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.

scholarly journals Estimation of Safety Performance Functions (SPF) at signalized intersections in Medellín Colombia

DYNA ◽  
2020 ◽  
Vol 87 (214) ◽  
pp. 215-220
Author(s):  
Víctor Gabriel Valencia-Alaix ◽  
Basilio Restrepo Betancur ◽  
Cristhian Lizarazo Jimenez ◽  
Raul Andres Pineda Mendez
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Signalized Intersections ◽  
Safety Performance ◽  
Design Criteria ◽  
Binomial Model ◽  
Crash Frequency ◽  
Traffic Conditions ◽  
Safety Performance Functions

One of the objectives of road infrastructure sustainability is to ensure that users are treated equally and their quality of life is improved by providing better mobility and traffic safety. When designing roads, it is important to evaluate different design criteria alternatives - in this case, we look at traffic safety principles. For this, we used the Safety Performance Functions (SPF) tool to obtain the expected crash frequency. The data used were Medellín’s crash records from 2012 to 2016, as well as the geometric features and traffic conditions at signalized intersections. A negative binomial model was fitted to estimate the SPF. Exposure, geometry, and traffic volume were found to be statistically significant in determining the expected crash frequency for collisions where there was property damage only (PDO) and fatal or injury (FI). It was found that accidents were less likely on T-junctions compared to four-leg junctions, one-way approaches were found to be safer whereas right turns were found to increase collisions causing FI. 

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 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.

scholarly journals Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand

2021 ◽  
Vol 13 (16) ◽  
pp. 9011
Author(s):  
Nopadon Kronprasert ◽  
Katesirint Boontan ◽  
Patipat Kanha
Keyword(s):  
Prediction Models ◽  
Safety Performance ◽  
Horizontal Curve ◽  
Crash Frequency ◽  
Rural Roads ◽  
Crash Prediction ◽  
Road Crashes ◽  
Highway Safety Manual ◽  
Safety Performance Functions ◽  
Crash Prediction Models

The number of road crashes continues to rise significantly in Thailand. Curve segments on two-lane rural roads are among the most hazardous locations which lead to road crashes and tremendous economic losses; therefore, a detailed examination of its risk is required. This study aims to develop crash prediction models using Safety Performance Functions (SPFs) as a tool to identify the relationship among road alignment, road geometric and traffic conditions, and crash frequency for two-lane rural horizontal curve segments. Relevant data associated with 86,599 curve segments on two-lane rural road networks in Thailand were collected including road alignment data from a GPS vehicle tracking technology, road attribute data from rural road asset databases, and historical crash data from crash reports. Safety Performance Functions (SPFs) for horizontal curve segments were developed, using Poisson regression, negative binomial regression, and calibrated Highway Safety Manual models. The results showed that the most significant parameter affecting crash frequency is lane width, followed by curve length, traffic volume, curve radius, and types of curves (i.e., circular curves, compound curves, reverse curves, and broken-back curves). Comparing among crash prediction models developed, the calibrated Highway Safety Manual SPF outperforms the others in prediction accuracy.

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.

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