Improving the Rider's Transit Experience with Big Data

Transcription

1 Improving the Rider's Transit Experience with Big Data The Hype Cycle tells us that we must consider applying Big Data to improve our ridership of which, none of us appears to have the slightest idea as to what it is or what to do with 1

2 What is Big Data? Big Data refers to the inability of traditional data architectures to efficiently handle the new datasets. Characteristics of Big Data that force new architectures according to NIST are: Volume (i.e., the size of the dataset); Variety (i.e., data from multiple repositories, domains, or types); Velocity (i.e., rate of flow); and Variability (i.e., the change in other characteristics) All of the above define the concept of Big Data but Variety is a super component. NIST: The US National Institute of Standards and Technology 2

3 Big Data Coming of Age 2016 was considered a landmark year for Big Data:, more and more organizations are: Storing Processing Analyzing Extracting value The year 2017 is already seeing systems who are supporting large volumes of structured and unstructured data that will only continue to expand into 2018 The market place is already demanding platforms that help organizations to: Manage Govern (How and when data is used, privacy issues) Securing of Big Data 3

4 Big Data Coming of Age, the Standards Big Data is more than just being talked about: Standards committees nationally and internationally are addressing Big Data to establish definitions, formats, etc. NIST developed in late 2015:Big Data Interoperability, Taxonomies, Architectural Framework: Special Publication ,2,6 others ISO/IEC JTC 1/WG9, Information technology-2015/2016: TR Big Data Analytics Integration of results Big Data Engineering - Storage and Data Manipulation Big Data Models logical and computational Big Data Paradigm distribution of data to achieve scalability, etc. INCITS/ANSI: Established a national working group in

5 Initial Benefits derived from Big Data Did you know that Big Data is already being used to better understand and manage future transit investment decisions, such as accumulating the following Big Data results: As of 2016; 52% of all Americans spend an hour or less per day commuting? Or that large city public transit riders such as in Los Angeles travel over 7.5 miles/trip Or that large city riders may spend more than 2-hours a day on publictransport Or that non-frequent public transit riders are often concerned with using public transit because of first and last mile connections 5

6 A Sampling of Big Data: Where are the transit oriented populations within a region? Where are the employment, event, retail malls centers located? When, Where, Why and Now Often do people travel and specifically on public transit? What modes of travel are they using, and are there better options? Where are the gaps in public transit service? What technology do riders have and prefer to use? What methods of payment are used and or preferred? What is the amount of time expended per trip? What riders types travel as individual or in groups? What does social media say about our agency? Transit rail and bus, etc. on-time performance data. 6

7 So why Consider Big Data? The rider s transit experience is ever more multimodal and is expecting a seamless cost effective, connective and convenient trip planning Transit agencies are faced with higher ridership expectations and a growing private competitive transport environment Transit agencies are now expected to understand the riders total landscape? Having the data derived from the big picture, is a good start: Street Car Rail Bus Transit Parking City/Private Parking Tolling Bike Share Uber or Lyft Private Transport Commuter Rail Pubic Transport Airlines and Amtrak Retail Partners Entertainment Venue 7

8 How might the Transit Industry make use of Big Data? Implementing Big Data may be challenging but is also realistic and may now actually be a necessity for public transit operators The proliferation of Smart phones and recent standardization achievements has changed everything Transit agencies must consider applications that bring personalization to the rider s trip planning experience, players in the industry must understand how to achieve this? To ensure a useful Big Data implementation of a personalized transit rider s experience, consider the following: 8

9 How might the Transit Industry make use of Big Data?, Continued Standardized and secure open standards protocols for lower-level data and security interoperability such as, OSPT-CIPURSE or other transactional protocols utilizing: ISO/IEC, NIST and ANSI/ INCITS standards Become familiar with what Big Data represents and what it can provide in understand agency long-term transit planning needs by leveraging Big Data Consider system integrators and software processing providers that can also integrate Big Data into the total transit back office experience Let Big Data contribute to the personalized rider experience on smart media and especially transit provided or directed mobile applications giving the rider a complete transit experience at his/her finger tips. 9

10 Improving the Rider's Transit Experience with Big Data Thank You! Our Contact Information: Al Chan ALINC Consulting Walt Bonneau Jr. ALINC Consulting Greg Coogan Infineon Technologies