluate the performance of these alternative models. To our knowledge, the study provides a first of its kind comparison exercise of the performance of ordered and unordered response models for examining the impact of exogenous factors on the driver injury severity. The research also captures the effect of potential under reporting on alternative choice frameworks by artificially creating an under reported data sample from the driver injury severity sample.

The empirical analysis is based on the 2010 General Estimates System (GES) data base. The comparison exercise clearly highlights the superiority of the GOL model on the estimation and the validation sample in terms of data fit compared to the OL and MNL models. The estimation with the artificial under reported sample consistently obtains the wrong elasticities and these errors are substantially reduced for both GOL and MNL models with the correction measures for the thresholds/constants of these models based on the true aggregate shares. The most striking finding is the fact that the MNL model does not perform any better in the under reporting context. In fact, the GOL elasticity effects of under reported estimates with corrections are closer to the true elasticity effects than that of the MNL model. Overall, the results of the empirical comparison provide credence to the belief that an ordered system that allows for exogenous variable effects to vary across alternatives offer superior fit compared to unordered systems in modeling driver injury severity.


Keywords: Driver injury severity, ordered response models, unordered response models, generalized ordered logit, comparison of alternative discrete choice models, presence of under reporting

INTRODUCTION

The problem of morbidity and mortality from motor vehicle crashes is now acknowledged to be a global phenomenon. According to World Health Organization (WHO), more than one million people get killed in traffic accidents each year (WHO 2004). These incidents affect the society as a whole both emotionally and economically (Subramanian 2006, Blincoe et al. 2002). These road crashes not only result in loss of life, but also impact the quality of life and productivity of the motor vehicle crash survivors. Given the import of the consequences of motor vehicle crashes, the issue has received significant attention from researchers and practitioners. In particular, the emphasis is on examining the influence of several factors, comprising of driver characteristics, vehicle characteristics, roadway design and operational attributes, environmental factors and crash characteristics on motor vehicle crash related severity.

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