Performance assessment of urban streets adressing improvement issues for automobile mode of transport

Abstract

The present study focusses on modelling automobile drivers’ response pattern to assess the service quality provided by urban streets in developing countries. Several Quality of service attributes, affecting driver’s riding quality were investigated from 102 urban street segments under widely varying geometric and traffic conditions. From Pearson’s’ correlation analysis, total nine variables are found out to be significantly affecting drivers’ satisfaction level. Two novel Artificial Intelligence technique i.e. Artificial Neural Network (ANN) and Functionally Linked Artificial Neural Network (FLANN) are applied in this study to predict Automobile drivers’ level of satisfaction score (ALOS_score). The prediction performance of developed models is assessed in terms various statistical parameters of a Modified Rank Index. Among the ANN models Bayesian Regularization Neural Network (BRNN) algorithm has given the best fitted model in both training and testing data sets. However, application of FLANN model shows better prediction performance in the present context. Because, there exists no hidden layer and all the input layer neurons are directly linked with output layer neurons with lesser number of connections. Hence, it’s advantageous over ANN to reduce the accumulated error. The result shows that 73% of studied segments are offering inferior service quality. Sensitivity analyses have reported that Pavement condition is the most important variable with relative importance of 26.78% to influence the drivers’ riding quality. Similarly, other parameters were also ranked in decreasing order of their relative importance, which will help the highway authorities to prioritize budgets for future investments to improve provided service quality.