Using Big Data for Public Transit Planning
Corresponding Author: Catherine Manzo, StreetLight Data
Presented By: Kim Harrison, StreetLight Data, Inc.
Traditionally, the expansion and design of public transit systems has been informed by the behavior of existing transit users, and potential transit users are identified as those that live or work near transit stations. But what about the people who are driving, walking, or biking? Their travel patterns can be equally valuable to planners when it comes to expanding and improving public transit systems. With accurate, comprehensive data on the mobility behavior of non-transit users, planners are better positioned to develop public transit systems that increase ridership and convert vehicle trips to other modes.
In her paper and presentation, Catherine Manzo of StreetLight Data will explain how Big Data from mobile devices such as cellphones and connected cars can reveal valuable insights for public transit. Using Denver, Colorado as a case study, Catherine will walk conference attendees through the process of using geospatial records from mobile devices to create origin-destination matrices. She will outline the process for two origin-destination studies: one on drivers with origins and destinations in Denver’s core downtown area, where there are ample public transit options, and one regional origin-destination study. Next, Catherine will analyze the top zip codes and census block groups with destinations downtown, the distance of trips, and the variation in behavior across weekends and weekdays as well as peak AM and peak PM hours.
By overlaying Denver’s public transit stations with the top origin-destination pairs identified in these Big Data analyses, Catherine will identify: 1) the routes that are not currently served by public transit, but that, based on the high volume of vehicle trips, would benefit greatly, and 2) the routes that are currently served by public transit, but, based on the high volume of vehicle trips, are not fully utilized. In both cases, digging into the average duration and length of trips completed by drivers can yield key guideposts for planners when it comes to maximizing every dollar invested in public transit systems. Catherine will conclude her presentation by identifying these guideposts, thereby providing planners with an actionable set of recommendations for using Big Data for public transit design.