Traffic Flow Anomaly Detection Based on Robust Ridge Regression with Particle Swarm Optimization Algorithm

<table class="table-group" id="tab2"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Hyperparameter</td><td class="align_center">Value range</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">Threshold <svg height="8.68572pt" id="M176" style="vertical-align:-0.0498209pt" version="1.1" viewbox="-0.0498162 -8.6359 12.9526 8.68572" width="12.9526pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M962 650H795L470 145L347 650H176L170 622C268 613 275 606 244 503L190 322C150 188 132 126 118 91C102 50 80 33 18 28L12 0H245L251 28C175 35 170 48 174 93C177 128 191 180 220 284L292 542H294C331 392 383 150 409 4H432L774 555H776L714 137C700 40 694 34 612 28L606 0H868L874 28C793 34 784 37 797 137L849 533C859 612 863 616 956 622L962 650Z"></path></g></svg></td><td class="align_center"><span style="width: 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<div>Model hyperparameters and value ranges.</div>

Mathematical Problems in Engineering

tab2

Table 2

Table 2: Traffic Flow Anomaly Detection Based on Robust Ridge Regression with Particle Swarm Optimization Algorithm 