Estimating congestion zones and travel time indexes based on the floating car data

Abstract Efficiently predicting traffic congestion benefits various traffic stakeholders, from regular commuters and logistic operators to urban planners and responsible authorities. This study aims to give a high-quality estimation of traffic conditions from a large historical Floating Car Data (FCD) with two main goals: (i) estimation of congestion zones on a large road network, and (ii) estimation of travel times within congestion zones in the form of the time-varying Travel Time Indexes (TTIs). On the micro level, the traffic conditions, in the form of speed profiles were mapped to links in the road network. On the macro level, the observed area was divided into a fine-grained grid and represented as an image where each pixel indicated congestion intensity. Spatio-temporal characteristics of congestion zones were determined by morphological closing operation and Monte Carlo simulation coupled with temporal clustering. As a case study, the road network in Croatia was selected with spatio-temporal analysis differentiating between the summer season and the rest of the year season. To validate the proposed approach, three comparisons were conducted: (i) comparison to real routes' travel times driven in a controlled manner, (ii) comparison to historical trajectory dataset, and (iii) comparison to the state-of-the-art method. Compared to the real measured travel times, using zone's time-varying TTIs for travel time estimation resulted in the mean relative percentage error of 4.13%, with a minor difference to travel times estimated on the micro level, and a significant improvement compared to the current Croatian industrial navigation. The results support the feasibility of estimating congestion zones and time-varying TTIs on a large road network from FCD, with the application in urban planning and time-dependent routing operations due to: significant reduction in the data volume without notable quality loss, and meaningful reduction in the pre-processing computation time.