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The Data Conundrum: Turning Transit Data into Real-world Applications

Two men looking for information on a mobile phone.

The data generated from the time an operator arrives at work until the last bus of the night pulls into the yard is staggering. Public transit agencies are only beginning to grasp its volume and understand how to leverage it.

While research methods developed by academia to harness and make this data actionable for transit agencies has grown steadily, putting this information into a form accessible to the industry has proven elusive. Transit agencies generate data that make it possible to understand ridership at an extremely granular level, but how do they make it manageable? To date, there’s been minimal use of analytics to harness and act on them to actually improve the service provided to riders.

For those of you who were able to attend ThinkTransit this year, I led a session about what we’re doing to help advance North American transit and specifically how to address scheduling and planning department challenges. Teaming up with industry partners like Trapeze, we’ve embarked on a Research and Development project that we hypothesize will have a significant impact on the transit industry when its findings can be turned into action. The program will build on some of the Business Intelligence solutions that are already in action and available from Trapeze. A key aspect of the study is addressing the data conundrum.

What is the study trying to address when it comes to data?

· Expand transit evaluation from service-based metrics to more holistic user-based metrics

· Provide a web-based platform to allow agency users to easily apply the methods for various scenarios and compare results

But, how?

· By developing a cloud-based solution for use by Trapeze & its clients and university researchers. For those customers already on the cloud with Trapeze, this will enhance their technology mix seamlessly. · By connecting the Trapeze Scheduling Suite with the University of Toronto, Transportation Research Institute (UTTRI)’s simulation software and new methods for planning and scheduling

· By addressing strategies for:

- Accelerated service (stopping buses at all stops slows down service, so how can we optimize?)

- Adaptive transit signal priority (develop adaptive TSP for improved reliability)

- Improved transfer synchronization (in short, transfers are a pain, so how can we improve the experience?)

- Bus bridging planning for Metro disruption response (we want to provide better measures of how well shuttle buses are used and the impact on train and bus passengers).

How will this help with your scheduling and planning activities?

When the project comes to fruition, agencies will be able to leverage an easy-to-use interface to apply advanced service and schedule adjustment methods.

Where are we now?

· Research and development prototype has been constructed

· This prototype provides an analytics platform to test out strategies

· Work remains on some modules

· We are in an exploratory phase for advancement to a full end-user software

So what are our next steps? Well, we need to do three things: move from R&D to an end-user product; investigate multi-intersection cooperative and adaptive transit signal priority; and create bus-bridging plans. When we move forward on these initiatives, we will finally be able to make the connection between research and academia and real-life scenarios, adding real value to how transit agencies respond to the demands of their communities. That time is coming and I can’t wait to see it happen. To learn more about our ongoing studies, and methodologies, visit the University of Toronto, Transportation Research Institute.

Dr. Siva Srikukenthiran is a Research Associate in the University of Toronto Transportation Research Institute.His main areas of research are in better understanding how the behavior and movement of crowds impact transit network performance, developing tools to improve operational response to disruptions in transit networks, and in survey methods to collect data on traveler behavior.
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