Safety Performance Functions for Rural Two-Lane County Road Segments

2018 ◽  
Vol 2672 (52) ◽  
pp. 226-237 ◽  
Author(s):  
Steven Y. Stapleton ◽  
Anthony J. Ingle ◽  
Meghna Chakraborty ◽  
Timothy J. Gates ◽  
Peter T. Savolainen
Keyword(s):  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
Strong Association ◽  
Random Effect ◽  
Safety Performance ◽  
Federal Aid ◽  
Rural County ◽  
Design Standards ◽  
Safety Performance Functions ◽  
Lane County

Safety performance functions (SPFs) were developed for rural two-lane county roadway segments in Michigan. Five years of crash data (2011 to 2015) were analyzed for greater than 6,500 mi of rural county roadways, covering 29 of Michigan’s 83 counties and representing all regions of the state. Three separate models were developed to estimate annual deer-excluded total and injury crashes on rural county roadways: 1) paved federal-aid segments, 2) paved non-federal-aid segments, and 3) paved and gravel non-federal-aid segments with fewer than 400 vpd. To account for the unobserved heterogeneity associated with differing county design standards, mixed effects negative binomial models with a county-specific random effect were utilized. Not surprisingly, the county segment SPFs generally differed from traditional models generated using data from state-maintained roadways. County federal-aid roadways general showed greater crash occurrence than county non-federal-aid roadways, the Highway Safety Manual (HSM) two-lane rural roadways model, and rural state highways in Michigan. County non-federal-aid paved roadways showed crash occurrence rates that were remarkably similar to the HSM base rural two-lane roadway model, whereas gravel roadways showed greater crash occurrence rates. The presence of horizontal curves with design speeds below 55 mph had a strong association with the occurrence of total and injury crashes across all county road classes. Increasing driveway density was also found to be associated with increased crash occurrence. However, lane width, roadway surface width, and paved shoulder width had little to no impact on total or injury crashes.

Safety Performance Functions for Low-Volume Rural Stop-Controlled Intersections

2019 ◽  
Vol 2673 (5) ◽  
pp. 660-669 ◽  
Author(s):  
Steven Y. Stapleton ◽  
Timothy J. Gates ◽  
Raul Avelar ◽  
Srinivas R. Geedipally ◽  
Ramin Saedi
Keyword(s):  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Negative Binomial Regression ◽  
Safety Performance ◽  
Cross Sectional ◽  
Minor Road ◽  
Safety Performance Functions ◽  
Traffic Volumes ◽  
Low Volume

This study involved the development of safety performance functions for rural, low-volume, minor road stop-controlled intersections in Michigan. Facility types included three-leg stop-controlled (3ST) and four-leg stop-controlled (4ST) intersections under state or county jurisdiction and were sampled from each of Michigan’s 83 counties. To isolate lower-volume rural intersections, major roadway traffic volumes were limited to the range of 400–2,000 vehicles per day (vpd). Data were compiled from several sources for 2,023 intersections statewide. These data included traffic crashes, volumes, roadway classification, geometry, cross-sectional features, and other site characteristics covering the period of 2011–2015. Random effects negative binomial regression models were specified for each stop-controlled intersection type considering factors such as driveway density, lighting presence, turn lane presence, and intersection skew, in addition to volume. To account for the unobserved heterogeneity between counties, mixed effects negative binomial models with a county-specific random effect were utilized. Furthermore, unobserved temporal effects were controlled through the use of a year-specific random effect. Separate models were developed for fatal/injury crashes, property damage crashes, and select target crash types. The analysis found that skew angles of greater than five degrees led to significantly greater crash occurrence for both 3ST and 4ST intersections, while greater than two driveways near the intersection led to significantly greater angle crashes at 4ST intersections. Other factors were found to have little impact on crash occurrence. Comparison with the Highway Safety Manual (HSM) base models showed that the HSM models over-predict crashes on 4ST intersections and 3ST intersections with volumes between 1,200 and 2,000 vpd.

Application of different negative binomial parameterizations to develop safety performance functions for non-federal aid system roads

2021 ◽  
Vol 156 ◽  
pp. 106103
Author(s):  
Ali Khodadadi ◽  
Ioannis Tsapakis ◽  
Subasish Das ◽  
Dominique Lord ◽  
Yingfeng Li
Keyword(s):  
Negative Binomial ◽  
Safety Performance ◽  
Federal Aid ◽  
Safety Performance Functions

Examining the Safety Performance and Injury Severity Characteristics of Rural County Roadways

2019 ◽  
Vol 2673 (10) ◽  
pp. 405-415 ◽  
Author(s):  
Srinivas R. Geedipally ◽  
Timothy J. Gates ◽  
Steven Stapleton ◽  
Anthony Ingle ◽  
Raul E. Avelar
Keyword(s):  
Injury Severity ◽  
Negative Binomial ◽  
Random Effect ◽  
Multinomial Logit Model ◽  
Distribution Functions ◽  
Safety Performance ◽  
Occurrence Rate ◽  
Rural County ◽  
Rural Highways ◽  
Traffic Volumes

Much of the earlier work on rural safety focused on state-maintained roadways and little is known about the safety performance of low-volume county-maintained roads. This study involved the estimation of safety performance for rural county roadways (paved and gravel). This was accomplished through the development of safety performance functions (SPFs) to estimate the number of annual crashes at a given highway segment, crash modification factors to determine the impacts associated with various roadway and geometric characteristics, and severity distribution functions (SDFs) to predict the crash severity. County road segment data were collected across a sample of 30 counties representing all regions of Michigan. Because of the overwhelming proportion of deer crashes, only non-deer-related crashes were considered. To minimize the influence of variability among counties, the random effect negative binomial model was used to develop SPFs. In addition, a multinomial logit model was used to develop SDFs. Paved county roadways showed approximately double the crash occurrence rate of typical state-maintained two-lane rural highways, and gravel roadways showed a substantially greater crash occurrence rate than paved county roadways across the equivalent range of traffic volumes. The economic analysis showed that it is beneficial to pave a gravel road when the traffic volume is greater than 600 vehicles per day. The random effect variable is significant in all the calibrated models, which shows that there is a considerable variability among counties that cannot be captured with the available variables. Not considering the random effects will result in biased estimation of crashes.

Association between Driveway Land Use and Safety Performance on Rural Highways

2020 ◽  
pp. 036119812096523
Author(s):  
Meghna Chakraborty ◽  
Timothy J. Gates
Keyword(s):  
Land Use ◽  
Traffic Safety ◽  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
Transportation Network ◽  
Negative Binomial Regression ◽  
Safety Performance ◽  
Land Utilization ◽  
Design Standards ◽  
Rural Highways

Rural roads are a critical component of the transportation network in the U.S., including Michigan, where county roads comprise of a majority of the state’s roadway mileage. The rates of fatal crashes on rural highways are substantially higher than that on urban roads. Previous research has investigated the safety impacts of driveway density, but the effects of driveway land use on rural roadway safety performance, particularly for county roadways, remains under-researched. This study analyzed the safety impacts of various classifications of driveway land utilization on rural two-lane state and county roads. Non-animal segment crashes from 2011 to 2015 were analyzed along with roadway data for over 5,556 mi of state highways and 5,890 mi of paved county segments from across Michigan. To account for the unobserved heterogeneity associated with varied county design standards and site characteristics, mixed-effects negative binomial regression with county- and site-specific random effects was utilized. Separate models were developed for state highways and paved county roads. The results indicated that commercial driveways possess a stronger effect on crash occurrence than other driveway land use types, including residential and industrial driveways. The effect of driveway density on crash frequency was also found to be stronger on state highways compared with the county roads. This study contributes to the limited body of knowledge in relation to the relationship between traffic safety and driveway land use for rural roadway segments, particularly for county roads, which typically possess design and travel characteristics that are considerably different from those of state highways.

Regional Perspective of Safety Performance Functions and Their Application to Florida Intersections in Suburban Residential and Urban General Context Classification Categories

2021 ◽  
pp. 036119812110084
Author(s):  
Ghalia Gamaleldin ◽  
Haitham Al-Deek ◽  
Adrian Sandt ◽  
John McCombs ◽  
Alan El-Urfali
Keyword(s):  
Negative Binomial ◽  
Regression Tree ◽  
Influential Factors ◽  
Safety Performance ◽  
General Context ◽  
Boosted Regression Tree ◽  
Safety Performance Functions ◽  
Traffic Volumes ◽  
Intersection Types ◽  
The Individual

Safety performance functions (SPFs) are essential tools to help agencies predict crashes and understand influential factors. Florida Department of Transportation (FDOT) has implemented a context classification system which classifies intersections into eight context categories rather than the three classifications used in the Highway Safety Manual (HSM). Using this system, regional SPFs could be developed for 32 intersection types (unsignalized and signalized 3-leg and 4-leg for each category) rather than the 10 HSM intersection types. In this paper, eight individual intersection group SPFs were developed for the C3R-Suburban Residential and C4-Urban General categories and compared with full SPFs for these categories. These comparisons illustrate the unique and regional insights that agencies can gain by developing these individual SPFs. Poisson, negative binomial, zero-inflated, and boosted regression tree models were developed for each studied group as appropriate, with the best model selected for each group based on model interpretability and five performance measures. Additionally, a linear regression model was built to predict minor roadway traffic volumes for intersections which were missing these volumes. The full C3R and C4 SPFs contained four and six significant variables, respectively, while the individual intersection group SPFs in these categories contained six and nine variables. Factors such as major median, intersection angle, and FDOT District 7 regional variable were absent from the full SPFs. By developing individual intersection group SPFs with regional factors, agencies can better understand the factors and regional differences which affect crashes in their jurisdictions and identify effective treatments.

scholarly journals The variability of urban safety performance functions for different road elements: an Italian case study

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Paolo Intini ◽  
Nicola Berloco ◽  
Gabriele Cavalluzzi ◽  
Dominique Lord ◽  
Vittorio Ranieri ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Prediction Models ◽  
Safety Performance ◽  
Common Trends ◽  
Main Research ◽  
Pavement Markings ◽  
Italian Case ◽  
Sight Distance ◽  
Safety Performance Functions ◽  
Urban Safety

Abstract Background Urban safety performance functions are used to predict crash frequencies, mostly based on Negative Binomial (NB) count models. They could be differentiated for considering homogeneous subsets of segments/intersections and different predictors. Materials and methods The main research questions concerned: a) finding the best possible subsets for segments and intersections for safety modelling, by discussing the related problems and inquiring into the variability of predictors within the subsets; b) comparing the modelling results with the existing literature to highlight common trends and/or main differences; c) assessing the importance of additional crash predictors, besides traditional variables. In the context of a National research project, traffic volumes, geometric, control and additional variables were collected for road segments and intersections in the City of Bari, Italy, with 1500 fatal+injury related crashes (2012–2016). Six NB models were developed for: one/two-way homogeneous segments, three/four-legged, signalized/unsignalized intersections. Results Crash predictors greatly vary within the different subsets considered. The effect of vertical signs on minor roads/driveways, critical sight distance, cycle crossings, pavement/markings maintenance was specifically discussed. Some common trends but also differences in both types and effect of crash predictors were found by comparing results with literature. Conclusion The disaggregation of urban crash prediction models by considering different subsets of segments and intersections helps in revealing the specific influence of some predictors. Local characteristics may influence the relationships between well-established crash predictors and crash frequencies. A significant part of the urban crash frequency variability remains unexplained, thus encouraging research on this topic.

Safety Performance of Rural Curved Corner Intersections with Regional Effects

2021 ◽  
pp. 036119812110444
Author(s):  
Anthony Ingle ◽  
Timothy J. Gates
Keyword(s):  
Negative Binomial ◽  
Signalized Intersections ◽  
Regression Modeling ◽  
Safety Performance ◽  
Radius Of Curvature ◽  
Rural Roads ◽  
Produce Safety ◽  
Regional Effects ◽  
Random Intercept ◽  
Safety Performance Functions

This study evaluates the intersection of rural roads where a curved roadway segment connects the major flow of through traffic from orthogonal directions. A system of up to three intersections in combination can be represented singly by the situation modeled in this paper as a curved corner intersection site. This paper evaluates the application of random intercept negative binomial (NB) regression modeling to produce safety performance functions, and compares the outcome with NB models using fixed regional effects. At curved corner intersections, installing a combined/merged intersection approach near the midpoint of the curve is a potential countermeasure that by comparison with three-leg configurations experienced 20% fewer intersection crashes. A larger radius of curvature along the curved segment at these types of intersections is also very favorable for safety performance. Each 100 ft increase in the radius of a three-leg or four-leg curved corner intersection is estimated to reduce total non-animal crash occurrence by 5% and 7%, respectively. This study can help safety engineers to prioritize the improvement of rural un-signalized intersections.

Roadway Context Classification Approach for Developing Regional Safety Performance Functions for Florida Intersections

2020 ◽  
Vol 2674 (2) ◽  
pp. 191-202
Author(s):  
Ghalia Gamaleldin ◽  
Haitham Al-Deek ◽  
Adrian Sandt ◽  
Alan El-Urfali ◽  
Md Imrul Kayes ◽  
...  
Keyword(s):  
Classification System ◽  
Negative Binomial ◽  
Safety Performance ◽  
P Value ◽  
Department Of Transportation ◽  
Florida Department ◽  
Safety Performance Functions ◽  
Traffic Volumes ◽  
Road Width ◽  
Data Elements

Understanding how the type and location of intersections affect crashes is important to reduce these crashes effectively. This paper discusses the development of regional safety performance functions (SPFs) based on a new context classification system developed by the Florida Department of Transportation (FDOT). This classification system (which has not previously been used) categorizes intersections into eight different categories based on land use and other parameters, allowing SPFs to be developed for up to 32 different types of intersections. The Model Inventory of Roadway Elements (MIRE) 2.0 was used as the standard inventory for the data elements collected. Using MIRE 2.0 allows for the procedures conducted in this study to be easily implemented in other states. SPFs were developed for two intersection groups. First, a linear regression model was built to predict missing minor traffic volumes. This statistically significant model ( p-value < 0.05) had an adjusted R-square of 0.7648. Data were collected for over 25 potential predictor variables (including a regional variable for FDOT districts) and used to fit a negative binomial model to each studied intersection group. Some variables (such as major traffic volume) were significant for both groups, but each SPF had unique variables (such as speed limit and road width). Different regions were significant for each group, showing how crashes vary for different intersection types in different regions. By allowing for the development of SPF models for many intersection classifications, FDOT’s context classification system can be used by other agencies to identify crash-influencing factors better for different conditions.

Bicycle Safety Analysis at Intersections from Crowdsourced Data

2019 ◽  
Vol 2673 (4) ◽  
pp. 1-14 ◽  
Author(s):  
Moatz Saad ◽  
Mohamed Abdel-Aty ◽  
Jaeyoung Lee ◽  
Qing Cai
Keyword(s):  
Field Observation ◽  
Negative Binomial ◽  
Population Distribution ◽  
Safety Performance ◽  
Observation Data ◽  
Major Step ◽  
Bicycle Safety ◽  
Safety Performance Functions ◽  
Crowdsourced Data ◽  
Field Observation Data

Cycling is encouraged in countries around the world as an economic, energy efficient, and sustainable mode of transportation. Although there are many studies focusing on analyzing bicycle safety, they have limitations because of the shortage of bicycle exposure data. This study represents a major step forward in estimating safety performance functions for bicycle crashes at intersections by using crowdsourced data from STRAVA. Several adjustments in respect of the population distribution and field observations were made to overcome the disproportionate representation of the STRAVA data. The adjusted STRAVA data which include bicycle exposure information were used as input to develop safety performance functions. The functions are negative binomial models aimed at predicting frequencies of bicycle crashes at intersections. The developed model was compared with three counterparts: the model using the unadjusted STRAVA data, the model using the STRAVA data with field observation data adjustments only, and the model using the STRAVA data with adjusted population. The results revealed that the case of STRAVA data with both population and field observation data adjustments had the best performance in bicycle crash modeling. The results also addressed several key factors (e.g., signal control system, intersection size, bike lanes) which are associated with bicycle safety at intersections. Additionally, the safety-in-numbers effect was acknowledged when bicycle crash rates decreased as bicycle activities increased. The study concluded that crowdsourced data are a reliable source for exploring bicycle safety after the appropriate adjustments.

Sign in / Sign up

Export Citation Format

Share Document