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The focus of this study is to examine the service quality of unsignalized intersections based on the perception of automobile drivers. This was achieved by developing Automobile Level of Service (ALOS) models using two computational intelligence methods – Functional Linked Artificial Neural Network (FLANN) and Differential Evolution (DE). The required data were collected at 47 unsignalized intersections in India with widely varying driving environments. Traffic simulation models were developed to estimate parameters that could not be directly measured in the field. DE model exhibited higher prediction efciencies with coefcient of determination ( R 2) of 0.94 and 0.93 for training and testing datasets, respectively. Applying sensitivity analysis, significant parameters affecting ALOS were arranged in descending rank of their relative influence. Pavement condition is the most significant parameter influencing ALOS of unsignalized intersections. The proposed model will help the transportation administrators in prioritizing the key factors for investment in infrastructural development.

Assessment of service quality at unsignalized intersections using traffic simulation and computational intelligence