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. There is a huge opportunity for cities to capture this demand by presenting options that create a faster, better and more seamless travel experience. But that’s no easy task.
Leveraging insights from data analytics can help. 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, take 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 the day’s busiest activity. Such on-the-fly system optimization is becoming possible as analytics move beyond descriptive to becoming predictive and prescriptive. Descriptive analytics tells us what has happened before; predictive analytics tells us what will likely happen in the future and prescriptive analytics tells us what actions to take based on those predictions that will improve outcomes.
Predictive and prescriptive analytics are even more powerful if data on leading indicators of transit use are 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. 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.
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 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.
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 near will keep the system operating at optimized levels.
In this innovator’s brief you will read about Conduent Seamless® Transportation System, a next-gen approach to ticketless public transit which operates with a smartphone and Near Field Communication (NFC) tags installed in a transport network. In addition to making ticketing and access much more convenient for the traveler, Seamless provides a rich source of data for predictive and prescriptive analytics. The use of data is certainly not limited to public transit and another seismic shift of the horizon is the shift from fixed toll roads and pricing to road usage charging which you will also read about in this newsletter.
I hope that you enjoy the rest of this issue of the innovator’s brief.
Cheers to innovation in transportation,