Lyndon Henry Railway Age Magazine Urban Rail Today Consulting


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Analytics and Big Data — Rail Public Transportation is a Leader

Lyndon Henry

Railway Age Magazine

Urban Rail Today Consulting

Austin, Texas




INTRODUCTION
Two concepts currently at the leading edge of today's information technology (IT) revolution are Analytics and Big Data. Analytics is high-technology applied to data processing, complex calculations, and automation; Big Data is the current term referring to significantly large volumes of data, on virtually every facet of human activities and characteristics, that can be rapidly processed via Analytics, yielding a broad spectrum of highly useful results. Recent technological advances have sparked what amounts to a "revolution" in the application of these cognitive and informational tools.

Apparently without realizing it, the public transportation industry, has, for many decades, been at the forefront in utilizing and implementing Analytics and Big Data, from ridership forecasting to transit operations. Rail transit systems have been especially involved with these IT concepts, and tend to be especially amenable to the advantages of Analytics and Big Data because they are generally "closed" systems that involve sophisticated processing of large volumes of data. In virtually any American city, on any normal weekday, one is likely to see the results of analytics literally in motion — the operation of transit buses and trains that are essential to maintaining the mobility of the metro area.

The more that public transportation professionals and decisionmakers understand the role of Analytics and Big Data in their industry in perspective, the more effectively they will be able to utilize its promise. Furthermore, it is useful for both the public and the industry to realize how significantly public transportation has been a leading pioneer in the rich and extensive historic development of these tools, the roots of which in some cases extend back to 19th century rail technology.

MAJOR APPLICATIONS OF ANALYTICS
Some of the most salient applications of Big Data and Analytics in today's urban rail transit are summarized in the following sections. [1] These range from urban planning activities with computerized processing of massive amounts of demographic and geographic data, to complex signaling and train dispatching or control systems, to communications, train tracking, and passenger information operations using increasingly common modern technologies like GPS, Wi-Fi, and cellular phone systems. The intensive use of Analytics is particularly underscored by the incorporation of "automatic" and "automated" in so many of the common technical features of modern rail operations: terms such as Automatic Block Signals, Automatic Train Control, Automated Passenger Counting, etc.

Travel Demand Modeling



Surely one of the biggest deployments of Big Data has been in planning new public transportation services and infrastructure. At least since the 1950s, this relatively gigantic undertaking has involved modeling (projecting) future travel demand in various urban areas.

Not only have the public at large generally not realized the magnitude of this task, but transportation planners themselves seem to have been unaware of the extent to which systems modeling — involving projections ranging from travel demand to modal split to ridership — has, for more than half a century, represented one of the most widespread and intensive deployments of Analytics and Big Data.

The modeling process typically involves splintering up all the census tracts in a multicounty metro region into segments (each one often called a travel (or transportation or traffic) analysis zone, or TAZ), tallying the total households in each TAZ, then assigning some demographic characteristics (e.g., income level) to proportional household categories based on available data. Next the model uses sophisticated algorithms to project future growth in population, economic activity, and perhaps other critical elements. Then (typically using estimates of factors such as travel time and cost) more algorithms project an estimate of all trips (for an average weekday) among all the households and centers of employment (and other activity centers, such as educational facilities, retail centers, etc.) among all these TAZs. [2]

From this complex process enormous volumes of trips (Big Data) are projected, which are then assigned (via the model Analytics) into travel corridors. Ultimately, final projections are produced for transportation facilities such as new road routes, additional freeway lanes, and major new transit lines (such as light rail).

This highly complex procedure, involving massive amounts of data and networks of intricately interrelated algorithms, has been repeated in metro areas across the country, for many decades, well before the terms Big Data and Analytics were fashioned. And planning for rail transit systems has certainly been at the leading edge, driving this effort and refining the methodology from city to city, year after year. Furthermore, the incorporation of global positioning systems (GPS) and geographic information systems (GIS), with associated Big Data Analytics, has facilitated major advancements in essential planning tasks such as pinpointing locations, determining land areas accurately, and overlaying and correlating large volumes of demographic data with geographic areas (e.g., census tracts, transit service areas, etc.).
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