Identifying commonly used and potentially unsafe transit transfers with crowdsourcing

Public transit is an important contributor to sustainable transportation as well as a public service that makes necessary travel possible for many. Poor transit transfers can lead to both a real and perceived reduction in convenience and safety, especially for people with disabilities. Poor transfers can expose riders to inclement weather and crime, and they can reduce transit ridership by motivating riders who have the option of driving or using paratransit to elect a more expensive and inefficient travel mode. Unfortunately, knowledge about inconvenient, missed, and unsafe transit transfers is sparse and incomplete. We show that crowdsourced public transit ridership data, which is more scalable than conducting traditional surveys, can be used to analyze transit transfers. The Tiramisu Transit app merges open transit data with information contributed by users about which trips they take. We use Tiramisu data to do origin-destination analysis and identify connecting trips to create a better understanding of where and when poor transfers are occurring in the Pittsburgh region. We merge the results with data from other open public data sources, including crime data, to create a data resource that can be used for planning and identification of locations where bus shelters and other infrastructure improvements may facilitate safer and more comfortable waits and more accessible transfers. We use generalizable methods to ensure broader value to both science and practitioners. We present a case study of the Pittsburgh region, in which we identified and characterized 338 transfers from 142 users. We found that 66.6% of transfers were within 0.4 km (0.25 mi.) and 44.1% of transfers were less than 10 min. We identified the geographical distribution of transfers and found several highly-utilized transfer locations that were not identified by the Port Authority of Allegheny County as recommended transfer points, and so might need more planning attention. We cross-referenced transfer location and wait time data with crime levels to provide additional planning insight.