Safe from Crime at Location-Specific Transit Facilities

Abstract

Transit agencies identify two types of exposure to crime: the safety of riders and security. Transit operators have long monitored crime and are cognizant of high incident locations. However, they lack data-driven tools to readily match crime events spatially with the locations of individual transit facilities, and temporally with transit service periods. This pilot project explored the use of data-driven tools to (1) identify concentrations of criminal activity near transit facilities, and (2) assist decision-making regarding the selection of countermeasures and the allocation of future safety investments, using the results of models estimating environmental and socioeconomic predictors of crime near transit facilities. The project used two novel data sets: location-specific, police-reported crime incidents by type; and individual ORCA card (electronic transit fare payment system) transaction records, yielding transit ridership data. Two sets of models were developed to examine exposure to crime while waiting for transit (within 100 m of transit stops) and while walking to transit (within 400 m of transit stops). The hypotheses were that within 100 m of a stop, amenities provided at each stop could act as a deterrent of crime; and within 400 m different characteristics of the built, social, and transportation environment would be associated with crime. Analyses were restricted to the City of Seattle, and models were run using all stops as well as only stops located in the City’s urban villages (hosting 90 percent of the City’s ridership and where 74 percent of the stops fell in the highest tertile of crime). We found that amenities at stops had mixed associations with crime, suggesting that amenities serve to provide riders with added comfort but not necessarily more safety. Higher ridership provides safety while waiting for transit (100-m models), but it exposes riders to more crime as they walk to and from transit (400-m models). Higher employment densities in neighborhoods around transit stops are protective of crime. In urban villages, sidewalks are associated with a lower likelihood of crime. However, a more connected street network, which characterizes the oldest, most urban areas of Seattle, is associated with more crime. The project illustrated how novel sets of disaggregated data on both crime and transit ridership can serve to develop models assessing the safety of transit riders at specific locations. Future research should continue to examine how transit riders can be protected from crime while they wait for transit as well as while they walk to and from it.