Estimating traffic demand of different transportation modes using floating smartphone data

Abstract

Traffic demand is crucial for traffic planning, helping to understand traffic volume, identify congestion points, and develop strategies for efficient and sustainable transportation. This paper introduces a method to estimate origin-destination (OD) matrices for drivers, cyclists, and pedestrians using floating smartphone data (FSD) and the information minimisation (IM) model. The FSD is used in the gravity model (GM) to provide more accurate inputs for the IM model by estimating an a priori matrix and traffic demand at intersections. Additionally, FSD allowed a direct route choice proportions deriviation without needing a traffic assignment model. The proposed estimation model was tested with real data and analyzed in a simulation environment, examining the impact of increasing the penetration rate (PR) of FSD. Results indicate that traffic demand in the study area was accurately estimated with an average Rxy value of 92%, aligning well with simulation results.