scholarly journals Safety performance assessment of freeway interchanges, ramps and ramp terminals

2021 ◽  
Author(s):  
Bishnu Prasad Parajuli
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Safety Performance ◽  
Index Method ◽  
Safety Improvement ◽  
Binomial Regression ◽  
Safety Performance Functions ◽  
Network Screening ◽  
Traffic Volumes ◽  
Functional Forms

Existing safety performance functions for mainline interchanges and ramps of Ontario freeways are updated using negative binomial regression. The functional forms of the updated models are different from the existing models. In addition, new safety performance functions for ramp terminal sites are developed. Network screening to identify sites in need of safety treatment has been illustrated using two different methods, one based on a potential for safety improvement (PSI) index and, the other based on an index of a high proportion of a specific accident type. A comparison for rankings for 3-legged signalized ramp terminals by the two methods indicates reasonably consistent results, with some key differences. The method of screening for high proportion of specific accidents can be a possible alternative to PSI index method where safety performance functions and/or traffic volumes are not available since, unlike the PSI Index method, it does not require these inputs.

scholarly journals Safety performance assessment of freeway interchanges, ramps and ramp terminals

2021 ◽  
Author(s):  
Bishnu Prasad Parajuli
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Safety Performance ◽  
Index Method ◽  
Safety Improvement ◽  
Binomial Regression ◽  
Safety Performance Functions ◽  
Network Screening ◽  
Traffic Volumes ◽  
Functional Forms

Existing safety performance functions for mainline interchanges and ramps of Ontario freeways are updated using negative binomial regression. The functional forms of the updated models are different from the existing models. In addition, new safety performance functions for ramp terminal sites are developed. Network screening to identify sites in need of safety treatment has been illustrated using two different methods, one based on a potential for safety improvement (PSI) index and, the other based on an index of a high proportion of a specific accident type. A comparison for rankings for 3-legged signalized ramp terminals by the two methods indicates reasonably consistent results, with some key differences. The method of screening for high proportion of specific accidents can be a possible alternative to PSI index method where safety performance functions and/or traffic volumes are not available since, unlike the PSI Index method, it does not require these inputs.

scholarly journals Modelling the Safety Performance of Roadways for Specific Collision Types

2021 ◽  
Author(s):  
Tony Chiu
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Constant Factor ◽  
Safety Performance ◽  
Annual Average Daily Traffic ◽  
Collision Type ◽  
Binomial Regression ◽  
Safety Performance Functions ◽  
The Individual ◽  
Screening Approaches

Safety performance functions (SPFs) are rarely developed for specific accident types because this can be very lengthy especially when relevant data are unavailable. Because of this constraint, a factor is applied along with the SPF for all accident types to estimate the safety for specific accidents types. This factor is the proportion of the individual collision type in the entire population of all accidents. However, there is no reason to believe that this factor is a constant which is independent of Annual Average Daily Traffic (AADT). Accordingly, a constant factor and the proportion model are applied to the SPF for all accident types combined to estimate the SPF for specific accident types on both rural road segments and Two-Way Stop-Controlled (TWSC) intersections. The validity of these factors are tested using the state-of-the-art network screening approaches. Furthermore, a detailed investigation on Property Damaage Only (PDO) estimates is carried out on certain aspects of safety performance functions, using negative binomial regression. PDO estimates are then evaluated based on three different approaches.

scholarly journals Modelling the Safety Performance of Roadways for Specific Collision Types

2021 ◽  
Author(s):  
Tony Chiu
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Constant Factor ◽  
Safety Performance ◽  
Annual Average Daily Traffic ◽  
Collision Type ◽  
Binomial Regression ◽  
Safety Performance Functions ◽  
The Individual ◽  
Screening Approaches

Safety performance functions (SPFs) are rarely developed for specific accident types because this can be very lengthy especially when relevant data are unavailable. Because of this constraint, a factor is applied along with the SPF for all accident types to estimate the safety for specific accidents types. This factor is the proportion of the individual collision type in the entire population of all accidents. However, there is no reason to believe that this factor is a constant which is independent of Annual Average Daily Traffic (AADT). Accordingly, a constant factor and the proportion model are applied to the SPF for all accident types combined to estimate the SPF for specific accident types on both rural road segments and Two-Way Stop-Controlled (TWSC) intersections. The validity of these factors are tested using the state-of-the-art network screening approaches. Furthermore, a detailed investigation on Property Damaage Only (PDO) estimates is carried out on certain aspects of safety performance functions, using negative binomial regression. PDO estimates are then evaluated based on three different approaches.

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.

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.

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.

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