Transit agencies invest millions to transport people in the most seamless, efficient, safe and reliable manner. Whether public or private, mass transit agencies and companies have always relied on information from representative surveys to make investment decisions. These surveys tell the story of where the average customer goes during the typical times. Decisions based on their findings impact every aspect of the traveler experience: frequency of bus arrivals, comfort, stop locations, and accessible destinations. Data on the average rider during those times are insufficient for effective customer-service decisions, which means the traditional approach to data collection simply is inadequate.
Why? Because there is so much data out there that can provide a nuanced view of what other market segments need, why they need it, and when they need it (e.g. grocery shoppers in disadvantaged communities or elderly traveling to the doctor). Times are changing due to the increasing use of smart phones and tablets by information-savvy customers who demand real-time information and responsive actions. Their evolving expectations require a new approach to leveraging data to provide superior transit services. This is why the industry needs to focus on Big Data or risk losing customers to emerging transportation services.
Big Data and Mass Transit
To appreciate big data’s importance to mass transit, imagine its offerings for decision makers beyond typical surveys. Only big data can:
- Identify users by nuanced market segment, time of use, purpose of use (potentially by linking with financial transaction or social media data), connecting modes and other aspects for the entire population of riders
- Help planners target bus service to match changing mobility patterns, such as re-routing buses in the evening to accommodate a flourishing restaurant scene in a particular neighborhood
- Give asset managers up-to-the-minute information on the condition of rolling stock using vibration and temperatures sensors on engines and wheels to keep vehicles in a state of good repair
The use of big data helps agencies understand how structural changes in demographics (e.g. urbanization), technology (e.g. automated/connected vehicles) and the economy impact transit operations. This is not to suggest that surveys, public feedback, ticketing on demand and passenger counts are no longer relevant. Of course they are. By taking advantage of sophisticated data and a powerful performance-based approach to decision-making, agencies can move beyond intuition to decisions based on a more complete understanding of reality.
It may be uncomfortable for some transit agencies to compare themselves with such rapidly growing upstarts like Uber and Lyft, but there are lessons to be learned from these two industry newcomers and their experience with big data. An August 2016 article on BostInno, a Boston website, “An Uber Data Scientist Shares What Trips Reveal about a City” lists several items that Uber considers important for setting up ride sharing in specific areas. The three subjects identified for data analysis can rightfully be viewed as important subject matter for mass transit:
- Bar closing times. “You can make a good guess based on when ride requests spike at night,” the scientist says.
- How communities connect. The article cites Uber’s focus on “transportation deserts” defined as those areas that lack adequate mass-transportation services.
- Use of all transportation services by city residents. “Uber has a broader picture of general transportation in a city,” the article states.
Big Data as an Indispensable Resource
Big data has transformed many industries. For example, its competitive advantage has long been understood by retailers as a valuable source of information and decision-making. They use analytics as a powerful tool to understand what their customers want, why and when they want it, and what affects their buying habits. Through detailed analytics, retailers are able to identify specific elements for customized messaging through smart phones and Internet ad placements, elevating market segmentation to unprecedented levels.
Contrast the comprehensive retail model with the limited information available to transit agencies that lack big data analytics. While they have the basic statistics — trips, time of trips and when they were taken — what about all the other data available outside the agency’s system? In addition to those previously mentioned, these might include rental patterns from craigslist.com, jobs from LinkedIn, or trips planned but not taken. Such data can be as important as basic trip information, which is why analyzing them can help detect patterns and alter agency operations.
No one has to convince Rachel Bain, assistant secretary of the Office of Performance Management and Innovation at the Massachusetts Department of Transportation of the importance and relevance of big data. She cites the experience of MBTA (Massachusetts Bay Transportation Authority), which, like many other transit agencies, has collected automatic vehicle location (AVL) and automated fare collection (AFC) data for 10 years. What sets MBTA apart is the use of this data to understand the customer and modify its operations accordingly. “It was critical to provide insights and potential interventions into operational challenges and to plan for the future with concrete analysis of past performance,” Bain said.
Bain said MBTA is able to access “more disaggregated detail” than it could in the past. “It has impacted the way we handle operations by providing insight on things such as bus bunching,” she said, and “has totally redesigned the way we measure reliability…to create a customer-weighted as opposed to vehicle-centric on-time performance metric.”
Big Data and Transit Planning
Agencies that are considering the implementation of a big data strategy need to begin by establishing clearly defined goals and a set of performance metrics to measure progress — a decision that will likely require the input of internal and external stakeholders. Then determine what can be gleaned from big data to track performance. The role of passenger-centric data should be among the priorities — where passengers go, why and when, as the Uber example illustrates. Customer feedback on quality of service, including analysis from social-media outlets, should be closely examined to improve the customer experience. Once goals are set and measures identified, develop a data-collection strategy to enhance existing information by purchasing available big data sets (e.g. financial transactions) and collecting new ones, for example instrumenting buses. All this information enables agencies to track results and make investment decisions to improve performance for customers.
While challenging, guidance is available from vendors who have a history of successfully using data to make decisions. “Give it time … there will be growing pains,” advises MBTA’s Rachel Bain to companies considering using big data to make customer-focused decisions. Her other advice is to visualize data. “Without good data visualization, some of the insight is lost on the rest of the organization,” she said.
The transit industry cannot afford to ignore Big Data. It offers an unprecedented opportunity for transit agencies to better meet stakeholder needs, enhance agility and, perhaps most important, provide better service to their customers.
Nathan Higgins is a senior associate, transportation planning and management, for Cambridge Systematics Inc., Cambridge, Massachusetts.
