Models for Safety Analysis of Road Surface Transit
infrastructure - stop, infrastructure - vehicle, planning - safety/accidents, land use - planning, place - urban, mode - bus, mode - tram/light rail
Vehicle miles of travel, Urban transit, Toronto (Canada), Streetcars, Strategies, Strategic planning, Service planning, Priorities, Objectives, Nearside (Bus stops), Mathematical prediction, Intracity bus transportation, Goals, Farside (Bus stops), Decision support systems, Crashes, Crash prediction models, Collisions, Bus transit
A study was done to explore the development of zonal- and arterial-level collision prediction models that incorporate characteristics applicable to urban transit planning. A generalized linear modeling approach with a negative binomial regression error structure was employed by using a data set from Toronto, Ontario, Canada. The zonal-level models indicate that vehicle kilometers traveled, bus or streetcar kilometers traveled, arterial road kilometers, bus stop density, percentage of near-sided stops, and average posted speed have significant associations with occurrences of transit-involved collisions. The arterial-level models, which were developed for collisions involving all motor vehicles, suggest that average annual daily traffic, transit frequency, segment length, presence of on-street parking, and percentage of near-sided stops are all associated with increased frequency of these collisions, whereas percentage of far-sided stops and average stop spacing are linked with reduced collision frequency. It is evident that models such as those developed in this study can provide transit agencies with decision-support tools for considering safety implications in the strategic and service-planning processes. These models can also be used as a tool to predict future levels of transit-involved collisions for an existing and a new transportation network or arterial route.
Cheung, Carl, Shalaby, Amer, Persaud, Bhagwant, Hadayeghi, Alireza, (2008). Models for Safety Analysis of Road Surface Transit. Transportation Research Record: Journal of the Transportation Research Board, 2063, pp 168-175.