Other presenters at Transportation Techies’ Bus Hack Night shared projects to help riders better understand their local bus routes
Though the D.C. region has one of the busiest bus systems in the country, with more than 120 million trips in 2016, it’s still part of the nationwide movement to stem recent bus ridership declines. As such, WMATA is looking to better understand how to provide reliable, efficient service that keeps daily riders and draws others back.
Catherine Vanderwaart of WMATA’s Office of Intermodal Planning is working on just that. Speaking at Tuesday’s Transportation Techies meetup, “Bus Hack Night,” she presented a wide range of findings pulled from multiple aspects of bus performance and rider behavior.
Chart by Catherine Vanderwaart, WMATA
Vanderwaart presented the time costs of fare payment and her findings that tapping a SmartTrip card averages two to four seconds per transaction. The time it takes passengers to pay by cash or reload their card varies widely, however, taking anywhere from 10 to 60 seconds per person, which can impact a bus’s dwell time at a stop, and therefore its overall performance. Because this dwell time accounts for 19 to 25 percent of a bus’s run-time, according to another WMATA staff member, speeding up the payment and boarding process could make a noticeable difference along some routes.
Given its unique service changes, WMATA’s ongoing SafeTrack campaign has provided abundant information on how riders react to disruptions. Vanderwaart’s office has collected data on the shuttle buses (called “bus bridges”) that connect closed stations to better understand how to deploy them. Since, prior to SafeTrack, self-reported data only existed on established routes, the agency at first faced delay issues with its shuttles, but eventually established methods to automatically track shuttle ridership. With more robust tracking during each surge, WMATA now has a better sense of how to space out the bus bridges and improve their service.
A day of shuttles from Surge 4. Chart by Catherine Vanderwaart, WMATA.
Vanderwaart also presented lessons from last year’s system-wide rail safety shutdown, which provided a unique chance to examine reactions on bus ridership. Using anonymous SmartTrip data from the previous 30 days as a baseline for typical ridership, Vanderwaart compared it with those riders’ behaviors during the shutdown to determine how people shifted their commutes. Those who typically combine bus and rail dropped out of the system that day – avoiding transit or working remotely – but a large number of new or infrequent users tried the bus. Overall, Metrobus saw 20,000 more riders, a 5 percent increase, than on a typical day.
Turning around bus performance
JD Godchaux, of civic tech group NiJeL, worked with TransitCenter to convert New York MTA buses on-time performance into an advocacy tool for better bus policy. Bus Turnaround NYC collects historical data on every bus route in New York and provides a performance report card. These categorize the problems facing the Metropolitan Transportation Authority’s bus system and help explain why it is losing ridership despite a growing population. Now, Bus Turnaround is developing report cards for the buses of every elected official’s district in the region, to draw attention to the need for a better bus network and the ways to fix it.
Back in D.C., the District Department of Transportation’s District Mobility project has helped to visualize the broad concepts of congestion and reliability and their effects on accessibility. The site’s tools show the most crowded roads, bus routes, and even individual stops, as well as on-time performance in an effort to define and measure the idea of urban mobility.
What’s in a wait?
On the ground, there are a number of tools in development to help passengers understand the services available to them and how long one can expect to wait for a bus.
- Michael Eichler of WMATA shared Metrobus Explorer, which maps the Metrobus system and shows users how they can navigate it from any point. By selecting an individual stop, or drawing a box around a group of them, users can identify routes and the frequency of buses at each location and get a sense for how the tangle of lines translate into bus lines.
- Mobility Lab’s Michael Schade built a similar tool that maps all of the region’s transportation operators. Users can select agencies to see their service area, and select individual routes to highlight and to pick out their stops in order to see how they fit into the region’s larger transportation network. Schade built this using MapZen’s Transitland project, a “community-edited data service” that aggregates the feeds of transportation services around the world, which MapZen’s Dave Nesbitt briefly demoed.
- MetroHero, Max Grossman, and Daniel Turse are all building tools to estimate bus wait times and when to expect them. Turse’s wait-time tool uses PlanItMetro’s historical data, which includes bus positions but also time between stops, dwell time, and what every bus did at every stop, such as skipping one. With that, the tool helps users determine how wait times vary for any route across the region and by time of day.
- Grossman’s DC Latebus uses WMATA’s live bus position information to visualize bus lateness along every segment of a route. By comparing arrival times at each stop to the published schedule, the tool measures median deviation to show which parts of every route are most likely to bog down your bus. Grossman and Turse’s projects launched a discussion of how to measure bus delay, especially taking into account how riders might ignore schedules and focus more on frequency.
- MetroHero‘s bus-tracking tool, a beta webpage in the same fashion of their original Metrorail app, shows current bus positions along their routes, and allows users to click on each one for performance information. Users can also click on specific stops to see estimated arrival times, and how many stops separate them from each predicted bus.
- Ranjani Prabhakar of Fehr & Peers dove into the gritty details of traffic planning by explaining the Poisson Distribution that planners can use to predict the probability of events over time, such as if cars traveling behind a bus might be backed up into the “upstream” intersection. By understanding the flow of traffic on any stretch of road, and how buses travel along them, planners can work out the likelihood that a bus stop’s location will cause nearby vehicles to actually increase congestion.
Photo: A Metrobus picks up passengers in Rosslyn, Arlington, Va. (Sam Kittner for Mobility Lab, www.kittner.com).
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