ONS Local has been established by the Office for National Statistics (ONS) to support evidence-based decision-making at the local level. We aim to host insightful events that connect our users with exciting developments happening in subnational statistics and analysis at the ONS and across other organisations. In recent months, the ONS Data Science Campus has published data and insights on feasible travel at a hyperlocal level and rail schedule disruptions at every station in Great Britain. We are currently scoping the need for further work in this area, which may include topics such as reliability, cost, international comparability, and the type and quality of services accessible. In this webinar, we present existing work and gathered user needs from local institutions for data and statistics in this area. If you have any questions, please contact ons.local@ons.gov.uk.

1. Spotlight on transport
ONS Data Science Campus
Levelling Up squad
01 March 2023
Levelling Up

2. Slides
• Introducing the Levelling Up team /
overview of the Campus' work
• Recent work in transport
• Hyperlocal public transport availability
• Visualising rail schedules
• Level and punctuality of bus service
• Q&A
Levelling Up

3. Who's who?
Andy Banks Lead Data Scientist
Dan Coles Apprentice Data Scientist
Edward Jackson Data Scientist
Ethan Moss Data Scientist
Iva Špakulová Senior Data Scientist
Maddy Lunskey Delivery Manager
Martin Wood Data Scientist
Niovi Karathodorou Senior Data Scientist
Rich Leyshon Senior Data Scientist
Sam Stock Graduate Data Scientist
Sergio Recio Rodriguez Graduate Data Scientist
Levelling Up

4. Levelling Up
Supporting UK wide data initiatives
Supporting public monitoring and
evaluation of Levelling Up missions /
investment
Improve quality, timeliness or
granularity of official statistics, to
better inform the public about
social and economic matters
Supporting ONS subnational stats,
and ONS local teams

5. Levelling Up
Assisting central government
(DLUHC)
Assisting local government /
combined authorities
Insight for the development and
evaluation of public policy

6. Levelling Up
Assisting central government
(DLUHC)
Assisting local government /
combined authorities
Build data and data science
capability, across the UK public
sector and internationally

7. Using open data to understand hyperlocal
differences in UK public transport availability
View the project blog here
For further information on this project, please e-
mail the team: ethan.moss@ons.gov.uk &
iva.spakulova@ons.gov.uk
To discuss the wider work of the Data Science
Campus and collaboration opportunities, please
e-mail: DSC.Projects@ons.gov.uk
Levelling Up

8. Levelling Up
Measuring Public Transport Availability
Read more here
Experimental
Investigate public transport
accessibility variation across UK,
at (granular) subnational level
and use consistent method
across all 4 UK countries
Motivation
Feed UK-wide public transport
timetable and map data into Open
Trip Planner (OTP)
Method
Generates travel isochrones for
all output areas (or equivalent)
across a range of travel times
Outputs

9. Levelling Up
Blue
markers
show
location
of Job
Centres
Possible Further Analyses
Q: How does accessibility to
services/amenities vary across a
region?
A: ‘Count’ / Score no. of reachable
services by region
e.g., Access to job centres in the North West
Access to Services
Q: How many jobs are reachable
from a point of interest?
A: Derive job counts per origin to
compare job opportunities.
e.g., Reachable jobs (by SIC code) near
reopening train stations in the North East
Job Opportunities
Experimental
Read more here

10. Experimental
Read more here
Further Remarks
• Data Science Campus Blog Post – further methods + insights.
• Data available on ONS Geoportal – Isochrones via build download or API.
• Poster presented at ONS Subnational conference in January 2023.
Publications
• Share work wider and engage with further interested stakeholders.
• Is an improved methodology feasible?
Next Steps
Levelling Up

11. Visualising rail schedules using open data
View the project blog here
For further information on this project, please e-
mail the team: edward.jackson@ons.gov.uk,
ethan.moss@ons.gov.uk &
martin.wood@ons.gov.uk
To discuss the wider work of the Data Science
Campus and collaboration opportunities, please
e-mail: DSC.Projects@ons.gov.uk
Levelling Up

12. Levelling Up
Context
• Rail Delivery Group publishes rail schedules twice daily. Service stop data
for every station in Great Britain are available.
• Schedule updates are collected from train operating companies, capturing
the picture for all services:
• permanent – originally timetabled
• temporary – new/replacement services
• cancelled – originally timetabled but known not to be running
• amendments – originally timetabled but now scheduled with changes
• All permanent schedules unaffected by other schedule types are assumed
still to be running.
Read more here

13. Levelling Up
Visualisations
• In this example, we see the
percentage of timetabled service
stops scheduled to run on a given day
for every station in the south England
and Wales.
• We use open-source visualisation
tools and Python.
• Interactive visuals allowed Cabinet
Office to better understand localised
impacts of anticipated events:
emergency timetables, engineering
works, industrial action etc.
Read more here

14. Better understanding local bus service levels
and punctuality
This work is currently in an exploratory phase
It looks possible to generate several metrics
described in the Levelling Up missions’ technical
annex (public transport connectivity) directly.
For further information on this project, please e-
mail the team: edward.jackson@ons.gov.uk &
martin.wood@ons.gov.uk
To discuss the wider work of the Data Science
Campus and collaboration opportunities, please
e-mail: DSC.Projects@ons.gov.uk
Levelling Up

15. Context, metrics & insights
Levelling Up
Percentage of frequent and non-
frequent bus services running on
time*
Average excess waiting time for
frequent bus services*
Example
82% of services from Freeman
Hospital (to Denton Burn) departed
'on-time'.
Example
On Route Y, the average excess
waiting time (over and above the 5
minutes delay allowed) was 1.4 mins.
* taken directly from Levelling Up technical annex – Public Transport Connectivity mission
How does punctuality compare for
Service Z between 7.30-9.30am1 and
1.30-3.30pm2?
Example
On average, buses running Service Z
arrive at destination X 7 minutes later
in time window 1 than in window 2.
How do different services stopping at
Location T compare?
Example
On average, services from Whitby
arrive 2 minutes behind schedule
whereas services from Saltburn are
16 minutes delayed.
The Bus Open Data Service (BODS) provides real-time bus
location data for all services in England. In addition, timetables for
all regions are refreshed daily.
We use the BODS API to ingest real-time data; this is a rich data
source which allows for deep analysis.

16. Levelling Up
Service stops &
punctuality
All service stops (individual buses
servicing each physical stop) are flagged
as acceptable (1 minute early <> 5
minutes late) or not.
We calculate % of arrivals that are
“punctual” at every stop (plotted).
In these examples, we use one week’s
data, covering 9am – 11am.
Punctuality and frequency are captured;
these can be aggregated flexibly, e.g. on
specific routes, destinations, times of day
etc.

17. Levelling Up
Service stops &
punctuality
In another example, we
calculate the average times
between subsequent buses for
all bus stops within areas of
interest.
Areas of concern would have
high average time, low
punctuality.
Average time (m) Mean punctuality (%) Locale
30 33.56 Ponteland
29 47.74 Corbridge
5 57.21 Arthurs Hill
7 60.98 Byker
28 64.15 Beamish
33 64.66 Scotswood Road
16 64.96 Whickham
9 67.57 Walker
13 69.19 Elswick

18. Levelling Up
Bus service
proximity to
Teesworks
Taking a specific destination – a new
wind turbine factory at Teesside -
stops for all routes with services that
stop within 1 km of the site can be
analysed:
- Punctuality: 67 %
- Avg time between buses: 41 minutes
- Routes: 64, X3A
- IMD 2019 decile layer in green
(Inset: Continuation of coastal bus route
past Redcar)
Brown < Red < Blue, punctuality
Size proportional to number services

19. Levelling Up
Average service
stop punctuality
by LSOA
All service stops (individual
buses servicing each physical
stop) within each LSOA
The fraction of service stops
being acceptably punctual gives
a sense of disruption across the
region
Why this is useful:
overview allows the public and
local institutions to understand
the travel reliability in their area
LSOAs only where sufficient service stop activity exists

20. Levelling Up
Hex-bin
reliability map
This aggregation method allows for
clearer visualisation of service
locations and levels: service stops
within a hexagon
Why this is useful:
local institutions and the public can
compare location coverage and
reliability more easily

21. Q&A
We welcome your questions, suggestions and
challenges at this point.
We are keen to understand the issues affecting
areas across the UK. Perhaps there are
opportunities for us to collaborate with you.
For further information on today's projects,
please e-mail the team.
To discuss the wider work of the Data Science
Campus and collaboration opportunities, please
e-mail: DSC.Projects@ons.gov.uk
Levelling Up