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- W2408502658 abstract "We develop a Bayesian particle filter for tracking traffic flows that is capable of capturing non-linearities and discontinuities present in flow dynamics. Our model includes a hidden state variable that captures sudden regime shifts between traffic free flow, breakdown and recovery. We develop an efficient particle learning algorithm for real time on-line inference of states and parameters. This requires a two step approach, first, resampling the current particles, with a mixture predictive distribution and second, propagation of states using the conditional posterior distribution. Particle learning of parameters follows from updating recursions for conditional sufficient statistics. To illustrate our methodology, we analyze measurements of daily traffic flow from the Illinois interstate I-55 highway system. We demonstrate how our filter can be used to inference the change of traffic flow regime on a highway road segment based on a measurement from freeway single-loop detectors. Finally, we conclude with directions for future research." @default.
- W2408502658 created "2016-06-24" @default.
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- W2408502658 date "2018-02-01" @default.
- W2408502658 modified "2023-10-14" @default.
- W2408502658 title "Bayesian Particle Tracking of Traffic Flows" @default.
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- W2408502658 doi "https://doi.org/10.1109/tits.2017.2650947" @default.
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