Getting to Zero Deaths on Ohio’s Low-Volume Roads

2018 ◽  
Vol 2672 (32) ◽  
pp. 40-48
Author(s):  
Victoria F. Beale ◽  
Derek Troyer ◽  
Alejandro Chock ◽  
Cory Hopwood ◽  
Mike McNeill
Keyword(s):  
Highway Safety ◽  
Improvement Program ◽  
Crash Data ◽  
Societal Benefit ◽  
Low Volume Roads ◽  
Grant Program ◽  
Low Volume ◽  
Safety Countermeasures ◽  
Benefit Cost ◽  
Serious Injuries

Ohio faces the challenge, as do many other states, of how to utilize Highway Safety Improvement Program (HSIP) funding to improve safety on its low-volume roadways while meeting the data-driven safety funding requirements of the Fixing America’s Surface Transportation (FAST) Act. Low-volume roads present unique challenges because data is rarely available and other factors, such as roadway ownership, affect the implementation of safety countermeasures on this system. Beginning in 2014, the Ohio Department of Transportation (ODOT) created a township safety signage grant program designed to address the issues with utilizing HSIP funding on low-volume roadways. The grant program’s goal was to drive down the number of fatalities, serious injuries, and overall crashes occurring on Ohio’s low-volume roads. ODOT took into consideration overriding issues regarding low-volume roads in how it structured the grant program. ODOT also utilized the direction from its Strategic Highway Safety Plan in choosing a safety countermeasure which met the needs of its roadway departure and intersection crash trends. The program has been actively engaged in by Ohio townships and now has a large enough amount of post-safety countermeasure installation data available to quantify its initial success. This paper presents the successful results by highlighting the human capital and comprehensive societal benefit/cost analyses for the first 24 townships with 12 months of post-grant completion crash data.

Safety Performance Functions of Low-Volume Roadways

2019 ◽  
Vol 2673 (12) ◽  
pp. 798-810 ◽  
Author(s):  
Subasish Das ◽  
Ioannis Tsapakis ◽  
Songjukta Datta
Keyword(s):  
High Volume ◽  
Highway Safety ◽  
Safety Performance ◽  
Improvement Program ◽  
Crash Data ◽  
Safety Issues ◽  
Safety Performance Functions ◽  
Traffic Volumes ◽  
Low Volume ◽  
State And Local

The Fixing America’s Surface Transportation Act (FAST Act) mandates a Highway Safety Improvement Program (HSIP) for all states that “emphasizes a data-driven, strategic approach to improving highway safety on all public roads that focuses on performance.” To determine the predicted crashes on a specific roadway facility, the most convenient and widely used tool is the first edition of Highway Safety Manual (HSM), which provides predictive models [known as safety performance functions (SPFs)] of crash frequencies for different roadways. Low-volume roads (LVRs) are defined as roads located in rural or suburban areas with daily traffic volumes of less than or equal to 400 vehicles per day (vpd). LVRs cover a significant portion of the roadways in the U.S. While much work has been done to develop SPFs for high-volume roads, less effort has been devoted to LVR safety issues. This study used 2013–2017 traffic count, and roadway network and crash data from North Carolina to develop six SPFs for three LVRs, which can be used to predict total crashes, as well as fatal and injury crashes. This study also performed a sensitivity analysis to show the influence of traffic volumes on expected crash frequencies. The SPFs developed in this study can provide guidance to state and local agencies with the means to quantify safety impacts on LVR networks.

Network-Level Application of Roads Economic Decision Model in Nicaragua

2003 ◽  
Vol 1819 (1) ◽  
pp. 67-73 ◽  
Author(s):  
Rodrigo Archondo-Callao ◽  
Douglas Méndez Talavera ◽  
Lubina Cantarero Zeas
Keyword(s):  
Decision Making ◽  
Consumer Surplus ◽  
Decision Making Process ◽  
Improvement Program ◽  
Unpaved Roads ◽  
Economic Decision ◽  
The Road ◽  
Low Volume Roads ◽  
Low Volume ◽  
Poverty Indicators

A network-level application of the Roads Economic Decision (RED) model in Nicaragua is presented. The RED model was developed by the World Bank to improve the decision-making process for development and maintenance of low-volume roads. The model adopts the consumer surplus approach to estimate transport benefits and is customized to the characteristics of low-volume roads, such as the high uncertainty in the assessment of the model inputs, the difficulties in characterizing the road condition of unpaved roads, and the need for a comprehensive analysis of generated traffic to clearly define all accrued benefits. The network-level application was designed to define a rational maintenance and improvement program for a network of secondary unpaved roads with particular attention to the alternative of improving the network by surfacing roads with concrete blocks and to include in the decision-making process not only economic considerations but also poverty indicators and priorities perceived by local administrators.

Identifying Candidate Locations for Safety Improvements on Low-Volume Rural Roads: The Oregon Experience

2019 ◽  
Vol 2673 (12) ◽  
pp. 690-698
Author(s):  
Ahmed Al-Kaisy ◽  
Levi Ewan ◽  
Fahmid Hossain
Keyword(s):  
Rural Areas ◽  
Risk Index ◽  
Significant Proportion ◽  
Safety Data ◽  
The State ◽  
Crash Risk ◽  
Rural Roads ◽  
Crash Data ◽  
Low Volume Roads ◽  
Low Volume

Low-volume roads constitute a significant proportion of the roadway network in rural areas, but they are usually associated with sparse crash data. This makes it impractical to rely on crash history alone to identify candidate locations for more detailed safety investigations and potential improvements. This paper presents the development of a prioritization scheme, in the form of a crash risk index, to be used in ranking candidate sites for safety improvements on low-volume roads in the State of Oregon. The index developed utilizes information on highway geometry, roadside features, traffic exposure, and crash occurrence in assessing risk, rather than relying solely on crash history in identifying hazardous locations. A roadway sample with a total length of around 830 mi was used in this study to represent different geographic regions in the state. Subsequently, extensive roadway, traffic, and safety data for the study sample were acquired and utilized in the development of the proposed index. A case study application of the proposed crash risk index on a 16-mi low-volume road corridor is presented which shows how to apply the index practically on a typical low-volume road using information readily accessible to the agency.

Evaluating the Effectiveness of the Safety Improvement Program in Saskatchewan Using an Observational Before-After Study with the Full-Bayes Approach

2021 ◽  
Author(s):  
Emanuele Sacchi ◽  
Saeid Tayebikhorami
Keyword(s):  
Rural Areas ◽  
Safety Management ◽  
Highway Safety ◽  
Safety Performance ◽  
Property Damage ◽  
Improvement Program ◽  
Safety Improvement ◽  
Crash Modification Factors ◽  
Safety Countermeasures ◽  
Bayes Approach

A key step in highway safety management is to determine whether the frequency and/or severity of collisions have been reduced after implementing a specific improvement program. This research focused on evaluating the safety performance of 50 sites that have been improved under the Saskatchewan Ministry of Highways and Infrastructure’s (MHI) Safety Improvement Program (SIP). SIP projects were designed to reduce the frequency and severity of collisions on provincial highways in rural areas through the implementation of different safety countermeasures. The methodology adopted was an observational before-after study with the full Bayes approach. The results showed that SIP projects reduced total collisions by 14.8% and severe (fatal-plus-injury) collisions by 25.4%. The reduction of property-damage-only collisions was not found to be statistically significant. Crash modification factors (CMFs) for the two most frequent SIP treatments, i.e., right-turn lanes and delineation lighting at intersections, were estimated and compared to the results of the literature.

Identifying Pedestrian High-Crash Locations as Part of Florida’s Highway Safety Improvement Program: A Systematic Approach

2003 ◽  
Vol 1828 (1) ◽  
pp. 83-88 ◽  
Author(s):  
Elio R. Espino ◽  
Javier S. Gonzalez ◽  
Albert Gan
Keyword(s):  
Goodness Of Fit ◽  
Highway Safety ◽  
Dade County ◽  
Goodness Of Fit Test ◽  
Improvement Program ◽  
Crash Data ◽  
Safety Improvement ◽  
Florida Department ◽  
Pedestrian Crashes ◽  
High Crash Locations

From 1997 to 2001, pedestrian fatalities represented 25.9% (2,065 fatalities) of all traffic fatalities in Florida. The latest available statewide crash data from the Florida Department of Highway Safety and Motor Vehicles reveals 8,487 pedestrian crashes, resulting in 510 deaths and 7,894 injuries, in 2001. However, a methodology is not currently available to identify pedestrian high-crash locations in Florida as part of the Highway Safety Improvement Program (HSIP). A study was conducted to provide the framework for the systematic identification of pedestrian high-crash locations on the state highway system as part of the HSIP. The study methodology uses the Poisson distribution to determine abnormally high pedestrian crash frequencies in a year for 1-mi roadway segments. Four-lane and six-lane divided roadways with continuous sidewalks on both sides of the road in Miami-Dade County were included. The crash data, the latest available from the crash database of the Florida Department of Transportation, correspond to the years 1997, 1998, and 1999. A χ2 goodness-of-fit test was performed to determine how well the data could be modeled by a Poisson process. The goodness-of-fit test was significant at the 2.5% level for the 1999 data, at the 10% level for the 1998 data, and less than 1% for the 1997 data. With a confidence level of at least 90%, a pedestrian crash frequency of three crashes in a 1-mi segment was found to be abnormally high for the fourlane divided facilities. For the six-lane divided facilities, four pedestrian crashes per 1-mi segment were established as the threshold value. From these threshold values, 22 1-mi segments were identified as pedestrian high-crash locations in Miami-Dade County for 1999.

Study & Experimental Analysis of Pervious Concrete In Low Volume Roads

2018 ◽  
pp. 172-176
Author(s):  
Suraj Pinate ◽  
Hitesh Sonawane ◽  
Jayesh Barhate ◽  
Mayur Chaudhari ◽  
Utkarsha Dhok ◽  
...  
Keyword(s):  
Experimental Analysis ◽  
Pervious Concrete ◽  
Low Volume Roads ◽  
Low Volume

Examining Driver Injury Severity in Single-Vehicle Road Departure Crashes Involving Large Trucks

2021 ◽  
pp. 036119812110101
Author(s):  
Mehdi Hosseinpour ◽  
Kirolos Haleem
Keyword(s):  
Rural Areas ◽  
Injury Severity ◽  
Factors Affecting ◽  
Crash Data ◽  
Natural Logarithm ◽  
Concrete Barriers ◽  
Local Roads ◽  
Single Vehicle ◽  
Safety Countermeasures ◽  
Injury Severities

Road departure (RD) crashes are among the most severe crashes that can result in fatal or serious injuries, especially when involving large trucks. Most previous studies neglected to incorporate both roadside and median hazards into large-truck RD crash severity analysis. The objective of this study was to identify the significant factors affecting driver injury severity in single-vehicle RD crashes involving large trucks. A random-parameters ordered probit (RPOP) model was developed using extensive crash data collected on roadways in the state of Kentucky between 2015 and 2019. The RPOP model results showed that the effect of local roadways, the natural logarithm of annual average daily traffic (AADT), the presence of median concrete barriers, cable barrier-involved collisions, and dry surfaces were found to be random across the crash observations. The results also showed that older drivers, ejected drivers, and drivers trapped in their truck were more likely to sustain severe single-vehicle RD crashes. Other variables increasing the probability of driver injury severity have included rural areas, dry road surfaces, higher speed limits, single-unit truck types, principal arterials, overturning-consequences, truck fire occurrence, segments with median concrete barriers, and roadside fixed object strikes. On the other hand, wearing seatbelt, local roads and minor collectors, higher AADT, and hitting median cable barriers were associated with lower injury severities. Potential safety countermeasures from the study findings include installing median cable barriers and flattening steep roadside embankments along those roadway stretches with high history of RD large-truck-related crashes.

scholarly journals GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 362
Author(s):  
Arshad Jamal ◽  
Tahir Mahmood ◽  
Muhamad Riaz ◽  
Hassan M. Al-Ahmadi
Keyword(s):  
Real Time ◽  
Control Charts ◽  
Negative Binomial ◽  
Highway Safety ◽  
Monitoring Methods ◽  
Count Data Models ◽  
Crash Data ◽  
Safety Surveillance ◽  
Study Results ◽  
Best Fit

Statistical modeling of historical crash data can provide essential insights to safety managers for proactive highway safety management. While numerous studies have contributed to the advancement from the statistical methodological front, minimal research efforts have been dedicated to real-time monitoring of highway safety situations. This study advocates the use of statistical monitoring methods for real-time highway safety surveillance using three years of crash data for rural highways in Saudi Arabia. First, three well-known count data models (Poisson, negative binomial, and Conway–Maxwell–Poisson) are applied to identify the best fit model for the number of crashes. Conway–Maxwell–Poisson was identified as the best fit model, which was used to find the significant explanatory variables for the number of crashes. The results revealed that the road type and road surface conditions significantly contribute to the number of crashes. From the perspective of real-time highway safety monitoring, generalized linear model (GLM)-based exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are proposed using the randomized quantile residuals and deviance residuals of Conway–Maxwell (COM)–Poisson regression. A detailed simulation-based study is designed for predictive performance evaluation of the proposed control charts with existing counterparts (i.e., Shewhart charts) in terms of the run-length properties. The study results showed that the EWMA type control charts have better detection ability compared with the CUSUM type and Shewhart control charts under small and/or moderate shift sizes. Finally, the proposed monitoring methods are successfully implemented on actual traffic crash data to highlight the efficacy of the proposed methods. The outcome of this study could provide the analysts with insights to plan sound policy recommendations for achieving desired safety goals.

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