The general relationships between hourly accident rates and hourly traffic volume/capacity ( v/c) ratios were examined. A 26 km (16 mi) segment of Interstate I-94 in the Detroit area was selected as the study segment. The v/c ratios were calculated from average hourly traffic volume counts collected in 1993 and 1994 from three permanent count stations. Accident rates were derived from hourly distributed number of accidents in the same 2 years. The correlation between v/c values and accident rates follows a general U-shaped pattern. The study of all observed accidents combined indicates that accident rates are highest in the very low hourly v/c range, decrease rapidly with increasing v/c ratio, and then gradually increase as the v/c ratio continues to increase. U-shaped models also explain the relationship between v/c and accident rates for weekdays and weekend days, multivehicle, rear-end, and property-damage-only accidents. On the other hand, single-vehicle, fixed-object, and turnover accidents, and accidents involving injury and fatality follow a generally decreasing trend with increasing v/c ratio. Traffic conflict is viewed as a major contributing factor to high accident rates observed in the high v/c range, whereas night conditions and driver inattention were identified as explanatory factors for the occurrence of high accident rates in the low v/c ranges.

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Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.

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