Investigating Total Annual Average Daily Traffic as a Surrogate for Motorcycle Volumes in Estimating Safety Performance Functions for Motorcycle Crashes

2017 ◽  
Vol 2637 (1) ◽  
pp. 9-16
Author(s):  
Craig Lyon ◽  
Bhagwant Persaud ◽  
Scott Himes
Keyword(s):  
Safety Management ◽  
Safety Performance ◽  
Volume Data ◽  
Annual Average ◽  
Annual Average Daily Traffic ◽  
Safety Performance Functions ◽  
Traffic Volumes ◽  
Crash Modification Factors ◽  
Crash Types ◽  
Motorcycle Crashes

Data on traffic volumes are required to estimate the safety performance functions (SPFs) used to develop crash modification factors and for various safety management applications. Estimation of SPFs for motorcycle crashes can be especially challenging because few jurisdictions collect motorcycle traffic volume data systematically. To address this challenge, analyses with data from Florida, Pennsylvania, and Virginia were conducted to explore how much predictive power for an SPF was lost when motorcycle traffic volumes were not known. The results of the analyses showed that when motorcycle volumes were unknown, the use of total annual average daily traffic on its own was sufficient to develop motorcycle crash SPFs. The potential bias from missing motorcycle-specific annual average daily traffic was sufficiently negligible where it existed, not to preclude SPF development. A more significant issue in the development of motorcycle crash SPFs is to work with a crash type that is relatively rare, so that SPFs cannot be developed for all motorcycle crash types or site types.

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.

Evaluation of Safety Performance Functions Based on Experimental Design Using a Cross-Validation Method

2017 ◽  
Vol 2636 (1) ◽  
pp. 62-72 ◽  
Author(s):  
Jung-Han Wang ◽  
Mohamed A. Abdel-Aty ◽  
Jaeyoung Lee
Keyword(s):  
Goodness Of Fit ◽  
Cross Validation ◽  
Safety Performance ◽  
Major Road ◽  
Highway Safety Manual ◽  
Annual Average Daily Traffic ◽  
Minor Road ◽  
Safety Performance Functions ◽  
The One ◽  
Leave One Out

The Highway Safety Manual (HSM) Part C provides a series of safety performance functions (SPFs) for different roadway conditions. The SPFs suggested in the HSM are formulated on the basis of exposure variables: the logarithms of the annual average daily traffic (AADT) on the major road and on the minor road under the base condition. In this research, data from 7,802 intersections in Florida were collected and processed. These intersections were categorized into seven types based on area type (rural or urban), number of legs (three or four), and number of approaches controlled by stop signs. Twenty-two SPF formulations, including the one suggested by the HSM, were developed for each intersection type for examination of the goodness-of-fit measures of the SPFs. In addition, the goodness of fit of each model of the 22 SPFs in each category was examined with 10-fold leave-one-out cross-validation (LOOCV). With a comparison of the delta values generated with the LOOCV method, it is suggested that the SPF with the logarithm of the total entering vehicle volume and the ratio of the AADT on the minor road and the AADT on the major road are important. In addition, the SPFs with the AADT on the major road and the AADT on the minor road and their logarithmic transformations are also important. Therefore, it is suggested that the future HSM compare these two SPF formulations—as suggested in the current research, along with the original SPF formulation in the manual—and select the one with the best model fit on the basis of the delta value using LOOCV.

Development of safety performance functions and crash modification factors for expressway ramps

2017 ◽  
Vol 22 (2) ◽  
pp. 804-812 ◽  
Author(s):  
Yoon Hwan Choi ◽  
Sung Ho Park ◽  
Hangeom Ko ◽  
Kyung Hyun Kim ◽  
Ilsoo Yun
Keyword(s):  
Safety Performance ◽  
Safety Performance Functions ◽  
Crash Modification Factors

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.

Changing the Future?: Development and Application of Pedestrian Safety Performance Functions to Prioritize Locations in Seattle, Washington

2017 ◽  
Vol 2659 (1) ◽  
pp. 212-223 ◽  
Author(s):  
Libby Thomas ◽  
Bo Lan ◽  
Rebecca L. Sanders ◽  
Alexandra Frackelton ◽  
Spencer Gardner ◽  
...  
Keyword(s):  
High Frequency ◽  
Empirical Bayes ◽  
Threshold Value ◽  
Safety Performance ◽  
Crash Prediction ◽  
Safety Improvement ◽  
Safety Performance Functions ◽  
Pedestrian Crashes ◽  
Crash Types ◽  
Treatment Priorities

This study aimed to use robust analysis methods to identify and screen locations at risk for pedestrian crashes and injuries to help Seattle, Washington, a Vision Zero city, broaden treatment priorities beyond only high-crash locations. For this objective, data from the entire network were used to develop safety performance functions (SPFs) for two pedestrian crash types: total pedestrian crashes at intersections (a high frequency type) and a subset of intersection crashes involving through motorists striking crossing pedestrians (a high severity type). Many variables from roadway, built environment, census, and activity measures were tested. A similar but not identical set of variables, including measures of activity and intersection size and complexity, significantly contributed to crash prediction in both models. Pedestrian volume exhibited a curved relationship to crashes and demonstrated a tendency for expected crashes to begin to decline above a threshold value; however, the causes of this relationship were unknown. The SPFs were used in several ranking methods, including SPF-predicted crashes, empirical Bayes estimated crashes, and potential for safety improvement, to aid in prioritization of locations that might have been candidates for safety improvement but that had not necessarily experienced a high frequency of crashes. On the basis of this example, this approach is feasible for jurisdictions that wish to be more proactive in addressing potential crashes and injuries. Jurisdictions must, however, begin routinely collecting the data needed to implement the method efficiently.

scholarly journals Safety Impacts of the Actuated Signal Control at Urban Intersections

2016 ◽  
Vol 28 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Sang Hyuk Lee ◽  
Daeseok Han
Keyword(s):  
Safety Performance ◽  
Impact Factors ◽  
Peak Time ◽  
Traffic Conditions ◽  
Safety Performance Functions ◽  
Time Periods ◽  
Actuated Signal ◽  
Actuated Signal Control ◽  
Crash Types ◽  
Optimal Signal

To reduce travel time, the actuated signal controls have been implemented at urban intersections. However, the safety impacts of actuated signal controls thus far have rarely been examined. In this assessment of the safety impact of urban intersections with semi-actuated signal controls, the safety performance functions and EB approaches were applied. The semi-actuated signal controls have increased injuries and total crashes in all crash types by around 5.9% and 3.8%, respectively. Regarding the most common crash types, such as angle, sideswipe & rear-end, and head-on crashes, semi-actuated signal controls have been seen to decrease injuries by 7.7%. Total crashes have been reduced by over 9.2% through the use of semi-actuated signal controls. This may be result of optimal signal timings considering traffic conditions during peak time periods. In conclusion, safety impact factors which have been established in this study can be used to improve safety and minimize travel times using semi-actuated signal controls.

Jurisdiction-Specific versus SafetyAnalyst-Default Safety Performance Functions: Case Study on Two-Lane and Multi-Lane Arterials

2019 ◽  
Vol 2673 (10) ◽  
pp. 501-509
Author(s):  
Hector Vargas ◽  
Asif Raihan ◽  
Priyanka Alluri ◽  
Albert Gan
Keyword(s):  
Empirical Bayes ◽  
Goodness Of Fit ◽  
Safety Management ◽  
Safety Performance ◽  
Regression To The Mean ◽  
Absolute Deviation ◽  
Local Conditions ◽  
Screening Criteria ◽  
Rural And Urban ◽  
Safety Performance Functions

Network screening is the most important step in the highway safety management process. Screening criteria based on the empirical Bayes (EB) approach are considered to be most reliable as it accounts for the regression-to-the-mean bias. However, the EB approach requires safety performance functions (SPFs), preferably calibrated to local conditions, which are often unavailable. The SafetyAnalyst software, developed by the Federal Highway Administration, automates the EB approach using the default SPFs which were developed using multiple states’ data. Local agencies are encouraged to develop jurisdiction-specific SPFs to better reflect local conditions. However, the benefits of developing local SPFs for rural and urban two-lane and multi-lane highway facilities are unclear and may vary from state to state. This research compares the performance of Florida-specific SPFs with SafetyAnalyst-default SPFs calibrated to Florida data using mean absolute deviation, mean squared predicted error, and Freeman-Tukey R-square goodness-of-fit measures. The results showed that Florida-specific SPFs generally produced better-fitted models than the calibrated SafetyAnalyst-default SPFs. In contrast, when the crash prediction capabilities of the already-available local SPFs, calibrated to the latest time period for which they will be applied, are compared with the calibrated SafetyAnalyst-default SPFs, the calibrated SafetyAnalyst-default SPFs in general were found to better predict crash frequencies compared with the existing Florida-specific SPFs calibrated to the latest data. Therefore, the local SPFs are recommended when developed using present data; however, the calibrated SafetyAnalyst-default SPFs could be used if local SPFs developed from present data are not available.

Safety Performance of Rural Offset-T Intersections

2020 ◽  
pp. 036119812096109
Author(s):  
Anthony Ingle ◽  
Timothy J. Gates
Keyword(s):  
Rural Areas ◽  
Negative Binomial ◽  
Motor Vehicle ◽  
Separation Distance ◽  
Safety Performance ◽  
Mixed Effect ◽  
Crash Data ◽  
Offset Distance ◽  
Crash Modification Factors ◽  
Crash Types

Offset-T intersections represent a special geometric configuration where two three-leg intersections adjoin the major road from opposite directions within a short separation distance. The prevalence of offset-T intersections in rural areas coupled with the lack of research evaluating their safety performance led to the development of a series of safety performance functions for rural stop-controlled intersections that considered the effects of the offset direction and separation distance. In addition, crash modification factors (CMFs) were developed to estimate the change in crash frequency associated with converting a rural offset-T intersection into a conventional four-leg intersection. A series of mixed-effect negative binomial models for crash occurrence was generated based on 10 years of crash data from a sample of 299 offset-T intersections and 301 four-leg intersections with minor stop control along rural two-lane highways in Michigan. Compared with conventional four-leg intersections, offset-T intersections exhibited 35% more crashes regardless of the offset distance or direction. Considering crash types, single motor vehicle crashes occurred more frequently at offset-T intersections, and increased as the offset distance increased. Rear-end crashes also occurred more frequently at offset-T intersections, with left offsets having greater crash occurrence than right offsets. However, angle crashes were 40%–69% lower at offset-T intersections because of the elimination of the direct crossing maneuver. Considering the ranges of offset distance and direction utilized within this study, the total (non-animal) CMF for converting an offset-T into a four-leg intersection was 0.74.

Using Seasonal and Day-of-Week Factoring to Improve Estimates of Truck Vehicle Miles Traveled

1996 ◽  
Vol 1522 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Herbert Weinblatt
Keyword(s):  
Short Duration ◽  
Single Unit ◽  
Vehicle Miles Traveled ◽  
Annual Average ◽  
Vehicle Class ◽  
Traffic Counts ◽  
Annual Average Daily Traffic ◽  
Traffic Volumes ◽  
Total Vehicle ◽  
Unbiased Estimates

Procedures developed by FHWA for “factoring” short-duration traffic counts for seasonal and day-of-week variations in traffic volumes are capable of producing estimates of annual average daily traffic (AADT) that are quite accurate. Moreover, there is virtually no bias in these estimates, so AADT estimates for a set of road sections can be used to produce unbiased estimates of total vehicle miles traveled (VMT) for systems of roads. Unfortunately, corresponding procedures are not generally used for estimating AADT by vehicle class, and the less sophisticated procedures that are commonly used contribute to substantial overestimates of truck AADT and VMT. Current procedures apparently overestimate VMT by 25 to 40 percent for combination trucks and possibly more for single-unit trucks. Modified versions of the FHWA factoring procedure that are capable of producing substantially improved estimates of truck VMT and of AADT of combination trucks are presented. These procedures use seasonal and day-of-week factoring to reduce the errors in truck AADT estimates and to eliminate the upward bias in truck VMT estimates that result from the use of unfactored weekday classification counts.

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