Estimating lateral placement and lane indiscipline of urban mixed traffic of a developing country: an ANN-assisted approach

Abstract

Information on lateral placement and lane indiscipline is useful in simulation of a mixed traffic stream and in identifying the distressed portion of a pavement. Despite this utility, inadequate investigation has been made to estimate the lateral placement of vehicles under prevailing traffic conditions. In a typical mixed traffic situation, vehicles having different static and dynamic characteristics take any lateral gap across the carriageway left empty by other surrounding vehicles and move in an untidy manner. This leads to variation in lateral placement of vehicles, governed by the subject vehicle type. This paper explores the potential factors that influence lateral placement of vehicles and presents an artificial neural network (ANN)-based approach to quantify lateral placement and lane indiscipline in the context of undivided urban roads. Further, sensitivity analysis revealed how different traffic parameters like traffic volume, traffic composition, and directional split influence lateral placement and lane indiscipline within each vehicle category.