Showing papers in "Journal of transportation and statistics in 2014"

Investigation of the Impact of Corner Clearance on Urban Intersection Crash Occurrence

Abstract: Corner clearance is defined as the distance between the corner of an intersection of two roadways and the first driveway Vehicles turning into a driveway adjacent to an intersection or vehicles merging into the mainline from such a driveway may pose a safety hazard to other traffic Adequate corner clearance is important to effectively separate conflict points and allow drivers enough time to make safe maneuvers Although previous studies have investigated and identified factors influencing crash frequency at intersections, corner clearance has not been well studied In this study, the authors used crash count data collected from all signalized intersections of major roadways in the cities of Las Vegas and North Las Vegas, Nevada, to investigate the impact of corner clearance on crash frequency The authors estimated and compared results from four models: Poisson, Negative Binomial, and Zero-Inflated (Poisson and Negative Binomial) Model comparison test results indicated that the Zero-Inflated Negative Binomial was the best fitted model for the data at hand As expected, it was revealed that longer corner clearance tends to reduce the number of crashes occurring at an urban intersection In addition to corner clearance, the results indicated that land-use type, entering volume, number of left-turn lanes, as well as number of through lanes, have significant impact on the number of crashes occurring at an intersection Sensitivity results revealed that adequate corner clearances have greater potential of improving safety at signalized intersections when compared to other factors considered in this study Language: en

Application of the Bayesian Model Averaging in Predicting Motor Vehicle Crashes

Abstract: Developing reliable statistical models is critical for predicting motor vehicle crashes in highway safety studies. However, the conventional statistical method ignores model uncertainty. Transportation safety analysts typically select a single “best” model from a series of candidate models (called model space) and proceed as if the selected model is the true model. This paper proposes a new approach for deriving more reliable and robust crash prediction models than the conventional statistical modeling method. This approach uses the Bayesian model averaging (BMA) to account for model uncertainty. The derived BMA crash model is an average of the candidate models included in the model space weighted by their posterior model probabilities. To examine the applicability of BMA to the Poisson and negative binomial (NB) regression models, the approach is applied to the crash data collected on 338 rural interstate road sections in Indiana over a five-year period (1995 to 1999). The results show that BMA was successfully applied to Poisson and NB regression models. More importantly, in the presence of model uncertainty, the proposed approach can provide better prediction performance than single models selected by conventional statistical techniques. Thus, this paper provides transportation safety analysts with an alternative methodology to predict motor vehicle crashes when model uncertainty is suspected to exist.

Modeling School Bus Crashes Using Zero-Inflated Model

Abstract: School bus crashes are rare, but their occurrence can have devastating effects on the school children involved. Such crashes are infrequent and random, and some roadway segments may not experience any school bus related crashes for a number of years (zero crashes). Despite the fact that no crashes may have occurred along particular stretches of road, these zerocrash road segments cannot be termed as safe sites, and they cause a dual state of crash experience (no crashes, but still at risk for crashes) compared to a single state of non-zero crash prone sections where risk is confirmed. Literature indicates that for extremely rare and random count data, such as school bus crashes, Poisson and Negative Binomial (NB) distributions become more applicable for modeling. Apart from Poisson and NB, there exists an alternative discrete distributional model that is used to model extra-zero discrete data, such as school bus crashes,that allows exploration of the impact of zero segments. This alternative modeling approach called zero-inflated negative binomial (ZINB) model is introduced in this study for evaluation of variables influencing school bus crashes. Although crash data rarely reveal variability, the ZINB model provides a more flexible modeling framework for school bus crashes. The study found that, ZINB yields better prediction (tight standard errors and higher z-statistics), compared to NB model though same variable coefficient signs. Presence of median and outside shoulders was found to have tendency of reducing school bus crashes. On the other end, wider medians, outside shoulders, inside shoulders, and lane widths were found to reduce the probability of these crashes. Presence of curb and gutter and two-way left turn lane (TWLTLL, high posted speed limits, multilane segments, and congested segments were found to increase the probability of school bus crashes. Language: en