Big Data Analytics in Transportation Systems

Management and OperationsBob McQueen, Bob McQueen and Associates, Orlando, Florida

Petros Xanthopoulos, Stetson University, DeLand, Florida

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Topics

• Instructional objectives• Transportation Systems Management and Operations (TSM&O)• TSM&O from an analytics perspective• Predictive and prescriptive analytics• Machine learning and big data• Summary• Discussion

Instructional objectives• At the end of this webinar, you should be able to:

1. Provide an overview of TSM&O2. Define the challenges associated with TSM&O3. Explain the value of analytics in TSM&O4. Define Big Data5. Define big data analytics and relevance to transportation6. Explain the value of Use Cases7. Define an effective approach to Smart Data Management8. Explain predictive and prescriptive analytics9. Describe the relevance of machine learning to TSM&O

Transportation Systems Management and Operations

(TSM&O)Bob McQueen

Transportation Management and Operations• Traffic incident management• Traffic signal coordination• Freeway management • Management of multi-modal transportation systems• Full spectrum from planning to maintenance: transportation as a single system

Project managementProject deliveryTestingCommissioningPartnership management

Define projectsSelect technologyEstimate costDevelop design conceptsDevelop detailed design

Monitor statusCollect dataDevelop informationBuild intelligenceDefine strategiesImplement strategies

Develop maintenance policiesMonitor device statusIdentify intervention pointsAssess device performance

Design Build Operate Maintain

Match supply and demandExplore alternativesUnderstand effectsDevelop results-driven investment programs

Plan

Transportation as a Single System• What is a system?

• It has clarity of purpose• It is connected together• We can find out its status at any

given time• It can adapt to changes in the

environment• “Single system” also includes

alignment between planning, design, project delivery, operations, and maintenance

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Paraphrased from the speech by Samuel J. Palmisano, Intelligent Transportation Society of America, 2010 Annual Meeting & Conference, Houston, Texas, May 5, 2010

TSM&O Analytics Examples

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Services AnalyticsAsset and maintenance management Asset performance index, asset maintenance standards compliance measure, optimal intervention point analytic

Connected vehicleLane changes per mile, steering angle compared to road geometry, brake applications per mile, driving turbulence index, minutes per trip, trip time reliability index, no of stops per trip

Connected, involved citizens Citizens awareness levels index, citizens satisfaction levels

Integrated electronic paymentTransit revenue per passenger, transit seat utilization, toll revenue per vehicle and per trip, premium customer identification index, parking revenue per slot, payment system revenue achieved compared to forecast and addressable market

Intelligent sensor-based infrastructure Data quality index, transportation conditions index, trip time variability indexLow cost efficient, secure and resilient ICT Network load compared to capacity index, network latency, cost of data transfer, network security index

Smart grid, roadway electrification and electric vehicleElectric vehicle charging points per mile, electric vehicle charging points per head of population, number of electric vehicles as a percentage of the total fleet, electric vehicle miles per day, electric vehicle miles per trip, electric vehicle miles between charges

Smart land-use Observed trip generation rates for different land uses, observed actual trips between zones, land value transportation index, zone accessibility index

Strategic business models and partnering Percentage of private sector investment, number of partnerships, improvement in service delivery for each private sector dollar invested

Transportation governanceTransportation efficiency for each dollar spent, supply and demand matching index, transportation agency coordination index, partnership cost-saving index, cost of data storage and manipulation compared to services provided

Transportation managementMobility index, citywide job accessibility index, citywide transportation efficiency index, reliability index, end-to-end time including modal interchanges index

Traveler information Traveler satisfaction index, decision quality information index, behavior change index

Urban analytics Number of analytics in use, value of services managed by analytics, money saved through efficiencies gained by analytics

Urban automationPercentage of automated vehicles within the entire citywide fleet, percentage of automated vehicles in use by city agencies and private fleets, proportion of deliveries made by automated vehicles, proportion of passengers carried by automated transit

Urban delivery and logisticsAverage cost of urban delivery, average time for end-to-end delivery, freight and logistics user satisfaction index, freight management satisfaction index

User focused mobility Citywide mobility index, user satisfaction index, transportation service delivery reliability index

Importance of Operations

• Operations as a significant data generator

• SANDAG• 1 TB per day• Assumed 200 days per year operation• 200 TB per annum

• Connected vehicle• 2 ZB per annum

• The impact of operations on safety, efficiency, and user experience

• Coordination of planning, design, project delivery, operations, and maintenance to deliver quality services

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Proportion of the data originating

Planning 20%Design 10%Project delivery 5%Operations 50%Maintenance 15%Total 100%

Planning

Design

Project delivery

Operations

Maintenance

Traffic anomaly detection and communications1 Towing and recovery

management2

Mobility as a service10

Transportation Operations Use Case Catalog Version 1

Results driven investment3

Asset management4 Transportation network management5

Transportation systems management and operation impact analysis

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Developer fee management7 Regionwide safety analysis8 Regionwide speed in

bottleneck analysis9

Connected citizens and travelers11 Project tracking and

coordination12

TSM&O Use Cases

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Smart Data ManagementNot So Smart Data Management Smart Data Management

Predictive and Prescriptive AnalyticsPetros Xanthopoulos

Analytics

• Descriptive analytics• Prepares and analyzes historical data• Identifies patterns from samples for

reporting of trends• Predictive analytics

• Predicts future probabilities and trends• Finds relationships in data that may not be

readily apparent with descriptive analysis• Prescriptive analytics

• Evaluates and determines new ways to operate

• Targets business objectives• Balances all constraints

”The scientific process of transforming data into insights for the purpose of making better decisions.”

Institute for Operations Research and Management Science (INFORMS)

Descriptive Analytics

• More than just descriptive statistics

Examples

• Foursquare check-ins show the pulse of New York City

https://vimeo.com/75413842

• Data Categories (Labels are not known)

• Used for exploratory/preliminary analysis of the data

Predictive Analytics - Clustering

Stetson Faculty/Staff

UCF faculty/staff

UF undergrad alumni

UF grad colleagues

Classmates from Greece (TUC)

Example Social Media Graph Clustering- LinkedIn

Graphs drawn with SociLab.com

• “Final frontier of analytic capabilities”

• Transform trends and patterns into actionable insights

• Can predictions from Data results in REAL actions???

Prescriptive Analytics

Giving Viewers What They Want

• Fact0: Netflix has 27million subscribers in the US and 33 million worldwide.

• Fact 1: A lot of users had streamed the work of Mr. Fincher, the director of “The Social Network,” from beginning to end.

• Fact 2: films featuring Mr. Spacey had always done well.

• Fact 3: British version of “House of Cards” had done well too.

Giving Viewers What They Want

Potential Benefits in Transportation

McKinsey Global Institute “The age of analytics: competing in a data-driven world” (December 2016)

What Do The Following Have In Common?

• Toilets

• Dog house

• Onesies

• Mattress cover

• Egg tray

They All Can Be Connected To The Internet

The Internet of Things (IoT)

Number Of Devices Connected To The Internet

*from http://www.solidsmack.com/

Evolution of Computer Processors

Evolution of Computer Storage

Big Data

A Data Universe

*from https://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm

Data Sophistication

Continuous update and time-sensitive queries

become important

OPERATIONALIZINGApplying insights to

transportation operations

Event-based triggering takes hold

ACTIVATINGAutomated transportation

back office

Primarily batch and some ad hoc reports

Increase in ad hoc analysis

ANALYZINGMechanisms

related to transportation demand and

supply

REPORTINGHistorical

Performance Reporting

Analytical modeling

grows

PREDICTINGFuture

Transportation Demand and

Supply

Single View of Transportation – Better, Faster Decisions – Drive Safety, Efficiency, User Experience

Wor

kloa

d C

omp

lexi

ty

Batch

Ad Hoc

ContinuousUpdate/Short Queries

Analytics

Event-basedTriggering

Growth in Query complexity, Workload mixture, Depth of history, Number of users, Expectations

Database Requirement: Analytic foundation must handle multi-dimensional growth!

Machine Learning and TSM&O

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Summary

• Transportation Systems Management and Operations (TSM&O)

• TSM&O from an analytics perspective

• Predictive and prescriptive analytics

• Machine learning and big data

Discussion