An ensemble Kalman filtering approach to highway traffic estimation using GPS enabled mobile devices

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The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data (2011, background): “Historically, the estimation of trafﬁc congestion has been limited to highways, and has relied mostly on a static, dedicated sensing infrastructure such as loop detectors or cameras [41].”
Traffic state estimation on highway: A comprehensive survey (2017, background): “Work et al. (2008, 2010) developed a first-order traffic flow model whose state variable is speed.”
Singapore in Motion: Insights on Public Transport Service Level Through Farecard and Mobile Data Analytics (2016, methodology): “Mobile geolocation data has been used for instance for real-time traffic monitoring [32] and path inference [14] in the context of GPS-based participatory sensing, combined with other sources for city planning [26], and even as a potential replacement for the fixed sensing infrastructure, in particu”
A traffic model for velocity data assimilation (2010, methodology): “running in real-time during the experiment, detected the accident’s resulting bottleneck and corresponding shockwave [35], and broadcast the results to the web.”
Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction (2021, methodology): “Different from EKFs, the ensemble KFs was developed by Lighthill Whitham Richards (LWR) [10], [11] and was used for freeway network estimation problems [12], [13].”