William H. Schneider

Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations

Standard multinomial logit (MNL) and mixed logit (MXL) models are developed to estimate the degree of influence that bicyclist, driver, motor vehicle, geometric, environmental, and crash type characteristics have on bicyclist injury severity, classified as property damage only, possible, nonincapacitating or severe (i.e., incapacitating or fatal) injury. This study is based on 10,029 bicycleinvolved crashes that occurred in the State of Ohio from 2002 to 2008. Results of likelihood ratio tests reveal that some of the factors affecting bicyclist injury severity at intersection and non-intersection locations are substantively different and using a common model to jointly estimate impacts on severity at both types of locations may result in biased or inconsistent estimates. Consequently, separate models are developed to independently assess the impacts of various factors on the degree of bicyclist injury severity resulting from crashes at intersection and non-intersection locations. Several covariates are found to have similar impacts on injury severity at both intersection and non-intersection locations. Conversely, six variables were found to significantly influence injury severity at intersection locations but not non-intersection locations while four variables influenced bicyclist injury severity only at non-intersection locations. In crashes occurring at intersection locations, the likelihood of severe bicyclist injury increases by 14.8 percent if the bicyclist is not wearing a helmet, 82.2 percent if the motorist is under the influence of alcohol, 141.3 percent if the crash-involved motor vehicle is a van, 40.6 percent if the motor vehicle strikes the side of the bicycle, and 182.6 percent if the crash occurs on a horizontal curve with a grade. Results from non-intersection locations show the likelihood of severe injuries increases by 374.5 percent if the bicyclist is under the influence of drugs, 150.1 percent if the motorist is under the influence of alcohol, 53.5 percent if the motor vehicle strikes the side of the bicycle and 99.9 percent if the crash-involved motor vehicle is a heavy-duty truck.

Examination of factors determining fault in two-vehicle motorcycle crashes

Motorcycle crashes frequently involve a combination of high-risk behaviors by the motorcyclist or the other crash-involved driver. Such behaviors may include riding or driving without appropriate licensure or while under the influence of alcohol, as well as deciding not to use a safety device such as a helmet or safety belt. Given that these factors frequently occur in combination with one another, it is difficult to untangle the specific effects of individual factors leading up to the crash outcome. This study assesses how various rider-, driver-, and other crash-specific factors contribute to at-fault status in two-vehicle motorcycle crashes, as well as how these same factors affect the propensity for other high-risk behaviors. Furthermore, the interrelationships among fault status and these other behaviors are also examined using a multivariate probit model. This model is developed using police-reported crash data for the years 2006-2010 from the State of Ohio. The results show that younger motorcyclists are more likely to be at-fault in the event of a collision, as are riders who are under the influence of alcohol, riding without insurance, or not wearing a helmet. Similarly, motorcyclists were less likely to be at-fault when the other driver was of younger age or was driving under the influence of alcohol, without insurance, or not wearing their safety belt. Crash-involved parties who engaged in one high-risk behavior were more likely to engage in other such behaviors, as well, and this finding was consistent for both motorcyclists and drivers. The results of this study suggest that educational and enforcement strategies aimed at addressing any one of these behaviors are likely to have tangential impacts on the other behaviors, as well.

Driver Injury Severity Resulting from Single-Vehicle Crashes Along Horizontal Curves on Rural Two-Lane Highways

Horizontal curves have been identified as a target area for improving safety on rural two-lane highways in Texas. This study involved the development of multinomial logit models to assess driver injury severity resulting from single-vehicle crashes on such roads. Likelihood ratio tests warranted the development of separate injury severity models for curves of small, medium, and large radius. Various driver, vehicle, roadway, and environmental characteristics were found to affect injury severity among the 10,029 crashes analyzed. Run-off-the-road crashes, particularly those resulting in collisions with roadside objects, were found to increase injury severity significantly. Females were more likely to sustain injury and older drivers to be critically injured, particularly on curves of smaller radius. Various driver actions and behaviors were also significant determinants of injury severity. Unbelted drivers were up to 10 times more likely to suffer fatal injuries, and drivers who were uninsured, fatigued, or under the influence of drugs or alcohol were more likely to be seriously injured. Several of these behavioral factors were more pronounced on sharper curves.

Effects of Horizontal Curvature on Single-Vehicle Motorcycle Crashes Along Rural Two-Lane Highways

Motorcycle crashes and fatalities have been increasing rapidly during the past 12 years both in Ohio and across the rest of the United States. In response to these issues, various studies have examined aspects of motorcycle safety in recent years. However, there has been limited research on the effects of site-specific roadway geometry on the frequency of motorcycle crashes, particularly at nonintersection locations. Typically, researchers employ Poisson and negative binomial crash prediction modeling techniques in these types of studies. The research presented in this paper uses a negative binomial model, applying full Bayes methods to improve model performance and to assess the impacts of horizontal curvature and other geometric features on the frequency of single-vehicle motorcycle crashes along segments of rural two-lane highways. The data used in this study include crash records for the years 2002 through the spring of 2008, in combination with available geometric design information, for those curves maintained by the State of Ohio. The analysis data set includes 30,379 horizontal curves that experienced a total of 225 motorcycle crashes during the study period. The findings show that the radius and length of each horizontal curve significantly influence the frequency of motorcycle crashes, as do shoulder width, annual average daily traffic, and the location of the road segment in relation to the curve.

Bayesian Analysis of the Effect of Horizontal Curvature on Truck Crashes Using Training and Validation Data Sets

The research on the effects of roadway geometry on truck crashes is relatively limited in comparison with predictive models developed for total vehicle crashes. The most common predictive models currently used are Poisson and negative binomial models. This study uses a negative binomial model but applies the full Bayes’ methods for improving model performance. To use Bayes’ methods successfully, a learning process was used to develop a final model, which was then compared with a separate validation data set to verify its accuracy. The data set used for this study is based on rural two-lane collector and arterial horizontal curves in Ohio, comprising 15,390 observations from crash records between 2002 through 2006. Specific areas of interest in this study include the impact of shoulder width, horizontal curve radius, curve length, and other traffic parameters. The final results indicate a significant increase in truck crashes due to both horizontal curvature and passenger vehicle volumes. The final models predictions were improved compared with the initial model, indicating that the learning process is a viable tool for future crash model development.

Innovative Real-Time Methodology for Detecting Travel Time Outliers on Interstate Highways and Urban Arterials:

Bluetooth devices are a rich source of travel time data for transportation engineers. Like any other source, however, Bluetooth devices can generate outliers that may bias travel times and corridor speeds. In this study, the proposed, innovative statistical methodology is capable of real-time deployment when travel time and vehicle speed outliers collected from Bluetooth data collection systems are detected. The proposed statistical methodology identifies outliers by using a data points standard residual in a robust Greenshields model. The efficiency and the feasibility of this statistical methodology are evaluated for both Interstate highways and urban arterial corridors. These roadways are selected because they display a wide range of traffic patterns and outlier generators. The effectiveness of one methodology, especially on a wide range of traffic patterns, would improve the potential for widespread implementation. Four subsets of the collected data are provided within this study to highlight the efficiency and capability of the methodology. The results of using the Shapiro–Wilk statistics show that in both Interstate highway and urban arterial deployment the proposed method is effective at identifying Bluetooth outliers and is capable of working in a real-time environment. A second comparison between the two road types shows that the methodology performs better for Interstate highways than urban arterials, for which the added performance is based on the higher volume of hits and less fluctuation.

A comparison of contributing factors between alcohol related single vehicle motorcycle and car crashes

INTRODUCTION Alcohol related crashes have accounted for approximately 35% of fatal crashes per year since 1994 nationwide, with approximately 30% involving impairment over the legal blood alcohol content limit of 0.08%. Educational campaigns and law enforcement efforts are two components of multi-faceted programs aimed toward reducing impaired driving. It is crucial that further research be conducted to guide the implementation of enforcement and educational programs. METHOD This research attempts to provide such guidance by examining differences in alcohol-involved crashes involving motorcycles and passenger cars. Prior safety research has shown that motorcyclists follow a significantly different culture than the average passenger car operator. These cultural differences may be reflected by differences in the contributing factors affecting crashes and the severity of the resulting injuries sustained by the driver or motorcyclist. This research is focused on single-vehicle crashes only, in order to isolate modal effects from the contribution of additional vehicles. The crash data provided for this study are from the Ohio Department of Public Safety from 2009 through 2012. RESULTS The injury severity data are analysed through the development of two mixed logit models, one for motorcyclists and one for passenger car drivers. The models quantify the effects of various factors, including horizontal curves, speeds, seatbelt use, and helmet use, which indicate that the required motor skills and balance needed for proper motorcycle operation compounded with a lack of mechanical protection make motorcyclists more prone to severe injuries, particularly on curves and in collisions with roadside objects. PRACTICAL APPLICATIONS The findings of this study have been incorporated into combined motorcycle and sober driving educational safety campaigns. The results have shown to be favorable in supporting national campaign messages with local justification and backing.

Probabilistic Model Based on the Effective Range and Vehicle Speed to Determine Bluetooth MAC Address Matches from Roadside Traffic Monitoring

The use of Bluetooth technology to determine vehicle travel times offers several benefits over traditional methods—most notably, an increased amount of data points. An understanding of the Bluetooth device discovery procedure provides insight on the impacts of vehicle speed and device effective range on the number of in-range scan intervals, allowing the data collection setup to be modified to improve the probability of device detection. It is determined that the number of in-range scan intervals increases as vehicle speed decreases and that vehicle speed has a greater influence when the device effective range is decreased. Our match probability is defined as the probability that a single vehicle containing a discoverable Bluetooth radio will be detected by two different scanning radios, not a sampling rate produced by the ratio of MAC detections to total number of passing vehicles. The probability of device detection escalates for roadways with lower functional classification, slower posted speed limits,...

Discriminant Analysis for Assigning Short-Term Counts to Seasonal Adjustment Factor Groupings

The assignment of short-term counts to groupings of seasonal adjustment factors is the most critical step in the annual average daily traffic estimation process; this step is also extremely sensitive to error resulting from engineering judgment. In this study, discriminant analysis is examined, and several variable selection criteria are investigated to develop 12 assignment models. Continuous traffic volume data, obtained in the state of Ohio during 2005 and 2006, are used in the analysis. Seasonal adjustment factors are calculated with individual volumes of the two directions of travel as well as the total volume of a roadway segment. The results reveal that the best-performing directional volume–based model, which employs the Raos V algorithm, produces a mean absolute error (MAE) of 4.2%, which can be compared with errors reported in previous studies. An average decline in the MAE by 58% and in the standard deviation of the absolute error by 70% is estimated over the traditional roadway functional classification. In addition, time-of-day factors are slightly more effective in identifying similar patterns of short-term counts than when they are combined with the average daily traffic. When directional-specific factors are used instead of total volume–based seasonal adjustment factors, the improvement in the average MAE is approximately 41%. This conclusion is consistent with previous research findings and may result from the division of the data set by direction essentially doubling the sample size, which in turn increases the number of assignment options for a short-term count.

A Bayesian analysis of the effect of estimating annual average daily traffic for heavy-duty trucks using training and validation data-sets

The precise estimation of annual average daily traffic (AADT) is of significant importance worldwide for transportation agencies. This paper uses three modeling frameworks to predict the AADT for heavy-duty trucks. In total, 12 models are developed based on regression and Bayesian analysis using a training data-set. A separate validation data-set is used to compare the results from the 12 models, spanning the years 2005 through 2007 and taken from 67 continuous data recorders. Parameters of significance include roadway functional class, population density, and spatial location; five regional areas – northeast, northwest, central, southeast, and southwest – of the state of Ohio in the USA; and average daily truck traffic. The results show that a full Bayesian negative binomial model with a coefficient offset is the most efficient model framework for all four seasons of the year. This model is able to account for between 87% and 92% of the variability within the data-set.