Methodology for Developing Real-time Motorway Traffic Risk Identification Models Using Individual Vehicle Data

摘要

Most of existing motorway traffic safety studies using disaggregate traffic flow data aim at developingmodels for identifying real-time traffic risks by comparing pre-crash and non-crash conditions. One ofserious shortcomings in those studies is that non-crash conditions are arbitrarily selected and hence, notrepresentative, i.e. selected non-crash data might not be the right data comparable with pre-crash data; thenon-crash/pre-crash ratio is arbitrarily decided and neglects the abundance of non-crash over pre-crashconditions; etc. Here, we present a methodology for developing a real-time MotorwaY Traffic RiskIdentification Model (MyTRIM) using individual vehicle data, meteorological data, and crash data. Non11crash data are clustered into groups called traffic regimes. Thereafter, pre-crash data are classified intoregimes to match with relevant non-crash data. Among totally eight traffic regimes obtained, four highlyrisky regimes were identified; three regime-based Risk Identification Models (RIM) with sufficient pre14crash data were developed. MyTRIM memorizes the latest risk evolution identified by RIM to predictnear future risks. Traffic practitioners can decide MyTRIM’s memory size based on the trade-off betweendetection and false alarm rates. Decreasing the memory size from 5 to 1 precipitates the increase ofdetection rate from 65.0% to 100.0% and of false alarm rate from 0.21% to 3.68%. Moreover, criticalfactors in differentiating pre-crash and non-crash conditions are recognized and usable for developingpreventive measures. MyTRIM can be used by practitioners in real-time as an independent tool to makeonline decision or integrated with existing traffic management systems.