Understanding Big Data in Freight Transportation Task Force Mission, Goals, Findings

Transcription

1 Understanding Big Data in Freight Transportation Task Force Mission, Goals, Findings Transportation Research Board Annual Meeting Donald Ludlow, MCP, AICP January 10, 2017

2 Presentation Map What is Understanding Big Data in Freight Task Force? What do Big Data allow us to do Challenges of using Big Data 2

3 Task Force Overview ABJ92T Task Force on Understanding Big Data in Freight Transportation Three-year task force established May 2014 Task force goals: 1. Explore current state of knowledge regarding the use of big data to understand and manage the movement of goods throughout the supply chains. 2. Address how both private and public sector managers can use big data to improve the performance of the freight system. 3. Coordinate interests of both freight and transportation data committees within TRB.

4 Task Force Activities Big data panelists informed panel Defense Aviation Manufacturing Supply chains Government Big data analytics White paper commissioned Circular under development

5 Innovations in Freight Data Workshop Irvine May 17-18, 2017 Applications Granularity (gaps in commodity coverage, cost data, short-haul and local movements) Linkages (gaps in domestic movements of international trade, O-D data by commodity and vehicle, whole trip data across modes) Consistency and Quality (inconsistency in data taxonomy, data collection, data synthesis, data quality problems) Some examples of innovations include: Hardware developments Software or smartphone applications Data processing or analysis methods Communications and outreach improvements.

6 Innovations in Freight Data Application

7 Big Data Definition Big Data is a term that describes large volumes of high velocity, complex and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information. Demystifying Big Data TechAmerica Foundation

8 Big Data Challenges 4 V s Volume how to capture, store, analyze? Variety many types, many sources, how to reconcile? Velocity real-time availability, how to harness? Veracity how to validate to make sure it s right? Adapted from Mayer-Schonberger and Cukier, 2013

9 What are Big Data Sources? (initial scan) Agency sources Sensors Cameras / images Customer data Transaction data Safety data Data collection servers Existing big data sets Public Sources Social media, articles (text mining) Vendor Data GPS data Other sources Photo: Yousuf Fahimuddin

10 Sources of Freight Transportation Big Data Category Big Data Sources White Paper commissioned 2015 to explore Task Force Questions, including source of big data for freight Transportation Network In-roadway sensors Over-roadway and roadside sensors Vehicle-based technologies Satellite imagery and aerial observations Geo-location-based social media applications Mode of Transport Vehicle-based technologies Social media Satellite imagery and aerial observations Vehicle Operators Driver monitoring devices Cargo RFID Mobile/wireless technology Import/export trade data Visual technologies Industry Import/export trade data Commodity trading prices Stock market E-commerce data Newsfeeds Consumers E-commerce data Social media Geo-location-based social media applications Point-of-sale transactions Natural Environment Weather Emission sensors Newsfeeds Social media

11 Context: Transportation Agencies and Big Data The Challenge The Opportunity The application of big data sources holds significant potential to assist transportation agencies in better managing and improving their assets. Most agencies have not developed or implemented strategies to capture, analyze, and integrate big data in operations, planning, and asset management. WE ARE BEHIND

12 We know that we are behind... Analysts declared 2013 to be the start of the Big Data era in supply chain. 27% of shippers and 30% of 3PLs surveyed are planning or currently engaged in big data initiatives. 97% of shippers and 93% and 3PLs (93%) believe that improved, data-driven decision-making is essential to the future success ~50% of shippers and 3PLs believe that big data drives these improvements in decision making 18th Annual Third-Party Logistics Study produced by Dr. C. John Langley and Capgemini Consulting. And 2016 Third-Party Logistics Study, Korn Ferry International.

13 Where is the Task Force Going? Major focus is Innovations Workshop Reassess progress in May 2017 Big Data will soon just be data Natural fit within core mission of Freight Transportation Data Committee (ABJ 090)

14 Presentation Map What is Understanding Big Data in Freight Task Force What do Big Data allow us to do? Challenges of using Big Data 14

15 Big Data Allow us to Capture and analyze vast amounts of data In lieu of surveys and traditional data collection Instrumented supply chain Vehicles, vessels, operators, cargo Find unanticipated patterns, trends

16 Example: Real-Time Inventory Mgmt. Wal-Mart Supplier Portal Allowing Retail Coverage (SPARC) Real-time app Allows vendors to monitor and replace inventory. Source: Mike Kalasnick (Creative Commons) Inventory Source: Wal-Mart

17 Example: Observed Truck Behavior Source: ATRI

18 Can Big Data Close Persistent Data Gaps? Granularity Linkages Consistency Poor commodity coverage (biofuels) Local delivery and short haul not accounted Scarce data on private trucking carriers Difficult to obtain / estimate freight cost data Lack of information on vehicles Domestic movements of international trade O-D data by commodity and vehicle Quality of linked trips across modes (through supply chains) Linking data sets (vehicles + commodities + value + safety + performance) Consistent regional truck classification counts Consistency in data taxonomy Consistency data collection / surveys 18

19 Evolution of Freight Data in Practice Big data sourced

20 Big Data Applications in Freight Transportation

21 Presentation Map What is Understanding Big Data in Freight Task Force? What do Big Data allow us to do? Challenges of using Big Data

22 Transportation Agency Challenges Common challenges and approaches to overcome Data maintenance and reliability over time warrants program commitments Internal capacity to support vast datasets can be an issue however Costs associated with databases and servers to manage data can be overcome by turning to open source tools Asking the Right Questions What are major operational or planning challenges? Can big data solve the challenges? IN BIG DATA, SHEPHERDING COMES FIRST STEVE LOHR, NY TIMES DEC 15, 2014 Aspiring big data software companies find themselves training, advising and building pilot projects for their customers, acting far more as services companies than they hope to be eventually. 22

23 Discussion Donald Ludlow, MCP, AICP Managing Director 1050 Connecticut Ave. NW, Suite 500, Washington, DC T: C:

24 Introduction to CPCS Global management consulting firm (formerly consulting arm of Canadian Pacific Railway, est. 1969) Strategy, economic analysis, policy, specific to transportation and energy sectors Multimodal transportation practice (road, rail, air, marine, pipeline) Global presence and experience Over 1000 projects in more than 90 countries Recent experience: Arizona State Freight Plan (ongoing) Understanding and Using New Data Sources to address Urban and Metropolitan Freight Challenges (NCFRP 49) Florida DOT Freight Data Strategies to Improve Last-Mile Truck Information Toronto Off-Peak Truck Deliveries Project Twin Cities Regional Truck Corridor Study Dozens of multimodal freight studies CPCS countries of work experience (shaded) and offices 24

25 Summary of Recent CPCS Experience Freight Rail 100+ Strategy projects 8 Transactions $3+ billion in deals Port & Terminals 35+ Strategy projects 30+ Transactions $5+ billion in deals Multimodal Transport 30+ Strategy projects Passenger & Transit 10+ Strategy projects 3 transactions $3 billion in deals 25