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Big Ideas about Big Data; Industry Leaders Weigh In on Value of Big Data

"Big data"--a term that identifies data sets so large, complex and dissimilar that they are difficult to process and analyze using traditional data management techniques--can be both a boon and a bust to public transportation. Passenger Transport recently asked several members the following question regarding big data and the role such information plays in the industry.

How can industry agencies and businesses harness and apply big data to strengthen their operations?

Paul Jablonski
Chief Executive Officer, San Diego Metropolitan Transit System (CA)

There is no doubt, big data can return big results to public transit agencies.

The San Diego Metropolitan Transit System (MTS) is not unlike other large public transit systems. We collect billions of data points every day. Fares, boardings and alightings, vehicle analytics, preventive maintenance, on-time performance, web hits ... the list is almost endless. WeÊhave struggled with traditional analytic tools to make sense of it all.

Making data useful and ensuring that its potential is realized and becomes a boon to the agency is critical. MTS has made a huge commitment to invest in its data infrastructure. We have made multimillion-dollar investments in software development. We have a dedicated team of more than 20Êpeople from all disciplines and appropriate contractors working on this challenge.

Over the next two years, we will be building our transit asset management and our enterprise asset management systems. When done, this effort, combined with other existing data mining techniques, will provide many benefits. Some ways to use this data when harnessed include the following:

Leverage the assessment of asset conditions and preventive maintenance data to perform predictive maintenance analytics; fully analyze and communicate our Key Performance Indicators in an almost real-time environment to better manage our people and assets; combined with GIS analysis, analyze our rider use patterns on a more granular level to better allocate resources and to anticipate future capacity and growth; and boarding patterns on a spatial level could assist in fare pricing models and developing targeted advertising and personalized promotional campaigns.

Our ability to collect data will continue to grow exponentially. Big data is a relative term. What is big today will be commonplace tomorrow. We need to design our systems with an eye to the future so that we continue to turn all of the data into powerful, actionable information.

Phil Silver
Director, Business Development, Urban Insights

It is becoming widely acknowledged that today's standard reporting tools and methods are no longer effective in providing transportation operators with adequate information for understanding and benefiting from the overwhelming amount of operational data they are collecting from enterprise information systems in addition to vast sensor networks.

The design of these legacy systems typically inhibit the inferences and insights that are possible today through the application of big data tools and proven data science methodology to reliably and repeatedly model, forecast, plan, simulate, visualize and inform policymakers, planners and operators on how to improve operational efficiency and serve travelers' needs better. They also fail to account for the rapid demographic shifts that require rethinking how mobility services will be provisioned and delivered five or 10 years in the future.

Commercial enterprises in logistics and fleet operations have already gained incredible insights from applying these new capabilities to diverse data sources, and as a result are improving customer satisfaction, fleet efficiency and the financial performance of their organizations.

It is time for urban public transportation agencies and authorities to achieve similar results by applying these same tools, techniques and methodology to the immediate and future challenges of their communities and customers. Then they will be able to explore the implications and opportunities that exist to better align the transit network and provisioned services with current and anticipated future traveler needs and preferences.

Sean Murphy
Technical Programs Specialist, Intelligent Transportation Society of America, Washington, DC

Successful applications of big data require a thorough rethinking and retooling of organizational attitudes toward data. This change requires both a top-down and a bottom-up approach.

First, executive level decision makers must fully embrace the potential that data and existing capabilities to extract knowledge from that data have to transform operations for the better. This means hiring the talent necessary ("data scientists") and making the appropriate investments in data mining software and means to acquire, store and manage vast sets of data. It also means reforming reporting practices to include fully integrated data-driven insight on every line of business.

Second, public transportation professionals who understand the value of big data technologies the most must effectively communicate the benefits of implementation in order to help decision makers realize a return on investment. Because funding streams in the public transportation industry can be uncertain at best, agency executives must be able to understand how big data techniques can deliver a return. That is why it is so critical for the business analysts, computer engineers and other technology-savvy professionals of public transportation to clearly tie big data capabilities to business results. For example, the potential for data mining to optimize scheduling or maintenance routines should be clearly demonstrated.

Initiating a change to an organizational culture of data-driven decision making is not easy. There are certainly other technical hurdles to realizing the benefits of big data in public transportation, such as ensuring data meet a level of quality where it can be successfully analyzed. But these technical issues can't even begin to be addressed until there is widespread buy-in to data, and the promise it holds, in the first place.

Steve Callas
Manager, Service and Performance, Analysis, Tri-County Metropolitan Transportation District of Oregon (TriMet), Portland, OR

Over the past 20 years, TriMet has worked to capture and record as much data about our services as possible. To achieve this, the agency made a substantial investment in database technology to house the growing streams of data being produced by vendor and internally developed systems.

The greatest benefit we have seen using big data are the possibilities it creates in understanding and improving transit operations. For example, last year we began collecting five-second location data from all in-service buses, allowing us to conduct detailed and specific intersection delay analysis and evaluate the effectiveness of transit signal priority treatments. This information will be invaluable in guiding future BRT improvement projects.

One of the biggest challenges remaining with big data systems is long-term data storage. Even with our ongoing investments, storing more than 18 months of detailed raw data is difficult. At present, we have created "summary tables" for essential datasets that need to be stored for longer periods of time. As TriMet works to address this need, the agency is also looking at ways to integrate existing and future datasets into other business intelligence tools to deliver a better product to our customers.

Laura M. Minns
Senior Project Manager, LYNX, Orlando, FL

I see the use of big data as twofold. There are the operational aspects. By leveraging big data generated by our daily operations, transit agencies can track the health of their systems--rolling stock, facilities, what have you. It can be used as a way of tracking assets, particularly when it comes to maintenance and replacements. Ultimately, the use of big data as an asset management tool would allow the agency to better plan for major capital costs to be proactive rather than reactive.

The second purpose of mining big data would be to track key performance indicators (KPIs) and the overall health of the agency. From a business perspective, decision makers can leverage big data to monitor how well the agency is meeting its business goals. These KPIs can then be converted to a dashboard report showing various trends in ridership, ticket sales, on-time performance and even monitoring procurement and inventories.

The good thing is all this data is being collected nearly every second of every day. We are a sound-bite-driven society these days and the need for information in a timely, easily digestible format is key. It does the industry no good to have massive amounts of data without understanding what it means for the agency.

Scott Lacy
Director of Product, TransLoc, Durham, NC

Big data is a huge part of any industry's future, and this is particularly true for public transit. TransLoc is investing heavily in the development of rider and vehicular datasets that will enable the next wave of transit innovation. We are entering an era of mobility driven by software intelligence, and big data is the fuel that software intelligence consumes.

TransLoc collects a tremendous amount of data to understand how riders interact with transit systems--where they start, board, alight and end up. This anonymous, aggregated data drives the intelligence of the tools we create. But that's just the start. We're also leveraging big data to understand how riders move when they're not on buses and instead moving through cities in cars, on bikes or on foot. The implications of this data cannot be understated: It is the key to extending the reach, ubiquity and appeal of public transit.

We see public transit as a growth industry, one on the brink of explosive growth. When you marry trusted modes of public transit with big data insights, new models begin to emerge. These models won't eradicate transit as it exists today; they will extend it, empower it and make it more attractive to people who have never used it before.

TransLoc is focused on helping public transit move greater numbers of people through America's cities and suburbs. To do this, we are mining huge amounts of rider data to build a software platform that routes transit vehicles based on rider demand. This kind of software intelligence, powered by big data, will enable public transit to maintain its leadership as new competitors for riders emerge.

Within the walls of TransLoc, we talk a lot about making public transit the preferred mode of transit for everyone. Big data will play a huge role in making that vision a reality.
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