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10 Reasons Transit Agencies Need Data Analytics

“Most public transit systems lack three things – time, money, and real estate …0 Transit authorities must make better use of current infrastructure.” – Sanford Weinberg, vice president for Fare Collection, Public Transit North America

Metropolitan transit systems consistently face the same combinations of challenges: As their populations escalate, the demands on the public transit systems become increasingly more complex. That’s why data analytics are now an essential component in the mass-transit decision-making toolbox.

Today, the average consumer uses a smartphone or credit card to pay for their daily expenses rather than cold, hard cash. They also no longer travel between only two locations on a regular basis. Some citizens drive an automobile to a local parking garage before jumping on the train to head to work, while others take a series of trains, subterranean metros, and bus systems to conduct their daily affairs throughout the city.

Unfortunately, most public transit systems lack three things – time, money, and real estate. Building new infrastructure to accommodate the new demands is rarely an option. Transit authorities must make better use of current infrastructure. Without an excess of financial capital, the improvements can only occur through highly targeted cost-saving strategies.


Data analytics tools address all of these issues and more.

1 – See a city-wide picture of transportation

As ridership on public transit systems increase, travel between only two locations is no longer the standard model. Cities must design their infrastructure to accommodate a more multi-modal system of transportation. By taking advantage of the combined data analytics from buses, trains, metros, transit-adjacent parking, tolling systems, and even bicycle traffic, a more accurate city-wide view of the transportation landscape begins to take form. When transit executives also begin including data from other public resources — such as community calendars, weather forecasts, fluctuating local demographics, and social and sensor networks — the data analytics tools become more system-agnostic.

2 – Visualize all the data

Today’s transit authorities must be able to respond to the public’s evolving traffic patterns. As ridership numbers fluctuate and typical traffic patterns shift on an almost daily basis, the need to make faster decisions with near pinpoint accuracy is crucial. A data analytics tool can take all of the different data inputs, and create charts or graphs that can be used to identify challenges at a glance. Now, transit executives can base their decisions on information acquired in real-time, without the need for tedious number-crunching or hypothetical guesswork.

3 – Predictive analysis such as what-if scenarios

Decisive action requires the ability to predict successfully the possible positive and negative impacts that result from any proposed change in the transportation infrastructure. Data analytics helps generate possible outcomes from what-if scenarios more accurately and nearly instantaneously. Public transport executives can now offer more trustworthy solutions by relying on predictive analysis strategies  that use a combination of up-to-the-minute data and historical statistics.

4 – Back up management decisions with data

The Mobility Analytics Platform

The Mobility Analytics Platform provides operators with interactive, dynamic displays. Here: the number of validations on the Adelaide (Australia) tramway .

No public transit system operates in a vacuum. Industry executives at all levels make decisions every day that affect the efficiency of the overall system. Even closing a few city streets for a two-day street fair can produce a negative ripple effect across the entire community. Data analytics supports proposed solutions for current or future challenges, so upper management can sign-off on the new programs more quickly and with greater confidence.

5 – Login to one system that combines all reporting

Decisions based on real-time data analytics reduces the need for in-house meetings of perhaps twenty or more highly paid executives. Data analytics lets agencies use a single system to generate a multitude of different reports simultaneously, each based on the very same data. Transit authorities save even more time because they no longer have to re-enter data into several different databases before printing out multiple spreadsheets at different times.

6 – Identify operational inefficiencies so that you can make improvements

Because data analytics tools calculate in real-time, identifying operational inefficiencies is more accurate and timely as a result. Big data systems can instantly alert transit executives of decreasing ridership numbers on individual train lines, buses that spend too much time in a single location, or even individual transit operators who are not meeting their scheduled stops consistently. Since guesswork is no longer a factor, corrections and modifications can occur more rapidly, which significantly reduces possible negative consequences.

7 – Maximize ridership with location or route planning

The effective implementation of data analytics tools helps transit authorities to create new travel routes, or modify existing ones to maximize ridership numbers. Marketing strategies might include offering incentive programs to specific classifications of riders, or adjusting individual routes to accommodate the community’s changing demands. As more communities move to a more multimodal transportation system, this data becomes even more critical.

8 – Cost analysis capabilities

Because most urban transit systems lack an abundance of real estate and financial resources, building new infrastructure is rarely an option. Cities must now make better use of the current infrastructure already in place. Big data systems allow executives to identify and resolve profitability challenges of individual components of the overall transit system quickly and accurately. Meanwhile, transit authorities can then use the cost-savings to maintain and update existing transit infrastructure.

9 – Look at historical information, real-time data, and plan for the future

Before the age of big data, planning for the future was an arduous process. As transit executives came and went, the ideologies of new team members could stagnate the decision-making process indefinitely. Today, local transit authorities can combine the real-time information of data analytics with the historical statistics of years past to generate a more decisive and accurate plan for the future and in far less time.

Want more info? Download the full version of the white paper, “Big Data in Transit.”

10 – Innovate and create an even better experience for riders

To some extent, all public transit authorities must be highly effective at marketing their product to the community. To attract new ridership, a focus on the typical travel experience is essential. Customers want innovation. They demand comfort, accessibility, speed, limited wait times between stops, and easy payment options. By evaluating the trip analysis data of its riders, local transport systems can respond to consumer demand faster and more accurately by targeting improvements to the proper areas.

Public transit systems that use data analytics tools improve the quality of life for its citizens, save money, and boost efficiency. However, many of these cities are witnessing other very dramatic, positive side effects, as well. With increased ridership comes a reduction in traffic congestion on city streets and highways. In addition, air quality levels improve simultaneously.

Big data tools allow you to manage and manipulate traveler-specific data so that transit authorities can streamline a multi-modal system of transportation with greater ease and cost-efficiency.