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Author: John Sanders Miller Publisher: ISBN: Category : Roads Languages : en Pages : 68
Book Description
Intersection crashes cost the nation more than $40 billion annually, account for more than one-fifth of all highway crash fatalities nationally, and totaled almost 75,000 in the Virginia Department of Transportation's (VDOT) Northern Virginia District for the period 2001 through 2006. Although VDOT maintains several databases containing more than 170 data elements with detailed crash, driver, and roadway attributes, it was not clear to users of these databases how these data elements could be used to identify causal factors for these intersection crashes for two reasons: (1) the quality of some of the data elements was imperfect, and (2) and random variation is inherent in crashes. This study developed an approach to address these two issues. To address the first issue, the completeness and accuracy of the 179 data elements that comprise the VDOT CRASHDATA database were assessed. For the 76 data elements for which the quality of the data was imperfect, eight rules for using these elements were developed. The rules indicate which data elements should be used in certain circumstances; which data elements are incomplete; and how to manipulate the data for certain applications. To address the second issue, classification trees and crash estimation models (CEMs) were developed. The trees showed that specific causal factors, such as the approach alignment or surface condition, successfully indicate whether a given crash was a rear-end or angle crash. By extension, the trees suggested that intersection crashes were not purely random. Accordingly, it was feasible to develop CEMs that for 17 intersection classes predicted the number of crashes for a 1-year period for four crash types: rear-end, angle, injury, and total. The 68 CEMs showed deviance-based pseudo R-square values between 0.07 and 0.74, suggesting that the causal factors explained some, but not all, of the variation in intersection crashes. The CEMs varied by intersection class. Two actions with regard to crash data analysis may be taken as detailed in this report. First, the eight crash data quality rules developed in this study should be considered for use on a case-by-case basis for studies requiring intersection crash data. Second, when they are collected at the crash scene, the variables that successfully classified rear-end and angle crashes may be given increased attention such that every effort is made to ensure these data elements are accurately recorded.
Author: John Sanders Miller Publisher: ISBN: Category : Roads Languages : en Pages : 68
Book Description
Intersection crashes cost the nation more than $40 billion annually, account for more than one-fifth of all highway crash fatalities nationally, and totaled almost 75,000 in the Virginia Department of Transportation's (VDOT) Northern Virginia District for the period 2001 through 2006. Although VDOT maintains several databases containing more than 170 data elements with detailed crash, driver, and roadway attributes, it was not clear to users of these databases how these data elements could be used to identify causal factors for these intersection crashes for two reasons: (1) the quality of some of the data elements was imperfect, and (2) and random variation is inherent in crashes. This study developed an approach to address these two issues. To address the first issue, the completeness and accuracy of the 179 data elements that comprise the VDOT CRASHDATA database were assessed. For the 76 data elements for which the quality of the data was imperfect, eight rules for using these elements were developed. The rules indicate which data elements should be used in certain circumstances; which data elements are incomplete; and how to manipulate the data for certain applications. To address the second issue, classification trees and crash estimation models (CEMs) were developed. The trees showed that specific causal factors, such as the approach alignment or surface condition, successfully indicate whether a given crash was a rear-end or angle crash. By extension, the trees suggested that intersection crashes were not purely random. Accordingly, it was feasible to develop CEMs that for 17 intersection classes predicted the number of crashes for a 1-year period for four crash types: rear-end, angle, injury, and total. The 68 CEMs showed deviance-based pseudo R-square values between 0.07 and 0.74, suggesting that the causal factors explained some, but not all, of the variation in intersection crashes. The CEMs varied by intersection class. Two actions with regard to crash data analysis may be taken as detailed in this report. First, the eight crash data quality rules developed in this study should be considered for use on a case-by-case basis for studies requiring intersection crash data. Second, when they are collected at the crash scene, the variables that successfully classified rear-end and angle crashes may be given increased attention such that every effort is made to ensure these data elements are accurately recorded.
Author: Nicholas J. Garber Publisher: ISBN: Category : Roads Languages : en Pages : 68
Book Description
In recent years, significant effort and money have been invested through research and implemented safety projects to enhance highway safety in Virginia. However, there is still substantial room for improvement in both crash frequency and severity. As there are limits in the available funds for safety improvements, it is crucial that allocated resources for safety improvement be spent at highway locations that will result in the maximum safety benefits. In addition, intersection crashes play a significant role in the safety conditions in Virginia. For example, crashes at intersections in Virginia for the period 2003 through 2007 account for 43.8% of all crashes and 26% of fatal crashes. Therefore, identifying intersections for safety improvements that will give the highest potential for crash reduction when appropriate safety countermeasures are implemented will have a significant impact on the overall safety performance of roads in Virginia. The Federal Highway Administration (FHWA) has developed a procedure for identifying highway locations that have the highest potential for crash reduction (ITT Corporation, 2008). A critical component of this method is the use of safety performance functions (SPFs) to determine the potential for crash reductions at a location. An SPF is a mathematical relationship (model) between frequency of crashes by severity and the most significant causal factors on a specific highway. Although the SafetyAnalyst User's Manual presents several SPFs for intersections, these were developed using data from Minnesota. FHWA also suggested that if feasible, each state should develop its own SPFs based on crash and traffic volume data from the state, as the SPFs that are based on Minnesota data may not adequately represent the crash characteristics in all states. SPFs for intersections in Virginia were developed using the annual average daily traffic as the most significant causal factor, emulating the SPFs currently suggested by SafetyAnalyst. The SPFs were developed for both total crashes and combined fatal plus injury crashes through generalized linear modeling using a negative binomial distribution. Models were also developed for urban and rural intersections separately, and in order to account for the different topographies in Virginia, SPFs were also developed for three regions: Northern, Western, and Eastern. This report covers Phases I and II of the study, which includes urban and rural intersections maintained by VDOT. Statistical comparisons of the models based on Minnesota data with those based on the Virginia data showed that the specific models developed for Virginia fit the Virginia crash data better. The report recommends that VDOT's Traffic Engineering Division use the SPFs developed for Virginia and the specific regional SPFs suggested in this report to prioritize the locations in need of safety improvement.
Author: Nicholas J. Garber Publisher: ISBN: Category : Roads Languages : en Pages : 68
Book Description
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: Nicholas J. Garber Publisher: ISBN: Category : Low-volume roads Languages : en Pages : 60
Book Description
Considerable efforts have been made in recent years to make highway travel safer. Traffic engineers continue to emphasize the identification of causal factors for crashes on individual sections and on different functional classes of highways as an area of emphasis. If precise causal factors and corresponding countermeasures can be identified, traffic engineers in the roadway design field would be able to use that information to make Virginia's highways safer. The purpose of this study was to identify causal factors of crashes on two-lane highways and corresponding effective countermeasures that should significantly reduce these crashes. The scope of the research was limited to two-lane highways in Virginia with data from 2001 through 2004. The researchers identified 143 five- to ten-mile stretches of two-lane highways in Virginia that proportionally represented each of the counties in Virginia. Relevant data elements that included time of crash, road and weather conditions, driver action, and type of collision were extracted from the relevant police reports. Traffic volumes and speed data were obtained from VDOT publications. Global positioning system data collected for each site provided information on grading and curvature of the sites. Signing and speed limit data were also collected for each site. The final dataset consisted of nearly 10,000 crashes and more than 30 variables, grouped under different highway classifications (urban primary, urban secondary, rural primary, rural secondary) and collision type (rear-end, angle, head-on, sideswipe, run-off-the-road [ROR], deer, and other). Fault tree analysis was used to identify the associated causal factors, and generalized linear models were developed from which the significant causal factors were identified. The results indicated that ROR crashes were the predominant type of crash, followed by rear-end, angle, and deer crashes. These crashes represented nearly 70% of all crashes. The significant causal factors for ROR crashes were found to be curvature and annual average daily traffic. One of the four recommendations is that a plan for correcting the geometric deficiencies of the significant causal factors at sites with high ROR crashes be developed and implemented. The economic benefits of improving the radii at locations with predominantly ROR crashes were investigated using a sensitivity analysis on the benefit/cost ratios for different levels of improvements and expected crash reductions. In all cases, the ratio was higher than 1, with a range of 1.16 to 9.60.
Author: U.s. Department of Transportation Publisher: Createspace Independent Publishing Platform ISBN: 9781723492846 Category : Languages : en Pages : 38
Book Description
Crash factors in intersection-related crashes : an on-scene perspective /
Author: Mafruhatul Jannat Publisher: ISBN: Category : Automobile drivers Languages : en Pages : 241
Book Description
A Right-hook (RH) crash is a common type of bicycle-motor vehicle crash that occurs between a right-turning vehicle and through-moving bicycle at an intersection. At signalized intersections, RH crashes can occur at the onset of the green or during the latter portion of the green phase. In spite of the frequency and severity of this crash type, no experimental studies have provided compelling evidence as to the root causes of RH crashes at signalized intersections. This research provided improved understanding of RH crash causal factors during the latter portion of the green phase through an online survey and driving simulator experiment. From the 209 self-reported online survey responses, it was found that 78% of bicyclists were unaware of their stopping position with respect to stopped vehicles queued at an intersection during a red indication, and 19% of motorists (n = 246) reported that they would not yield to the adjacent bicyclist approaching from behind if they were detected in rear-view or side-view mirrors. The driving simulator experiment (n = 51) investigated RH crash causal factors related to the motorist and built environment using three different motorist performance measures: i) visual attention, ii) situation awareness (SA) and iii) crash avoidance behavior. Motorist's visual attention measure revealed that in the presence of oncoming vehicular traffic, motorists spent the majority of their visual attention looking at the oncoming traffic that posed immediate hazard to them and failed to detect a bicyclist approaching from behind. Motorists' SA measure indicated that motorists detect a bicyclist riding in their forward field of view more successfully than a bicyclist approaching from behind in the vehicle's blind spot. Motorist's crash avoidance behavior revealed that 92% of 26 observed crashes occurred with a bicyclist approaching from behind in the vehicle's blind spot and oncoming vehicles were present in 88% of those crashes. Also, 81% of observed crashes occurred due to inadequate surveillance.
Author: John Sanders Miller
Author: John Sanders Miller
Author: Nicholas J. Garber
Author: Nicholas J. Garber
Author: Santhosh K. Korukonda
Author: Nicholas J. Garber
Author: Rachael Elizabeth Abel
Author: 
Author: 
Author: U.s. Department of Transportation
Author: Mafruhatul Jannat