Biomedical subjects

Dominique Lord

Publications and source records attributed to Dominique Lord.

6 recordsLinked to original sources

PubMed2006

A strategy to reduce older driver injuries at intersections using more accommodating roundabout design practices.

This paper briefly summarizes a laboratory study investigating strategies designed to improve the ability of our most vulnerable drivers, the elderly, to safely negotiate the most dangerous and demanding of all traffic situations--intersections--through increased use of modern roundabouts. Compared to conventional intersections, roundabouts have demonstrated the potential to significantly reduce the most injurious (angle) type of crashes and slow the operating speed of all vehicles, while maintaining a high capacity for moving traffic through an intersection. This research sought to develop and evaluate countermeasures with the potential to improve the perceived comfort, confidence, and/or safety of seniors in using roundabouts. Research methods included focus groups and structured interviews utilizing photographs, which had been edited to include novel traffic control devices. The results suggest that design elements that improve the path guidance for older drivers are necessary to encourage roundabout use by this group. Recommendations for improved practice related to advance warning signs, guide signs, yield treatments, directional signs, and exit treatments are presented.

Accidents, Traffic↗

PubMed2006

Further notes on the application of zero-inflated models in highway safety.

The intent of this note is to succinctly articulate additional points that were not provided in the original paper (Lord et al., 2005) and to help clarify a collective reluctance to adopt zero-inflated (ZI) models for modeling highway safety data. A dialogue on this important issue, just one of many important safety modeling issues, is healthy discourse on the path towards improved safety modeling. This note first provides a summary of prior findings and conclusions of the original paper. It then presents two critical and relevant issues: the maximizing statistical fit fallacy and logic problems with the ZI model in highway safety modeling. Finally, we provide brief conclusions.

Accidents, Traffic↗

PubMed2006

Modeling motor vehicle crashes using Poisson-gamma models: examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter.

There has been considerable research conducted on the development of statistical models for predicting crashes on highway facilities. Despite numerous advancements made for improving the estimation tools of statistical models, the most common probabilistic structure used for modeling motor vehicle crashes remains the traditional Poisson and Poisson-gamma (or Negative Binomial) distribution; when crash data exhibit over-dispersion, the Poisson-gamma model is usually the model of choice most favored by transportation safety modelers. Crash data collected for safety studies often have the unusual attributes of being characterized by low sample mean values. Studies have shown that the goodness-of-fit of statistical models produced from such datasets can be significantly affected. This issue has been defined as the "low mean problem" (LMP). Despite recent developments on methods to circumvent the LMP and test the goodness-of-fit of models developed using such datasets, no work has so far examined how the LMP affects the fixed dispersion parameter of Poisson-gamma models used for modeling motor vehicle crashes. The dispersion parameter plays an important role in many types of safety studies and should, therefore, be reliably estimated. The primary objective of this research project was to verify whether the LMP affects the estimation of the dispersion parameter and, if it is, to determine the magnitude of the problem. The secondary objective consisted of determining the effects of an unreliably estimated dispersion parameter on common analyses performed in highway safety studies. To accomplish the objectives of the study, a series of Poisson-gamma distributions were simulated using different values describing the mean, the dispersion parameter, and the sample size. Three estimators commonly used by transportation safety modelers for estimating the dispersion parameter of Poisson-gamma models were evaluated: the method of moments, the weighted regression, and the maximum likelihood method. In an attempt to complement the outcome of the simulation study, Poisson-gamma models were fitted to crash data collected in Toronto, Ont. characterized by a low sample mean and small sample size. The study shows that a low sample mean combined with a small sample size can seriously affect the estimation of the dispersion parameter, no matter which estimator is used within the estimation process. The probability the dispersion parameter becomes unreliably estimated increases significantly as the sample mean and sample size decrease. Consequently, the results show that an unreliably estimated dispersion parameter can significantly undermine empirical Bayes (EB) estimates as well as the estimation of confidence intervals for the gamma mean and predicted response. The paper ends with recommendations about minimizing the likelihood of producing Poisson-gamma models with an unreliable dispersion parameter for modeling motor vehicle crashes.

Accidents, Traffic↗

PubMed2005

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory.

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states-perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of "excess" zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to "excess" zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed-and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros.

Accidents, Traffic↗

PubMed2005

Modeling crash-flow-density and crash-flow-V/C ratio relationships for rural and urban freeway segments.

There has been considerable research conducted in recent years into establishing relationships between crashes and various traffic flow characteristics for freeway segments. Most of the research has focused on determining the relationship between crashes and highway traffic volumes, while little attention has been focused on the relationships of vehicle density, level of service (LOS), vehicle occupancy, V/C ratio and speed distribution. Despite overall progress, there is still no clear understanding about the effects of different traffic flow characteristics on safety. In fact, several studies reviewed in this work were found to have methodological limitations. These include using predictive models with a normal error structure, aggregated crash rates, and inadequate functional forms for the data at hand. The original research on which this paper is based is aimed to determine the statistical relationship using commonly applied predictive models (i.e., functional forms) between crashes and hourly traffic flow characteristics, such as traffic volume, vehicle density and V/C ratios, for rural and urban freeway segments respectively. To accomplish this objective, predictive models have been developed from data collected on freeway segments located in downtown and outside of Montreal, Quebec. Three different functional forms are evaluated. The results show that predictive models that use traffic volume as the only explanatory variable may not adequately characterize the accident process on freeway segments. Functional forms that incorporate density and V/C ratio offer a richer description of crashes occurring on these facilities, whether they are located in a rural or urban environment. Finally, separate predictive models for single- and multi-vehicle crashes should be developed rather than one common model for all crash types.

Accidents, Traffic↗

PubMed2004

Estimating the safety performance of urban road transportation networks.

Transportation planning models are typically used to estimate future traffic patterns, peak period traffic, travel time, and various environmental or other related traffic flow characteristics. Unfortunately, traffic safety is seldom, if ever, explicitly considered proactively during the transportation planning process. This omission is attributed to various factors, including the lack of available tools needed to estimate the number of crashes during this process. To help fill this void, the research on which this paper is based aimed, as a primary objective, to develop a tool that would allow the estimation of crashes on digital or coded urban transportation networks during the planning process. The secondary objective of the research was to describe how the predictive models should be applied on these networks and explain the important issues and limitations surrounding their application. To accomplish these objectives, safety performance functions specifically created for this work were applied to two sample digital networks created with the help of EMME/2, a software package widely used in transportation planning. The results showed that it is possible to predict crashes on digital transportation networks, but confirmed the reality that the accuracy of the predictions is directly related to the precision of the traffic flow estimates. The crash predictions are also sensitive to how the digital network is coded, and it is shown how appropriate adjustments can be made.

Accidents, Traffic↗