Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning

<figure class="figure-group responsive"><div class="sub-figure" id="a"><object data="https://static-preview.hindawi.com/articles/jat/volume-2019/3072495/figures/3072495.fig.001a.svgz" name="3072495.fig.001a" type="image/svg+xml"></object><div class="fig-label"><b>(a) </b>Train passes balise B<sub>1</sub></div></div><div class="sub-figure" id="b"><object data="https://static-preview.hindawi.com/articles/jat/volume-2019/3072495/figures/3072495.fig.001b.svgz" name="3072495.fig.001b" type="image/svg+xml"></object><div class="fig-label"><b>(b) </b>Train passes balise B<sub>2</sub></div></div><div class="sub-figure" id="c"><object data="https://static-preview.hindawi.com/articles/jat/volume-2019/3072495/figures/3072495.fig.001c.svgz" name="3072495.fig.001c" type="image/svg+xml"></object><div class="fig-label"><b>(c) </b>Train passes balise B<sub>3</sub></div></div><div class="sub-figure" id="d"><object data="https://static-preview.hindawi.com/articles/jat/volume-2019/3072495/figures/3072495.fig.001d.svgz" name="3072495.fig.001d" type="image/svg+xml"></object><div class="fig-label"><b>(d) </b>Train passes balise <svg height="11.6608pt" id="M11" style="vertical-align:-3.27282pt" version="1.1" viewbox="-0.0498162 -8.38798 21.3961 11.6608" width="21.3961pt" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M372 352C427 368 507 407 507 502C507 548 486 592 451 616C420 638 376 650 301 650H44V622C124 616 128 610 128 527V123C128 40 122 34 36 28V0H257C336 0 402 12 453 40C512 72 548 124 548 191C548 291 471 335 372 350V352ZM213 362V558C213 591 216 602 226 608C235 614 258 618 282 618C377 618 415 558 415 490C415 406 369 362 260 362H213ZM213 328H258C380 328 450 283 450 181C450 76 377 35 296 34C231 34 213 53 213 125V328Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,7.644,3.132)"><path d="M719 650H483V622C553 619 576 606 581 560C585 532 588 487 588 394V148H585L167 650H20V622C67 618 88 610 109 585C127 562 129 557 129 484V264C129 173 126 124 123 93C118 44 92 31 35 28V0H272V28C204 32 181 44 176 95C173 124 170 172 170 264V515H172L598 -9H629V394C629 487 631 532 635 563C639 606 663 619 719 622V650Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,14.405,3.132)"><path d="M300 253H71L57 198H286L300 253Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,17.645,3.132)"><path d="M311 0V25C221 32 216 41 216 113V635C162 613 103 594 39 583V559L88 556C131 552 135 546 135 500V113C135 41 131 32 40 25V0H311Z"></path></g></svg></div></div></figure>

<p>Parking area and balises distribution [<a href="/journals/jat/2019/3072495/#B18">17</a>].</p>

Journal of Advanced Transportation

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Figure 1: Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning 