Abstract
This paper evaluates the optimum detector spacing to monitor the crash risk of hazardous locations on urban expressways in real-time. For this, crash data and traffic flow data (flow and speed) were collected for two years (December, 2006 to November, 2008) from 30 loop detectors on Shinjuku 4 Tokyo Metropolitan Expressway, Japan. Six different detector spacings were evaluated and Bayesian Network was used as the modeling method. The 5-minute cumulative flow difference and average speed difference between upstream and down stream detectors were identified as suitable predictors. The optimum detector spacing was identified and an implementation strategy was presented.
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