This document outlines a plan for a smart city collaboration between a Business Improvement District and various partners to improve public space waste management using sensors, data analysis, and citizen reporting. The plan involves installing trash bin sensors to provide real-time fill level data, collecting micro-reports from an app, analyzing the data to predict waste levels and optimize routes, and establishing metrics to evaluate the results with goals of reducing labor/costs and improving cleanliness perceptions.

1. Sense,
Report,
Act,
and
Refine:
A
Smart
City
Collabora8on

2. • Rou8ne
opera8ons
• Surge
condi8ons
• Situa8onal
awareness
• Mul8-­‐objec8ve
resource
op8miza8on
• Service
Rou8ng
• Changing
uses
Management
Expecta8ons
• Clean
• Safe
• Walkable
• Reliable
opera8ons
• Special
events
• Livability
• Healthy
environment
• Fresh
food
• Web
sources
and
blogs
• Real8me
informa8on
(traffic,
weather,
transit,
…)
• Ci8zen
par8cipa8on
vs
Our
Challenge

3. The
relevant
Sustainable
DC
goals
to
this
project
include:
• Develop
a
Zero
Waste
plan
for
the
city
• Ban
polystyrene
from
the
city
• Decrease
all
citywide
waste
streams
• Increase
recycling
bins
in
public
realm
• Coordina8ng
a
city-­‐wide
educa8on
programs
The
Sustainable
DC
Plan,
developed
with
extensive
ci8zen
input,
established
2032
goals
and
ac8ons
designed
to
make
Washington
DC
the
most
sustainable
city
in
North
America.
The
2016
BID
management
goals
include:
• More
efficient
use
of
labor
and
equipment
• Ability
to
adapt
to
address
special
event
impacts
• Reducing
contamina8on
in
recycling
bins
• Enhancing
public
percep8ons
regarding
cleanliness
• Effec8ve
use
of
technology
and
data
How
to
Improve
Public
Space
Waste
Management
Our
Goals

4. Real-­‐8me
Trash
Situa8ons
from
Sensors
and
Micro-­‐Reports
Trash
Bin
Sensors
Data
Micro
Reports
from
Krumbs
Filter
Aggregate
Filter
Real-­‐Time
Trash
Fill
Level
Situa8on
in
EventShop

5. Predic8on
based
on
Events
History
Events
Data
Real-­‐Time
Trash
Fill
Level
Situa8on
5
32
Now
Predicted
Trash
Fill
Level
in
30
minutes
at
a
given
loca8on
45
95
30
minutes
10
30
40
70
90
100
20
0
20
40
60
80
100
120
7:30
8:00
8:30
9:00
9:30
10:00
10:30
Projected
Trash
Fill
Level
at
a
given
loca8on
based
on
Event
History
0
35
50
90
0
20
40
60
80
100
7:30
8:00
8:30
9:00
9:30
10:00
10:30
Real-­‐Time
Fill
Level
Situa8ons
at
a
given
loca8on
of
an
event

6. Situa8on
Recogni8on
Model
in
ES
OP
OP
OP
OP
OP
DS
DS
DS
Situa8on

7. DowntownDC
BID’s
plans
have
so
far
focused
on:
• Refining
routes
for
personnel
and
equipment
• Gaining
an
understanding
of
the
8ming
of
services
• Cataloging
major
events
and
ac8vi8es
• Mapping
all
public
space
elements
in
GIS
This
project
will
enable
the
BID
to
effec8vely
use
data,
technology
and
analy8cs
to:
Micro-­‐reports
Event
data
GIS
data
Route
informa8on
Sense,
Report
and
Refine
Our
Approach

8. Timeline
and
KPI’s
April
-­‐
June
2016
–
Install
sensors,
established
baselines,
develop
incen8ves,
and
determine
metrics.
Ini8al
prototype.
June
–
October
2016
Prototype
system
and
evalua8on
for
trash
management,
Evalua8on
for
scaling
efforts
and
incorpora8on
into
other
services
November
-­‐
March
2017:
Review
program
and
revise,
create
winter
services
plan,
develop
city-­‐wide
expansion
plan,
April
-­‐
June
2017
Scaled
effort
and
KPI
evalua8on
• Reduc8on
in
8me
spent
servicing
bins
• Par8cipa8on
in
Micro
Repor8ng
• Reduc8on
in
Recycling
Contamina8on
• Improved
percep8ons
of
downtown
cleanliness
• Enhanced
reputa8on
of
BID
services

9. Our
Team
ScoH
Pomeroy
Bill
Updike
Dr.
Dhananjay
Anand
Dr.
Eswaran
Subrahmanian
Dr.
Ramesh
Jain
Dr.
Asquith
Bailey
Marvin
Kau
DowntownDC
Business
Improvement
District
DC
Department
of
Energy
and
Environment
Na8onal
Ins8tute
for
Standards
and
Technologies
Carnegie
Melon
University/NIST
University
of
California
Irvine
Krumbs
Enevo