Vehicle mobility is evolving, from traditional rental and fleet services, to car sharing, ride hailing, and future driverless services. Mobility providers need an agile, scalable, digital platform to manage all aspects of their fleet and its usage. In this session, Avis Budget Group (ABG) and Slalom walk through their serverless mobility platform using the AWS connected vehicle reference architecture, Amazon SageMaker, Amazon Kinesis Data Analytics, and AWS Lambda. Learn the practical application of using AWS IoT to connect vehicles and Amazon SageMaker to apply machine learning to uncover insights for use cases, including vehicle inventory, shuttling efficiency, driver behavior, and vehicle trajectory analysis to identify fraudulent vehicle usage. We dive deep into the overall solution and services mentioned above, as well as the operations dashboard ABG created with Uber's open source framework, deck.gl.

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Easy Rider: How ML, Serverless, and IoT Drive Mobility as a Service
A M T 3 0 2
Indra(Neel) Mitra
Solutions Architect
AWS
Christopher Cerruto
Vice President
Avis Budget Group
Saken Kulkarni
ML / AI Practice Lead
Slalom

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Intro
Indra(Neel) Mitra
Solutions Architect
Amazon Web Services
Outside of work

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Agenda
1. Amazon in Automobiles
2. AWS Connected Vehicle Solution Overview
a. Greenfield Environments
b. Brownfield Environments
3. Avis Budget Group
a. Our Vision
b. Current State & Challenges
c. Future State on AWS
4. Slalom Consulting
a. Use case Deep Dive
b. Architecture
c. Demo
5. Q&A

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Amazon’s approach to connected vehicles

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AWS Connected Vehicle Solution Technology Stack
Platform APIs
Application services
Data services
Ingestion (AWS IoT)
Amazon API Gateway
AWS Lambda Amazon SNS Amazon SQS Amazon Cognito
Amazon S3 Amazon DynamoDB Amazon Kinesis Amazon ML
Device Gateway Registry Rules Engine Message Broker
Protocols
Connectivity
Vehicle
HTTP MQTT MQTT + WebSockets
Mobile Carrier
Telemetry Hardware AWS Greengrass x.509 certificates Automotive Grade Linux

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AWS Connected Vehicle Solution Benefits

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How do you get started?
Start prototyping with a ‘well architected’ architecture in
minutes using the AWS Connected Vehicle Quick Start
Deploy into your own AWS account
Customize use cases or develop your own
Remove or add other AWS and partner services
Easily connect your own data or your own devices
https://aws.amazon.com/answers/iot/connected-vehicle-solution/

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AWS Connected Vehicle Solution Reference Architecture
Authentication
Connected Vehicle Data Ingestion
Device Gateway Rules Engine
AWS IoT
Connected Vehicles
Certificate
JSON
Just-in-Time Registration
Rule 1
Message Topic Action
OBD Telematics connectedcar/telemetry/<VIN> Publish
Vehicle Trip Info connectedcar/trip/<VIN> Publish
Diagnostic Trouble Code connectedcar/dtc/<VIN> Publish
Anomaly Alert connectedcar/alert/<VIN>/anomaly Subscribe
DTC Alert connectedcar/alert/<VIN>/dtc Subscribe
Driver Score Alert connectedcar/alert/<VIN>/driverscore Subscribe
Vehicle Provisioning connectedcar/vehicle/<VIN>/provision Publish
Sample Message Payload
{
"name": "speed", ”
value": 47.4,
"vin": "1NXBR32E84Z995078",
"trip_id": "799fc110-fee2-43b2-a6ed-a504fa77931a",
"timestamp": "2018-02-15 18:50:18.000000000"
}

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AWS Connected Vehicle Solution Reference Architecture
Rule 2
Raw Data
Telemetry
Data
JSON
Manufacturers Dealers Consumers
Personas
Data Visualization
Telemetry Data
MQTT (near-real-time)
Reference Data Stores
DTC Reference Table Owner Data Table Vehicle Health
Report Table
Authentication
Connected Vehicle Data Ingestion
Device Gateway Rules Engine
AWS IoT
Connected Vehicles
Anomalous Data
Anomaly Detection & Alerting
Anomaly Table
User Pool
Logs
Web UI Mobile App
Microservices
DTC Detection & Alerting
Rule 5
DTC Data
JSON
DTC Table
Location-based Services
Geolocation Data
JSON
Rule 6
Marketing Table
Driver Safety Score Processing
Rule 4
Aggregated Telemetry Data
[ Ignition Status ]
JSON
Trip Aggregation Processing
Rule 3
Aggregated Telemetry Data
JSON
Trip Aggregation Table
Certificate
JSON
Just-in-Time Registration
Rule 1

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Data Ingestion
REST ENDPOINTS
WEBSOCKETS
LMPP
OTHER PROTOCOLSAmazon EC2 Amazon Elastic
Container Service
Elastic Load
Balancing
Authentication Device Gateway Rules Engine
AWS IoT
Connected Vehicle Data Ingestion

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Data Storage & Analysis
Raw Data
Reference Data Stores
DTC Reference Table Owner Data Table Vehicle Health
Report Table
Anomalous Data
Anomaly Detection & Alerting
Anomaly Table
DTC Detection & Alerting
DTC Table
Location-based Services
Marketing Table
Driver Safety Score Processing
Trip Aggregation Processing
Trip Aggregation Table
NOSQL
TSDB
Mainframe
APIs
Applications

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Data Consumption
Manufacturers Dealers Consumers
Personas
Data Visualization
User Pool
Logs
Web UI Mobile App
Microservices
Raw Data
Data analysts
Data scientists
Business users
Schemaless
Amazon ES
Direct Query
Amazon Athena
Semi/Unstructured
Amazon EMR
Enterprise Apps
Oracle, Teradata, SQL Server,
MySQL, et
Data Warehouse
Amazon Redshift
Automation / events
Engagement platforms
Data ServingData Lake
Machine Learning
Amazon SageMaker
ETL
AWS Glue
Business Intelligence
Amazon QuickSight
Data Warehouse
Amazon Redshift Spectrum
Enterprise Apps
Amazon Aurora

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Summary

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Learn more
Thurs, November 29
AMT303 – Deep Dive into the AWS Connected Vehicle Reference Solution
12:15PM – 2:30PM | Mirage, Grand Ballroom G
AMT304 – Building Volkswagen Group’s Digital Ecosystem
1:00PM – 2:00PM | MGM, Level 1, Grand Ballroom 111
Check the re:Invent website for videos of these 2018 Automotive sessions
AMT301 – Alexa, Where’s My Car? A Test Drive of the AWS Connected Vehicle Solution
AMT305 – Building BMW Group’s Digital Customer Engagement Platform
AMT201 – Automotive Leadership Session, Paving the Way for the Future of the Automotive Industry

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Intro
Christopher Cerruto
Vice President, Global Architecture and Analytics
Avis Budget Group
Things I love…

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180
COUNTRIES
WORLDWIDE
650k
GLOBAL
FLEET
$9B
ANNUAL
REVENUE
30k
EMPLOYEES
WORLDWIDE
72
YEARS IN
BUSINESS

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22 | AVIS BUDGET GROUP NEW MOBILITY 101
OUR VISION
“We envision a world
where mobility is
completely connected,
integrated and on-
demand.”

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Reinventing
Rental
Digitizing
the Business
Developing
New Models
OUR APPROACH

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BUSINESS INTELLIGENCE ARCHITECTURE
OUR NEXT-GENERATION MOBILITY PLATFORM
Functional View
DATAINGESTIONARCHITECTURE
DATA LAKE
API, SERVICES &
ORCHESTRATION
DEVELOPMENT
PLATFORM
TELEMATICS
DATA/EVENT
STREAMS
3rd PARTY
DATA/EVENT
STREAMS
INTERNAL
DATA
SOURCES
EXTERNAL
DATA
SOURCES
OPERATIONAL
DATA STORES
WEB
APPLICATIONS
MOBILE
APPLICATIONS
OUTBOUND EVENT
STREAMS
DATA PUBLISHING
ARCHITECTURE
DATA QUERYING
ON-DEMAND DATA
EXTRACTION
DATA CACHES
ENTERPRISE DATA HUB
DEVELOPER API
PORTAL
3rd PARTY DATA
CONSUMERS
3rd PARTY API
CONSUMERS
DATA VISUALIZATION
BUSINESS RULES AND
COMPLEX EVENT
PROCESSING ENGINE
DATA WAREHOUSE
ANALYSIS AND
MODELING
SYSTEMS
REPORTING INSIGHTS
EXPLORATORY
ANALYSIS
MODELS
MACHINE
LEARNING
MODELS
API CATALOG
CONSENT/PRIVACY
DATA ARCHITECTURE BUSINESS SERVICES ARCHITECTURE
ANALYTICS & DATA SCIENCE ARCHITECTURE
CLOUD INFRASTRUCTURE, CODE AND RESOURCE ORCHESTRATION ARCHITECTURE

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ABG CURRENT STATE OF THE WORLD
Pain Points
o Anchored to Legacy
Systems
o Rigid Infrastructure
o 3rd Party Supported
Data Center and
Ops
o “Big Data” Problem
• Size: Breadth
and Depth
• Normalization
• Pace of Change
o Limited/No Event
Handling and Event
Publishing
o No Real-Time
Operational Analytics
o Performance and
Scaling
o Time to Market

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INITIAL
CONNECTED CAR
ARCHITECTURE INGESTION
SERVICES
RABBIT MQ
CSS
WebLogic
(Java)
JMS
Amazon EMR
CONNECTED CAR MANAGEMENT PLATFORM BATCH ANALYTICS PLATFORM
FRONT OFFICE
PROGRAMS
SERVICES
PORTAL
TELEMATICS
DATA
(limited, based
on geofence
event)
LIMITED DATA
(daily)
LIMITED DATA
(daily)
Oracle IMS DB2
BACK OFFICE
PROGRAMS
IBM MQ
Amazon
S3CASSANDRA
VEHICLE
SERVICES
OTHER
SERVICES
OMEGA PLATFORM (SOA) SYSTEM Z
TCU (UTP)
TCU (Z01)
Z01
Platform
UTP
Platform
…
COAP/UDP
LMPP
REST
WebSocket
REST
LMPP

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Edge Cloud Enterprise Applications
Telemetry
Endpoints/
Data Sources
Gateway
TCU (UTP)
TCU (Z01)
Z01
Platform
UTP
Platform
…
REST
WebSocket
COAP/UDP
LMPP
EC2
Containers
S3 Aurora PostgresCassandra
Rabbit
MQ
Ingest &
Real Time
Process
1 Data Analysis, Modeling &
Machine Learning
Raw
Data
Normalized
Data
Real Time
Services
Amazon
SageMaker
Lambda Amazon
EMR
Real Time & Batch Analysis
and Machine Learning
Lambda Kinesis Data
Analytics
Near Real Time /
Micro Batch Analysis
Kinesis Data
Streams
Lambda
(Data xForm)
Business
Intelligence &
Visualization
Amazon
Redshift
Athena
Tableau
Fleet
Management
Corporate Application
Legacy Data/
Services
5
Wizard
Mainframe
ODB Fleet Location
Edge Users
Enterprise
Users
REST
APIs
(HTTP/
SOAP)
OUR NEXT-GENERATION MOBILITY PLATFORM
Technical View
REST
LMPP
2 3 4
EC2
Containers
OMEGA

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ABG DEVELOPER PROGRAM
The Avis developer platform
provides the products you need to
start building applications that
revolve around cars—from on-
demand luxury ride requests to
traditional rental car reservations
with access to our Avis, Budget and
Zipcar product catalog.
Use our simplified ReST APIs to
create memorable trip experiences
for your users across the globe.
http://developer.avis.com
JOIN US!

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31. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Intro
Saken Kulkarni
Practice Area Lead, Advanced Analytics
Slalom
Things I love…

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The Avis Budget Group, Slalom, and AWS teams had
several use cases to build on the Connected Car
platform
Use Case Description
1 Continually rent out Program (Re-purchase/ Lease) Cars
2 Load balance mileage across Risk Cars
3 Incorporate customer usage into car assignment logic
4 Incorporate mileage optimization into one way car assignments
Phase 1 focuses on
Use Case #1 and
Use Case #2

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The challenge is clear – vehicles are not being utilized as
effectively as possible
Vehicles that are
underutilized
based on vehicle
age
• Each point is a vehicle
• Vehicles further from the slope and with a lower than optimized mileage require prioritization

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Initial deployment will take place at the Avis Budget Group parking lot at
Newark International Airport

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The model assigns vehicles based on mileage accrued in order to load
balance vehicles
Vehicles are placed in priority lanes to be washed, cleaned, and prepared
for rental based on the model results

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Our model development followed an agile, sprint
based process
Identify data fields
required to build model
Reservation
Brand
Reservation Status
Booking Date
Check In/Out Date
Check In/Out Time
Car Class
Check In Location
Rental
Brand
MVA #
VIN #
Expected CI/CO Time
Length of Rental
Fuel Reading
Transform data
into a usable and
normalized state
analysis
Performing modeling in
Amazon SageMaker
• Integer Programming for
Optimization
• Regression (Load
Balancing)
Integrate model results
into new Rental
Application & Device
Obtain feedback and
implement changes in
subsequent model
iterations
Fleet
Vehicle Make & Model
Vehicle Purchase Date
Vehicle Delivery Date
Vehicle Manufacturer
Market Segment Indicator
Vehicle Age

37. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Mileage Optimization Machine Learning Architecture
On Premise Cloud
REST TLS 1.2/ HTTPS
Integer Programming &
Regression
TLS 1.2/ HTTPS
Key Points
• Leverages Amazon’s
Connected Vehicle
Framework as a
foundation and expands
upon it for key use cases
• Takes the modern
architecture stack into
consideration by
including both on
premise and AWS
architecture
• Designed for real time
analytics and for
operational efficiency

38. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Mileage Optimization Machine Learning
Architecture: On Premise
Component Usage
Operational
Client Server
& Databases
• Feed mileage optimization engine and AWS
components with transactional and operational
data, including reservation, rental, and fleet data
• Fleet, Reservation & Rental data will be fetched from
Oracle Operational Database.
• Java application would read data from MQ queue
and write above data set to ActiveMQ setup in ABG
Datacenter. Java application would also fetch
Readyline data from IMS DB through a Omega
service and store it as message on ActiveMQ.
• ActiveMQ would be setup to synchronize with
ActiveMQ on AWS.
Omega
Service
• Data will be synced with the AWS environment via
synchronous API Calls

39. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Mileage Optimization Machine Learning
Architecture: Data Processing
Component Usage
Amazon API
Gateway
• The station operator scans barcode on vehicle. This
triggers invocation of new Omega service.
• Omega service then invokes Private API on Amazon
API Gateway. Device_Id & Location_Id are sent to
this Private API.
• API Gateway invokes Lamba function
AWS Lambda
• Receives data inputs from API and logs request
details in Amazon S3 (future model analysis)
• Queries Postgres operational data store and
retrieves Fleet and Reservation information for that
particular location
Invocation and process will occur on a near-real time basis in order to ensure
that Avis Budget Group front line employees are able to obtain optimized
mileage results on an hourly basis

40. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Mileage Optimization Machine Learning
Architecture: Machine Learning
Cloud
Integer Programming &
Regression
Component Usage
Lambda
• Invokes Amazon SageMaker endpoint and sends
input data
• Receives optimized result from Amazon SageMaker
• Stores response in Amazon S3 bucket ( for future
model analysis)
• Sends response back to calling API
Amazon
SageMaker
• Imports relevant Python libraries
• Generates supply and demand for the specified time
horizon
• Incorporates upgrade and substitution logic
• Simulate slope and average miles per day (MPD)
• Calculates “ideal” mileage for any particular vehicle
• Calculates ideal vs actual ratio

41. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The model takes key operational components into
consideration for deployment
Feature Description
Cars on
Readyline
Rentable cars that are already
parked in the Readyline
Demand
Horizon
Helps determine if there is a
demand (reservation) for a
particular car class in the next
horizon (particular number of
hours) of time
Primary vs.
Exclusive
The cars are divided into two (2)
cohorts, where substitutions can
happen within a cohort.
Exclusive Cars
by Class
Demand for an exclusive car class
can only be satisfied by that car
class.
Substitution
Demand for a primary class car can
be satisfied by other Primary class
cars.
Contention
Satisfying a reservation with an
upgrade car class if a better car is
available.

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Dashboards powered by Uber’s deck.gl
Geolocation analysis
of intra-lot
movements, and
details associated
with those
movements

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Dashboards powered by Uber’s deck.gl
Geofenced location
and identification of
a set of vehicles
within that geofence

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What is next?

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47. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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