The objective of this project was to investigate and describe the effect of the lack of motorcycle annual average daily traffic (AADT) data on the performance of motorcycle safety evaluations aimed at developing motorcycle crash-specific crash modification factors (CMFs) and safety performance functions (SPFs). Motorcycle volumes would intuitively be required for modeling motorcycle crashes and studying the safety effectiveness of countermeasures due to the strong relationship between crashes and exposure. However, few jurisdictions collect motorcycle traffic volume data systematically. A second purpose of the research was to investigate and demonstrate methods and provide the mathematical models required for jurisdictions that lack motorcycle volumes when undertaking the evaluation of motorcycle related safety countermeasures. The project scope included the following tasks: • Identify current practices for statistical modeling of motorcycle crashes and motorcycle countermeasure evaluations. • Identify the availability, applicability, quality, and other related features of motorcycle safety data resources. • Perform a quantitative analysis of the effect of the lack of motorcycle AADT on safety evaluations. • Determine data limitations, gaps, and future data needs to support future motorcycle safety evaluations. • Identify opportunities to promote the findings and support follow up research through a marketing, communications, and outreach plan. A literature review found many studies concerned with motorcycle safety, but very few focused on the prediction of crash frequency. The limited international research does suggest that motorcycle crash frequency models can be developed based only on total AADT for all vehicle types. Guided by lessons learned from the literature review, especially the promise in seeking alternatives to directly including motorcycle volumes in motorcycle crash frequency prediction models, a number of analytical approaches were developed and undertaken. The intention of the models were to serve two broad purposes, namely the following: • To explore how much predictive power for an SPF is lost when motorcycle volumes are unknown and how this lack of information may affect a study of motorcycle countermeasures estimating a CMF. • To explore alternative methods for deriving accurate predictions of motorcycle crashes or motorcycle volume data. The project team investigated two groups, or avenues, of methods. The methods for avenue A focused on investigating (1) the difference in predictive performance for motorcycle SPFs calibrated with motorcycle AADT versus total AADT, (2) the relation of total crash SPFs and motorcycle crash SPFs so jurisdictions without motorcycle volumes could predict motorcycle crashes using total crash SPFs, and (3) methods to predict segment-level motorcycle AADT. The methods for avenue B focused on the differences in CMF estimates found when using motorcycle AADT versus total AADT when applying before-after or cross-sectional regression CMF estimation methods on simulated crash count data. For developing the avenue A models, data were collected from Florida and Pennsylvania. Both States had a large number of locations with an estimated motorcycle AADT, which could provide linkable roadway inventory, traffic, and crash data. Virginia also provided data for the purpose of validating the models developed. The avenue B analyses used the roadway inventory, total AADT, and motorcycle AADT collected for the avenue A methods in Florida and Pennsylvania. For motorcycle crashes, SPFs developed in the avenue A models simulated crash counts as the initial starting point. The findings of both the avenue A and avenue B modeling indicate that when motorcycle volumes are unknown, using total AADT on its own is sufficient for developing SPFs and CMFs. The potential bias due to missing motorcycle-specific AADT is sufficiently negligible, where it exists so as not to preclude SPF and CMF development. However, in the analysis undertaken, SPFs could not be developed for all motorcycle crash or site types. This is a significant issue in working with relatively rare crash types. The findings also conclude that attempting to predict motorcycle volumes is not possible using typically available roadway and county-level data. Improvements could possibly be made in trip generation-type modeling at a disaggregate scale, although, given the success of the SPFs using total AADT, such an effort may not be worthwhile. The research identified a number of data limitations and motorcycle SPF and CMF research gaps through the assessment of available data sources, analytic methods, and evaluation results. Data limitations identified relate to traffic volumes (AADT) addressing technology requirements in particular; crash data focusing on quality issues; and roadway inventory data emphasizing roadway class/ownership and missing data issues. For 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 reasons for this gap are likely twofold: motorcycle crashes are not usually the focus of safety-related countermeasures, and the rarity of motorcycle crashes combined with scarcity of treatment locations would result in a small sample size for study. With respect to SPFs for application 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 evaluating site types for which no SPF was developed and ensuring that SPFs exist that calibrate well in all jurisdictions. This final project report organizes the findings of these tasks as follows: • Chapter 2 discusses current practices for assessing motorcycle safety. • Chapter 3 discusses the analysis methods. • Chapter 4 discusses the data collection and a summary of the data. • Chapter 5 discusses the analysis and results. • Chapter 6 provides conclusions and recommendations. • Chapter 7 discusses data and analysis limitations and future research needs. (Author/publisher)
Samenvatting