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Data Usage and Route Analysis in North Yorkshire
Allan McVeighIntegrated Transport Group Manager
North Yorkshire County Council1
Introduction
• Casualty reduction in North Yorkshire
• Financial/other challenges
• Current Guidance
• Route Analysis techniques/scheme identification
• How we do it (process, outputs)
• How system works
• Advantages
• Weaknesses
•Making better use of data generally
•What next 2
Integrated Transport Group Manager, NYCC and Chair of 95 Alive
Officer Working Group. Responsibility for teams dealing with:
oTraffic Engineering (CPR, Traffic Signals, policy);
oRoad Safety & Travel Awareness (ETP, SCP, sustainability);
oTransport & Development (major planning applications);
oTransport Planning (LTP, modelling, air quality, noise, LSTF bids
etc);
oTransport Projects (CPE, LEP etc);
oManagement/coordination of the Highways Capital Works
Programme.
Introduction
3
Casualty Reduction in North Yorkshire
Partnership History
•York & North Yorkshire Road Safety
Partnership;
•Founded in 2004;
•Well established partnership;
•‘Coalition of the willing’ coupled with
formal governance framework;
•Performance Reward Grant of £100k
p.a. for further three years of delivery
to higher risk groups;
•Exhibition vehicle and trailer.
4
Casualty Reduction in North Yorkshire
KSI casualties & economic outlook
5
Casualty Reduction in North Yorkshire
“The results…suggest that the larger-than-expected
fall in (US) road fatalities is partly a consequence of
the disproportional decreases in both rural and
leisure driving… The amount of rural driving is more
influenced by general economic conditions, with the
amount of leisure driving reflecting the price of
gasoline. “
- Dr M Sivak, University of Michigan (2009)
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Casualty Reduction in North Yorkshire
Recession and severe winters help explain road deaths drop, says DfT
“The economic downturn, falling traffic levels for the
last three years and continued reductions in free-
flow speeds have played a part in the sharp drop in
road fatalities.”
- Local Transport Today (October 2011)
7
• People travelling less and when they do – more slowly;
• As the economy improves, collision numbers will start to rise;
• Need to maintain the downward trend.
NYCC Budget
•So understanding data/making best use is key;
•Despite falling collision numbers, in North Yorkshire the cost of all reported
road accidents resulting in injury i.e. fatal, serious or slight, is in the region of
£180M p.a.(Based on figures for average value of prevention per reported road accident - DfT Reported Road Casualties Great
Britain 2012 Annual Report)
•Societal loss - economic output, health costs and pain, grief and suffering;
•Direct impact on North Yorkshire: emergency services, hospital admissions,A&E, social care etc; Public Health – local government function;
•NYCC Local Safety Scheme budget - £400K pa;
•Focus though on road repairs; significant revenue budgetary pressures; need
to explore new funding streams. Public Health/PCC?
8
Financial Situation
Mostly Bad
9
Source: Road Safety Analysis, National Signpost Report: 2013
10
• Cluster site accident numbers reducing; more difficult to target highest
potential accident savings; but
•External influences such as EuroRAP generating greater public and media
scrutiny of ‘dangerous roads’;
•Localism.
Challenges
11
Situation Audit•(LTP3 – “Manage, Maintain, Improve”) – NYCC;
•Falling casualty numbers BUT influenced by externalities:
•Global financial/funding crisis;
•Ever increasing expectations:
•Public;
•Media;
•Politicians (local, MPs);
•Amplified through prism of ‘Localism’?
•Local authorities’ - ad hoc? approach to route investigations:
•manual/visual interrogation of collision data (time consuming and prone to human error);
or
•political pressure – democratic; but
•but generally too reactive;
•No statistical analysis per se, based only on accident numbers;
•Little or no account taken of risk factors in scheme identification;
•Traditional lack of coordination with other data-sets – silo mentality?
•Basic before/after post scheme monitoring.
12
Current Guidance
• “Movement towards route-based studies, often embedding risk-based
assessments for targeted road user groups.”
• “Route identification should involve consideration of the accident rate on various
similar routes over several years.”
• “Multiple and complementary datasets are being used to analyse and
understand accident trends.”
• “Greater integration between engineering, enforcement and education.” 13
Current Guidance
“It is hard to imagine a time when the basic tools (single
site, route, area and mass action treatments) will no longer be
relevant, but that is not to say they could not be
improved or used more effectively, or that new tools
should not be used.
New tools that address the shortcomings of the basic
methods should be particularly interesting to road
safety practitioners and their managers.“
- Collision Prevention and Reduction Guidelines
CIHT (2007)
14
Risk
•The probability (chance/likelihood) of an accident
occurring:
•‘Objective’ risk: the risk calculated by system designers;
•‘Subjective’ risk: the risk perceived by the road user;
•Risk compensation or behavioural adaptation;
•How can risk be measured?
•Exposure – typically accidents per:
•100,000 population;
•Distance travelled;
•Unit length of road.
15
Analysis TechniquesTECHNIQUE ADVANTAGES DISADVANTAGES
Number of
accidents –
‘Frequency’
• A ranked list of sites can be developed
quickly and is easy to understand – cluster
sites;
• Sites with a large proportion of KSI
accidents can be quickly identified and the
largest accident cost savings targeted;
•Easy to understand.
• Accident numbers are falling and clusters
becoming more difficult to identify;
• No consideration of risk, eg two sites may
have same number of accidents but may carry
totally different levels of traffic;
•No acknowledgement of UTAR or RTM
effects;
•Risk of bias by selection.
Accident rate –
‘Exposure’
• Can be used to identify where the exposure
to the risk of an accident is greatest;
• Rates can be compared to national or
international average values for similar road
types, to find which roads require
investigation.
• Accurate traffic flow data needed. A costly
traffic model may be required for larger areas;
•Mvkm travelled? Road Length? Per head of
population?
Statistical spatial
analysis
• A statistical score is calculated for a road
link, and compared to all other links in the
study area, to identify statistically significant
links;
• Statistics can give greater meaning or
weight to other results.
• Methods and results not widely used or
understood.
16
•“Oh, people can come up with statistics to prove
anything. 14% of people know that.”
- Homer Simpson
17
Our Solution
NYCC’s term consultants were commissioned to:
• Provide a system and methodology to allow in-house analysis and
prioritisation of routes for treatment (whole population or split by road user
type);
•Significance testing;
•Confidence levels;
•Defensible methodology based on tried and tested techniques.
18
The process
1. Export required selection of
accidents from AccsMap.2. Assign accidents to road
links using MapInfo.
4. Use table to create thematic map in
MapInfo and display results graphically.3. Statistical calculation in Microsoft
Access. Ranked table of links is produced.
19
Output from the process
5. Final map produced from MapInfo.
20
Traditional accident plot Cluster sites
Motorcycle collisions:
‘A’ Class Roads
21
Motorcycle collisions:
‘A’ Class Roads
Traditional accident plot Spatial statistic output
22
Motorcycle collisions:
All Road Classes
Motorcycle collisions:
‘A’ Class Roads
23
•Hypothesis – accidents are rare, random, multifactorial
events – assume normally distributed (for individual
sites – Poisson distribution) – no pattern;
• Gi* Statistic – measure of significance. Attaches a z-
score (measure of standard deviation) to each feature
(eg road link) and p-value (probability). Higher the z-
score (higher than you would expect if randomly
distributed) and the lower the p-value (probability due to
random variation), the stronger the association between
links/collisions that are occuring;
• A z-score of >1.96 implies a 95% confidence level
that the results are statistically significant, ie where
resources should be targeted.
How System Works
24
Advantages
•Data led: informed analysis; uses tried and tested statistical methodology to
find links with greatest exposure to risk;
•Flexible: wide range of accident attributes can be tested e.g. motorcycles / wet
/ dark / older & younger drivers…;
• Link to area wide analysis – IMD, SOA, other demographic data sets etc;
•VFM: effective use of limited resources – focus on the worst links where
potential accident savings are greatest.
25
Advantages• Sharing of info with:
• Road Safety & Travel Awareness Team; and • 95 Alive road safety partners to initiate a joint approach in tackling worst routes for young
driver, older driver and motorcycle accidents etc;• Speed Management Protocol.
Education, Training & Publicity
Public HealthPolice and Crime Commissioner
Traffic Engineering
26
Advantages
• Can supply Highway Asset Management team with info on links with wet
accidents for comparison against SCRIM data.
Section highlighted in route analysis as
statistically significant for accidents in the wet.
Same section highlighted in SCRIM results as
falling below investigatory level.
27
•Results only as good as the imported accident data… ;
•Unable (at this stage) to make use of flow data, so can’t apply to accident
rates;
•Instability; subtle changes to key variables can produce differing outputs;
•Accidents must be plotted on/close to carriageway or may not be picked up in
MapInfo… ;
•Fairly user intensive; but in overall terms, use of inferential statistics generates
time savings – reduced need for detailed analysis and good BCR.
Weaknesses
28
1. Basic Before/After studies:
• Blunt tool;
• No account of UTAR or RTM effects;
2. Comparative testing, eg K-test using control sites:
• Choice of control site important, eg similar to treated site/in close
proximity, must be un-treated..!
3. Chi-square distribution – test for significance; again, using control sites.
Appraisal techniques:Making Better Use of Data
29
What Next•Health and Social Care ‘Act’:
•Health and wellbeing duty;•Health and wellbeing Boards;
•Greater use of SPSS:•Trend forecasting;•Significance testing;•Other distributions - Poisson? Negative binomial? To identify changes in risk;
•Further roll-out of route analysis capability and risk identification internally and with partners:
•Police and Crime Commissioner;•Speed Management Protocol – mobile camera site identification;•Sustainable Travel;•Through 95 Alive;•Localism Act – “ability to pay” V “evidenced based”?
• “I’ll show you mine if you show me yours”;•Openness (internally and externally);
•Be receptive to constructive criticism and new ideas. 30
"Anyone who has never made a mistake has never
tried anything new.”
- Albert Einstein
… and take risks..!
31
