A Unified Framework for Modeling Traffic Crashes from Hierarchical Spatial Resolutions

Music City Center 

Independent traffic crash modeling approaches do not account for the embedded relationships
related to the multi-resolution data structure, leading to mis-specified estimations. The recently
developed integrated frameworks demonstrate the capability of addressing this drawback. The
current study proposes an integrated framework that accommodates information from multiple
spatial units and observation resolutions. Specifically, the study develops an integrated model
system that allows for the influence of independent variables from disaggregate crash record,
micro-facility (segment and intersection) and macro (traffic analysis zone) level simultaneously
within the macro level propensity estimation. The empirical analysis considers disaggregate
crash records of 1,818 segments and 4,184 intersections from 300 traffic analysis zones in the
City of Orlando, Florida. These crash records contain crash-specific factors, driver and vehicle
factors, roadway, road environmental and weather information of each crash record. For micro-
facility and macro levels, an exhaustive set of independent variables including roadway and
traffic factors, land-use and built environment attributes, and sociodemographic characteristics
are considered. The proposed model system can also accommodate for hierarchical correlations
among the data across observation resolutions and parameter variability across the system. The
empirical analysis is augmented by employing several goodness of fit and predictive measures.
The results clearly demonstrate the improved performance offered by the proposed integrated
model system relative to the non-integrated model. A validation exercise also highlights the
superiority of the proposed framework. The application of the proposed integrated framework
can allow transportation professionals to adopt policy-based, site-specific, and outcome-specific
solutions simultaneously.