1. Motorcycle AADT, Safety Analysis, and Intersection Treatments
Bob Scopatz
Traffic Records Forum 2018 Milwaukee, WI
The following presentation discusses a recently completed project sponsored by FHWA to investigate how the lack of motorcycle AADT data affects the accuracy of crash prediction models and Crash Modification Factors.

2. Items To Be Covered Three Projects
Safety analyses in the absence of MC AADT
Motorcycle AADT recommendations (NCHRP)
Intersection infrastructure treatments
This presentation briefly overviews the project objectives, current practices and the research approach. The focus is on results, their implications and future research opportunities.

3. Principle Team Members
Kim Eccles (VHB)
Craig Lyon (P&L)
Bhagwant Persaud (P&L)
Bob Scopatz (VHB)
Frank Gross (VHB)
Scott Himes (VHB)
Joshua DeFisher (VHB)
Dan Middleton (TTI)
Patty Turner (TTI)
Hassan Charara (TTI)
Srinivas Geedipally (TTI)
The principle team members included:

4. Why Is AADT Data Important?
AADT is the average annual daily traffic
Traffic exposure is the primary contributor to crash risk (up to 60% of the variance)
Accounting for exposure is necessary in various safety management tasks
Development of SPFs
Development of CMFs
Network screening
Evaluating potential countermeasures
SPFs and CMFs are required in EB method, HSM, SafetyAnalyst, IHSDM
Average annual daily traffic is the primary contributor to crash risk. It must be considered in safety management tasks including the development of Safety Performance Functions (SPFs), Crash Modification Factors (CMFs) and conducting Network Screening or countermeasure evaluations.
The empirical Bayes method, Highway Safety Manual, SafetyAnalyst and Interactive Highway Safety Design Model all require SPFs and CMFs.

5. What Are SPFs?
This slide shows a graphical representation of an SPF for intersections. The dotted points represent the observed number of crashes per year at a given level of entering AADT. The fitted line of best fit represents the predictions from the SPF.

6. What Are CMFs?
CMF is a multiplier representing expected change in crashes due to treatment
Safety factors not related to the treatment are necessary to consider when estimating CMFs
Traffic volume is variable over time and differences between sites are important
A CMF is multiplied by the expected crash rate to estimate the new crash rate if a treatment was applied.
CMF estimation needs to consider factors that can affect safety but are not related to the treatment, including traffic volume changes over time and between sites.

7. CMF Example
4-legged signalized intersection on rural multilane road; major road AADT of 30,000 and minor road AADT of 5,000; no turn lanes Consider adding a left-turn lane on one approach of major road. CMF = 0.82 Expected crashes without left-turn lane =6.3 Expected crashes with left-turn lane = (6.3)(0.82) = 5.2
This slide shows an example application of a CMF.
The example applies a CMF for adding a left-turn lane to a major road approach at a 4-legged signalized intersection.

8. Empirical Bayes Overview
* SPF
Crashes/year
AADT
* OBS
* EB
SPF
(Observed frequency)
(Predicted frequency)
(Expected frequency)
This slide illustrates how the empirical Bayes method adjusts a very high crash count towards that estimated by an SPF.

9. Why Undertake This Research?
Motorcycle safety is an increasing concern
Safety management needs reliable quantitative methods
SPFs and CMFs are lacking for motorcycle crashes
Lack of motorcycle volumes is one potential reason
We need to understand the impact of missing motorcycle volumes for future work
Motorcycle riding and crashes are increasing, as is interest in mitigating these crashes.
While SPFs and CMFs are needed for motorcycle crashes they are rare. One reason may be the lack of motorcycle AADT.
Given that motorcycle volumes are often not available, there is a need to understand if the lack of data impacts SPFs and CMFs to such a degree that they cannot be developed or if other surrogates may be used, e.g. total AADT.

10. Current Practices
Most research focused on motorcycle crashes deals with severity of crashes, given one has occurred
VERY few studies are developing motorcycle crash SPFs or CMFs
A review of current practices found that most research on motorcycle crashes focused on the probability of a given severity, given a crash has occurred.
Very few studies dealt with developing SPFs or CMFs for motorcycle crashes.

11. Current Practices – Study Examples
Impact of longitudinal barriers on injury severity
Likelihood of crash, given stated attitudes and behavior
Impact of blood alcohol concentration (BAC) on fatality risk
Impact of factors such as age, BAC, pavement surface, highway type, horizontal curvature on injury severity
Effects of universal helmet laws on injury severity
While valuable, these studies don’t estimate expected crashes or crash reductions
This slide lists several examples of research undertaken on motorcycle crashes.
While these studies were useful they do not provide estimates of crash frequency of expected crash frequency reductions for countermeasures.

12. Current Practices Most states have limited motorcycle AADT data
A few states have extensive class count programs providing motorcycle AADT estimates, including FL, PA, VA
With regards to data, most states have few counts of motorcycle AADT.
A few states were identified that had extensive class count programs that provide motorcycle counts, including Florida, Pennsylvania and Virginia.

13. Data Collected
Data collected for developing models from Florida and Pennsylvania
Data collected for model validation from Virginia
Roadway geometry, total AADT, motorcycle AADT, crash data collected at segment level
County level data on motorcycle registrations, licenses and census data collected
Analysis ultimately focused on freeways and arterials, both urban and rural
To conduct the Avenue A research data were collected from Florida and Pennsylvania. Additional data were collected from Virginia to validate any models developed.
In all states road geometry, total AADT, motorcycle AADT and crash data were collected.
For the A3 models, county level data on motorcycle registrations, motorcycle licenses and several census variables were collected.
The analysis focused on freeway and arterial roads in urban and rural environments. At lower level roads the sample size of motorcycle crashes did not allow for any models to be developed.

14. A1 Model Results
Performance of total AADT SPFs generally similar to motorcycle AADT SPFs
Total AADT SPFs better or worse depending on case
SPFs developed all reasonable
Validation of FL and PA SPFs using VA show bias
What does this mean?
Using total AADT is a reasonable approximation
Potential bias of total AADT is sufficiently small so as not to preclude SPF development
SPFs may not transfer well, assessment is necessary
The overall findings of the A1 models indicate that SPFs using total AADT perform similarly to those using motorcycle AADT. All the SPFs developed appear to provide a reasonable fit to the data.
A validation of the SPFs using the data collected in Virginia did however show some bias in the predictions across the range of motorcycle AADT in the Virginia data. This finding shows that it is important, as for all SPFs, to assess the transferability of SPFs when applying in another jurisdiction.

15. B1 and B2 Results What does this mean?
Use of total AADT is a reasonable substitution
Potential bias of total AADT is sufficiently small so as to not preclude CMF development
Larger issue is the variance in CMF estimates due to small sample sizes
The overall findings of the Avenue B modeling are:
That total AADT is a reasonable substitution for motorcycle AADT when estimating motorcycle crash CMFs.
The potential bias due to using total AADT instead of motorcycle AADT is sufficiently small so as to not preclude the estimation of CMFs.
A larger issue in CMF estimation for motorcycle crashes is the difficulty in estimation due to the small sample sizes of motorcycle crashes.

16. AADT Data Limitations Accuracy of motorcycle counts is hard to assess
Difficulties due to motorcycles:
Having small amount of metal
Riding near lane lines
Riding in close groups
Difficulties due to measuring equipment:
Calibration of detectors affects small vehicle accuracy first
Locations of permanent counters may not reflect motorcycle patterns
Weekend/Weekday riding may not be reflected in temporary counting
Road tubes not tested for accuracy
Technology
MC Accuracy
Non-MC Accuracy
Infrared (IR) Classifier
95%
98%
Inductive loops/piezoelectric sensors (full lane-width)
45%
Magnetometers
80%
Multi-technology system
50%
n/a
Tracking video system
75%
90%
The study also investigated the limitations in AADT data with respect to motorcycle volumes.
The accuracy of motorcycle AADT estimates is hard to assess. There are several difficulties in measuring motorcycle AADTs listed on this slide.
The table shows the accuracy for motorcycle and non-motorcycle volumes summarized in NCHRP Report 760. It is evident that estimating motorcycle AADTs is more difficult than other vehicles. Road tubes, the most common method for counting traffic, were not included in this comparison.
Comparisons of five detector technologies from NCHRP 760.
Counting Motorcycles (NCHRP Project 08-36, Task 92), Transportation Research Board, Washington, D.C., February 2010.
Improving the Quality of Motorcycle Travel Data Collection (NCHRP Report 760; Project 08-81), Transportation Research Board, Washington D.C., November, 2013.

17. Analysis Limitations
Limitations similar to the study of other rare crash types
Small sample sizes of crashes
Rarity of infrastructure treatments focused on motorcycle safety
Results of this study confirm these issues
In terms of analysis limitations, the study of motorcycle crashes holds the same limitations as for other rare crash types, including:
Small sample sizes of crashes
Infrastructure treatments aimed solely and motorcycle crashes are rare

18. NCHRP Study Similar conclusions:
You can predict where crashes will occur based on total AADT from permanent counters even if the motorcycle classification counts aren’t reliable.
Accuracy is reasonable, and very good on an area-wide basis.
There are ways to configure sensors so that motorcycles are counted. It’s not cheap.

19. Safety at intersections
Infrastructure treatments are rarely designed just to improve motorcycle safety.
The data are limited here too:
Inaccurate classification counts.
Counts not updated even annually.
Lots of candidate sites with zero baseline crashes.

20. Intersection Treatments
Signal phasing
Warning signs
High friction surfacing

21. Research Opportunities
Research and estimate reliable CMFs for motorcycle related infrastructure treatments
Research new motorcycle SPFs for other safety management tasks
Investigate alternate sources of motorcycle volumes
Cellular phone data
Motorcycle trip smartphone APPs
Investigate alternate route choice and weekday vs weekend riding
Differences in commuter vs recreational riding may affect SPFs and CMFs for example
There are many future research opportunities for motorcycle crashes. These include estimating CMFs for motorcycle related infrastructure treatments and more SPFs for motorcycle crashes for other road types, site types (e.g. intersections) and states.
Other opportunities include:
Alternate sources of motorcycle volume data such as cell phone data or smartphone applications
Investigating the difference in route choice and expected crashes from commuter versus recreational riding

22. Research Opportunities
Application of case-control study method for CMFs
Applied to rare crash types
Used recently to evaluate shoulder/lane width combinations for vehicle crashes
Holds promise for motorcycle crash CMF estimation
Calibration of existing CMFs for motorcycle crashes
NCHRP developing methods to calibrate CMFs based on crash type proportions
CMFTOTAL=0.4(TYPE1PROP)+1.0(TYPE2PROP)
CMFs for each crash type estimated in the process
3) The application of case-control methods to develop CMFs. Case-control methods are often applied to rare crash types and have been used recently in CMF estimation.
4) The calibration of CMFs for motorcycle crashes using the methods being developed by NCHRP Project and existing CMFs for total crashes. This project is developing a method to calibrate CMFs for a given site and in the process estimates disaggregate CMFs by crash type.

23. Research Opportunities
Explore methods for identifying potential cost-effective projects
Site identification methods in HSM may not be efficient because of rarity
Methods based on identifying routes or corridors may be more appropriate
Explore data and methods for analyzing intersection crashes
Motorcycle counts for intersections are non-existent or sparse
Research needed to see if total AADT is adequate for intersections
Revisit whether motorcycle volumes could be routinely obtained or estimated at intersections
5) Because of the rarity of motorcycle crashes, the network screening methods from the Highway Safety Manual may be inefficient and methods focused on routes may be more relevant. The development of such methods for identifying cost-effective projects could be explored.
6) This study focused on road segments. Similar research into the impact of the lack of motorcycle volumes at intersections or ways to estimate intersection motorcycle volumes could be explored.

24. Research Opportunities
Applying probability models
Potential for using probability models for identifying specific treatments at problem sites
Have the advantage of not requiring exposure data
Example is study finding that an increase in curve radius of 1,000 ft. decreasing likelihood of severe injury in single-vehicle motorcycle crashes
HSM freeway chapter will estimate severity using probability models and apply to SPF for total crashes
May be a method for estimating motorcycle crash frequency
7) Probability models have often been used to estimate the effects on rider crash severity given a crash has occurred. There is potential for using such models for identifying potential treatments at high crash locations. These models do not require exposure data. The Highway Safety Manual freeway chapter will use probability models to estimate crashes by severity when applied to a total crash SPF, a similar approach could work for specific crash types such as motorcycle crashes.

25. Questions?

26. Resources FHWA HRT-16-054 Craig.thor@dot.gov
NCHRP
FHWA-SA (in prep)