Passenger Crowding: Tools and Data for Transit Providers

With COVID-19 on the minds of transit providers and their passengers, full buses are no longer a marker of success in the transit industry.

Instead, the ideal operating environment is one where buses run below their capacity, where riders and drivers have space between one another and are less likely to spread the coronavirus. This extra space is critical for the health of drivers and those essential workers who rely on transit today, and it will also be important for attracting crowd-wary customers back to transit as our economy reopens.

What is GMV doing about this new reality? We have a number of features related to crowded buses that are especially useful in this environment. But first, how can we think about passenger crowding data? Like a lot of other transit data, we can think of crowding information as either internal or external, and realtime or historical. Here’s what that ecosystem looks like:

Crowding data can be internal or external, realtime or historical.

Dispatch

Detailed and accurate crowding information is made possible when agencies install automated passenger counting (APC) systems onboard their vehicles. There are several manufacturers and sensor types on the market, and all of them are compatible with GMV’s intelligent transportation system. When APC equipment is integrated with our CAD/AVL technology, we instantly push passenger count data to dispatchers at every bus stop.

Our Dispatch software features a vehicle’s passenger load in realtime. This data generally comes via APC sensors on the bus, or drivers of vehicles without APC technology can update the passenger load via the touchscreen Mobile Data Terminal. In List view, dispatchers can sort active vehicles by how crowded they are and instantly understand where there are crowding problems in their system.

Dispatchers can sort vehicles by their passenger load in our Dispatch List.

We share realtime passenger load information with riders in our agency-branded mobile apps and by GTFS-Realtime in third party applications like Google Maps and Transit. When agencies share this information, their riders know what crowding conditions they can expect on their trip. If a rider has some flexibility in their departure time, they may choose to wait for a less crowded bus. This data also helps these third-party services aggregate crowding trends and inform trip planning users if a particular bus is likely to be crowded in the future.

Passenger load information in our custom rider app and in the Transit app.

Reporting

We’ve also made improvements to our reporting tools to allow clients to better analyze historical crowding data. Our Stop Times Export tool, which already documented the number of passengers on a vehicle throughout a trip, now also includes the vehicle’s capacity. Managers and planners can now see when and where vehicles have historically been crowded in a detailed spreadsheet. When exported, Stop Times reports can be manipulated in Excel or other quantitative analysis software.

Crowding data will also be available in Insights Plus, our new business intelligence tool that will be released later this fall. Insights Plus will allow agencies to explore crowding data and compare it against other service measures in an easy to use drag-and-drop interface. Want to see if there’s a correlation between crowding and average headways? What about crowding and time of day? Day of week? With Insights Plus, agencies can easily create custom reports directly within Sync and take appropriate action.

Users can compare crowding data in Insights Plus against other service performance measures.

Passenger crowding is no longer merely an inconvenience; it’s a very real concern for drivers and riders. With GMV technology, transit providers have better data to pinpoint when and where crowding occurs in their system, both historically and in realtime. As dispatchers, planners, and managers prepare for increasing ridership, these insights can help inform them where to funnel limited resources to help maintain social distancing. And as our economy reopens, realtime crowding data can give riders the confidence they need to return to transit.