{"id":5751,"date":"2026-08-19T15:50:31","date_gmt":"2026-08-19T19:50:31","guid":{"rendered":"https:\/\/dcp.ufl.edu\/iadapt\/?page_id=5751"},"modified":"2026-10-14T16:16:37","modified_gmt":"2026-10-14T20:16:37","slug":"urban-planning-ai","status":"publish","type":"page","link":"https:\/\/dcp.ufl.edu\/iadapt\/urban-planning-ai\/","title":{"rendered":"Urban Planning AI"},"content":{"rendered":"\n<div class=\"wp-block-uagb-container uagb-block-8b94bf20 alignfull uagb-is-root-container\"><div class=\"uagb-container-inner-blocks-wrap\">\n<h1 class=\"wp-block-heading has-ast-global-color-5-color has-text-color has-link-color wp-elements-467d241c43e6f623f0cc936d4627eaa6\" style=\"font-size:3rem;font-style:normal;font-weight:900\">Urban Planning AI<\/h1>\n\n\n\n<p class=\"has-ast-global-color-5-color has-text-color has-link-color wp-elements-45cf34691a51731a8c749634fc2beb06 wp-block-paragraph\" style=\"font-size:1.25rem;font-style:normal;font-weight:500\">We investigate how AI can collaborate with planners, support plan-making and decision-making, and facilitate responsible planning automation. Our research also examines human\u2013AI interaction and develops governance frameworks for transparent, accountable, and effective Urban Planning AI.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-uagb-container uagb-block-d41da8b8 alignfull uagb-is-root-container\"><div class=\"uagb-container-inner-blocks-wrap\">\n<div class=\"wp-block-uagb-container uagb-block-ddfba918\">\n<figure class=\"wp-block-image size-large\" style=\"margin-top:0;margin-bottom:0\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2026\/10\/2025-10-14_HiPerGator-Unveiling_064-1024x683.jpg\" alt=\"Rows of black server racks labeled HiPerGator 4th Gen University of Florida in a data center.\" class=\"wp-image-6393\" style=\"aspect-ratio:1;object-fit:cover\" srcset=\"https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2026\/10\/2025-10-14_HiPerGator-Unveiling_064-1024x683.jpg 1024w, https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2026\/10\/2025-10-14_HiPerGator-Unveiling_064-300x200.jpg 300w, https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2026\/10\/2025-10-14_HiPerGator-Unveiling_064-768x512.jpg 768w, https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2026\/10\/2025-10-14_HiPerGator-Unveiling_064-1536x1025.jpg 1536w, https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2026\/10\/2025-10-14_HiPerGator-Unveiling_064-2048x1366.jpg 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-container uagb-block-f4e5d983\">\n<h2 class=\"wp-block-heading has-text-align-left has-text-color has-link-color wp-elements-45759c5eaaf2355f8263feca84b1bfab\" style=\"color:#002657;margin-top:0;margin-bottom:0;font-style:normal;font-weight:700\">Concept of Urban Planning AI<\/h2>\n\n\n\n<p class=\"has-text-align-left wp-block-paragraph\" style=\"margin-bottom:0;font-size:1.15rem\">Urban Planning AI refers to the use of artificial intelligence agents within the planning process itself, whether to support, augment, automate, or eventually take on independent roles in plan preparation and policy formation. This distinguishes it from AI applications that merely analyze cities without entering the institutional process of making public planning decisions.<\/p>\n\n\n\n<p class=\"has-text-align-left wp-block-paragraph\" style=\"margin-bottom:0;font-size:1.15rem\">Interest in this area has grown quickly across the planning field. Recent literature explores generative AI for urban design and automated plan generation, alongside a parallel and increasingly urgent conversation about governance: how to keep AI-assisted planning transparent, accountable, and reviewable as its role expands, rather than letting technical capability alone decide what AI is allowed to do in public decision-making.<\/p>\n<\/div>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-uagb-container uagb-block-c3903917 alignfull uagb-is-root-container\"><div class=\"uagb-container-inner-blocks-wrap\">\n<h2 class=\"wp-block-heading has-text-align-center has-text-color has-link-color wp-elements-2ac7743aa0ab5073cc4baed6fe716b1a\" style=\"color:#002657;margin-top:0;margin-bottom:var(--wp--preset--spacing--40);font-style:normal;font-weight:700\">Key Questions We Aim to Answer<\/h2>\n\n\n\n<div class=\"wp-block-uagb-container uagb-block-06e1f5b9 alignfull uagb-is-root-container\"><div class=\"uagb-container-inner-blocks-wrap\">\n<div class=\"wp-block-uagb-container uagb-block-6026bbbd\">      \n\t\t\t<div class=\"uagb-icon-wrapper uagb-block-9e3083af  wp-block-uagb-icon--has-margin    \"\n\t\t\tstyle=\"\" >\n\t\t\t\t\t\t\t\t<div class='uagb-icon-margin-wrapper'>\n\t\t\t\t\t\t\t\t\t<span class=\"uagb-svg-wrapper\" \n\t\t\t\t\t aria-label=\"computer\"\t\t\t\t\ttabindex=\"0\">\t\t\n\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox= \"0 0 640 512\" role=\"graphics-symbol\" aria-hidden=\"false\" aria-label=\"\"><path d=\"M400 32C426.5 32 448 53.49 448 80V336C448 362.5 426.5 384 400 384H266.7L277.3 416H352C369.7 416 384 430.3 384 448C384 465.7 369.7 480 352 480H96C78.33 480 64 465.7 64 448C64 430.3 78.33 416 96 416H170.7L181.3 384H48C21.49 384 0 362.5 0 336V80C0 53.49 21.49 32 48 32H400zM64 96V320H384V96H64zM592 32C618.5 32 640 53.49 640 80V432C640 458.5 618.5 480 592 480H528C501.5 480 480 458.5 480 432V80C480 53.49 501.5 32 528 32H592zM544 96C535.2 96 528 103.2 528 112C528 120.8 535.2 128 544 128H576C584.8 128 592 120.8 592 112C592 103.2 584.8 96 576 96H544zM544 192H576C584.8 192 592 184.8 592 176C592 167.2 584.8 160 576 160H544C535.2 160 528 167.2 528 176C528 184.8 535.2 192 544 192zM560 400C577.7 400 592 385.7 592 368C592 350.3 577.7 336 560 336C542.3 336 528 350.3 528 368C528 385.7 542.3 400 560 400z\"><\/path><\/svg>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\n\n\n<p class=\"wp-block-paragraph\" style=\"margin-top:0;margin-bottom:0;font-size:1.15rem\">How much of the planning process can and should be delegated to AI, and how does the planner&#8217;s role change as AI capability grows from data support toward plan generation?<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-container uagb-block-48875d16\">      \n\t\t\t<div class=\"uagb-icon-wrapper uagb-block-4f23dff1  wp-block-uagb-icon--has-margin    \"\n\t\t\tstyle=\"\" >\n\t\t\t\t\t\t\t\t<div class='uagb-icon-margin-wrapper'>\n\t\t\t\t\t\t\t\t\t<span class=\"uagb-svg-wrapper\" \n\t\t\t\t\t aria-label=\"book-open\"\t\t\t\t\ttabindex=\"0\">\t\t\n\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox= \"0 0 576 512\" role=\"graphics-symbol\" aria-hidden=\"false\" aria-label=\"\"><path d=\"M144.3 32.04C106.9 31.29 63.7 41.44 18.6 61.29c-11.42 5.026-18.6 16.67-18.6 29.15l0 357.6c0 11.55 11.99 19.55 22.45 14.65c126.3-59.14 219.8 11 223.8 14.01C249.1 478.9 252.5 480 256 480c12.4 0 16-11.38 16-15.98V80.04c0-5.203-2.531-10.08-6.781-13.08C263.3 65.58 216.7 33.35 144.3 32.04zM557.4 61.29c-45.11-19.79-88.48-29.61-125.7-29.26c-72.44 1.312-118.1 33.55-120.9 34.92C306.5 69.96 304 74.83 304 80.04v383.1C304 468.4 307.5 480 320 480c3.484 0 6.938-1.125 9.781-3.328c3.925-3.018 97.44-73.16 223.8-14c10.46 4.896 22.45-3.105 22.45-14.65l.0001-357.6C575.1 77.97 568.8 66.31 557.4 61.29z\"><\/path><\/svg>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\n\n\n<p class=\"wp-block-paragraph\" style=\"margin-top:0;margin-bottom:0;font-size:1.15rem\">How can AI&#8217;s involvement in planning be governed so that community priorities become binding constraints rather than advisory input?<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-container uagb-block-618385e7\">      \n\t\t\t<div class=\"uagb-icon-wrapper uagb-block-1415477d  wp-block-uagb-icon--has-margin    \"\n\t\t\tstyle=\"\" >\n\t\t\t\t\t\t\t\t<div class='uagb-icon-margin-wrapper'>\n\t\t\t\t\t\t\t\t\t<span class=\"uagb-svg-wrapper\" \n\t\t\t\t\t aria-label=\"gavel\"\t\t\t\t\ttabindex=\"0\">\t\t\n\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox= \"0 0 512 512\" role=\"graphics-symbol\" aria-hidden=\"false\" aria-label=\"\"><path d=\"M512 216.3c0-6.125-2.344-12.25-7.031-16.93L482.3 176.8c-4.688-4.686-10.84-7.028-16.1-7.028s-12.31 2.343-16.1 7.028l-5.625 5.625L329.6 69.28l5.625-5.625c4.687-4.688 7.03-10.84 7.03-16.1s-2.343-12.31-7.03-16.1l-22.62-22.62C307.9 2.344 301.8 0 295.7 0s-12.15 2.344-16.84 7.031L154.2 131.5C149.6 136.2 147.2 142.3 147.2 148.5s2.344 12.25 7.031 16.94l22.62 22.62c4.688 4.688 10.84 7.031 16.1 7.031c6.156 0 12.31-2.344 16.1-7.031l5.625-5.625l113.1 113.1l-5.625 5.621c-4.688 4.688-7.031 10.84-7.031 16.1s2.344 12.31 7.031 16.1l22.62 22.62c4.688 4.688 10.81 7.031 16.94 7.031s12.25-2.344 16.94-7.031l124.5-124.6C509.7 228.5 512 222.5 512 216.3zM227.8 238.1L169.4 297.4C163.1 291.1 154.9 288 146.7 288S130.4 291.1 124.1 297.4l-114.7 114.7c-6.25 6.248-9.375 14.43-9.375 22.62s3.125 16.37 9.375 22.62l45.25 45.25C60.87 508.9 69.06 512 77.25 512s16.37-3.125 22.62-9.375l114.7-114.7c6.25-6.25 9.376-14.44 9.376-22.62c0-8.185-3.125-16.37-9.374-22.62l58.43-58.43L227.8 238.1z\"><\/path><\/svg>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\n\n\n<p class=\"wp-block-paragraph\" style=\"margin-top:0;margin-bottom:0;font-size:1.15rem\">How can AI-generated planning options remain contestable, with humans retaining final authority to accept, modify, or reject them?<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-container uagb-block-53de6226\">      \n\t\t\t<div class=\"uagb-icon-wrapper uagb-block-cd4cfbb3  wp-block-uagb-icon--has-margin    \"\n\t\t\tstyle=\"\" >\n\t\t\t\t\t\t\t\t<div class='uagb-icon-margin-wrapper'>\n\t\t\t\t\t\t\t\t\t<span class=\"uagb-svg-wrapper\" \n\t\t\t\t\t aria-label=\"people-group\"\t\t\t\t\ttabindex=\"0\">\t\t\n\t\t\t\t\t\t\t\t\t\t<svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox= \"0 0 640 512\" role=\"graphics-symbol\" aria-hidden=\"false\" aria-label=\"\"><path d=\"M184 88C184 118.9 158.9 144 128 144C97.07 144 72 118.9 72 88C72 57.07 97.07 32 128 32C158.9 32 184 57.07 184 88zM208.4 196.3C178.7 222.7 160 261.2 160 304C160 338.3 171.1 369.8 192 394.5V416C192 433.7 177.7 448 160 448H96C78.33 448 64 433.7 64 416V389.2C26.16 371.2 0 332.7 0 288C0 226.1 50.14 176 112 176H144C167.1 176 190.2 183.5 208.4 196.3V196.3zM64 245.7C54.04 256.9 48 271.8 48 288C48 304.2 54.04 319.1 64 330.3V245.7zM448 416V394.5C468 369.8 480 338.3 480 304C480 261.2 461.3 222.7 431.6 196.3C449.8 183.5 472 176 496 176H528C589.9 176 640 226.1 640 288C640 332.7 613.8 371.2 576 389.2V416C576 433.7 561.7 448 544 448H480C462.3 448 448 433.7 448 416zM576 330.3C585.1 319.1 592 304.2 592 288C592 271.8 585.1 256.9 576 245.7V330.3zM568 88C568 118.9 542.9 144 512 144C481.1 144 456 118.9 456 88C456 57.07 481.1 32 512 32C542.9 32 568 57.07 568 88zM256 96C256 60.65 284.7 32 320 32C355.3 32 384 60.65 384 96C384 131.3 355.3 160 320 160C284.7 160 256 131.3 256 96zM448 304C448 348.7 421.8 387.2 384 405.2V448C384 465.7 369.7 480 352 480H288C270.3 480 256 465.7 256 448V405.2C218.2 387.2 192 348.7 192 304C192 242.1 242.1 192 304 192H336C397.9 192 448 242.1 448 304zM256 346.3V261.7C246 272.9 240 287.8 240 304C240 320.2 246 335.1 256 346.3zM384 261.7V346.3C393.1 335 400 320.2 400 304C400 287.8 393.1 272.9 384 261.7z\"><\/path><\/svg>\n\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\n\n\n<p class=\"wp-block-paragraph\" style=\"margin-top:0;margin-bottom:0;font-size:1.15rem\">What happens when an AI-assisted deployment meets a formal rule on paper but still produces unequal outcomes on the ground, and how should governance catch that gap?<\/p>\n<\/div>\n<\/div><\/div>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-uagb-container uagb-block-236b138b alignfull uagb-is-root-container\"><div class=\"uagb-container-inner-blocks-wrap\">\n<h2 class=\"wp-block-heading has-text-align-left has-text-color has-link-color wp-elements-fd9e43f3328a9e5deccd8a6dc8705c24\" style=\"color:#002657;margin-top:0;margin-bottom:var(--wp--preset--spacing--50);font-style:normal;font-weight:700\">Featured Research &amp; Projects<\/h2>\n\n\n\n<div class=\"wp-block-uagb-container uagb-block-931edb8a\">\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-7387b849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:50%\">\n<figure class=\"wp-block-image size-full has-custom-border is-style-default\" style=\"margin-top:0;margin-bottom:0\"><img loading=\"lazy\" decoding=\"async\" width=\"1030\" height=\"810\" src=\"https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2026\/09\/human-ai-transit-featured-image.jpg\" alt=\"A city map displaying 10 colored bus routes, bus stop walksheds in yellow, and blue circles of varying sizes representing demand magnitude at each stop.\" class=\"wp-image-6363\" style=\"border-top-left-radius:15px;border-top-right-radius:0px;border-bottom-left-radius:15px;border-bottom-right-radius:0px;aspect-ratio:1;object-fit:contain;object-position:51% 59%\" srcset=\"https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2026\/09\/human-ai-transit-featured-image.jpg 1030w, https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2026\/09\/human-ai-transit-featured-image-300x236.jpg 300w, https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2026\/09\/human-ai-transit-featured-image-1024x805.jpg 1024w, https:\/\/dcp.ufl.edu\/iadapt\/wp-content\/uploads\/sites\/28\/2026\/09\/human-ai-transit-featured-image-768x604.jpg 768w\" sizes=\"auto, (max-width: 1030px) 100vw, 1030px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"padding-top:30px;padding-right:30px;padding-bottom:30px;padding-left:10px;flex-basis:66.66%\">\n<h3 class=\"wp-block-heading has-text-color has-link-color wp-elements-d7283bd578b8d4131de22baf554b9556\" style=\"color:#001a84;margin-top:0;margin-bottom:var(--wp--preset--spacing--20);font-size:1.17rem\"><strong>Human-AI Collaboration in Transit Network Design<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"margin-top:0;margin-right:0;margin-bottom:var(--wp--preset--spacing--30)\"><em>A Data-Driven Planning Case from Tampa<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"margin-top:0;margin-bottom:var(--wp--preset--spacing--40)\">As artificial intelligence increasingly shapes how cities are planned and managed, a key question for planners is whether AI can move beyond supporting analysis to actually generating spatial plans. This study explores that question in the context of public transit network design, using downtown Tampa, Florida as an empirical testbed &#8230;<\/p>\n\n\n\n<div class=\"wp-block-uagb-buttons uagb-buttons__outer-wrap uagb-btn__default-btn uagb-btn-tablet__default-btn uagb-btn-mobile__default-btn uagb-block-77a2135c\"><div class=\"uagb-buttons__wrap uagb-buttons-layout-wrap \">\n<div class=\"wp-block-uagb-buttons-child uagb-buttons__outer-wrap uagb-block-1712a4e2 wp-block-button\"><div class=\"uagb-button__wrapper\"><a class=\"uagb-buttons-repeater wp-block-button__link\" aria-label=\"\" href=\"https:\/\/dcp.ufl.edu\/iadapt\/human-ai-collaboration-in-transit-network-design\/\" rel=\"follow noopener\" target=\"_self\" role=\"button\"><div class=\"uagb-button__link\">Read More<\/div><span class=\"uagb-button__icon uagb-button__icon-position-after\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 448 512\" aria-hidden=\"true\" focussable=\"false\"><path d=\"M438.6 278.6l-160 160C272.4 444.9 264.2 448 256 448s-16.38-3.125-22.62-9.375c-12.5-12.5-12.5-32.75 0-45.25L338.8 288H32C14.33 288 .0016 273.7 .0016 256S14.33 224 32 224h306.8l-105.4-105.4c-12.5-12.5-12.5-32.75 0-45.25s32.75-12.5 45.25 0l160 160C451.1 245.9 451.1 266.1 438.6 278.6z\"><\/path><\/svg><\/span><\/a><\/div><\/div>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-buttons uagb-buttons__outer-wrap uagb-btn__default-btn uagb-btn-tablet__default-btn uagb-btn-mobile__default-btn uagb-block-478f44ad\"><div class=\"uagb-buttons__wrap uagb-buttons-layout-wrap \">\n<div class=\"wp-block-uagb-buttons-child uagb-buttons__outer-wrap uagb-block-e57f4343 wp-block-button\"><div class=\"uagb-button__wrapper\"><a class=\"uagb-buttons-repeater wp-block-button__link\" aria-label=\"\" href=\"https:\/\/dcp.ufl.edu\/iadapt\/research-projects\/\" rel=\"follow noopener\" target=\"_self\" role=\"button\"><div class=\"uagb-button__link\">Explore More Research<\/div><\/a><\/div><\/div>\n<\/div><\/div>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-uagb-container uagb-block-a972683a alignfull uagb-is-root-container\"><div class=\"uagb-container-inner-blocks-wrap\">\n<h2 class=\"wp-block-heading has-text-align-left has-text-color has-link-color wp-elements-fa8fcdceb14e5bd6bcd1128fde5df179\" style=\"color:#002657;margin-top:0;margin-bottom:0;font-style:normal;font-weight:700\">Selected Publications<\/h2>\n\n\n\n<p class=\"has-text-align-left wp-block-paragraph\" style=\"margin-bottom:0;font-size:1.15rem\">Peng, Z.-R., Lu, K.-F., Liu, Y., &amp; Zhai, W. (2024). The pathway of urban planning AI: From planning support to plan-making. <em>Journal of Planning Education and Research, 44<\/em>(4), 873\u2013885. <a href=\"https:\/\/doi.org\/10.1177\/0739456X231180568\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1177\/0739456X231180568<\/a><\/p>\n\n\n\n<p class=\"has-text-align-left wp-block-paragraph\" style=\"margin-bottom:0;font-size:1.15rem\">Peng, Z.-R., Lu, K.-F., Liu, Y., Hou, Q., &amp; Zhang, Q. (2026). Symbiotic Planning Theory: The CORE framework for AI-human co-creation in urban planning. <em>Journal of the American Planning Association.<\/em> <a href=\"https:\/\/doi.org\/10.1080\/01944363.2026.2640038\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1080\/01944363.2026.2640038<\/a><\/p>\n\n\n\n<p class=\"has-text-align-left wp-block-paragraph\" style=\"margin-bottom:0;font-size:1.15rem\">Peng, Z.-R. (2026). <em>Symbiotic Planning for Urban Futures: A Paradigm for Human-AI Co-Creation.<\/em> Elsevier.<br><a href=\"https:\/\/shop.elsevier.com\/books\/symbiotic-planning-for-urban-futures\/peng\/978-0-443-45040-2\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/shop.elsevier.com\/books\/symbiotic-planning-for-urban-futures\/peng\/978-0-443-45040-2<\/a><\/p>\n\n\n\n<div class=\"wp-block-uagb-buttons uagb-buttons__outer-wrap uagb-btn__default-btn uagb-btn-tablet__default-btn uagb-btn-mobile__default-btn uagb-block-8c24fe9d\"><div class=\"uagb-buttons__wrap uagb-buttons-layout-wrap \">\n<div class=\"wp-block-uagb-buttons-child uagb-buttons__outer-wrap uagb-block-b282d6d2 wp-block-button\"><div class=\"uagb-button__wrapper\"><a class=\"uagb-buttons-repeater wp-block-button__link\" aria-label=\"\" href=\"https:\/\/dcp.ufl.edu\/iadapt\/publications\/\" rel=\"follow noopener\" target=\"_self\" role=\"button\"><div class=\"uagb-button__link\">Explore More Publications<\/div><\/a><\/div><\/div>\n<\/div><\/div>\n<\/div><\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Urban Planning AI We investigate how AI can collaborate with planners, support plan-making and decision-making, and facilitate responsible planning automation. Our research also examines human\u2013AI interaction and develops governance frameworks for transparent, accountable, and effective Urban Planning AI. Concept of Urban Planning AI Urban Planning AI refers to the use of artificial intelligence agents within [&hellip;]<\/p>\n","protected":false},"author":259,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"#primary {\n  margin: 0;\n}","site-sidebar-layout":"no-sidebar","site-content-layout":"","ast-site-content-layout":"normal-width-container","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-5751","page","type-page","status-publish","hentry"],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false},"uagb_author_info":{"display_name":"adoralin","author_link":"https:\/\/dcp.ufl.edu\/iadapt\/author\/adoralin\/"},"uagb_comment_info":0,"uagb_excerpt":"Urban Planning AI We investigate how AI can collaborate with planners, support plan-making and decision-making, and facilitate responsible planning automation. Our research also examines human\u2013AI interaction and develops governance frameworks for transparent, accountable, and effective Urban Planning AI. Concept of Urban Planning AI Urban Planning AI refers to the use of artificial intelligence agents within&hellip;","_links":{"self":[{"href":"https:\/\/dcp.ufl.edu\/iadapt\/wp-json\/wp\/v2\/pages\/5751","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dcp.ufl.edu\/iadapt\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/dcp.ufl.edu\/iadapt\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/dcp.ufl.edu\/iadapt\/wp-json\/wp\/v2\/users\/259"}],"replies":[{"embeddable":true,"href":"https:\/\/dcp.ufl.edu\/iadapt\/wp-json\/wp\/v2\/comments?post=5751"}],"version-history":[{"count":0,"href":"https:\/\/dcp.ufl.edu\/iadapt\/wp-json\/wp\/v2\/pages\/5751\/revisions"}],"wp:attachment":[{"href":"https:\/\/dcp.ufl.edu\/iadapt\/wp-json\/wp\/v2\/media?parent=5751"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}