The Near Miss Project: some

findings and implications

Rachel Aldred

rachelaldred.org

@RachelAldred

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

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.’

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.’

1532 UK diarists in 2014

3994 incidents

Headline results

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.

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

What are ‘incidents’?

Some – but not all – map closely to injury collision types

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

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)

Incident rates per hour

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???

Location analysis (ongoing)

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

Road type

0%

10%

20%

30%

40%

50%

60%

A Road B Road Not Classified Unclassified

Rural, non-London

Urban, non-London

London

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

“Primary cause”

0%

10%

20%

30%

40%

50%

60%

Rural, non-London

Urban, non-London

London

Impacts of incidents

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

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

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

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.

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)

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

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

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)

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?

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

The Near Miss Project: some

findings and implications

Rachel Aldred

rachelaldred.org

@RachelAldred