Big Data Analytics in Transportation Systems Management and Operations

Transcription

1 Big Data Analytics in Transportation Systems Management and Operations Bob McQueen, Bob McQueen and Associates, Orlando, Florida Petros Xanthopoulos, Stetson University, DeLand, Florida v3

2 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

3 Instructional objectives At the end of this webinar, you should be able to: 1. Provide an overview of TSM&O 2. Define the challenges associated with TSM&O 3. Explain the value of analytics in TSM&O 4. Define Big Data 5. Define big data analytics and relevance to transportation 6. Explain the value of Use Cases 7. Define an effective approach to Smart Data Management 8. Explain predictive and prescriptive analytics 9. Describe the relevance of machine learning to TSM&O

4 Transportation Systems Management and Operations (TSM&O) Bob McQueen

5 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 Match supply and demand Explore alternatives Understand effects Develop results-driven investment programs Define projects Select technology Estimate cost Develop design concepts Develop detailed design Project management Project delivery Testing Commissioning Partnership management Monitor status Collect data Develop information Build intelligence Define strategies Develop maintenance policies Monitor device status Identify intervention points Assess device performance Implement strategies Plan Design Build Operate Maintain

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

7 Services TSM&O Analytics Examples Asset and maintenance management Connected vehicle Connected, involved citizens Integrated electronic payment Intelligent sensor-based infrastructure Low cost efficient, secure and resilient ICT Smart grid, roadway electrification and electric vehicle Smart land-use Analytics Asset performance index, asset maintenance standards compliance measure, optimal intervention point analytic Lane 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 Citizens awareness levels index, citizens satisfaction levels Transit 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 Data quality index, transportation conditions index, trip time variability index Network load compared to capacity index, network latency, cost of data transfer, network security index Electric 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 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 Transportation governance Transportation management Traveler information Urban analytics Urban automation Urban delivery and logistics User focused mobility Percentage of private sector investment, number of partnerships, improvement in service delivery for each private sector dollar invested Transportation efficiency for each dollar spent, supply and demand matching index, transportation agency coordination index, partnership costsaving index, cost of data storage and manipulation compared to services provided Mobility index, citywide job accessibility index, citywide transportation efficiency index, reliability index, end-to-end time including modal interchanges index Traveler satisfaction index, decision quality information index, behavior change index Number of analytics in use, value of services managed by analytics, money saved through efficiencies gained by analytics Percentage 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 Average cost of urban delivery, average time for end-to-end delivery, freight and logistics user satisfaction index, freight management satisfaction index Citywide mobility index, user satisfaction index, transportation service delivery reliability index 7

8 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 Maintenance Operations Planning Proportion of the data originating Planning 20% Design 10% Project delivery 5% Operations 50% Maintenance 15% Total 100% Design Project delivery 8

9 TSM&O Use Cases Transportation Operations Use Case Catalog Version 1 1 Traffic anomaly detection and communications 2 Towing and recovery management 3 Results driven investment 4 Asset management 5 Transportation network management 6 Transportation systems management and operation impact analysis Developer fee 7 8 Regionwide safety analysis management 9 Regionwide speed in bottleneck analysis 10 Mobility as a service 11 Connected citizens and travelers 12 Project tracking and coordination 9

10 Smart Data Management Not So Smart Data Management Smart Data Management

11 Predictive and Prescriptive Analytics Petros Xanthopoulos

12 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)

13 Descriptive Analytics More than just descriptive statistics

14 Examples Foursquare checkins show the pulse of New York City

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16 Predictive Analytics - Clustering Data Categories (Labels are not known) Used for exploratory/preliminary analysis of the data

17 Example Social Media Graph Clustering- LinkedIn UF undergrad alumni UCF faculty/staff Graphs drawn with SociLab.com UF grad colleagues Stetson Faculty/Staff Classmates from Greece (TUC)

18 Prescriptive Analytics Final frontier of analytic capabilities Transform trends and patterns into actionable insights Can predictions from Data results in REAL actions???

19 Giving Viewers What They Want

20 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.

21 Potential Benefits in Transportation McKinsey Global Institute The age of analytics: competing in a data-driven world (December 2016)

22 What Do The Following Have In Common? Toilets Dog house Onesies Mattress cover Egg tray

23 They All Can Be Connected To The Internet

24 The Internet of Things (IoT)

25 Number Of Devices Connected To The Internet *from

26 Big Data Evolution of Computer Processors Evolution of Computer Storage

27 A Data Universe *from

28 Machine Learning and TSM&O Workload Complexity Growth in Query complexity, Workload mixture, Depth of history, Number of users, Expectations REPORTING Historical Performance Reporting ANALYZING Mechanisms related to transportation demand and supply PREDICTING Future Transportation Demand and Supply Database Requirement: Analytic foundation must handle multidimensional growth! OPERATIONALIZING Applying insights to transportation operations ACTIVATING Automated transportation back office Batch Ad Hoc Eventbased Triggering Analytics Continuous Update/Short Queries Primarily batch and some ad hoc reports Increase in ad hoc analysis Analytical modeling grows Continuous update and time-sensitive queries become important Event-based triggering takes hold Single View of Transportation Better, Faster Decisions Drive Safety, Efficiency, User Experience Data Sophistication 2 8

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

30 Discussion