Background Paragraph: Turner et al (2006) derived crash prediction models for this report’s predecessor and found a
pronounced ‘safety in numbers’ effect in the models. Using the crash prediction model for mid-block
locations, generic motorist and cyclist volumes can be used to demonstrate the impacts on the
expected crash rate of varying motor vehicle and cycle volumes. As shown in figure 2.20, an increase
in the proportion of cyclists to the overall traffic volume causes an increase in expected crashes at
mid-block locations, but the crash rate increases at a decreasing rate. That is to say, the crash rate per
cyclist goes down as the cycle volume increases. 
Story: There was a week long bike tournament. Day 1 had 500 cyclists left.  Day 2 had 400 cyclists left. Day 3 had 300 cyclists left. Day 4 had 200 cyclists left. Day 5 had 100 cyclists left. Day 6 had 50 cyclists left. Day 7 had 10 cyclists left. Tom won the tournament and got a big trophy.
What day had a lower crash rate per cyclist: Day 1 or Day 2? What day had a lower crash rate per cyclist: Day 1 or Day 3? What day had a lower crash rate per cyclist: Day 1 or Day 4? What day had a lower crash rate per cyclist: Day 1 or Day 5? What day had a lower crash rate per cyclist: Day 2 or Day 3? What day had a lower crash rate per cyclist: Day 2 or Day 4? What day had a lower crash rate per cyclist: Day 2 or Day 5? What day had a lower crash rate per cyclist: Day 2 or Day 6? What day had a lower crash rate per cyclist: Day 4 or Day 5? What day had a lower crash rate per cyclist: Day 4 or Day 7?