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Critical gap has been estimated to investigate the gap acceptance characteristics of U-turning vehicles at mid-block median opening (MBMO) because proper modeling of gap acceptance behavior coupled with the integration of an intelligent transport system (ITS) can offer an efficient and safe driving environment at MBMO. In this regard, support vector machine (SVM) and occupancy time (OT), have been used to estimate the spatial and temporal critical gap of U-turning vehicles. The outcome of this study suggests that the SVM method can perform with better veracity than the OT method in accessing the gap acceptance behavior of U-turning vehicles. The result of the study also indicates SVM method can be used for the prediction of gap acceptance and rejection of U-turning vehicles and further can be integrated with a cognitive architecture for safe and efficient traffic flow and management at MBMO in an ITS environment.

transportation letters

Categorization of gaps at mid-block median openings in heterogeneous traffic: adjudging the applicability of support vector machine and occupancy time methods