Modeling Frequency and Severity of Rural Highway Crashes to Identify Contributing Factors and Potential Countermeasures PDF Download

Author: Liyanage Indike Ratnayake
Publisher:
ISBN:
Category :
Languages : en
Pages : 242

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Author: Irfan Uddin Ahmed
Publisher:
ISBN:
Category : Automobile driving in bad weather
Languages : en
Pages : 222

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The primary objective of practitioners working on traffic safety is to reduce the number and severity of crashes. The Highway Safety Manual (HSM) provides practitioners with analytical tools and techniques to estimate the expected crash frequency and severity with the aim to identify and evaluate safety countermeasures. Expected crash frequency can be estimated using Safety Performance Functions (SPFs) provided in Part C of the HSM. The HSM provides simple SPFs which are developed using the most frequently used crash counts model, the negative binomial regression model. The rural nature of Wyoming highways coupled with the mountainous terrain (i.e., challenging roadway geometry) make the HSM basic SPFs unsuitable to determine crash contributing factors for Wyoming conditions. In this regard, the objective of this study is to implement advanced statistical methods such as the different functional forms of Negative Binomial, and Bayesian approach, to develop crash prediction models, investigate crash contributing factors, and determine the impact of safety countermeasures. Bayesian statistics in combination with the power of Markov Chain Monte Carlo (MCMC) sampling techniques provide frameworks to model small sample datasets and complex models at the same time, where the traditional Maximum Likelihood Estimation (MLE) based methods tend to fail. As such, a novel No-U-Turn Sampler for Hamiltonian Monte Carlo (NUTS HMC) sampling technique in a Bayesian framework was utilized to investigate the crash frequency, injury severity of crashes on the interstate freeways and some rural highways in Wyoming. The Poisson and the Negative Binomial (NB) models are the most commonly used regression models in traffic safety analysis. The advantage of the NB model can be further enhanced by providing different functional forms of the variance and the dispersion structure. The NB-2 is the most common form of the NB model, typically used in developing safety performance functions (SPFs) largely due to the mean-variance quadratic relationship. However, studies in the literature have shown that the mean-variance relationship could be unrestrained. Another introduced formulation of the NB model is NB-1, which assumes that there is a constant ratio linking the mean and the variance of the crash frequencies. A more general type of the NB model is the NB-P model, which does not constrain the mean-variance relationship. Thus, leveraging the power of this unrestrained mean-variance relationship, more accurate safety models could be developed, and these would lead to more accurate estimation of crash risk and benefits of potential solutions. This study will help practitioners to implement advanced methodologies to solve traffic safety problems of rural highways that have plagued the researchers for a long time now. The methodologies proposed in this study will help practitioners to replace the outdated and inefficient traditional models and obtain more accurate traffic safety models to predict crashes and the resulting crash injury severity. Moreover, this research quantified the safety effectiveness of some unique countermeasures on rural highways.

Author: Azad Salim Abdulhafedh
Publisher:
ISBN:
Category :
Languages : en
Pages : 243

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Modeling crash severity is an important component of reasoning about the issues that may affect highway safety. A better understanding of the factors underlying crash severity can be used to reduce the degree of crash severity injury, locate road hazardous sites, and adopt suitable countermeasures. In order to provide insights on the mechanism and behavior of the crash severity injury, a variety of statistical approaches have been utilized to model the relationship between crash severity and potential risk factors. Many of the traditional approaches for analyzing crash severity are limited in that they are based on the assumption that all observations are independent of each other. However, given the reality of vehicle movement in networked systems, the assumption of independence of crash incidence is not likely valid. For instance, spatial and temporal autocorrelations are important sources of dependency among observations that may bias estimates if not considered in the modeling process. Moreover, there are other aspects of vehicular travel that may influence crash severity that have not been explored in traditional analysis approaches. One such aspect is the roadway visibility that is available to a driver at a given time that can impact their ability to react to changing traffic conditions, a characteristics known as sight distance. Accounting for characteristics such as sight distance in crash severity modeling involve moving beyond statistical analysis and modeling the complex geospatial relationships between the driver and the surrounding landscape. To address these limitations of traditional approaches to crash severity modeling, this dissertation first details a framework for detecting temporal and spatial autocorrelation in crash data. An approach for evaluating the sight distance available to drivers along roadways is then proposed. Finally, a crash severity model is developed based upon a multinomial logistic regression approach that incorporates the available sight distance and spatial autocorrelation as potential risk factors, in addition to a wide range of other factors related to road geometry, traffic volume, driver's behavior, environment, and vehicles. To demonstrate the characteristics of the proposed model, an analysis of vehicular crashes (years 2013-2015) along the I-70 corridor in the state of Missouri (MO) and on roadways in Boone County MO is conducted. To assess existing stopping sight distance and decision sight distance on multilane highways, a geographic information system (GIS)-based viewshed analysis is developed to identify the locations that do not conform to AASHTO (2011) criteria regarding stopping and decision sight distances, which could then be used as potential risk factors in crash prediction. Moreover, this method provides a new technique for estimating passing sight distance along two-lane highways, and locating the passing zones and no-passing zones. In order to detect the existence of temporal autocorrelation and whether it's significant in crash data, this dissertation employs the Durbin-Watson (DW) test, the Breusch-Godfrey (LM) test, and the Ljung-Box Q (LBQ) test, and then describes the removal of any significant amount of temporal autocorrelation from crash data using the differencing procedure, and the Cochrane-Orcutt method. To assess whether vehicle crashes are spatially clustered, dispersed, or random, the Moran's I and Getis-Ord Gi* statistics are used as measures of spatial autocorrelation among vehicle incidents. To incorporate spatial autocorrelation in crash severity modeling, the use of the Gi* statistic as a potential risk factor is also explored. The results provide firm evidence on the importance of accounting for spatial and temporal autocorrelation, and sight distance in modeling traffic crash data.

Author: Nicholas J. Garber
Publisher:
ISBN:
Category : Roads
Languages : en
Pages : 68

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In 2004, a total of 95,020 vehicle crashes occurred on highways under the jurisdiction of the Virginia Department of Transportation (VDOT). Of these, 39,847 crashes occurred on primary highways, and 345 of these were fatal crashes. VDOT's traffic engineers continue to place increasing emphasis on identifying causal factors for crashes to enhance the selection of appropriate and effective countermeasures. The purpose of this study was to identify causal factors and appropriate countermeasures for crashes occurring at high-risk locations on multilane primary highways from 2001 through 2006. These high-risk locations were identified by Fontaine and Reed (2006) in a VDOT safety corridor study. A total of 365 sites, 1 to 2 mi in length, were used in the study. The statewide sites were located on rural and urban highways with divided, undivided, and traversable medians, with about 40 sites per VDOT district. Crash data were extracted from police crash reports, and geometric data were collected through site visits. Operational data were collected using VDOT's resources. The analysis involved more than 34,000 crashes and was conducted using fault tree analysis and generalized linear modeling. The fault tree analysis was used to determine the critical fault path based on the probability of an event occurring. Individual fault trees were constructed for each collision type and for each highway classification. The generalized linear models were developed for different highway classifications: urban divided, urban undivided, urban traversable (central lanes that can be used for left turns in both directions), and rural divided highways. Models were developed for rear-end crashes and total crashes, and separate models were developed for injury crashes, property damage only (PDO) crashes, and injury + PDO crashes. Appropriate potential countermeasures were then identified based on the significant causal factors identified in the models. The results indicated that rear-end crashes were the predominant type of crash, representing 56% of all PDO crashes on urban divided highways and 37% of all PDO crashes on rural divided highways. Implementing the recommended countermeasures for total, rear-end, and angle crashes for different assumed levels of rehabilitation is expected to result in a crash reduction of up to about 40% depending on the site and level of rehabilitation undertaken. A benefit/cost analysis showed that the benefit/cost ratios were higher than 1 for all levels of countermeasure implementation.

Author: Wan-Hui Chen
Publisher:
ISBN:
Category :
Languages : en
Pages : 444

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Author: National Research Council (U.S.). Transportation Research Board
Publisher:
ISBN:
Category : Traffic accidents
Languages : en
Pages : 148

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Transportation Research Record contains the following papers: Method for identifying factors contributing to driver-injury severity in traffic crashes (Chen, WH and Jovanis, PP); Crash- and injury-outcome multipliers (Kim, K); Guidelines for identification of hazardous highway curves (Persaud, B, Retting, RA and Lyon, C); Tools to identify safety issues for a corridor safety-improvement program (Breyer, JP); Prediction of risk of wet-pavement accidents : fuzzy logic model (Xiao, J, Kulakowski, BT and El-Gindy, M); Analysis of accident-reduction factors on California state highways (Hanley, KE, Gibby, AR and Ferrara, T); Injury effects of rollovers and events sequence in single-vehicle crashes (Krull, KA, Khattack, AJ and Council, FM); Analytical modeling of driver-guidance schemes with flow variability considerations (Kaysi, I and Ail, NH); Evaluating the effectiveness of Norway's speak out! road safety campaign : The logic of causal inference in road safety evaluation studies (Elvik, R); Effect of speed, flow, and geometric characteristics on crash frequency for two-lane highways (Garber, NJ and Ehrhart, AA); Development of a relational accident database management system for Mexican federal roads (Mendoza, A, Uribe, A, Gil, GZ and Mayoral, E); Estimating traffic accident rates while accounting for traffic-volume estimation error : a Gibbs sampling approach (Davis, GA); Accident prediction models with and without trend : application of the generalized estimating equations procedure (Lord, D and Persaud, BN); Examination of methods that adjust observed traffic volumes on a network (Kikuchi, S, Miljkovic, D and van Zuylen, HJ); Day-to-day travel-time trends and travel-time prediction form loop-detector data (Kwon, JK, Coifman, B and Bickel, P); Heuristic vehicle classification using inductive signatures on freeways (Sun, C and Ritchie, SG).

Author: Bhanu Sireesha Javvadi
Publisher:
ISBN:
Category : Traffic accidents
Languages : en
Pages : 150

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"This study examined the variables related to roadway geometry, environmental, driver and traffic factors to identify crash causal factors. It relied on three years of crash data from the Arkansas Highway Transportation Department (AHTD) and analyzed nonjunctions of rural and urban US highway systems. In the first part of this study, negative binomial modeling technique was used to model the frequency of crash occurrence. To further analyze the crash factors this study also analyzed crash severity and collision types. The second part identified the factors responsible for severe crashes and fatalities including using the binary logistic regression modeling technique. The third part used the multinomial logistic regression modeling technique to identify the factors associated with specific collision types (single vehicle, head-on, rear-end, sideswipe-same, and sideswipe-opposite direction). The crash data were analyzed statistically, and the factors significant for crash frequency proved to be surface width, roughness, left and right shoulder widths, road segment length, and Annual Average Daily Traffic. Driver related factors such as age, gender, restraint type, and alcohol consumption were significant in severe crashes. Variables such as horizontal and vertical road curvature, wet road surface, and darkness differentiated single-vehicle collisions from multi-vehicle collisions. This study clearly indicated the importance of using different analysis techniques to identify the main factors responsible for crashes"--Abstract, leaf iii.

Author: Jun Deng (Writer on transportation)
Publisher:
ISBN:
Category :
Languages : en
Pages : 112

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Safety at intersections is of significant interest to transportation professionals due to the large number of possible conflicts that occur at those locations. In particular, rural intersections have been recognized as one of the most hazardous locations on roads. However, most models of crash frequency at rural intersections, and road segments in general, do not differentiate between crash type (such as angle, rear-end or sideswipe) and injury severity (such as fatal injury, non-fatal injury, possible injury or property damage only). Thus, there is a need to be able to identify the differential impacts of intersection-specific and other variables on crash types and severity levels. This thesis builds upon the work of Bhat et al., (2013b) to formulate and apply a novel approach for the joint modeling of crash frequency and combinations of crash type and injury severity. The proposed framework explicitly links a count data model (to model crash frequency) with a discrete choice model (to model combinations of crash type and injury severity), and uses a multinomial probit kernel for the discrete choice model and introduces unobserved heterogeneity in both the crash frequency model and the discrete choice model, while also accommodates excess of zeros. The results show that the type of traffic control and the number of entering roads are the most important determinants of crash counts and crash type/injury severity, and the results from our analysis underscore the value of our proposed model for data fit purposes as well as to accurately estimate variable effects.

Author: Andrew P. Tarko
Publisher: Purdue University Press
ISBN: 9781622602117
Category : Transportation
Languages : en
Pages : 70

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Since 2006, INDOT has been preparing an annual five-percent report that identifies intersections and segments on Indiana state roads that require attention due to the excessive number and severity of crashes. Many of the identified intersections are two-way, stop-controlled intersections located on high-speed, multi-lane, rural roads. Some contributing design and human factors have been identified while other factors still await investigation. Multivariate ordered probit models have been developed to help identify additional factors of the frequency and severity of crashes. These models can estimate how much different factors increase the frequency of crashes at several levels of injury severity (fatal/incapacitating, non-incapacitating/ possible, property-damage-only). They have a unique ability to account for unobserved but common conditions that affect all of the crash severity levels. Recommendations for safety countermeasures are made based on both of these research results and our study of published reports of other authors.

Author: Ozgur Ozturk
Publisher:
ISBN:
Category : Road work zones
Languages : en
Pages : 231

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Book Description
Work zone presence is an important concern for drivers in terms of safety and congestion. In recent years, work zone safety has received much attention due to numerous highway renovation projects that have resulted in many work zone crashes. To minimize the effect of work zones on roadway safety risks and traffic conditions, potential factors need to be addressed and countermeasures need to be implemented to ensure that the motorist can drive in a safe manner. The impact of the work zones can be estimated by using descriptive analysis and different statistical modeling methods. To this end, this study focused on three major areas: the crash frequency at work zones, the crash severity at work zones and the change in traffic conditions at work zones. Statistically robust models were developed by incorporating integrated datasets that could identify significant factors affecting each of these study areas. To better understand this, different from the previous studies, model results were compared against reference conditions, such as work zone crash frequency and modeling parameters were compared with non-work zone parameters. In addition, different statistical modeling techniques were applied to examine the best model or set of variables to connect crash severity and possible causative factors for binary level and multiple level outcomes. Two crash severity indexes were proposed and used to estimate multilevel crash severity by using both maximum severity and the monetary cost weighted severity. Besides safety issues, different types of lane closures and crashes observed within lane closures were studied to examine if there would be a change in traffic conditions compared with normal time traffic. Comparisons of each concept provides an idea for agencies about the differences of work zone and non-work zone conditions which is important if indeed there is a specific impact for the work zone cases. Work zone presence was found to have an increasing effect on crash occurrence. Nighttime shifts were found to be safer when compared to daytime work zone periods. Injury crashes for two-lane closure cases were found to have a more marked impact on traffic volume compared with other cases studied in this dissertation. In the conclusions chapter, all of these findings are summarized along with specific recommendations.