Smart Cities UK 2018- Stream 1 Data

Chair's Welcome - Speaker:
Brian Bishop CEO, Data
Performance Consultancy
Ltd

How A.I. can influence UK
Cities and Towns - Speaker:
Vishal Chatrath, CEO and
co-founder, PROWLER.io

© PROWLER.io 2018 – www.prowler.io
Vishal Chatrath
Founder, CEO

In AI/machine learning
perception/classification
is considered solved

The next big challenge: decision-making AI
AI that can make transparent & reliable decisions in
complex, dynamic, uncertain & high-dimensional
environments in real-time

Decision-support tools
Complexity timeline
inputcomplexity
80s 90s 2000s 10s70s60s1950s
expert
systems

Deep Neural Nets: effective,
automated perception
DNNs are, a very efficient tool for recognition / perception
tasks:
● Are not data efficient. Use blind repetitive speed
instead of probability
● Can't handle complex behaviour with high
dimensionality (bounded environments)
● Employ a single, narrow learning technique
● Can’t handle uncertainty in the environment
Tool for classification
/perception tasks
PROWLER.io: adaptive, data efficient
AI systems for decision-making
PROWLER.io has created a transparent, dynamic decision
system that:
● Can learn and adapt as the environment evolves
● Can handle complex behaviour with high dimensionality
● Can work in a multi-agent environment
● Can handle uncertainty in the environment
Principled AI

Probabilistic modelling
Allows us to build a continuous probabilistic model of a
environment. Models can manage high dimensional factors influencing
the environment with a measure of uncertainty.
Reinforcement learning
Enables agents to plan and evolve strategies in complex goal
settings. The behaviour of the world is typically not deterministic, but
probabilistic. So RL needs probabilistic approaches to make good
decisions.
Multi-agent systems
Realistic scenarios have dynamic environments. Environments
dynamically change as agents react to each other’s actions. An
intelligent agent must model the behaviour of other agents in order to
understand their intentions and why they react the way they do.
Our unique combination
Three distinct branches of maths in one platform

● Every decision-making node is an autonomous AI agent.
● Each agent can sense and dynamically adapt to high-dimensional
inputs with 1000s of parameters.
● Inputs can be information about the environment or incentives, which
are used to automatically tune behaviour and interaction.
● Any actions by agents in the environment update the model of the
environment dynamically.
● Mechanism Design (Inverse Game Theory) enables the design of
incentives that steer agents toward desirable outcomes, producing
overall outcomes with the desired risk characteristics.
How do we do it?
Allow me to explain...

Platformcomponents
GamesFinance Logistics
Tunable AI
Bounded rationality
GP State Space Model
Resource allocation
Demand matching
Mechanism design
Hierarchical RL
Risk-sensitive RL
Spatio-temporal model
Probabilistic
modelling
Reinforcement
learning
Multi-agent
systems
Autonomous
systems
Decision-support
simulations
Deep GP
Model-based RL
Platform
Deployable Decision-Making System
Cloud Learning System
Our platform provides an AI toolbox
Decision-making across multiple industries

Demand
moving set of objects
to be served by agents
Supply
A fleet of agents
Supply & Demand
Distributions over space
Objective:
Minimise cost - difference between
distributions over time horizon and movement
penalty.

Pickup demand model
Taxis in Porto

Real demand
Spatio-temporal model

How do we use these predictions?

Resource allocation
Modelling the future
● Probabilistic models predict
the demand for tomorrow
● A series of possible tomorrows are
simulated (“Monte Carlo” samples)
We can evaluate the effect of taking decisions
● For each possible “tomorrow”,
we solve the resource allocation
problem in simulation
● See the effects of decisions
(e.g. number of trucks)

Resource allocation
Heterogeneous resources & tasks
Different
assignment
tasks

…
t2t1 tn
Long-term plans
Dynamic tasks & resources

Demand matching
& resource allocation
Dynamic allocation with
varying resource due to
drop-off

Conceptual structure of a system
Transportation example
Model of environment
● Form a continuous probabilistic
model of city environment
● Models can manage high complexity
of factors influencing the
environment
Training of agents
● Set targets at agent or system level
● Decide if agents collaborate and if they
impact environment with actions
● Find optimal strategies for agents
dependent on current and predicted
states of system
● One or many agents
Build a well coordinated
team of experts to
support decision making
● Operate in the real world using the
agents as decision support
or autonomously given the
latest state

model of market environment
environment
Training of agents
states of system
team of experts to
latest state
Financial example

model of game environment
environment
Training of agents
states of system
team of experts to
latest state
Games example

PROWLER.io
Adaptive, data efficient, highly complex applications
Application specific
Dataefficiency
Application complexity

• Our principled AI can make reliable decisions in complex, dynamic & high-
dimensional environments under uncertainty in real-time.
• We have moved AI from being a black box to something that is transparent reliable,
& controllable.
This is the world’s first principled AI platform
that generalises decision-making
PROWLER.io
Summary

Financing:
• Seed round of £1.5M closed in Aug 2016
• Series A investment round of £10M closed in Jul 2017
Leading independent AI company:
• 20 papers in 12 months by the team to date
• UK, Cambridge based; 60+ full time staff including 29 Ph.Ds
• World leading researchers in the areas of Probabilistic Modelling, Game
Theory and Reinforcement Learning have been attracted to join PROWLER.io’s journey
• Attracting talent from around the world - 24 nationalities and counting
Commercial traction across key verticals:
• Financial institutions, logistics, smart-cities, games
PROWLER.io
Today

The power of Big Data to
improve social and economic
outcomes - Speaker: George
Johnston, Founder, Nitrous

The power of big data to improve social and
economic outcomes
1 February 2018

2020: ⅓ of
procurement
from SMEs
Public sector innovation
2025: £20
billion market
Talent,
capital and
policy
B2G2C
Consumers =
citizens

Sustainable innovation
Government
Scale-ups
Data
Industry

Civtech
Service and data innovation
Service delivery improvements
Govtech
Back office, procurement data
innovation
Efficiencies and cost savings
Govtech and civtech

B2G2C
G = Government
C = Citizens
Mutually beneficial partnerships that develop data

Data driven approach
Open data platform
Platform
products
Central government
Local government
Private sector
Data scientists
SMEs
Talent
CityMapper of X?

www.nitrous.london george@nitrous.london

Mapping - 5G deployment:
think global, act local -
Speakers: Ordnance Survey,
Met Office and the University
of Surrey

Research and real world
planning for 5G networks
Think global act local
• FI FT H GENERAT I O N MOBI LE COMMU NI C AT I ON S
Richard Woodling
February 2018
© Ordnance Survey 2018

Today…
• Why 5G?
• The Bournemouth experience
• Building the 3D digital model
• Building a propagation model
• Interactive planning tool
• Real world planning

Why 5G?
“By 2023, over 30 billion connected devices1 are forecast, of which around
20 billion will be related to the IoT. Connected IoT devices include
connected cars, machines, meters, sensors, point-of-sale terminals,
consumer electronics2 and wearables. Between 2017 and 2023, connected
IoT devices are expected to increase at a CAGR of 19 percent, driven by
new use cases and affordability.” Ericsson IoT predictions
• 1,000x increase in capacity
• Support for 100+ billion
connections
• Up to 10Gbit/s speeds
• Below 1ms latency

The DCMS Project ask of geospatial
• To commission research into
geospatial considerations for
network planning:
• Brings together appropriate
geospatial and other data
for the siting of high
frequency (mmWave) radio
infrastructure
• Identifies the location of
optimum sites for radio
equipment to provide
efficient and effective
capacity coverage
Considerations:
• Frequency ranges above 6GHz (
specifically the IMT-2020 bands,
including 26GHz, 32GHz, 39GHz
and 60GHz)
• Vegetation, buildings, walls,
weather, other obstacles
• Process of validating the output
data
and verification in the field
• Implications, including those to
other countries

3D digital model

• Three detailed study zones
– dense urban
– suburban
– rural
• Rich civic data
• Rich 3D data
Bournemouth study zone

Point cloud
Full colour mesh
Terrain model
Mesh
Building a 3D mesh

Additional assets
Street furniture
o2000 lamp posts
o4028 sign posts
o2926 traffic lights
o292 park benches
o1987 bus shelters
Telecoms features
o1152 telephone boxes
o262 internet fibre
boxes
o365 grey boxes
o274 green boxes
o349 telephone
cabinets
o 30+ data sets
o 18+ tools
o 5.6Bn points in the cloud
o 2,700 Km of underground assets;
high and low voltage cable routes,
ducting and CCTV networks
o 2,500 Aerial images
o 87 bridge extent polygons

Location: positional accuracy
• Real world 3D point
cloud
• Optimised
infrastructure
• Efficient and effective
coverage
Supplied
location
Actual
location
OS data resolution 10cm

Propagation
algorithms
(aka ‘the maths’!) 𝑌𝑛
Weather
Frequency
Coverage
Capacity
Channel Quality
Signal interference noise ratio
Path loss parameters
Diffraction

Modelling matters for planning
“I can simply deploy loads of antennas (sites) – can’t I?”
• Signal to interference and noise ratio (SINR)
• Knife and shield diffraction
• Resolving received power
• Network initialisation (Base stations deployment)
• Simulation and dynamic evaluation
• Mapping of SINR to Capacity using Modulation /
Coding lookup tables
• Coverage maps

5GIC – Measurements for the built
and natural environment
• Shield & edge diffraction
• Vegetation loss
• Scattering
• Material characteristics
• Field validation

Weather impact modelling
T+0 UKPP @ 2 km T+0 UKPP @ 100 m
Stephan Havemann, Robert Scovell, Jean-Claude Thelin, Dave Jones
1. Select significant weather events from the archive
2. Downscale to “5G resolution” (100m)
3. Calculate the attenuation
Extinction
@ 26 32 42 71 84 GHz

The interactive
demonstrator tool
• Demonstrates how built and natural environment
including weather, impacts signal propagation
• Geared to planners and business users
• Built in core features for planners
• Incorporate user parameters
• Scalable

Planning tool

Weather impact
Hydrometeors (rain and sleet)

Planning in Bournemouth

Planning in the real world
(what matters?)
• Where?
• What’s the demand/service (use case)?
• Dynamic considerations
• Location geo-spatial challenges

Candidate locations for mmwave
• Urban streets and pedestrian
areas
• Transport hubs
• Retail complexes
• Large event sites and stadia
• Business districts
• Dense residential areas
• Small town environments
• Road and Rail networks

The role of Geo-spatial for planning
Example - Urban Streets and pedestrian areas

Real world challenges
Road signs
Trees
Difficult locations
Challenging surfaces
Busy traffic routes

What do I capture and how?
© Ordnance Survey 2018© Ordnance Survey 2018
• Area of coverage
• Scheduling (when)
• Resolution (spec.)
• Budget / cost
• Time to survey (how long)
• Access constraints
10cm 40cmor

Additional data sets
• Footfall in a shopping centre
• Individual retail unit customer
counts
• Car park usage
• Transport timetables and
ticketing information
• Major events planning
• Ticketing for events
• Professional surveys of people
movement such as that provided
by Health and Safety Labs (HSL)
• Others as available
• Currency - when was it last
updated and what is the update
period and method?
• Provenance – Can you trust the
source of this data? Who
created it and how?
• Accuracy – Does it provide
figures commensurate with the
intended use?

Conclusions
• A detailed 3D geospatial model is essential for accurate
modelling of coverage and capacity
• Multiple data sources are required, and much baseline data exists
• Quality of data can be variable
• Maintaining a 3D neutrally held geospatial model is non-trivial
• Use cases need to be clearly articulated
• Planning tools are essential
Detail matters – A global challenge needing a local view

Where next for OS?
• Exploring spatial enhancements to mapping
• Refining planning tool capability
• Working closely with Central and Local Government

Thank you
Questions
Richard Woodling
richard.woodling@os.uk

Intelligent real-time Data
for Transport - Speaker:
Cleverciti Systems

CHICAGO - MUNICH - NEUKIRCH - REDWOOD CITY
Intelligent Data for
Clever Parking Solutions in Cities
Chris Wortley
Director Sales Airports
1st. February 2018
Smart Cities UK

copyright Cleverciti Systems 2018
High search traffic
Air pollution
Threat by EU penalty fees
Non-payment for parking
Inefficient enforcement
Stressed cardrivers
Challenges for Cities

New data
& knowledge
Convenient
Parking
Traffic safety
& efficient
enforcement
Less costs,
More revenue
Reduction of
air pollution
Cleverciti‘s solutions provide …

Intelligent real-time parking
and traffic flow management
Focusing on outdoor parking
Highly efficient and patented
technology
Overhead sensors scanning
10 to 100 spaces per sensor
Easy-to-install system e.g.
at existing lamp posts
Full-service end-to-end solutions
or integration into existing data
platforms
Cleverciti‘s products

Detection

Installation

2. Navigation on-site by dynamic digital signage
Digital Signage

Using new or existing Apps
Optional: Reservation
Optional: Carfinder
1. Navigation from home to a free parking space
closest to your preferred destination
Mobile App

Circ 360°
Advertising
Navigation
Commerce
Messaging
Security

Winner Digital Innovation Award
DIGICON – Digital World Conference
Best Munich Startup
Winner Fast Mobility Category - Bits&Pretzels
Winner Innovation Award 'Deloitte Fast 50‘
Finalist Energy Awards 2016
Finalist Best New Parking Product
Smart Mobility Gulf Traffic Award, Dubai
Innovation Trail Parkex 2017
German Accelerator Program 2017
Ecosummit Award Berlin 2017 – Bronze
Winner Digital Cities - EIT Digital Award 2017
Cleverciti – Prices and Awards

30 Countries. 40 Installations.
Bad Hersfeld · London Westminster · Dubai · Dublin · Cologne · Munich · Berlin
Rotterdam · Lubljana · Vancouver · Caloundra, Australia · Chicago · Vienna Airport
Fuerth · Moscow · Florence · Boston · Gent · Weiden · Eindhoven · Geneva
Everywhere

Welcome...
www.cleverciti.com
Cleverciti System GmbH
Hofmannstrasse 54
81379 Munich, Germany
Cleverciti Systems Corp.
100 S Saunders 1st floor
Lake Forest,
IL 60045, USA
CLEVERCITI is a registered trademark
of CLEVERCITI SYSTEMS GMBH
Cleverciti Sensor Technology & Systems,
Cleverciti App Features, Cleverciti Circ CMS 360° are
internationally Patent Pending

Open data for Smart Cities
Speaker: Fanny
Goldschmidt,
OpenDataSoft

Smart Cities UK 2018- Stream 1 Data

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