Objectives: The accuracy of self-reported driving exposure has questioned the validity of using self-reported mileage to inform research questions. Studies examining the accuracy of self-reported driving exposure compared to objective measures find low validity, with drivers overestimating and underestimating driving distance. The aims of the current study were to (1) examine the discrepancy between self-reported annual mileage and driving exposure the following year and (2) investigate whether these differences depended on age and annual mileage.
Methods: Two estimates of drivers’ self-reported annual mileage collected during vehicle installation (obtained via prestudy questionnaires) and approximated annual mileage driven (based upon Global Positioning System data) were acquired from 3,323 participants who participated in the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study.
Results: A Wilcoxon signed rank test showed that there was a significant difference between self-reported and annual driving exposure during participation in SHRP 2, with the majority of self-reported responses overestimating annual mileage the following year, irrespective of whether an ordinal or ratio variable was examined. Over 15% of participants provided self-reported responses with over 100% deviation, which were exclusive to participants underestimating annual mileage. Further, deviations in reporting differed between participants who had low, medium, and high exposure, as well as between participants in different age groups.
Conclusions: These findings indicate that although self-reported annual mileage is heavily relied on for research, such estimates of driving distance may be an overestimate of current or future mileage and can influence the validity of prior research that has utilized estimates of driving exposure. 相似文献
ABSTRACTThe calculation of the combined uncertainty of the international estimated short-term intake (IESTI) of ethephon residues in apples is shown as an example. The ethephon residues in apples were reported by the Joint FAO (Food and Agriculture Organization of the United Nations)/WHO (World Health Organization) Meeting on Pesticide Residues (JMPR). The apple consumption data were taken from the IESTI (international short-term intake) calculation template used by the JMPR. The IESTI was calculated with the currently used method (case 2a) and a proposed one recommended by the EFSA (European Food Safety Authority)/RIVM (Dutch National Institute for Public Health) Scientific Workshop co-sponsored by FAO and WHO. In this example, the ratio of IESTIproposed/IESTIcurrent and their combined relative uncertainty are about 2.8, and 1.7, respectively. The larger IESTI and uncertainty obtained with the proposed equation are the consequence of calculation only with the large portion (LP) instead of its combination with unit mass, and the MRL instead of the highest residue (HR). The LP is the major contributor to the combined uncertainty. Both the calculated IESTI and its combined uncertainty depend on the actual food – pesticide residue combination, and should be calculated for each case. 相似文献
As green infrastructure gets its attention in hazard mitigation, it is necessary to improve general understanding on what makes green infrastructure important for hazard and resiliency research. To better understand how green infrastructure fits with more traditional notions of structural and nonstructural mitigation, this study examines the relationship between green infrastructure and ‘structural and nonstructural’ mitigation approaches for hazard mitigation. Also, this study discusses a new measurement of locational aspects and spatial patterns of green infrastructure by utilizing high-resolution data in urban areas, and its potential implementation in hazard mitigation. Compared to previous research using land-use land-cover datasets, the normalized difference vegetation index (NDVI) utilizing National Agriculture Imagery Program dataset provides an ability to capture green infrastructure in greater detail. A visual comparison suggests that the NDVI data are able to capture and identify more types of ‘green’ land uses in Harris County. The total green infrastructure percentages for Harris County, Texas, based on 1-m high resolution were found to be 61.5% of the area, compared to the 51.5% based on the National Land Cover Database. This study provides support for utilizing high-resolution data to establish guidelines for green infrastructure’s spatial characteristics and sustainable hazard mitigation. The outcomes of this study will be helpful in the strategic planning and implementation of green infrastructure in urban areas with hazard issues. 相似文献