1. Safety in Numbers for Cyclists in England: Measuring the Effect
George Ursachi & Richard Owen

2. Background Road Safety Analysis George Ursachi Richard Owen
Not-for-profit company limited by guarantee registered in England
Independent specialists in collision and casualty analysis, evaluation, online analysis systems, intervention design, training and more
George Ursachi
Research Analyst at Road Safety Analysis
PhD. in Marketing, specialised in research design and research instruments
Richard Owen
Director at Road Safety Analysis
Former manager at Thames Valley Safer Roads Partnership
Specialist in spatial analysis, GIS, and project management

3. Some of our clients

4. Safety in Numbers for Cyclists in England: Measuring the Effect
What is Safety in Numbers (SIN) and why does it happen?
Existing evidence
Our study
The Data
Preliminary analysis
Results
What does this mean?
Published in…

5. What is Safety in Numbers (SIN)?
Wikipedia:
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.

6. Safety in Numbers in nature
Birds are good on that, fish are good on that, lots of animals are good on that too…people should be also good at it, isn’t it?

7. Safety in Numbers for cyclists
Peter Jacobsen (2003):
‘More riders, fewer crashes; fewer riders, more crashes’
‘Safety in numbers: more walkers and bicyclists, safer walking and bicycling’, P.L. Jacobsen, Injury Prevention, 2003, Issue 9, pages

8. Why does SIN happen? Peter Jacobsen: CyclingUK:
Adaptation in motorists’ behaviour.
CyclingUK:
The drivers are more likely to be cyclists themselves and are therefore more sympathetic;
There is greater political will to improve cycling conditions.
There are more hypothesis about that. We will just mention a few.
(1) Safety in numbers: more walkers and bicyclists, safer walking and bicycling, P.L. Jacobsen, Injury Prevention, 2003, Issue 9, pages
(2)

9. European countries in 1998 (Jacobsen, 2003)
Walking and bicycling in eight European Countries in (Jacobsen 2003)

10. European countries in 1998 (Jacobsen, 2003)
EU countries cycling level and cycling risk in (Jacobsen 2003)

11. Different perspective – same conclusion
Source: OECD via
And there are loads of other examples!!!
* Organisation for Economic Co-operation and Development (OECD)

12. Our Study
Focus:
England cities (319) – Local Authority Districts (LADs)
Uses multiple datasets
Better (not perfect) metric - risk rate
Relationship between cycling risk and cycling level
Four categories of LADs based on their level of cycling and the cycling risk (above and below the average)

13. The Data Three datasets:
The last 5 years’ average adult cyclists’ casualties ( ), based on residence, from MAST Online. Residency is calculated by using the postcode of the casualty
The Proportion of residents who cycle (any length) for utility purposes at a given frequency in England, 2013 to 2014
Population number for each local authority district (LAD), from the GB statistics, for adults (16+)

14. Preliminary analysis
Cycling exposure or level (number of cyclists per 1,000 population)
Cycling risk (proportion of casualties out of cycling exposure) => cycling risk (per 1,000 cyclists or per 1, population)
4 groups of LADs (above and below the average level and above and below the average risk)
* The analysis described here is preliminary, not due to incompletion but because there are other considerations that have not been included which would fully explore the relationship between the number of cyclists and risk of injury. The sample sizes included within this analysis are more than sufficient for the results to stand on their own, but there is also potential for a wider debate into the topic.

15. Preliminary analysis Ll-Lr Ll-Hr Hl-Hr Hl-Lr Cycling Risk
Cycling Level
Cycling Risk
Average Cycling Risk
Average Cycling Level
Ll-Lr
Ll-Hr
Hl-Hr
Hl-Lr
The analysis described here is preliminary, not due to incompletion but because there are other considerations that have not been included which would fully explore the relationship between the number of cyclists and risk of injury. The sample sizes included within this analysis are more than sufficient for the results to stand on their own, but there is also potential for a wider debate into the topic.
LADs

16. Results Simple linear relationships: Type of LAD
Simple Risk-Level Linear Relationship
Significance (statistically)
Hl-Hr (high level – high risk)
Cycling Risk = *Cycling Level
Not significant
Hl-Lr (high level – low risk)
Cycling Risk = *Cycling Level
Ll-Hr (low level – high risk)
Cycling Risk = *Cycling Level
Significant
Ll-Ll (low level – low risk)
Cycling Risk = *Cycling Level
Whole population
Cycling Risk = *Cycling Level
* The analysis described here is preliminary, not due to incompletion but because there are other considerations that have not been included which would fully explore the relationship between the number of cyclists and risk of injury. The sample sizes included within this analysis are more than sufficient for the results to stand on their own, but there is also potential for a wider debate into the topic.

17. Results *Cambridge isn’t even on the chart
1. High Risk – High Level Blue
2. High Risk – Low Level Red
3. Low Risk – High Level Orange
4. Low Risk – Low Level Yellow

18. What this means There seem to be Safety in Numbers in England
The safety effects of increasing cycling seems to be more pronounced for cities with low level of cycling
For all types of cities the effect of increasing cycling is a decrease in risk rate BUT this doesn’t mean necessarily that is valid for each city (there are many things to consider when designing a cycling campaign, such as infrastructure, hot spots, cycling purpose, cyclists’ profiles)

19. Cycle risk index Collision Rate Present Future Exposure (E)
Count-based risk index = Exposure * Collision Rate
Maintain similar level of safety (collision rate)
Increase level of safety (lower collision rate)
Present
Future
When designing strategies one need to consider that the Count-based Risk Index is a product of Exposure and Rate
Trying to decrease count-based risk index while increasing exposure
We will continue with some images

20. Denmark vs. Portugal Anyone see any differences?
Which one is more similar to UK?
Let’s talk about their risk profile, let’s try to characterise them. Which looks to be more likely to be risk takers?
Where are the risks higher? What are the risks?

21. Denmark vs. England
More Denmark cyclist. The same feeling, the same type of people.
And British cyclists. Risk takers here? Why?
Improve safety could also imply bringing safer people on the bike? How do we do that?
Infrastructure, education, awareness.

22. UK challenges
There are some challenges every council have when designing cycling strategies: space and road structure is among the most common ones
Also, internal there are many differences between cities. You can’t compare cycling infrastructure and culture between Bolton or Luton and Cambridge or Oxford.

23. Bristol
From pictures found on Google, Bristol seem to be on the right path. Anyone can approve or disapprove me?
In our analysis Bristol in the High Level – Low Risk Group with a cycling level almost triple than England’s average and a cycling risk of almost half the national average.

24. Bristol Bristol – High Level of cycling – Low level of risk (123; 5.6)
I think it’s doing ok!

25. Things to consider when designing cycling strategies
Where should the cycling level be improved (routes, areas)?
Who will be the new cyclists?
Why will they cycle? What can we offer them? (better and safer routes?)
How will the other road participants be affected?
Who can also help? (other organisations like health organisations or other public bodies)
The arrow it’s a hint :D

26. Thank you. Safety in Numbers
For any information or help do not hesitate to contact the RSA team.