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Journal ArticleDOI

Alternative Ordered Response Frameworks for Examining Pedestrian Injury Severity in New York City

TLDR
In this article, the authors focus on identifying the appropriate ordered response structure for modeling pedestrian injury severity and compare the performance of these two model structures relative to the traditional OL model in the context of pedestrian injuries.
Abstract
This article focuses on identifying the appropriate ordered response structure for modeling pedestrian injury severity. The alternative ordered response approaches considered for the empirical analysis include ordered logit model (OL), generalized ordered logit model (GOL), and latent segmentation based ordered logit model (LSOL). The GOL and LSOL models enhance the traditional OL model in different ways. The GOL model relaxes the restrictive thresholds in the OL model by allowing for individual-level exogenous variable impacts on the threshold parameters. On the other hand, the LSOL model allows for differential impact on the alternatives by segmenting the pedestrian crash population into various segments with segment-specific OL parameters. In this study, the authors focus on examining the performance of these two model structures relative to the traditional OL model in the context of pedestrian injury severity. The performance of the formulated injury severity models are tested based on the New York City (NYC) Pedestrian Research Data Base for the years 2002 through 2006. To the authors’ knowledge, the study provides a first of its kind comparison exercise among OL, GOL, and LSOL models for examining pedestrian injury severity. The model estimation results clearly highlight the presence of segmentation based on the crash location attributes of pedestrian accidents. The crash location attributes that affect the allocation of pedestrians into these segments include regional county, functional classification of roadway, pedestrian location on roadway, number of travel lanes, and number of parking lanes in the roadway system. The key factors influencing pedestrian injury severity are weather condition, lighting condition, vehicle types, pedestrian age, and season. Overall, the results of the empirical analysis provide credence to the hypothesis that LSOL model is a promising ordered framework to accommodate population heterogeneity in the context of pedestrian injury severity.

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Citations
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Journal ArticleDOI

Analytic methods in accident research: Methodological frontier and future directions

TL;DR: A review of the evolution of methodological applications and available data in highway-accident research can be found in this article, where fruitful directions for future methodological developments are identified and the role that new data sources will play in defining these directions is discussed.
Journal ArticleDOI

Unobserved heterogeneity and the statistical analysis of highway accident data

TL;DR: In this article, a detailed discussion of the unobserved heterogeneity in highway accident data and analysis is presented along with their strengths and weaknesses, as well as a summary of the fundamental issues and directions for future methodological work that address this problem.
Journal ArticleDOI

A latent segmentation based generalized ordered logit model to examine factors influencing driver injury severity

TL;DR: In this article, an econometric model, referred to as the latent segmentation based generalized ordered logit (LSGOL) model, was proposed for examining driver injury severity.
Journal ArticleDOI

A comparison of the mixed logit and latent class methods for crash severity analysis

TL;DR: The authors investigated the differences between two preferred methods for accommodating individual unobserved heterogeneity, the mixed logit and latent class methods, in exploring the relationship between heavy truck crash severity and its contributing factors.
Journal ArticleDOI

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

TL;DR: In this article, a random thresholds hierarchical ordered probit model with random parameters is proposed to account for the fixed thresholds limitation of the traditional ordered probability models, which typically leads to incorrect estimation of outcome probabilities for the intermediate categories, and for the possibility of unobserved factors systematically varying across the observations.
References
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Book

Limited-Dependent and Qualitative Variables in Econometrics

G. S. Maddala
TL;DR: In this article, the authors present a survey of the use of truncated distributions in the context of unions and wages, and some results on truncated distribution Bibliography Index and references therein.
Book

Discrete Choice Analysis: Theory and Application to Travel Demand

TL;DR: In this article, the authors present the methods of discrete choice analysis and their applications in the modeling of transportation systems and present a complete travel demand model system presented in chapter 11, which is intended as a graduate level text and a general professional reference.
MonographDOI

The Handbook of Road Safety Measures

TL;DR: This title covers various areas of road safety including: traffic control; vehicle inspection; driver training; publicity campaigns; police enforcement; and, general policy instruments.
Journal ArticleDOI

A latent class model for discrete choice analysis: contrasts with mixed logit

TL;DR: This paper proposes a semi-parametric extension of the MNL, based on the latent class formulation, which resembles the mixed logit model but which relaxes its requirement that the analyst makes specific assumptions about the distributions of parameters across individuals.
Journal ArticleDOI

Comprehensive analysis of vehicle-pedestrian crashes at intersections in Florida.

TL;DR: It was found that pedestrian and driver demographic factors, and road geometric, traffic and environment conditions are closely related to the frequency and injury severity of pedestrian crashes.
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