Search

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.

Private car ownership plays a vital role in the daily travel decisions of individuals and households. The topic is of great interest to policy makers given the growing focus on global climate change, public health, and sustainable development issues. Not surprisingly, it is one of the most researched transportation topics. The extant literature on car ownership models considers the influence of exogenous variables to remain the same across the entire population. However, it is possible that the influence of exogenous variable effects might vary across the population. To accommodate this potential population heterogeneity in the context of car ownership, the current paper proposes the application of latent class versions of ordered (ordered logit) and unordered response (multinomial logit) models. The models are estimated using the data from Quebec City, Canada. The latent class models offer superior data fit compared to their traditional counterparts while clearly highlighting the presence of segmentation in the population. The validation exercise using the model estimation results further illustrates the strength of these models for examining car ownership decisions. Moreover, the latent class unordered response models perform slightly better than the latent class ordered response models for the metropolitan region examined.

Research on travel demand modeling has primarily focused on weekday activity–travel patterns. However, weekend activities and travel constitute a major component of individuals’ overall weekly activity–travel participation. This paper describes a modeling effort that focuses on weekend activity–travel demand for discretionary events. This study bridges the gap in the literature by modeling participation in discretionary types of events, the duration of participation, and accompaniment type jointly in a simultaneous equations model system. A joint discrete–continuous modeling framework is formulated for analysis of these dimensions as a choice bundle. Specifically, the combination of event type and accompaniment type constitutes the discrete component, whereas the duration of participation constitutes the continuous component. The model uses a copula-based sample selection approach that ties the discrete choice error component with the duration error component in a flexible manner. The data used in the paper are drawn from the 2008–2009 National Household Travel Survey sample of the greater Phoenix metropolitan area in Arizona. The results from the estimation process highlight the presence of sample selection in the joint modeling context. Furthermore, the results also highlight the flexibility of copula models in capturing such sample selection. The best copula model results are used to generate hazard profiles for various alternative related duration intervals. The generated profiles highlight the inaccurate predictions obtained by the use of approaches that ignore the presence of sample selection.

This paper formulates and estimates an econometric model, referred to as the latent segmentation based generalized ordered logit (LSGOL) model, for examining driver injury severity. The proposed model probabilistically allocates drivers (involved in a crash) into different injury severity segments based on crash characteristics to recognize that the impacts of exogenous variables on driver injury severity level can vary across drivers based on both observed and unobserved crash characteristics. The proposed model is estimated using Victorian Crash Database from Australia for the years 2006 through 2010. The model estimation incorporates the influence of a comprehensive set of exogenous variables grouped into six broad categories: crash characteristics, driver characteristics, vehicle characteristics, roadway design attributes, environmental factors and situational factors. The results clearly highlight the need for segmentation based on crash characteristics. The crash characteristics that affect the allocation of drivers into segments include: collision object, trajectory of vehicle's motion and manner of collision. Further, the key factors resulting in severe driver injury severity are driver age 65 and above, driver ejection, not wearing seat belts and collision in a high speed zone. The factors reducing driver injury severity include the presence of pedestrian control, presence of roundabout, driving a panel van, unpaved road condition and the presence of passengers.

Bangladesh Journal of Anatomy, January 2012, Vol. 10 No. 1 pp 27-31 DOI: http://dx.doi.org/10.3329/bja.v10i1.15756

Emergency vehicles (EVs) are used to provide essential services to society in the event of an emergency. Hence, crashes involving these vehicles are a concern, and investigating crash characteristics of EVs is the first step toward improving their safety. In particular, the contribution of any improper driver behavior or actions must be identified before (a) a program of behavior modification is recommended to authorities or (b) countermeasures are implemented to improve road safety, or both. This study attempts to identify the driver actions that contribute significantly to the severity of a crash involving at least one EV by using data from the Province of Alberta, Canada, for the period from 1999 to 2008. In addition, the impact of control variables formed from demographic, vehicle, environmental, and behavioral factors will also be explored. The results indicate that drivers’ violations of the road rules significantly contributed to increasing the severity of crashes. Non-EV drivers’ errors, nonrepairable damage to the vehicle, collision of an EV with a two-wheeler, and sun glare were some of the other variables that had significant influence on crash severity.

International audience; In two earlier case-control studies conducted in Montreal, nitrogen dioxide (NO2), a marker for traffic-related air pollution was found to be associated with the incidence of postmenopausal breast cancer and prostate cancer. These studies relied on a land use regression model (LUR) for NO2 that is commonly used in epidemiologic studies for deriving estimates of traffic-related air pollution. Here, we investigate the use of a transportation model developed during the summer season to generate a measure of traffic emissions as an alternative to the LUR model. Our traffic model provides estimates of emissions of nitrogen oxides (NOx) at the level of individual roads, as does the LUR model. Our main objective was to compare the distribution of the spatial estimates of NOx computed from our transportation model to the distribution obtained from the LUR model. A secondary objective was to compare estimates of risk using these two exposure estimates. We observed that the correlation (spearman) between our two measures of exposure (NO2 and NOx) ranged from less than 0.3 to more than 0.9 across Montreal neighborhoods. The most important factor affecting the "agreement" between the two measures in a specific area was found to be the length of roads. Areas affected by a high level of traffic-related air pollution had a far better agreement between the two exposure measures. A comparison of odds ratios (ORs) obtained from NO2 and NOx used in two case-control studies of breast and prostate cancer, showed that the differences between the ORs associated with NO2 exposure vs NOx exposure differed by 5.2-8.8%.

The study contributes to literature on bicycle safety by building on the traditional count regression models to investigate factors affecting bicycle crashes at the Traffic Analysis Zone (TAZ) level. TAZ is a traffic related geographic entity which is most frequently used as spatial unit for macroscopic crash risk analysis. In conventional count models, the impact of exogenous factors is restricted to be the same across the entire region. However, it is possible that the influence of exogenous factors might vary across different TAZs. To accommodate for the potential variation in the impact of exogenous factors we formulate latent segmentation based count models. Specifically, we formulate and estimate latent segmentation based Poisson (LP) and latent segmentation based Negative Binomial (LNB) models to study bicycle crash counts. In our latent segmentation approach, we allow for more than two segments and also consider a large set of variables in segmentation and segment specific models. The formulated models are estimated using bicycle-motor vehicle crash data from the Island of Montreal and City of Toronto for the years 2006 through 2010. The TAZ level variables considered in our analysis include accessibility measures, exposure measures, sociodemographic characteristics, socioeconomic characteristics, road network characteristics and built environment. A policy analysis is also conducted to illustrate the applicability of the proposed model for planning purposes. This macro-level research would assist decision makers, transportation officials and community planners to make informed decisions to proactively improve bicycle safety – a prerequisite to promoting a culture of active transportation.

Fatality Analysis Reporting System (FARS) and Generalized Estimates System (GES) data are most commonly used datasets to examine motor vehicle occupant injury severity in the United States (US). The FARS dataset focuses exclusively on fatal crashes, but provides detailed information on the continuum of fatality (a spectrum ranging from a death occurring within thirty days of the crash up to instantaneous death). While such data is beneficial for understanding fatal crashes, it inherently excludes crashes without fatalities. Hence, the exogenous factors identified as critical in contributing (or reducing) to fatality in the FARS data might possibly offer different effects on non-fatal crash severity levels when a truly random sample of crashes is considered. The GES data fills this gap by compiling data on a sample of roadway crashes involving all possible severity consequences providing a more representative sample of traffic crashes in the US. FARS data provides a continuous timeline of the fatal occurrences from the time to crash - as opposed to considering all fatalities to be the same. This allows an analysis of the survival time of victims before their death. The GES, on the other hand, does not offer such detailed information except identifying who died in the crash. The challenge in obtaining representative estimates for the crash population is the lack of readily available "appropriate" data that contains information available in both GES and FARS datasets. One way to address this issue is to replace the fatal crashes in the GES data with fatal crashes from FARS data thus augmenting the GES data sample with a very refined categorization of fatal crashes. The sample thus formed, if statistically valid, will provide us with a reasonable representation of the crash population. This paper focuses on developing a framework for pooling of data from FARS and GES data. The validation of the pooled sample against the original GES sample (unpooled sample) is carried out through two methods: (1) univariate sample comparison and (2) econometric model parameter estimate comparison. The validation exercise indicates that parameter estimates obtained using the pooled data model closely resemble the parameter estimates obtained using the unpooled data. After we confirm that the differences in model estimates obtained using the pooled and unpooled data are within an acceptable margin, we also simultaneously examine the whole spectrum of injury severity on an eleven point ordinal severity scale - no injury, minor injury, severe injury, incapacitating injury, and 7 refined categories of fatalities ranging from fatality after 30 days to instant death - using a nationally representative pooled dataset. The model estimates are augmented by conducting elasticity analysis to illustrate the applicability of the proposed framework.

A most commonly identified exogenous factor that significantly affects traffic crash injury severity sustained is the collision type variable. Most studies consider collision type only as an explanatory variable in modeling injury. However, it is possible that each collision type has a fundamentally distinct effect on injury severity sustained in the crash. In this paper, we examine the hypothesis that collision type fundamentally alters the injury severity pattern under consideration. Toward this end, we propose a joint modeling framework to study collision type and injury severity sustained as two dimensions of the severity process. We employ a copula based joint framework that ties the collision type (represented as a multinomial logit model) and injury severity (represented as an ordered logit model) through a closed form flexible dependency structure to study the injury severity process. The proposed approach also accommodates the potential heterogeneity (across drivers) in the dependency structure. Further, the study incorporates collision type as a vehicle-level, as opposed to a crash-level variable as hitherto assumed in earlier research, while also examining the impact of a comprehensive set of exogenous factors on driver injury severity. The proposed modeling system is estimated using collision data from the province of Victoria, Australia for the years 2006 through 2010.

This paper focuses on the relevance of alternate discrete outcome 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), mixed generalized ordered logit (MGOL) and for the unordered response framework-multinomial logit (MNL), nested logit (NL), ordered generalized extreme value logit (OGEV) and mixed multinomial logit (MMNL) model. A host of comparison metrics are computed to evaluate the performance of these alternative models. The study provides a comprehensive comparison exercise of the performance of ordered and unordered response models for examining the impact of exogenous factors on driver injury severity. The research also explores the effect of potential underreporting on alternative frameworks by artificially creating an underreported data sample from the driver injury severity sample. The empirical analysis is based on the 2010 General Estimates System (GES) data base—a nationally representative sample of road crashes collected and compiled from about 60 jurisdictions across the United States. The performance of the alternative frameworks are examined in the context of model estimation and validation (at the aggregate and disaggregate level). Further, the performance of the model frameworks in the presence of underreporting is explored, with and without corrections to the estimates. The results from these extensive analyses point toward the emergence of the GOL framework (MGOL) as a strong competitor to the MMNL model in modeling driver injury severity.

School bus safety is a community concern because parents expect their children to be transported to and from school safely. However, relatively few studies have been devoted to examining the factors contributing to school bus crashes. In this study, a logistic regression model is used to delineate the factors that contribute to school bus collisions from collisions involving other types of buses. As expected, we find significant differences in crash factors arising from differences in exposure and operating characteristics. Surprisingly, we also find that school bus drivers are more likely to commit driving violations or errors than non-school bus drivers.

Traffic collisions and fatalities during the holiday festive periods are apparently on the rise in Alberta, Canada, despite the enhanced enforcement and publicity campaigns conducted during these periods. Using data from 2004 to 2008, this research identifies the factors that delineate between crashes that occur during public holidays and those occurring during normal weekends. We find that fatal and injury crashes are over-represented during holidays. Amongst the three risky behaviors targeted in the holiday blitzes (driver intoxication, unsafe speeding and restraint use), non-use of restraint is more prevalent whereas driver intoxication and unsafe speeding are less prevalent during holidays. The mixed results obtained suggest that it may be time to consider a more balanced approach to the enhanced enforcement and publicity campaigns.

Given the import of the consequences of motor vehicle crashes, transportation safety researchers examined the influence of exogenous variables on vehicle occupant injury severity. Our study focuses on identifying the associated risk factors of driver fatalities while recognizing that fatality is not a single state but rather is made up of multiple discrete states from dying instantly to dying within the thirty days of crash by using the data from Fatality Analysis Reporting System (FARS). The research also simultaneously examines the whole spectrum of injury severity on an eleven point ordinal severity scale - no injury to fatality characterized as instant death by using a pooled dataset from FARS and General Estimates System (GES) dataset. The data pooling exercise is done to replace the “less informative” fatal crashes in the GES databases with the more detailed fatal crashes from FARS database. The data for the current study is sourced from the FARS and GES databases for the year 2010. The generalized ordered logit approach is considered for the empirical analyses. The important control variables that affect the both the early fatality risk and the injury severity outcome of the drivers include: driving under the influence of alcohol, medium or higher speed limits and increase in vehicle age.

Red light running at signalised intersections is a growing road safety issue worldwide, leading to the rapid development of advanced intelligent transportation technologies and countermeasures. However, existing studies have yet to summarise and present the effect of these technology based innovations in improving safety. This paper represents a comprehensive review of red light running behaviour prediction methodologies and technology-based countermeasures. Specifically, the major focus of this study is to provide a comprehensive review on two streams of literature targeting red light running and stop and go behaviour at signalised intersection (1) studies focusing on modelling and predicting the red light running and stop and go related driver behaviour and (2) studies focusing on the effectiveness of different technology based countermeasures which combat such unsafe behaviour. The study provides a systematic guide to assist researchers and stakeholders in understanding how to best identify red light running and stop and go associated driving behaviour and subsequently implement countermeasures to combat such risky behaviour and improve the associated safety., Published in IEEE ACCESS