Traffic Estimation And Prediction Based On Real Time Floating Car Data

By Corrado de Fabritiis, Roberto Ragona, Gaetano Emanuele Valenti. 2008 11th International IEEE Conference on Intelligent Transportation Systems, 2008

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Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data (2017, methodology): “…congestion estimation on an arterial transportation network can be roughly categorized into trafﬁc modeling on individual roads [Helbing 2001; Mu˜noz et al. 2003; Porikli and Li 2004; de Fabritiis et al. 2008] and on a road network [Pattara-Atikom et al. 2006; Yuan et al. 2010; Shang et al. 2014].”
Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments (2017, background): “Advancements in traffic state estimation [21, 22, 23] have facilitated high resolution traffic monitoring, through the advent of GPS smartphone sensors [24, 25, 26, 27] that are part of the flow—termed Lagrangian or mobile sensors.”
Ridesourcing systems: A framework and review (2019, methodology): “De Fabritiis et al. (2008) present a large-scale working application of a Floating-Car Data (FCD) system. They propose two algorithms, based on artificial neural networks and pattern-matching, using floating car data designed to perform online short-term predictions of link travel speeds using curre”
Estimate Dynamic Road Travel Time Based on Uncertainty Feedback (2014, background): “In our city,real-time travel time plays an increasingly important role to observe the performance of street segments in complex urban road networks [1].”