An evolutionary framework for estimating turning movements at road intersections

Abstract Turning movements are one of the key inputs required for several traffic studies. Several methods have been developed to measure them. However, present techniques have high operational or capital costs, which motivate researchers to develop new techniques to estimate turning movements. However, there is neither a flexible technique available to make best use of different available information types, nor a framework that supports deciding additional data to achieve a target accuracy. This paper proposes a new methodology using all available data to identify the subspace containing all solutions and determine its centroid; thus, providing the most realistic and non-extreme solution. In addition, a framework, including scenarios with different data combinations, is developed with capability to evaluate the proposed solution and then locate further measurements to achieve the target accuracy. The framework is validated using a considerable set of intersections at Edmonton city, Canada. The results show that the proposed framework can achieve the target accuracy with minimum field measurements saving time, effort and cost.