Transit Agencies Turn to Analytics to Improve Service

After a prolonged love affair with the automobile, Americans have rediscovered mass transit.

According to the American Public Transportation Association, 2014 saw a record 10.8 billion public-transit trips in the United States — the highest ridership in 58 years, despite falling gas prices.

Maintaining and improving service levels in the face of swelling ridership and limited resources for modernization presents a significant challenge. Many transit agencies have addressed it in part by harnessing Big Data and analytics to streamline service and attract and retain customers. These technologies shed light on everything from scheduling and route optimization to asset maintenance and future planning and budgeting.

“The real frontier is in discovering hidden data relationships in huge data volumes that transit agencies didn’t even know to explore in the past,” says Daniel Collins, general manager and vice president of analytics services for Urban Insights Associates.

Analytics have given Dallas Area Rapid Transit new insight into everything from passenger travel patterns to recurrent causes of delay, employee performance, crime patterns and equipment maintenance and repair trends, says Alan Gorman, information management and decision support senior analyst for DART. The agency can also harness external information to measure the impact on performance of weather, gas prices, traffic conditions, special events and new development and construction.

Transit Agencies Incorporating Analysis

DART embraced analytics as far back as the 1990s. “We conducted spatial analysis on our in-vehicle collected data, which led to many of our current customer-facing arrival time applications,” Gorman says.

Momentum increased with a computer-aided dispatch automatic vehicle locator system (CAD AVL) that delivers huge volumes of operational information, including on-time performance based on more than 3 million data points per quarter versus a mere 400 previously. The CAD AVL system measures performance by integrating location data with routing and scheduling information.

“We conducted spatial analysis on our in-vehicle collected data, which led to many of our current customer-facing arrival time applications.” — Dallas Area Rapid Transit Information Management and Decision Support Senior Analyst Alan Gorman.

Photo: Trevor Paulhus

DART uses a combination of IBM Cognos, IBM SPSS, SAP Crystal Reports and Tableau to glean new insights from these and other data sources. IBM Cognos can bring in financial data and modeling to enable the authority to gauge the impact of routing changes. “We can use these tools across multiple data realms,” Gorman says.

New information from the transit agency’s mobile payment application can even help identify the ultimate destinations that draw riders (see “Mobile Ticketing Offers Win/Win”). “Data analytics have improved scheduling tremendously, compared with a planner or scheduler sitting at a desk with limited opportunities for on-the-street observations,” Gorman notes. Nicole Fontayne-Bardowell, DART’s vice president and CIO, attributes the agency’s success with analytics to collaboration and standardization.

Transit Agencies Embrace Data Visualization

“It’s vital to engage the end-user departments in a data standardization process that achieves a single version of the truth,” Fontayne-Bardowell says. DART does this by assigning department data stewards who collaborate to manage and standardize diverse data (most of which is stored in Oracle and SQL Server databases) and maintain a strict data glossary. She finds that the departments' overwhelmingly positive reception to the data visualization software has helped the effort tremendously.

Predicting Service Levels

Philadelphia’s Southeastern Pennsylvania Transportation Authority also uses analytics to improve scheduling, generate customer information and evaluate the impact of multiple events on the on-time performance of its regional commuter rail operations, says Jim Fox, chief control center officer.

SEPTA uses its own proprietary analytics platform to extract data from its Centralized Traffic Control and signaling systems to pinpoint the real-time location of trains. “As the train rides down the rails, the wheels interrupt the electrical current in the rail system,” Fox says. “We can use that information to track train locations, time-stamp the train’s location and store all the information in a database.”

In the control center, the Automated Train Dispatch System runs location data against the schedule data to determine on-time performance. This data can then be analyzed to detect where delays are occurring. SEPTA analyzes affected trains, rail lines and times of day for recurrent performance issues and trends, such as scheduling conflicts, mechanical issues or infrastructure problems.

“If we’re single-tracking due to maintenance and construction or purchasing new train cars, we can see the impact that a newer, more reliable car can have on on-time performance versus our older fleet,” says Fox. “We also know where to focus our efforts in the mechanical, infrastructure, personnel and scheduling realms to maximize performance impacts.”

The transit authority pushes real-time location information to station digital signage, the SEPTA website and mobile apps that let riders know when the next train or bus will arrive. “We generate automatic Twitter posts to riders on each line to warn them of delays, and mobile app users can see on a map where trains are at all times,” says Fox.

Austin Capital Metro uses analytics across its fare collection, automatic passenger count, and CAD AVL system to aid future planning and help finance new infrastructure projects, says Melvin Clark, vice president of rail operations for the Texas agency. Analytics provide insight into where passengers board, how they move through the system and how ridership affects overall performance.

Securing Funding for Transit Agencies

Such analyses have helped Austin Capital Metro to gauge the ridership impact of transit-oriented real estate development and pinpoint the most strategic locations for double tracking in order to reduce scheduling headway by as much as half on some lines.

The detailed data analysis has also been invaluable for justifying investments to federal and state agencies and securing grants. For example, the Texas Department of Transportation last year awarded $50 million for the purchase of new rail cars and construction of a new, larger station in downtown Austin. And the Austin MetroRail service also received an $11.3 million Transportation Investment Generating Economic Recovery grant from the U.S. Department of Transportation for service improvements to increase frequency. “We were able to demonstrate the need and outline specific improvements,” Clark says.

Business intelligence also aided Austin Capital Metro in securing dedicated bus lanes downtown, which have increased transit use, says Jennifer Govea, manager of service analysis. What’s more, this knowledge has helped the agency plan for special events, such as the South by Southwest film, interactive and music festival and concert, and Formula 1 races.

The potential for transit agencies to tap hidden insights from more types of data is virtually unlimited. “Analytics and business intelligence are journeys in themselves,” Fontayne-Bardowell says. “It’s best not to try to bite off a huge chunk of the pie all at once. Start with small use cases that make a big impact.”

To learn more about how transit agencies are taking advantage of mobile ticketing, head here.