the near miss project - some findings and implications
TRANSCRIPT
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The Near Miss Project: some
findings and implications
Rachel Aldred
rachelaldred.org
@RachelAldred
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Why Near Misses are important
– Near misses may predict at least some types of
collision risk
– Growing evidence that near misses strongly affect
cycling experience
– Clarify relationships between ‘perceived’,
‘experienced’, and ‘objective’ risk
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Immediate impacts
– ‘My heart stopped. But this happens every time. This is a terrifying
interchange for cyclists.’
– ‘[Felt] a bit sad that everyone is in such a rush, as to cut others up
and make the morning stressful.’
– ‘My impatience was ever growing at just trying to get home and
being fed up of cars blocking me, I also felt that I had made a
mistake, and was scared of being hit.’
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Longer-term impacts
– ‘I already approach this junction, and indeed every portion of the
campus where I have to cycle on the road, with considerable
caution. I can only attempt to cycle even more cautiously in future.’
– ‘An accumulation of these events over the years has made me
super cautious, and I now believe it’s not sufficient simply to obey
the rules to stay alive. To stay alive one must also anticipate that all
others will be careless.’
– ‘At the moment I lack confidence & feel nervous when vehicles
come from behind. I’m fed up with drivers overtaking me towards
oncoming traffic & providing me with insufficient room &/or nearly
pushing the other vehicle off the road.’
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1532 UK diarists in 2014
3994 incidents
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Headline results
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Comparing injury and non-injury incident rates
Type of Incident Rate per year, regular UK
commuting cyclist
Death .000125 (once every 8,000 yrs)
Reported serious injury .0025 (once every 400 yrs)
Reported slight injury .015 (once every 67 yrs)
Any injury (reported or not) .05 (once every 20 yrs)
Harassed/abused 20
‘Very scary’ incident 60
Any non-injury incident 450
Final three figures derived from Near Miss Project data. First four derived from
published academic sources – see journal article for full details.
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Experiencing incidents
– Around six in seven participants experienced at
least one incident on their diary day
– A quarter experienced a ‘very scary’ incident, and
half a ‘very annoying’ incident
– Most incidents (85.2%) involved other road users;
70.0% involved motor vehicles.
– Incidents were allocated to eight categories using
inductive coding
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What are ‘incidents’?
Some – but not all – map closely to injury collision types
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Speed and incident rate
– For a given journey length, holding other factors
constant, faster cyclists experienced relatively
fewer incidents
– In a regression model, the unstandardized
coefficient for speed was -0.101; meaning a one
mile per hour increase in speed is associated with
a 9.6% decrease in incident rate per cycled mile
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Rates of problematic passes by gender
Rate per hr Rate per mile Rate per trip
stage
Women .70 .099 .33
Men .65 .066 .39
Rates of problematic passing incidents (usually close passes)
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Incident rates per hour
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Cultural safety in numbers?
– Cycling levels in 2011 or increase 2001-11 not
significant in regression model of per-mile near
miss rates
– Simple correlation showed positive relationship
– Suggests confounding factors
– Implications for injury risk???
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Location analysis (ongoing)
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Comparing incident types (5 most common)
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
BLOCK DRIVEAT HOOK PASS PULL
Rural, non-London
Urban, non-London
London
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Road type
0%
10%
20%
30%
40%
50%
60%
A Road B Road Not Classified Unclassified
Rural, non-London
Urban, non-London
London
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Junction proximity
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Rural, non-London Urban, non-London London
Not within 20m of a junction
Within 20m of a junction
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“Primary cause”
0%
10%
20%
30%
40%
50%
60%
Rural, non-London
Urban, non-London
London
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Impacts of incidents
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Impact of motor vehicle involvement
N MeanHow
annoying
No MV1198 1.95
MV involved2781 2.19
How scary No MV1183 .78
MV involved2768 1.50
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Impacts of incidents
– Incidents involving motors were more scary
(18.1% vs. 5.7% ‘very scary’)
– Incidents involving large vehicles were scarier
still (24.0% of HGV incidents, 22.8% of
bus/coach incidents
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Impacts of incidents
– Blocking events were less likely to generate fear
than other incident types
– Almost one in four dooring, hooking & passing
incidents were ‘very scary’, but only one in
twenty blocking incidents
• Regression model confirms important of vehicle
involvement & incident type
• Initial findings re: infrastructure; planning more
detailed analysis
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Preventing incidents
– Cyclists were asked what they thought might have
prevented an incident from happening. Most, they
thought, could have been prevented, with only
2.7% (109) definitely not preventable.– According to the cyclists 19.1% of incidents could have been
avoided by them doing something differently, 53.3% if road
condition, layout or infrastructure had been better, and 78.7%
by another person behaving differently.
– Overall, cyclists said 83.2% of incidents could
have been prevented by someone else behaving
differently and/or by the road environment being
improved, with only 3.9% definitely not
preventable by either.
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Driver behaviour
– Attribution of responsibility and driver factors noted
were often similar to those noted by officers in
injury collision cases (Knowles et al 2009)
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Driver behaviour issues
– Cyclists said almost four in five of all incidents
could have been prevented by another person
– This was around 6 in 7, for ‘very scary’ incidents.– Suggestions often involved drivers being more patient, slowing
down, or looking more carefully:
Characteristic/behaviour Count Percentage of all incidents (%)
Waiting/Patience 752 3.22
Looking/Awareness/Checking 657 2.81
Stopping 189 0.81
Giving Space/Room 304 1.30
Slowing Down 157 0.67
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Driver behaviour: looked and failed to see?
– ‘[It] made me feel vulnerable and reminded me that a driver’s failure
to look causes a great risk. It also left me frustrated because I
wasn’t sure that the driver even noticed that he’d nearly caused a
collision and because of this he won’t have reason to change his
behaviour in future.’
– ‘[I was] annoyed because the driver didn’t indicate and didn’t notice
me as I had lights and an orange jacket on. Either that or they did
notice me and just expected me to get out of their way.’
– ‘I feel like if I had been a car they would have paid more
attention/taken more care. I overtook them later in the traffic and
when they passed me again I don’t think they noticed me or cared
about my safety at all - annoying and unnerving.’
– Cf Basford et al 2001
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Culture and infrastructure
‘The inter-relation of culture and infrastructure means that, for
example, a ‘behavioural’ problem does not necessarily need to be
addressed only by ‘behavioural’ interventions. Shackel and Parkin
(2014) have shown that driver overtaking behaviour is influenced by
road characteristics. Thus, for example, changes to road widths might
reduce or increase problem overtaking behaviour. Implementing this
consistently through road design guidance and standards would be an
example of an infrastructural response to a ‘behavioural’ problem.
Moreover, behaviour moderates the impact of infrastructural
interventions and vice versa.’
(from second Near Miss paper)
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Culture and infrastructure
- Need to think about whether
policy sometimes inadvertently
reinforces ‘out-group stigma’
(Basford 2001)
- E.g. recent research on ‘share
the road’ messaging
- Need to research & monitor
impact of infrastructure
- E.g. cycle symbols indicating
cyclists should ‘take the lane’
- Does high quality
infrastructure improve
legitimacy and driver
behaviour?
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Final thoughts
– Near misses are important for cycling experience &
retention/uptake, and for highlighting factors that lead
to injury collisions (for some incident types)
– Speed is the variable most strongly associated with
incident rates
– Incidents involving large motor vehicles (HGVs, buses)
are scarier than those not, and incidents not involving
any motor vehicles the least scary
– Driver behaviour factors similar to those in Stats19 –
but additional qualitative insight on road cultures
– Need more research on pathways to better cycling
experiences
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The Near Miss Project: some
findings and implications
Rachel Aldred
rachelaldred.org
@RachelAldred