By Scott Silence, chief innovation officer, Public Sector, Conduent

Scott Silence

Scott Silence

The demand for public transit has increased by 20 percent in the last decade, according to the Federal Transit Administration. To be honest, I’m surprised that upsurge isn’t even higher! Our global study of transportation habits and perceptions found that 70 percent of respondents said they would likely be encouraged to ride public transit more frequently if the journey times were faster.

So there is a huge opportunity for cities to capture this demand by presenting options that create a faster, better and more seamless travel experience. It’s no easy task, but leveraging insights from data analytics can help.

Uncovering Better Data

Public transit systems already optimize routes based on fixed and static demand-for-service data. However, the data used is historical, based on infrequent and expensive surveys, and at best takes into account only large-scale seasonal variation.

While this is a good first step, there are other data sources to use that can better optimize a traveler’s trip. Here’s an example: Using real-time, or near-real-time, data from tap-on, tap-off fare collection systems to optimize route planning in a responsive way. With this data in hand, system operators can adjust on the fly – moving buses to busier lines and adjust the types of stops they make based on where the day’s busiest activity is.

Predictive and prescriptive analytics are even more powerful if data on leading indicators of transit use is incorporated. One powerful source of this data is trip planners, such as MyTix for NJ Transit, which is used by over half a million commuters. Although it only features the public transit legs of a trip, apps can provide leading indicators. For instance, operators can see when people book themselves on certain trips, but then don’t ride — useful data points on the way to planning an effective system.  More trip planners will be linked to bookings and payments, making data even more accurate.

There is a huge opportunity for cities to capture this demand by presenting options that create a faster, better and more seamless travel experience.

Also consider data from options outside of the public transit network – forms of travel that are used as substitutes or complements to public transit. Data about a traveler’s end-to-end trip, including public and private options, is a goldmine.

Multimodal trip planning apps, such as Go LA or Go Denver, can provide context around how and when people chose certain modes of transportation, and information about the proverbial “first mile” and “last mile” of travel. Integrating this type of data allows planners to sharpen their analyses of commuter/traveler behavior and propose better and more relevant public transit solutions.

Making the Data Work Even Harder

Higher levels of data analysis can lead to a more efficient and predictable transit experience for everyone by providing incentives for commuters to make different choices.

At the simplest level this is done today with peak versus off-peak pricing for transit fares. But as discussed above, this is typically based on historical data. Introduce real-time data into the picture, and operators can offer price incentives to individuals whose journeys allow them to pursue alternatives paths or modes of travel – to switch between the bus and subway, for instance, or to get off at a different stop. For example, if a subway is more crowded than usual, a system operator can send an alert to an expected traveler that their trip will be shorter – and cheaper – if they take the bus.

Public transit systems need mechanisms to get that message to travelers, and if an incentive is involved, the system needs a way to guarantee and deliver that pricing.

Transit apps offer a vehicle to execute these methods for shaping the choices travelers make. Importantly, this does not require a large percentage of public transit riders to use these apps. It is only near the margins – when demand approaches the supply limit – that public transit systems and their riders are distressed. Shifting of a small percentage of the demand when that limit is neared will keep the system operating near optimized levels.

The transportation industry is being transformed by insightful use of data. For public transit, this creates a great opportunity to grow ridership with a faster, more reliable and informative system that citizens want to choose – instead of dread using – for their commute and travels.