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This presentation contains information that is proprietary to ClickSoftware. Any copying, distribution, display, transmission or dissemination of the information contained in this presentation to third parties without the prior written consent of ClickSoftware is strictly prohibited. No part of this presentation may be reproduced, translated or transmitted in any form or by any means, electronic, optic or mechanical, including photocopying, recording, or any information storage or retrieval system without written prior permission from the owner of the copyright. ©2014 ClickSoftware Technologies Ltd. All rights reserved.

SMART CITY: OPERATED BY HUMANS FOR HUMANS

Israel Beniaminy

October 2015

THE OLD CITY

Data

Analysis &

Decision Action

THE DIGITAL CITY

Data

Analysis &

Decision Action

THE SMART CITY

Data

Analysis &

Decision Action

CASE STUDY 1

New York City Dept. of Sanitation

Normal times and mega storms

NYC Department of Sanitation (DSNY)

• 9,000 employees

• 2,230 collection trucks

• 450 mechanical street sweepers

• 275 specialized collection trucks

• 365 salt/sand spreaders

• 298 front end loaders, and

• 2,360 various other support vehicles

NORMAL OPERATIONS

• 53 dead

• Thousands of homes

• 250,000 vehicles

• Losses at least $18B

• Four million cubic yards of debris

HURRICANE SANDY (2012) IMPACT ON NYC

1. Documentation and damage assessment

2. Update headquarters for analysis and action: right personnel, equipment and vehicles

SURVEYING THE DAMAGE AFTER THE STORM

MAKING IT WORK

Connecting the information and actions of machines and people

Data

Analysis &

Decision Action

GATHERING DATA

ANALYSIS & DECISION

TAKING ACTION

How to know a machine is failing or going to fail

1. Equip it with sensors

2. Connect it to the network

3. Gather the sensor info

4. Learn

5. Analyze

6. Generate alerts

How to know what to do about it

1. Where is the machine?

2. What impact will its failure have?

3. What corrective action is required (time, resources, cost, …)?

4. What impact will this action have?

5. What resources are available (and when) for the action?

DECISION: THE NEED FOR CONTEXT

How to know a person needs service

Person calls and tells us, e.g.: a. Complaint about water service (possibly

not time-critical) b. 911 call (time-critical)

- Or - Monitors detect something, e.g.:

a. Networked health sensors (time-critical) b. Audio and video alerts in public places

(time-critical) c. Water leak (possibly not time-critical)

How to know what to do about it

1. Where is the person? 2. When is s/he available (if not time-

critical)? 3. How urgent is the service? 4. What action is required (time, resources,

cost, …)? 5. Where are the relevant crews (e.g.

medical, rescue, utilities)? 6. What are those crews doing (now, next

tasks)? When are they on and off shift? 7. Prioritization and optimization in the

context of other tasks

DECISION: PEOPLE ADD COMPLICATIONS

1. What safety steps are required? e.g. road blocks when repairing water pipes under the

street

2. What alternate services may be offered while service is down?

e.g. route around the roadblocks

3. What notifications are required? e.g. notify transportation dept., issue notices to the public on

web, mobile apps etc., handle collaboration with crews and citizens, …

4. Snowballing effect of the additional steps on affected depts and crews, citizens, resources, …

ACTION: YET MORE GLOBAL ISSUES

Humans …

1. … can and will complain

2. … don’t always see the whole picture

3. … are protected by rules and laws

4. … don’t like being told what to do – even if it’s for their own good

5. ... sometimes prefer not to share information

6.… are the top priority

HUMANS ARE MORE DIFFICULT THAN MACHINES

CASE STUDY 2

City of London

Santander Cycles

or “Boris bikes”

1. Over 10,000 cycles

2. 700 docking stations situated every 300 to 500 metres

3. ~400 cycles in maintenance at any time

4. Electric vans move cycles to stations where they are needed

OPERATIONAL CHALLENGE

THE SOLUTION

1. All the bikes are tagged and volumes per locations measures

2. Managing repair and redistribution tasks:

a. Optimized scheduling to create the pick up and drop off tasks, including scheduled and reactive maintenance

b. Mobile app for maintenance teams to report progress, plus provide a GIS view of where the van is within London at any time and how much stock it has

VISION: THE SMART CITY AS A MEDIATOR OF HUMAN AND DIGITAL COLLABORATION

Make existing services better

1. Know when the mechanisms are faulty and fix them before someone complains

2. Skip empty bins on the garbage truck’s route

Create new, better services

1. Go from fixed routes to dynamic routes using waste-level reports

2. Gather insight regarding where more bins might be needed

3. Improve recycling

IMPROVING THE OLD, CREATING THE NEW

Benefits:

1. Waste management trucks cause less road congestion and less emissions

2. Less waste on the street due to full cans or faulty mechanisms

Example: Smart Garbage Cans

Larger traffic volume expected before and after the event Change public transportation schedules

Higher public safety and health staffing requirements Change shift assignments, reconsider leave approvals

Even more people need to come in (e.g. restaurants) More effects on transportation, energy, water, …

Adjust routes (e.g. city bikes balancing, waste management) to match predicted need and predicted traffic

Publish the predictions so that malls, bars and restaurants can adjust their own plans

IMAGINE WHEN MORE AND MORE PIECES CONNECT EXAMPLE: LARGE SPORTS EVENT NEXT WEEK

1. Traffic, energy, water, sanitation, public safety, hospitality – any expected or unexpected event in one may impact any of the others

2. Increasingly, we know the schedule of people –both maintainers and consumers of city services

3.So we can have everything ready for everyone when they need it, as if by magic

What about privacy? Turns out this can be handled via interacting “agents” that don’t share private info

HOW FAR CAN IT GO?

25

TAKEAWAYS

TAKEAWAYS

Smart City Digital

Backbone

Assets

Sensors

People creating

data

People getting

data

People acting

on data

THE DIGITAL/SMART CITY AS MEDIATOR

Things to consider

1. It’s not enough to achieve overall efficiency Each participating human needs to receive benefits

2. Balance between: a. Sharing info for efficiency

b. Protecting info for privacy

3. The potential is huge!

THANK YOU!