Development of Artificial Intelligence-based Bicycle Level of Service Models for Urban Street Segments

This study deals with the development of artificial intelligence (AI)-based bicycle level of service (BLOS) models for urban road segments carrying heterogeneous traffic. To accomplish this, the required data sets on the road geometric, traffic and built-environmental conditions are collected from 84 road segments located in various parts of four Indian cities. The satisfaction levels of bicyclists at each site are also assessed using a Likert scale of 1–6 (excellent–worst). Subsequently, three promising AI techniques namely, Multivariate adaptive regression splines (MARS), Genetic programming (GP) and Bayesian regularization neural network (BRNN) are utilized to develop the BLOS models. All models are trained and tested with eight significant attributes of the road segments. Among all models, the MARS-based one has shown the best prediction performance in the present context with a coefficient of determination (R²) value of 0.92 with averaged observations. On the other hand, GP has produced the simplest (regression-like) but reliable model, which is the most favourable for field applications. The relative importance of input variables has concluded that the outermost lane width, traffic volume, on-street parking activities and pavement condition index are by far the most important variables in the present context. Hence, these attributes should be largely prioritized in the planning process to enhance the perceived BLOS effortlessly.