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Predicting crash risk in real-time is an emerging concept in proactive road safety management system which estimates the probability of crash in real-time using data from traffic flow sensing devices. This derives an additional use of the existing underground loop detectors or other traffic flow data sensing devices which are now-a-days commonly used for surveillance, traffic forecasting and/or continuous data collection. Oh et al. were among the pioneers to separate traffic dynamics into two categories – disruptive and normal, and assess the likelihood of crash data belonging to each category. Standard deviations of speed as well as average occupancy were found to be the most significant predictors. Some contemporary studies can be found with similar but different objectives, such as, to improve the prediction capability of the conventional non-real time models, identify crash patterns 5) and to develop proactive road safety management systems 6). Bayesian statistics , matched case-control logistic regression, probabilistic neural network 3), 7), 10) and a combination of multiple models 11) were the most common methods used so far. The modeling approach followed were relatively similar – i) separate traffic flow data into two categories, i.e., hazardous and normal, ii) collect data from a single detector or a set of detectors for each crash point, iii) select different descriptive statistics of the traffic flow parameters as variables and finally, iv) treat the problem as a classification problem and use a suitable method to predict the crash. Previous studies 1)-3), 7), 10) in most cases considered a 5 minute time period some times prior to the crash to represent the traffic condition leading to crash. Some studies 7), 10) argued that 5 minutes may not be sufficient enough to take any counter measure even if crash potential is accurately calculated and recommended for a time period which ends 10 minutes prior to crash. Regarding the non-crash traffic condition, Oh et al. 1)-3) defined it as a 5 minute time period ending 30 minutes prior to the crash, whereas, some studies defined it as a 5-minute time period corresponding to the traffic condition leading to crash, but taken from the same weekdays throughout the year except for the crash day. Thus, although the overall modeling approach was similar, the studies varied in their definition about crash and non-crash situations and the modeling methods.

OPTIMUM DETECTOR SPACING FOR REAL-TIME MONITORING OF HAZARDOUS LOCATIONS ON URBAN EXPRESSWAYS