Mouyid Bin Islam

Large Truck–Involved Crashes: Exploratory Injury Severity Analysis

In recent years, a growing concern related to large-truck accidents has increased due to potential economic impacts and level of injury severity that can be sustained. Yet, studies related to large-truck involved crashes are scarce and lack human behavior factors that can greatly influence crash outcomes. In this study, the authors present an analysis of data from the fusion of several national data sets addressing large-truck involved injury severity. This is done by considering human, road-environment, and vehicular factors in large-truck involved crashes on U.S. interstates. A random parameter ordered probit model was estimated to predict the likelihood of five injury severity outcomes—fatal, incapacitating, non-incapacitating, possible injury, and no injury. The modeling approach accounts for possible unobserved effects relating to human, vehicular, and road environment factors not present in the data. Estimation findings indicate that the level of injury severity is highly influenced by a number of complex interactions of factors and that the effect of the some of the factors can vary across the observations.

Factors Affecting Highway Safety, Health Care Services, and Motorization—An Exploratory Empirical Analysis using Aggregate Data

This paper uses aggregate data from the World Health Organization (WHO) and the International Road Federation (IRF) to identify the relationship between aggregate levels of road traffic safety, health service levels, motorization level, and associated factors. Two alternative modeling specifications are used to estimate the national fatality rate, number of hospital beds, and the number of registered vehicles per capita. The first specification is a system of seemingly unrelated regression equations (SURE) while the second is a set of regression models. The results suggest that a number of socio-economic explanatory factors, government laws and policies and their enforcement levels, and traffic and geographic characteristics, are significantly related to the three response variables. The paper shows that the SURE model is statistically superior to the separately-estimated regression models. The model findings are exploratory, but can still offer preliminary insights to planners to identify the extent to which traffic and motorization levels, regional and geographic characteristics, and most importantly, existing traffic laws and policies can influence traffic fatalities.

Hazard-Based Analysis of Travel Distance in Urban Environments: Longitudinal Data Approach

This paper focuses on identifying important factors that determine activity-based travel distance in urban areas. Building on past research that has demonstrated the conceptual equivalence of hazard models applied to either temporal or spatial settings, the length of the distance from origin to destination is statistically modeled as longitudinal data using hazard-based modeling methods with data from Athens, Greece. Based on the data analysis, the Weibull model with gamma heterogeneity provides the best statistical fit, and a number of factors significantly affect travel distance, including socioeconomics and demographics, trip characteristics, mode choice, trip frequency, time of day for the trip, and type of activity participation. The proposed methodological approach and the research findings help to better understand travel behavior in terms of trip distance in the urban areas, an issue of significant importance for both transportation researchers and planners.

Modeling injury outcomes of crashes involving heavy vehicles on Texas highways

Concern related to crashes that involve large trucks has increased in Texas recently because of the potential economic impacts and level of injury severity that can be sustained. However, detailed studies on large truck crashes that highlight the contributing factors leading to injury severity have not been conducted in Texas, especially for its Interstate system. The contributing factors related to injury severity were analyzed with Texas crash data based on a discrete outcome-based model that accounts for possible unobserved heterogeneity related to human, vehicle, and road–environment factors. A random parameter logit (i.e., mixed logit) model was estimated to predict the likelihood of five standard injury severity scales commonly used in the Crash Records Information System in Texas: fatal, incapacitating, nonincapacitating, possible, and none (i.e., property damage only). Estimation results indicated that the level of injury severity outcomes was highly influenced by several complex interactions between factors and that the effects of some factors could vary across observations. The contributing factors include driver demographics, traffic flow, roadway geometric features, land use, time characteristics, weather, and lighting conditions.

Comparative Evaluation of Public-Private Partnerships in Roadway Preservation

In a bid to reduce overall costs, manage risks, and attract private capital, highway agencies worldwide strive to increase private-sector participation in road infrastructure development, particularly at the developmental phases of construction and preservation. A common mechanism for private-sector participation is the concept of a public–private partnership (PPP). As agencies grapple with the decision about whether to adopt a specific PPP or the traditional contracting approach for a specific project, they lack a rational decision-support structure. In addressing this major gap in PPP-related literature, this paper presents a framework by which an agency may assess the performance (relative benefits) of different PPP contracting approaches for highway preservation. For the purposes of this paper, performance is expressed in relation to the likelihood and intensity of cost savings calculated with data from domestic (U.S.) and international projects. In addition, the influence of project and contract attributes (such as the expected project duration, work type, and project size) on PPP project performance is investigated. The framework can be used or duplicated by highway-related agencies and international organizations for identifying the superior contracting option for a given road preservation project on the basis of project characteristics and for quantifying the consequences of such choices for cost savings or other performance criteria.

Fatality rates for crashes involving heavy vehicles on highways: A random parameter tobit regression approach

ABSTRACT Few studies have analyzed the impacts of freight movements (large truck) on crash fatality rates. This study explores a novel application of a method, namely the random parameters tobit regression model to large truck fatality rates by using the nationwide Fatality Analysis Reporting System. By utilizing random parameter tobit regression the authors examined crash rates (instead of frequencies) in per million truck-miles traveled and ton-miles of freight in the United States as continuous censored variables. The empirical and statistical results illustrate that the random-parameters tobit regression model provides a better understanding of the fatality rates per million truck-miles traveled and ton-miles of freight over the fixed parameter tobit model. Factors related to the crash mechanism, temporal and spatial characteristics, road and environmental attributes, vehicle configuration, drivers and passenger attributes were found to be statistically significant. Some exposure to injury severity related factors also were found to be significant with random parameters that vary across the observations.

IDENTIFICATION OF FACTORS IN ROAD ACCIDENTS THROUGH IN-DEPTH ACCIDENT ANALYSIS

The rising trend of motorization and improving socio-economic status of Thai people directly influences the aggravating road safety situation with fatalities and permanently disabled injuries of about 130,000 and 500,000 respectively over the past decades. An estimated annual cost from road crashes amounts to about US

IS IT A TIMELY APPROACH FOR BUS ACCIDENT INVESTIGATION IN THAILAND

2,500 million, 3.4 percent of Gross National Product (GNP), undoubtedly inflicts Thailand with a burning public health concern in the South East Asian region. This paper addresses an in-depth study through crash investigation and reconstruction which has not yet been practised in Thailand to identify the contributory factors in road crashes by the concerned authorities. This research attempts to establish the linkage between the causes and consequences with event classification of an investigated case by highlighting the dynamic driving situation with initial traveling speed, pre-impact and post-impact speed of the involved vehicles to describe the crash scenario. Moreover, inaccurate risk assessment and late evasive action, absence of street-light facilities, inadequate lane marking and visibility were also outlined as major risk factors increasing the severity of crash and injury in this investigated case.

CONTRIBUTING FACTORS OF ROAD CRASHES IN THAILAND: EVIDENCES FROM THE ACCIDENT IN-DEPTH STUDY

Worldwide it is estimated that 1.2 million people lose their lives and as many as 50 million injuries and disabilities from road accidents every year. It is more inflicting burden for developing countries like Thailand when Thai statistics indicate over 12,000 people became fatal with accident costs comprising about 3.4 percent of GNP. Among all types of vehicles involved, bus accident is considered a major public concern as transportation of many innocent people is involved. The gravity of the situation is getting more public concern as annually 4,000 bus accidents occurred in Thailand. Single vehicle-bus accident with running off road accounts a major portion of bus accidents. This research attempts to investigate such single vehicle bus accidents to collect detailed information of crash scene, vehicle and occupants. The findings of this research indicate the possible factors leading to collision with a particular demonstration of the case studies.