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Publication Number:  FHWA-HRT-16-054    Date:  October 2016
Publication Number: FHWA-HRT-16-054
Date: October 2016


Investigating the Impact of Lack of Motorcycle Annual Average Daily Traffic Data in Crash Modeling and the Estimation of Crash Modification Factors


Chapter 7. Limitations and Future Research Needs

This section summarizes existing data limitations and research gaps identified through the assessment of available data sources, analytical methods, and the evaluation results.

Data Limitations

With respect to developing SPFs and CMFs for motorcycle crashes, there are a number of data limitations related to traffic volumes (AADT), crash data, and roadway inventory data.



States collect traffic volume counts using a variety of techniques and technologies and convert these counts into estimated AADT volume estimates. With respect to motorcycle counts, NCHRP 08-36, Task 92, Counting Motorcycles, and NCHRP Report 760, Improving Motorcycle Travel Data – Data Collection Protocols and Field Tests, provide excellent detail on the strengths and weaknesses of the existing counter technologies.(29,30)

The second of these reports tested five detector technologies for their ability to accurately classify all vehicle types, specifically motorcycles. The five detector types tested were as follows:

Table 85 compares the five technologies tested in NCHRP 760.(30) For each, there are two measures of accuracy: MC for motorcycle detection, and non-MC for detection of all other vehicles. Costs for two- and four-lane roadways are shown for initial installation. While there may be portable versions of many of the technologies, the only portable system tested was the TIRTL. The skill level required to set up and calibrate the detector and associated electronics/software is shown as well. Table 86 (table 2 from NCHRP 08-36) shows the technology used in 24 States.(29)

Table 85. Comparisons of five detector technologies from NCHRP 760.(30)
Technology MC Accuracy (Percent) Non-MC Accuracy (Percent) Initial Cost (Two-Lane) Initial Cost (Four-Lane) Portability Skill Level for Setupa
IR Classifier 95 98 $26,850 $26,850 Fixed/portableb Expert
Inductive loops/ piezoelectric sensors (full lane-width) 45 95 $33,000c $61,000 Fixed Field technician
Magnetometers 80 95 $10,204 $15,964 Fixedd Field technician
Multi-technology system 50 N/A $6,000 $12,000 Fixedd Field technician
Tracking video system 75 90 $15,000 $15,000 Fixedd Field technician

aSetup skill level required: expert versus field technician with proper training.
bTIRTL is available as either portable or fixed, but only portable TIRTL was tested in this research.
cEstimated by Texas Department of Transportation: $61,000 total for four-lane site and $33,000 total for two-lane site.
dSome components could be portable, or the detector could be portable with modification.
N/A = Not available.

Table 86. Use of various detector technologies in 24 States as reported in NCHRP 08-36.(29)
Technology Short Counts Short Counts Continuous Counts Continuous Counts
Tested Used Tested Used
Road tubes 13 20 n/a n/a
Piezoelectric cable 3 4 9 17
Conventional inductive loops 6 2 4 8
Piezoelectric film 1 0 4 3
Inductive loop signatures 1 0 2 1
Quadrupole loops 1 0 1 0
Magnetometers 1 0 2 0
Manual 0 1 n/a n/a
Radar 7 3 4 5
Video 1 2 2 1
IR, including TIRTLs 5 0 4 3
Acoustic 1 0 2 0

N/A = Not applicable.

As can be seen from table 86, road tubes are the most common type of detector for short counts—NCHRP 760 did not test the road tubes because they were not considered to be capable of providing accurate motorcycle classification counts.(30) Piezoelectric and inductive loops together account for the vast majority of detector installations in the 24 States that responded to the survey. Of the non-intrusive methods, the TIRTL is second-most common (after radar), but the use of the non-intrusive technologies still lagged far behind the inductive loops and piezoelectric installations as of 2010 when the project was completed.

NCHRP 760 concluded that the TIRTL was the most accurate system for detecting both motorcycles and non-motorcycles.(30) It has some higher skill level requirements than the other detectors, and a relatively higher price compared to the next-most accurate system, magnetometers. The two technologies that performed least well in the test (multi-technology and tracking video) are undergoing rapid technology improvements and may become more useful in the future. If accuracy is improved, then their pricing and required skill level for installation are attractive.

AADT Data Limitations

FHWA’s Traffic Detector Handbook provides details on the limitations of motorcycle count data available.(31) In general, the report finds the relatively small amount of metal in many motorcycles combined with the fact that many motorcyclists ride near lane lines in order to give themselves more time to avoid cars moving into their lanes means that inductive loop detectors and half-lane axle sensors often undercount motorcycles. When motorcycles ride in closely spaced groups, the closely spaced axles and cycles often confuse available traffic monitoring equipment that has not been designed to identify the resulting pattern of closely spaced axles and vehicles.(31)

The following summarizes the limitations of motorcycle AADT estimates in the United States:

For overall traffic safety analysis, these shortcomings indicate that motorcycle counts may be inaccurate, and the inaccuracies vary among the States in ways that are difficult to assess or make adjustments for an analysis. The data from any specific site (most likely a permanent count location in a State) will have unknown under-reporting problems depending on how closely the installation matches the ideal design.(30) With few exceptions, the health of the detector—its current ability to correctly detect motorcycles—is unknown. Even if a detector’s installation was well designed originally, its current status might not be available to researchers who might use flawed data without knowing that the detector was beginning to fail during the period in which their data was collected. Ultimately, all of the problems would appear to lead to under-reporting of motorcycle counts, and thus an analyst might feel secure in viewing the data as a minimum. However, even that assurance may be misplaced. Detailed knowledge of the detector system’s software and any user-defined settings is needed before analysts could be comfortable that they know what happens with mistaken detections of various types and how the software is set to record those counts in the various classification bins.

Crash Data Limitations

Crash data standards, completeness, and accuracy vary among States. The difficulties using crash data in safety analyses are well known but not particularly well documented in the literature. Practitioners are, however, well aware of the following data limitations:

Roadway Inventory Limitations

Safety analyses often make use of roadway inventory data in order to understand the roadway attributes’ association with crash risk. There are a few notes worthy of consideration because they relate to AADT and crash data and the statewide roadway inventory data.

Analysis Limitations

As discussed in chapter 2, most of the current research concerning motorcycle crashes has focused on discrete outcomes (i.e., the probability of a given crash severity, presence of a roadway or traffic control feature given that a crash has occurred, or probability of injury severity given that feature or crash type). Very little research focuses on developing SPFs or CMFs specifically for motorcycle crashes.

The same analysis methods available for estimating SPFs and CMFs for other crash types are applicable for motorcycle crashes. While having estimates of motorcycle volumes is preferred, the results of the analyses undertaken for this project indicate that using total AADT volumes is a reasonable substitution when motorcycle AADT is not available.

Where motorcycle use is a small portion of traffic volume, such as the United States, some research has attempted to use motorcycle licensing and/or registration data as a surrogate, but these data are only available at the county level and so are not very useful for modeling site-level data.

Issues that apply to other rare crash types also hinder analyses of motorcycle crashes in order to develop SPFs and/or CMFs. Firstly, with rare crash types, low crash sample sizes make the development of reliable SPFs and CMFs difficult. Statistical models may not be estimable, or even if they are, estimated parameters may be very imprecise, and few variables related to crashes may be included in the model.

Another sample size issue is that roadway infrastructure treatments aimed at reducing motorcycle crashes are not common. When conducting a before-after study, the rarity of such treatment sites combined with low crash frequencies presents a formidable challenge.

The results of the research conducted for this project confirmed all of the analysis limitations stated above. Even with large datasets containing a substantial mileage of roads, SPFs could not be estimated for all site types or subtypes of motorcycle crashes. (Single-vehicle and multi-vehicle were attempted as well as total motorcycle crashes.) The simulation results estimating CMFs through before-after studies and cross-sectional regression modeling showed that the CMF estimates vary substantially between simulations due to low motorcycle crash frequencies.

Research Gaps and Needs

In terms of research gaps with respect to motorcycle safety and CMFs, very little information is known on the effects of roadway geometric and traffic control features on motorcycle crash frequency and severity. The reason for this gap is likely twofold: motorcycle crashes are not usually the focus of safety related countermeasures, and, the rarity of motorcycle crashes combined with the scarcity of treatment locations would result in a small sample size for study. Future research will need to explore how to overcome the small sample size issue with appropriate methodologies. This discussion provides some thoughts in this regard.

With respect to SPFs for applying in network screening and other safety management tasks, few SPFs at the segment level or intersection level exist. The SPFs developed in this project may contribute to filling this void, but there remains work to be done in terms of site types for which no SPF was developed and ensuring that models exist that calibrate well in all jurisdictions.

A major need for the research community would be a database that includes countermeasures implemented that are expected to affect motorcycle crashes along with the location, date of treatment, and treatment description. This information would aid researchers in identifying treatments that are feasible for study.

In terms of analytical methods and other related gaps, the project team identified of the following research needs.



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