Monitoring innovation –

The DfT funded pothole spotter

Dr David Greenfield, SOENECS Ltd

Efficient and collaborative smart city tech

Introduction

The Department for Transport announced on Friday 13 January 2017 a trial with Thurrock, Wiltshire and York Councils and private sector SMEs, Gaist and SOENECS, to use HD Cameras on refuse collection vehicles, buses and bikes.

Gaist is an innovative technical consultancy that provides a range of services with a specialty in developing collaborative technologies, particularly in the highway asset management sector.

Gaist provide consultancy services and a range of services from surveying to software development which allows us to build 'end to end' solutions for our customers.

A strategic research and development practice with a specialism in environmental policy, resource management and insight to international Circular Economy activities through the Ellen McArthur Foundation, Zero Waste Scotland, Institution of Civil Engineers and Walter Stahl.

The team at SOENECS’s have a background in national and local government efficiency and transformation

What is the Pothole-spotter trial?

The Trial Areas

Area characteristics

• Urban/rural mix

• Huge diversity in population

• Royal Opera House and Lakeside

shopping centre

• Potholes a major political issue

• Significant HGV traffic

• Significant deterioration and

maintenance challenges

Wiltshire Council

Area characteristics

• Small market towns and multiple

isolated communities

• Fourth largest council in the country

• Very rural area

• Significant agricultural traffic

• MOD vehicles cause interesting

challenges

• Heritage of Stonehenge and the

Whitehorse's

Area characteristics

• Compact historical city with

cobbles and historic wall

• High volumes of traffic

• City with the highest number of

cycle journeys in the country

• Embracing Smart City & Digital

• Cultural and tourist hub

Three distinct parts of the trail

Data collection

Data interpretation

Data usage

The York Fleet

The York Trial Areas

Stage 2: Data analysis

All images: GAIST & SOENECS

Chronological imagery captures change

Baseline

Camera

Survey

Aug 2016

RCV Camera

Survey

February

2017

All images: GAIST & SOENECS

Chronological imagery captures - York

All images: GAIST & SOENECS

MetadataRegno

DatacaptureKPI's

05/09/2017

06/09/2017

07/09/2017

08/09/2017

09/09/2017

10/09/2017

11/09/2017

12/09/2017

13/09/2017

14/09/2017

15/09/2017

16/09/2017

17/09/2017

18/09/2017

19/09/2017

20/09/2017

21/09/2017

22/09/2017

23/09/2017

24/09/2017

tue

wed

thu

fri

sat

sun

mon

tue

wed

thu

fri

sat

sun

mon

tue

wed

thu

fri

sat

sun

Camerarecording(hours) 12.8 13.2 13.0 12.0 12.5 12.3 12.3 12.3 13.1 2.9 13.3 12.2 10.2 13.0 0.0

Datarecorded(GB) 5.0 5.0 7.0 6.0 9.0 10.0 13.0 3.0 33.0 0.0 41 10 3.0 30.0 0.0

Dataremainaing(GB) 924.0 816.0 809.0 759.0 739.0 729.0 716.0 924.0 891.0 891.0 836 813 810.0 780.0

null(KM) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.0 0.0

null(mins) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.0 0.0

Notinareaofinterest:(KM) 66.6 75.0 60.3 99.4 120.4 116.3 114.4 99.2 101.8 0.0 117.4 75.1 97.1 104.6

Notinareaofinterest:(mins) 142.0 189.0 255.0 255.0 237.0 244.0 228.0 316.0 200.0 67.0 254 163 210.0 221.0

GaintooHigh:(KM) 164.5 166.3 158.3 147.3 178.6 180.7 184.5 150.8 226.7 0.1 183.7 245.7 163.4 234.2

GaintooHigh:(mins) 351.0 532.0 459.0 399.0 440.0 421.0 41.0 339.0 487.0 105.0 442 529 358.0 461.0

StartedSection:(KM) 0.3 2.6 3.1 2.6 5.0 2.9 6.5 1.4 20.2 19 6.1 0.8 22.9

StartedSection:(mins) 1.0 4.0 4.0 4.0 7.0 5.0 8.0 3.0 7.0 25 7 2.0 33.0

StoppedSection:(KM) 0.1 11.0 1.4 9.2 3.7 5.7 3.3 7.5 11.6 4.9 1.4 1.9 8.9

StoppedSection:(mins) 0.0 12.0 2.0 11.0 5.0 8.0 5.0 35.0 14.0 8 4 4.0 18.0

Weekcommencing

NODATA

NODATA

NODATA

NODATA

NODATA

BF62

UYO

• Deterioration models

• Monitoring of new materials

• Long term planning

• etc

How does Gaist monitor the Pothole-spotter Data over time

All images: GAIST

Part 3:

Data usage

How does Gaist manage the Pothole-spotter data?

All images: GAIST & SOENECS

I mages Capt ured on Vehicle

• Transfer red using Wi-Fi

• How much can we t ransfer?

Server in Depot

• Uploaded on evenings/ Aut hor i t y “off-peak”

Dat a Cent er

• Col lect ed and prepared for analysis

Condi t ion Assement

• Undert aken by t rained Highway assessors

Dat a Del ivery t o counci l for use

•Updat ed on a mont hly basis

Conclusions

The insights gained through the trial will be

shared with local authorities across the UK,

enabling you to improve your management

of road repairs and maintenance, if needed

and to predict where and how potholes will

form.

We want prevent expensive emergency

repairs and compensation pay-outs and

optimise the use of existing assets, whilst

increasing safety for all road users.

www.pothole-spotter.co.uk

@potholesspotter