Safety in numbers
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Safety in numbers is the hypothesis that, by being part of a large physical group or mass, an individual is less likely to be the victim of a mishap, accident, attack, or other bad event. Some related theories also argue (and can show statistically) that mass behaviour (by becoming more predictable and "known" to other people) can reduce accident risks, such as in traffic safety – in this case, the safety effect creates an actual reduction of danger, rather than just a redistribution over a larger group.
- 1 Description
- 2 In road traffic safety
- 2.1 Research
- 2.2 Criticism
- 2.3 Helmet debate
- 2.4 Examples of substantial increases in cycling associated with reduction in danger
- 3 See also
- 4 References
Evidence often advanced for this position includes the flocking of birds and schooling of fish. In both of these instances, by being part of a large group, individuals face less risk of falling victim to predators than they would if traveling alone.
In road traffic safety
Safety in numbers is also used to describe the evidence that a motorist is less likely to collide with a pedestrian or cyclist as the numbers of pedestrians or bicyclists increase. This non-linear relationship was first shown at intersections. A Public Health Consultant showed this non-linear relationship also occurs at in cities and countries using ecologic data from cities in California and Denmark, and European countries, and time-series data for the United Kingdom and the Netherlands. He termed this relationship "safety in numbers." 
Substantial independent evidence shows that the number of pedestrians or bicyclists injured increases at a slower rate than would be expected based on their numbers. That is, the risk to the individual pedestrian or bicyclist decreases where there are more people walking or bicycling. 
A 2002 study into whether pedestrian risk decreased with pedestrian flow, using 1983-86 data from signalized intersections in a town in Canada, found that in some circumstances the risk per pedestrian decreased with increased pedestrian flow.
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Another author has written that, while such data shows a degree of correlation, conclusions of causality may very well be based on a statistically spurious relationship. In other words, the relationship of an increase in bicycle use and a decrease in cyclist injuries may have no actual causal connection due to a certain third, unseen factor (referred to in statistics as a "confounding factor" or "lurking variable"). A spurious relationship gives an impression of a worthy link between two groups that is invalid when objectively examined.
Safety-in-numbers is important in the debate over the efficacy of bicycle helmets, because compulsory helmet laws have been shown to discourage bicycling.  Charles Komanoff has concluded that in "diluting the effect of 'safety in numbers,' compulsory helmet laws could have the perverse effect of increasing serious injury rates among those who continue to cycle." 
Examples of substantial increases in cycling associated with reduction in danger
After cycling was promoted in Finland, the number of trips increased by 72% and there was a 75% drop in cyclists deaths.
In the United Kingdom, Transport for London, the body responsible for the London Congestion Charge, stated in their April 2005 review of that scheme, that motor vehicle traffic had decreased by 16%, bicycle use had increased by 28% and cyclist injuries had decreased by 20% in the first year of operation of the scheme.
In January 2008, a London newspaper reported that the number of cyclists in London being treated in hospitals for serious injuries had increased by 100% in six years. Over the same time, they report, the number of cyclists had increased by 84%.
Between 1975 and 2001, the total number of bicycle trips almost quadrupled. Between 1990 and 2007, the share of trips made by bicycle increased from 5% to 10%. Between 1992 and 2006, the number of serious bicycle injuries declined by 38%. 
Portland, Oregon, US
Between 1990 and 2000, the percentage of workers who commuted to work by bicycle rose from 1.1% to 1.8%. By 2008, the proportion has risen to 6.0%; while the number of workers increased by only 36% between 1990 and 2008, the number of workers commuting by bicycle increased 608%. Between 1992 and 2008, the number of bicyclists crossing four bridges into downtown was measured to have increased 369% between 1992 and 2008.
Between 1995 and 2006, cycling increased by 44% and the percent of people cycling to work increased from 31% to 36%. During the same period, the number of cyclists killed or seriously injured fell by 60% 
Comparing the periods 1991-93 and 1996–98, the share of trips made by bicycle rose from 15% to 18%. The number of bicyclists killed and seriously injured fell by 59%. 
Between 1980 and 2005, cycling increased by 45%, and cyclist fatalities decreased by 58%. 
Western Australia, Australia
During 7 years of the 1980s, cycling in Western Australia increased by 82% and admissions to hospital of cyclists declined by 5%. 
Between 1975 and 1998, the percent of trips made by bicycle rose from 8% to 12%, and cyclist fatalities fell by 66%. 
New York, New York, US
- Brüde, U., Larsson, J. (1993). "Models for predicting accidents at junctions where pedestrians and cyclists are involved. How well do they fit?". Accident Analysis and Prevention 25 (5): 499–509. doi:10.1016/0001-4575(93)90001-D. PMID 8397652. "According to results obtained, the risk - the number of accidents involving unprotected road users per unprotected road user - increases with increasing numbers of motor vehicles but decreases with increasing numbers of pedestrians and cyclists."
- Leden, L., Gårder, P., Pulkkinen, U. (2000). "An expert judgment model applied to estimating the safety effect of a bicycle facility". Accident Analysis and Prevention 32 (4): 589–599. doi:10.1016/S0001-4575(99)00090-1. PMID 10868762. "An analysis of the relationship between bicycle flow and the number of reported accidents in the experimental area shows that the relative risk — when risk is defined as the number of expected (reportable) accidents per passing bicyclist — decreases with increasing bicycle flow"
- Jacobsen, P. L. (2003). "Safety in numbers: more walkers and bicyclists, safer walking and bicycling". Injury Prevention 9 (3): 205–209. doi:10.1136/ip.9.3.205. PMC 1731007. PMID 12966006. "A motorist is less likely to collide with a person walking and bicycling if more people walk or bicycle."
- Elvik, R. (2009). "The non-linearity of risk and the promotion of environmentally sustainable transport". Accident Analysis and Prevention 41 (4): 849–855. doi:10.1016/j.aap.2009.04.009. PMID 19540975. "Several studies show that the risks of injury to pedestrians and cyclists are highly non-linear. This means that the more pedestrians or cyclists there are, the lower is the risk faced by each pedestrian or cyclist."
- Geyer, J. Raford, N., Pham, T., Ragland, D. (2006). "Safety in Numbers: Data from Oakland, California". Transportation Research Record 1982: 150–154. doi:10.3141/1982-20. "Estimates of the model parameters show that the number of pedestrian collisions increases more slowly than the number of pedestrians; that is, the collision rate decreases as the number of pedestrians increases, consistent with previous studies by Leden and Jacobsen. Specifically, a doubling of the number of pedestrians (increase of 100%) is associated with only a 52% increase in the number of vehicle-pedestrian collisions, with the corresponding rate decreasing by about 24%."
- Miranda-Moreno, L., Strauss, J., Morency, P., (2011). "Disaggregate Exposure Measures and Injury Frequency Models of Cyclist Safety at Signalized Intersections". Transportation Research Record 2236 (2236): 74–82. doi:10.3141/2236-09. "A 10% increase in bicycle flow was associated with a 4.4% increase in the frequency of cyclist injuries."
- Raford, N., Ragland, D. (2004). "Space Syntax: Innovative Pedestrian Volume Modeling Tool for Pedestrian Safety". Transportation Research Record 1978: 66–74. doi:10.3141/1878-09. "Downtown intersections experience slightly more pedestrian–vehicle collisions per year than the intersections in East Oakland but carry approximately three times as many pedestrians annually, indicating lower annual accident rate per pedestrian than that in East Oakland."
- Schneider, R., Chagas Diogenes, M., Arnold, L., Attaset, V., Griswold, J., Ragland, D. (2010). "Association Between Roadway Intersection Characteristics and Pedestrian Crash Risk in Alameda County, California". Transportation Research Record 2198: 41–51. doi:10.3141/2198-06. "[a]s the pedestrian volume increases, the expected number of pedestrian crashes increases at a decreasing rate (Figure 1a). As the pedestrian volume increases, the expected risk of a crash for each individual crossing decreases (Figure 1b)."
- Harwood, D.W., Torbic, D.J., Gilmore, D.K., Bokenkroger, C.D., Dunn, J.M., Zegeer, C.V., Srinivasan, R., Carter, D., Raborn, C., Lyon, C., Persaud, B. (2008). "Pedestrian Safety Prediction Methodology". NCHRP Web-only Document 129: Phase III. Transportation Research Board, Washington, DC.
- Jonsson, T. (2005). "Pedestrian Safety Prediction Methodology". Predictive models for accidents on urban links. A focus on vulnerable road users. Ph.D. Dissertation. Bulletin 226. Lund Institute of Technology, Department of Technology and Society, Traffic Engineering, Lund. "The validation indicated that exponents were 0.5 for both the flows of pedestrians and motor vehicles in models for accidents involving vulnerable road users, and 1.0 for the motor vehicle flow exponent in the models for motor vehicle accidents. For bicyclist accidents the correct exponent for bicyclist flows is likely to be somewhat lower than 0.5, close to 0.35."
- Lyon, C., Persaud, B.N. (2002). "Pedestrian collision prediction models for urban intersections". Transportation Research Record 1818: 102–107. doi:10.3141/1818-16. "Collisions are estimated to increase with AADT and pedestrian volumes, although these relationships are nonlinear (as shown by the exponents of AADT and PEDS being significantly less than 1). This would confirm that the use of collision rates is based on an erroneous assumption of a linear relationship between collisions and volumes."
- Robinson, D.L. (2005). "Safety in numbers in Australia: more walkers and bicyclists, safer walking and cycling". Health Promotion Journal of Australia 16 (1): 47–51. PMID 16389930. "As with overseas data, the exponential growth rule fits Australian data well. If cycling doubles, the risk per kilometre falls by about 34%; conversely, if cycling halves, the risk per kilometre will be about 52% higher."
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- Jensen, S.U. (1998). "DUMAS – Safety of Pedestrians and Two-wheelers". A publication of Road Directorate, Denmark: Note 51. "when pedestrian and cycle traffic increases, the casualty rate per kilometre decreases."
- Elvik, R. (2009). "The non-linearity of risk and the promotion of environmentally sustainable transport". Accident Analysis and Prevention 41 (4): 849–855. doi:10.1016/j.aap.2009.04.009. PMID 19540975.
- Daniels, S., Brijs, T., Nuyts, E., Wets, G. (2010). "Explaining variation in safety performance of roundabouts". Accident Analysis and Prevention 42 (2): 393–402. doi:10.1016/j.aap.2009.08.019. PMID 20159059. "Confirmation is found for the existence of a safety in numbers-effect for bicyclists, moped riders and – with less certainty – for pedestrians at roundabouts."
- Vandenbulcke, G., Thomas, I., de Geus, B., Degraeuwe, B., Torfs, R., Meeusen, R., Panis, L.I. (2009). "Mapping bicycle use and the risk of accidents for commuters who cycle to work in Belgium". Transport Policy 16 (2): 77–87. doi:10.1016/j.tranpol.2009.03.004. "Table2 shows that -- as expected -- the risk of cyclists becoming casualties of road accidents decreases as the proportion of cyclists increases."
- Ekman L. (1996). "On the treatment of flow in traffic safety analysis — a non-parametric approach applied on vulnerable road users". Lund, Sweden: Institutionen för Trafikteknik, Lunds Tekniska Högskola. Bulletin 136. "the conflict rate for bicyclists is twice as large at locations with low bicycle flow compared to locations with higher flow"
- Turner, S.A., Roozenburg, A.P., Francis, T. (2006). "Predicting accident rates for cyclists and pedestrians". Land Transport New Zealand. Research. Report 289. "A 'safety in numbers' effect is observed for cycle accidents at traffic signals, roundabouts and mid-block sites. An increase in cycle numbers will not therefore necessarily increase the number of accidents substantially. A 'safety in numbers' effect is also observed for pedestrian accidents at traffic signals and mid-block sites. Insufficient data exists to conclude whether a 'safety in numbers' effect occurs at roundabouts"
- Turner, S.A., Binder, S., Roozenburg, A.P. (2009). "Cycle Safety: Reducing the Crash Risk". Land Transport New Zealand. Research. Report 389. "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."
- Knowles, J., Adams, S., Cuerden, R., Savill, T. Reid, S., Tight, M. (2009). "Collisions involving pedal cyclists on Britain's roads: establishing the causes". Transport Research Laboratory Published Project. Report. PPR445. "The research assessed as part of this study is strongly suggestive that a safety in numbers effect exists."
- Noland, R.B., Quddus, M.A., Ochieng, W.Y. (2008). "The effect of the London congestion charge on road casualties: an intervention analysis". Transportation 35 (1): 73–91. doi:10.1007/s11116-007-9133-9. "While motorcycle casualties in Inner London seem to have increased after implementation of the congestion charge no similar effect is found for bicycle casualties. This is despite an increase in bicycle usage within the congestion charging zone."
- Ministerie van Verkeer en Waterstaat (2009). "Cycling in the Netherlands". p. 14.
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- Turner S.A., Wood, G.R., Luo, Q., Singh, R., Allatt, T. J., Affiliation: Civil and Natural Resources Engineering, University of Canterbury, New Zealand; Macquarie University, NSW, Australia; Beca Infrastructure Ltd, New Zealand. (2010). "Crash prediction models and the factors that influence cycle safety". Australas Coll Road Saf 21 (3): 26–36. "The key finding is that as cycle volumes increase, the risk per individual cyclist reduces - the 'safety in numbers' effect."
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- Daniels, S., Brijs, T., Nuyts, E., Wets, G (2011). "Extended prediction models for crashes at roundabouts". Safety Science 49 (2): 198–207. doi:10.1016/j.ssci.2010.07.016. "Confirmation is found for the existence of a 'safety-in-numbers' effect for different types of road users."
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- Nordback K, Marshall W, Janson B (2014). "Bicyclist safety performance functions for a U.S. city". Accident Analysis & Prevention 65: 114–122. doi:10.1016/j.aap.2013.12.016. "intersections with more cyclists have fewer collisions per cyclist, illustrating that cyclists are safer in numbers"
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