{"id":29532,"date":"2026-03-11T08:00:00","date_gmt":"2026-03-11T07:00:00","guid":{"rendered":"https:\/\/blog.ptvgroup.com\/?p=29532"},"modified":"2026-02-06T14:21:05","modified_gmt":"2026-02-06T13:21:05","slug":"urban-mobility-microsimulation-in-a-unesco-heritage-city","status":"publish","type":"post","link":"https:\/\/blog.ptvgroup.com\/en\/user-insights\/urban-mobility-microsimulation-in-a-unesco-heritage-city\/","title":{"rendered":"Urban Mobility Microsimulation in a UNESCO Heritage City"},"content":{"rendered":"\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:66.66%\">\n<div class=\"wp-block-yoast-seo-table-of-contents yoast-table-of-contents\"><h2>Table of contents<\/h2><ul><li><a href=\"#h-urban-mobility-microsimulation-project-background-and-stages\" data-level=\"2\">Urban mobility microsimulation: Project background and stages<\/a><\/li><li><a href=\"#h-methodology-calibrate-before-drawing-conclusions\" data-level=\"2\">Methodology: Calibrate before drawing conclusions<\/a><\/li><li><a href=\"#h-what-we-tested-and-why\" data-level=\"2\">What we tested &#8211; and why<\/a><\/li><li><a href=\"#h-what-the-calibrated-model-showed\" data-level=\"2\">What the calibrated model showed<\/a><ul><li><a href=\"#h-network-wide-indicators\" data-level=\"3\">Network\u2011wide indicators<\/a><\/li><li><a href=\"#h-travel-time-corridors\" data-level=\"3\">Travel\u2011time corridors<\/a><\/li><li><a href=\"#h-bus-lane-performance-scenario-1\" data-level=\"3\">Bus lane performance (Scenario 1)<\/a><\/li><li><a href=\"#h-average-delay-all-scenarios\" data-level=\"3\">Average delay (all scenarios)<\/a><\/li><li><a href=\"#h-average-speed-all-scenarios\" data-level=\"3\">Average speed (all scenarios)<\/a><\/li><\/ul><\/li><li><a href=\"#h-engaging-stakeholders\" data-level=\"2\">Engaging stakeholders<\/a><\/li><li><a href=\"#h-handover-and-capability-building\" data-level=\"2\">Handover and capability building<\/a><\/li><li><a href=\"#h-my-tips-for-planners\" data-level=\"2\">My tips for planners<\/a><\/li><li><a href=\"#h-closing-thought\" data-level=\"2\">Closing thought<\/a><\/li><\/ul><\/div>\n\n\n\n<p>Many cities are under pressure to manage growing travel demand within historic, space\u2011constrained environments. Penang, Malaysia, is one of them: a region of 1.8 million people where Georgetown\u2019s UNESCO World Heritage center combines dense streets, heritage limitations, and highly dynamic mobility patterns. To understand these conditions and test improvements before implementation, we turned to urban mobility microsimulation using PTV Vissim.<\/p>\n\n\n\n<p>When I joined the effort through the <em>ASEAN Australia Smart Cities Trust Fund (AASCTF) Penang Smart Mobility Micro-Simulation Model Development<\/em> program. Our mandate was clear: build a calibrated PTV Vissim model of Georgetown\u2019s core so that Digital Penang and the Penang Island City Council (MBPP) could confidently evaluate current and future mobility strategies.<\/p>\n\n\n\n<p>In this article, I walk through how the model was created, how four policy scenarios were assessed, and how the results informed real decisions for one of Southeast Asia\u2019s most complex urban environments. I also highlight tips for planners facing similar challenges in other cities.<\/p>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69d60fd179093&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69d60fd179093\" class=\"wp-block-image size-full wp-lightbox-container\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"500\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Untitled-design-2.png\" alt=\"\" class=\"wp-image-29530\" srcset=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Untitled-design-2.png 500w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Untitled-design-2-360x360.png 360w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Untitled-design-2-200x200.png 200w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Untitled-design-2-150x150.png 150w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><figcaption class=\"wp-element-caption\">A street in Georgetown, Penang<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-urban-mobility-microsimulation-project-background-and-stages\"><strong>Urban mobility microsimulation: Project background and stages<\/strong><\/h2>\n\n\n\n<p>Our priority was creating a robust, reusable base model of Georgetown&#8217;s <a href=\"https:\/\/blog.ptvgroup.com\/en\/modeling-planning\/ptv-vissim-traffic-calming-measures\/\" target=\"_blank\" rel=\"noreferrer noopener\">historic city center<\/a>. With this model, authorities could assess developer proposals and test policies and designs, including parking reduction, bus priority, micro\u2011mobility, and car\u2011free streets. They could also communicate impacts to decision\u2011makers and the public and build internal capacity to improve strategies.<\/p>\n\n\n\n<p>Instead of presenting lists of ideas, I aimed to show quantitatively how each change affected the network.<\/p>\n\n\n\n<p>I began with a pilot area &#8211; <a href=\"https:\/\/blog.ptvgroup.com\/en\/user-insights\/ptv-blog_stadium_traffic_simulation_vissim_viswalk\/\" target=\"_blank\" rel=\"noreferrer noopener\">a proof of concept<\/a> &#8211; to demonstrate the value of micro simulation for localized options and to highlight the differences between schemes.<\/p>\n\n\n\n<p>Then, we expanded to a full-area model of the UNESCO World Heritage Site in Georgetown. This model faithfully represented traffic conditions and revealed the benefits and trade-offs of different scenarios, especially those related to congestion.<\/p>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69d60fd1797b1&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69d60fd1797b1\" class=\"wp-block-image size-full wp-lightbox-container\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"281\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_slide2.png\" alt=\"Urban mobility microsimulation of Georgetown showing the expanded full\u2011area Vissim model\" class=\"wp-image-29526\" srcset=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_slide2.png 500w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_slide2-320x180.png 320w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_slide2-300x169.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-methodology-calibrate-before-drawing-conclusions\"><strong>Methodology: Calibrate before drawing conclusions<\/strong><\/h2>\n\n\n\n<p>It was more important to get the foundation right than to rush to scenarios. These steps ensured the model accurately supported urban mobility microsimulation of Georgetown\u2019s constrained street network:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Calibration targets:<\/strong> Turn volumes, parking performance, and queue lengths were iteratively matched. Only then did we progress to scenarios.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Data and network:<\/strong> We expanded the network to Stage 2 and conducted additional traffic and movement counts.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Demand and supply:<\/strong> I ran a <a href=\"https:\/\/blog.ptvgroup.com\/en\/user-insights\/triangulating-intersection-delay-with-ptv-vissim-in-hyderabad\/\" target=\"_blank\" rel=\"noreferrer noopener\">PCU matrix estimation<\/a>, then split it by vehicle class (car, taxi, LGV, HGV, bike, and bus) and segregated origin-destination (OD) demand versus parking demand.<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Parking realism:<\/strong> Since on-street parking is a key factor in Georgetown\u2019s traffic conditions, we surveyed parking occupancy and dwell times. We then used trip chains to reproduce turnover behavior in Vissim, but only after meeting GEH targets in <a href=\"https:\/\/blog.ptvgroup.com\/en\/user-insights\/calibrating-vissim-for-accurate-simulation-of-pedestrian-crossings\/\" target=\"_blank\" rel=\"noreferrer noopener\">calibration<\/a>.<br><\/li>\n<\/ul>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69d60fd17a104&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69d60fd17a104\" class=\"wp-block-image size-full wp-lightbox-container\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"281\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_slide4.png\" alt=\"Urban mobility microsimulation calibration showing traffic counts and network inputs for Georgetown\" class=\"wp-image-29524\" srcset=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_slide4.png 500w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_slide4-320x180.png 320w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_slide4-300x169.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69d60fd17a66c&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69d60fd17a66c\" class=\"wp-block-image size-full wp-lightbox-container\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"281\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide5.png\" alt=\"Urban mobility microsimulation calibration outputs illustrating queue length and GEH matching in Georgetown\" class=\"wp-image-29522\" srcset=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide5.png 500w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide5-320x180.png 320w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide5-300x169.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-we-tested-and-why\"><strong>What we tested &#8211; and why<\/strong><\/h2>\n\n\n\n<p>Using this calibrated base, we applied urban mobility microsimulation to assess the following four <a href=\"https:\/\/blog.ptvgroup.com\/en\/user-insights\/from-gridlock-to-flow-ptv-vissim-transforms-traffic-in-marrakesh\/\" target=\"_blank\" rel=\"noreferrer noopener\">policy scenarios<\/a> from the Penang Green Transport Master Plan. We then compared these interventions against the calibrated baseline.<\/p>\n\n\n\n<p><strong>1. Public Transport Priority:<\/strong> A bus lane on a key corridor without signal priority.<\/p>\n\n\n\n<p><strong>2. Pedestrian and cyclist priority:<\/strong> Conversion of selected streets to non-motorized transportation (NMT), with new footpaths and possible bike lanes.<\/p>\n\n\n\n<p><strong>3. Parking Reduction:<\/strong> Targeted removal of on-street parking spaces that impede buses and NMT.<\/p>\n\n\n\n<p><strong>4. Traffic Management:<\/strong> Two signalized crossings to the ferry terminal and two new signalized junctions will improve circulation.<\/p>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69d60fd17ae5d&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69d60fd17ae5d\" class=\"wp-block-image size-full wp-lightbox-container\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"281\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide7.png\" alt=\"Urban mobility microsimulation scenario overview showing four policy tests from the Penang Green Transport Master Plan\" class=\"wp-image-29520\" srcset=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide7.png 500w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide7-320x180.png 320w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide7-300x169.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-the-calibrated-model-showed\"><strong>What the calibrated model showed<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-network-wide-indicators\"><strong><strong>Network\u2011wide indicators<\/strong><\/strong><\/h3>\n\n\n\n<p>We compared each scenario to the base using Vissim outputs for delay, density, and speed.<\/p>\n\n\n\n<p>For scenarios 1-3, the <a href=\"https:\/\/blog.ptvgroup.com\/en\/user-insights\/vissim-microsimulation-helps-liverpool-cut-no%E2%82%82-vehicle-emissions\/\" target=\"_blank\" rel=\"noreferrer noopener\">network-wide differences<\/a> were generally small and consistent with the maps. Scenario 4 produced localized increases in delay and density (as well as lower speeds) near the new signals, which is an expected outcome when adding protected crossing time to a tight urban cycle.<\/p>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69d60fd17b5aa&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69d60fd17b5aa\" class=\"wp-block-image size-full wp-lightbox-container\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"281\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide17.png\" alt=\"Urban mobility microsimulation networkwide delay impacts across tested scenarios in Georgetown\" class=\"wp-image-29518\" srcset=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide17.png 500w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide17-320x180.png 320w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide17-300x169.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69d60fd17baed&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69d60fd17baed\" class=\"wp-block-image size-full wp-lightbox-container\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"281\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide18.png\" alt=\"Urban mobility microsimulation density results comparing baseline and scenarios in Georgetown\" class=\"wp-image-29516\" srcset=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide18.png 500w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide18-320x180.png 320w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide18-300x169.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69d60fd17c05b&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69d60fd17c05b\" class=\"wp-block-image size-full wp-lightbox-container\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"281\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide19.png\" alt=\"Urban mobility microsimulation average speed changes across scenarios in Georgetown\u2019s street network\" class=\"wp-image-29514\" srcset=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide19.png 500w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide19-320x180.png 320w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide19-300x169.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-travel-time-corridors\"><strong>Travel\u2011time corridors<\/strong><\/h3>\n\n\n\n<p>We tracked directional travel times on key corridors during the morning and afternoon commutes (AM and PM peaks). The results varied by corridor and intervention. Rather than cherry-picking a single link, we used the results to cross-check the network KPIs.<\/p>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69d60fd17c65d&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69d60fd17c65d\" class=\"wp-block-image size-full wp-lightbox-container\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"281\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide20.png\" alt=\"Urban mobility microsimulation travel\u2011time corridor analysis for AM and PM peaks in Georgetown\" class=\"wp-image-29512\" srcset=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide20.png 500w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide20-320x180.png 320w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide20-300x169.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-bus-lane-performance-scenario-1\"><strong>Bus lane performance (Scenario 1)<\/strong><\/h3>\n\n\n\n<p>On the eastbound test segment, the dedicated bus lane reduced public transit travel time by approximately 6% during the AM peak and 1% during the PM peak compared to the baseline.<\/p>\n\n\n\n<p>Even modest percentage gains can be operationally significant for maintaining schedules on short urban routes.<\/p>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69d60fd17cc21&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69d60fd17cc21\" class=\"wp-block-image size-full wp-lightbox-container\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"281\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide21.png\" alt=\"Urban mobility microsimulation showing bus lane performance improvements on the eastbound test segment\" class=\"wp-image-29510\" srcset=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide21.png 500w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide21-320x180.png 320w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide21-300x169.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-average-delay-all-scenarios\"><strong>Average delay (all scenarios)<\/strong><\/h3>\n\n\n\n<p>From the network averages:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Scenario 1 (PT lane): -4% AM, -14% PM (99s \u2192 85s).<\/li>\n\n\n\n<li>Scenario 2 (pedestrian\/cycle): -1% AM, -14% PM (99s \u2192 85s).<\/li>\n\n\n\n<li>Scenario 3 (parking reduction): -2% AM, -12% PM (99s \u2192 86s).<\/li>\n\n\n\n<li>Scenario 4 (Signals): +0.5% AM, -9% PM (99s \u2192 89s).<\/li>\n<\/ul>\n\n\n\n<p>These averages temper expectations. Visibility and safety benefits (e.g., safer crossings) may introduce minor AM costs in Scenario 4, while PM benefits remain substantial.<\/p>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69d60fd17d2ff&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69d60fd17d2ff\" class=\"wp-block-image size-full wp-lightbox-container\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"281\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide22.png\" alt=\"Urban mobility microsimulation results showing average delay comparison across all four scenarios\" class=\"wp-image-29508\" srcset=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide22.png 500w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide22-320x180.png 320w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide22-300x169.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-average-speed-all-scenarios\"><strong>Average speed (all scenarios)<\/strong><\/h3>\n\n\n\n<p>Network-wide average speeds:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Scenario 1: +1% AM; +9% PM (21 to 23 km\/h).<\/li>\n\n\n\n<li>Scenario 2: +1% AM; +9% PM.<\/li>\n\n\n\n<li>Scenario 3: +1% AM; +8% PM.<\/li>\n\n\n\n<li>Scenario 4: -0.3% AM; +6% PM (21 \u2192 22 km\/h).<\/li>\n<\/ul>\n\n\n\n<p>Scenario 4 again shows the expected AM trade-off where pedestrian priority is added, with a PM recovery.<\/p>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69d60fd17dacb&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69d60fd17dacb\" class=\"wp-block-image size-full wp-lightbox-container\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"281\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide23.png\" alt=\"Urban mobility microsimulation networkwide average speed results for all scenarios in Georgetown\" class=\"wp-image-29506\" srcset=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide23.png 500w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide23-320x180.png 320w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide23-300x169.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-engaging-stakeholders\"><strong>Engaging stakeholders<\/strong><\/h2>\n\n\n\n<p>How the results were used was equally important. The animations produced through urban mobility microsimulation helped me transform abstract network effects into a <a href=\"https:\/\/blog.ptvgroup.com\/en\/user-insights\/how-microsimulation-reduces-risk-in-complex-urban-projects\/\" target=\"_blank\" rel=\"noreferrer noopener\">shared visual language<\/a> for council officers, developers, business groups, and residents.<\/p>\n\n\n\n<p>When people could see queues forming and clearing, buses moving in the dedicated lane, or how a crossing reallocated time directly from the micro-simulation model, the discussions shifted from opinions to outcomes.<\/p>\n\n\n\n<p>That\u2019s how we achieved alignment on where and how to make changes.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-handover-and-capability-building\"><strong>Handover and capability building<\/strong><\/h2>\n\n\n\n<p>To ensure long-term value, we did more than just hand over the base year and scenario models to the authority. I also provided PTV-accredited training, covering Vissim fundamentals and advanced topics such as dynamic assignment and mesoscopic simulation.<\/p>\n\n\n\n<p>As a result, local planners can now <a href=\"https:\/\/blog.ptvgroup.com\/en\/modeling-planning\/ptv-hub-making-transportation-planning-collaborative-and-cloud-based\/\" target=\"_blank\" rel=\"noreferrer noopener\">maintain and extend<\/a> the model with confidence for future tasks.<\/p>\n\n\n\n<p>We concluded with a Sustainment Action Plan so that the model would remain a living asset rather than a one-off study.<\/p>\n\n\n\n<p>This project shows how urban mobility microsimulation helps heritage cities reduce risk and make informed mobility decisions.<\/p>\n\n\n\n<figure data-wp-context=\"{&quot;imageId&quot;:&quot;69d60fd17e236&quot;}\" data-wp-interactive=\"core\/image\" data-wp-key=\"69d60fd17e236\" class=\"wp-block-image size-full wp-lightbox-container\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"281\" data-wp-class--hide=\"state.isContentHidden\" data-wp-class--show=\"state.isContentVisible\" data-wp-init=\"callbacks.setButtonStyles\" data-wp-on--click=\"actions.showLightbox\" data-wp-on--load=\"callbacks.setButtonStyles\" data-wp-on-window--resize=\"callbacks.setButtonStyles\" src=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide24.png\" alt=\"Urban mobility microsimulation capacity\u2011building and training summary for Digital Penang and MBPP\" class=\"wp-image-29504\" srcset=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide24.png 500w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide24-320x180.png 320w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Penang_Slide24-300x169.png 300w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><button\n\t\t\tclass=\"lightbox-trigger\"\n\t\t\ttype=\"button\"\n\t\t\taria-haspopup=\"dialog\"\n\t\t\taria-label=\"Enlarge\"\n\t\t\tdata-wp-init=\"callbacks.initTriggerButton\"\n\t\t\tdata-wp-on--click=\"actions.showLightbox\"\n\t\t\tdata-wp-style--right=\"state.imageButtonRight\"\n\t\t\tdata-wp-style--top=\"state.imageButtonTop\"\n\t\t>\n\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"12\" height=\"12\" fill=\"none\" viewBox=\"0 0 12 12\">\n\t\t\t\t<path fill=\"#fff\" d=\"M2 0a2 2 0 0 0-2 2v2h1.5V2a.5.5 0 0 1 .5-.5h2V0H2Zm2 10.5H2a.5.5 0 0 1-.5-.5V8H0v2a2 2 0 0 0 2 2h2v-1.5ZM8 12v-1.5h2a.5.5 0 0 0 .5-.5V8H12v2a2 2 0 0 1-2 2H8Zm2-12a2 2 0 0 1 2 2v2h-1.5V2a.5.5 0 0 0-.5-.5H8V0h2Z\" \/>\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-my-tips-for-planners\"><strong>My tips for planners<\/strong><\/h2>\n\n\n\n<p><strong>1. Calibrate for the decisions you must make.<\/strong> Include parking turnover and dwell time if curb use affects operations. The trip chain approach was decisive in Georgetown.<\/p>\n\n\n\n<p><strong>2. Localize expectations.<\/strong> Network averages <a href=\"https:\/\/blog.ptvgroup.com\/en\/modeling-planning\/scenario-management-one-of-the-key-features-for-traffic-simulation-2\/\" target=\"_blank\" rel=\"noreferrer noopener\">may show small percentage shifts<\/a>, but corridor or user group gains (e.g., buses or pedestrians) may be the policy target. Report both.<\/p>\n\n\n\n<p><strong>3. Use visual evidence to manage trade-offs.<\/strong> Adding protected crossings comes at a cost. Showing the &#8220;why&#8221; and the &#8220;where&#8221; turns controversy into design iteration.<\/p>\n\n\n\n<p><strong>4. Embed capacity.<\/strong> Training and a sustainment plan transform the model into a platform for daily planning, development control, and public communication.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-closing-thought\"><strong>Closing thought<\/strong><\/h2>\n\n\n\n<p>For a heritage city that still needs to move, calibrated micro simulation in PTV Vissim provided the evidence needed to validate designs and phases of implementation, as well as to bring stakeholders along on the journey. The goal is not to prove a point, but rather to reduce risk before implementing changes.<\/p>\n\n\n\n<p>As a result of the project, MBBP (Penang Island City Council) is willing to continue using PTV Vissim for future traffic studies.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column cta-box is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:33.33%\">\n<div class=\"wp-block-cover\"><img loading=\"lazy\" decoding=\"async\" width=\"1254\" height=\"836\" class=\"wp-block-cover__image-background wp-image-25406\" alt=\"\" src=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2024\/10\/iStock-1312502184.jpg\" data-object-fit=\"cover\" srcset=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2024\/10\/iStock-1312502184.jpg 1254w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2024\/10\/iStock-1312502184-540x360.jpg 540w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2024\/10\/iStock-1312502184-936x624.jpg 936w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2024\/10\/iStock-1312502184-768x512.jpg 768w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2024\/10\/iStock-1312502184-300x200.jpg 300w\" sizes=\"auto, (max-width: 1254px) 100vw, 1254px\" \/><span aria-hidden=\"true\" class=\"wp-block-cover__background has-background-dim-80 has-background-dim wp-block-cover__gradient-background has-background-gradient\" style=\"background:linear-gradient(135deg,rgb(66,104,249) 28%,rgb(41,224,133) 88%)\"><\/span><div class=\"wp-block-cover__inner-container is-layout-constrained wp-block-cover-is-layout-constrained\">\n<p class=\"has-text-align-center\" style=\"font-size:24px;font-style:normal;font-weight:700\">Explore Traffic Simulation Solutions<\/p>\n\n\n\n<p class=\"has-text-align-center\">See how traffic simulation helps cities test scenarios, improve mobility, and make informed decisions \u2013 including in historic sites<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-14c487f4 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-fill\"><a class=\"wp-block-button__link has-white-background-color has-text-color has-background has-link-color has-text-align-center has-custom-font-size wp-element-button\" href=\"https:\/\/www.ptvgroup.com\/en-us\/application-areas\/traffic-simulation\" style=\"border-radius:8px;color:#6482f6;font-size:14px;font-style:normal;font-weight:500\" target=\"_blank\" rel=\"noreferrer noopener\">Get our e-guide<\/a><\/div>\n<\/div>\n<\/div><\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-cover\" style=\"min-height:300px;aspect-ratio:unset;\"><img loading=\"lazy\" decoding=\"async\" width=\"1254\" height=\"836\" class=\"wp-block-cover__image-background wp-image-25406\" alt=\"\" src=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2024\/10\/iStock-1312502184.jpg\" data-object-fit=\"cover\" srcset=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2024\/10\/iStock-1312502184.jpg 1254w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2024\/10\/iStock-1312502184-540x360.jpg 540w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2024\/10\/iStock-1312502184-936x624.jpg 936w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2024\/10\/iStock-1312502184-768x512.jpg 768w, https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2024\/10\/iStock-1312502184-300x200.jpg 300w\" sizes=\"auto, (max-width: 1254px) 100vw, 1254px\" \/><span aria-hidden=\"true\" class=\"wp-block-cover__background has-background-dim-80 has-background-dim wp-block-cover__gradient-background has-background-gradient\" style=\"background:linear-gradient(135deg,rgb(66,104,249) 28%,rgb(41,224,133) 88%)\"><\/span><div class=\"wp-block-cover__inner-container is-layout-constrained wp-block-cover-is-layout-constrained\">\n<p class=\"has-text-align-center\" style=\"font-size:24px;font-style:normal;font-weight:700\">Explore Traffic Simulation Solutions<\/p>\n\n\n\n<p class=\"has-text-align-center\">See how traffic simulation helps cities test scenarios, improve mobility, and make informed decisions \u2013 including in historic sites<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-14c487f4 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button is-style-fill\"><a class=\"wp-block-button__link has-white-background-color has-text-color has-background has-link-color has-text-align-center has-custom-font-size wp-element-button\" href=\"https:\/\/www.ptvgroup.com\/en-us\/application-areas\/traffic-simulation\" style=\"border-radius:4px;color:#6482f6;font-size:14px;font-style:normal;font-weight:700\" target=\"_blank\" rel=\"noreferrer noopener\">Get our e-guide<\/a><\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n<div style=\"height:24px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p class=\"card-text\">How a PTV Vissim model of Penang\u2019s historic center enabled planners to test multimodal strategies and make informed decisions in a UNESCO listed area with dense streets and complex transport needs. [&#8230;]<\/p>\n<p class=\"m-0\"><a class=\"btn btn-outline-secondary btn-read-more\" href=\"https:\/\/blog.ptvgroup.com\/en\/user-insights\/urban-mobility-microsimulation-in-a-unesco-heritage-city\/\">Read More<\/a><\/p>\n","protected":false},"author":5,"featured_media":29528,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8326,8322],"tags":[8377,8387,8371],"ppma_author":[8494],"class_list":["post-29532","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-modeling-planning","category-user-insights","tag-traffic-simulation","tag-transportation-modeling","tag-urban-mobility"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.0 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Urban Mobility Microsimulation in Heritage Cities | PTV Blog<\/title>\n<meta name=\"description\" content=\"See how urban mobility microsimulation supports planning, scenario testing, and decisions in complex UNESCO World Heritage city environments.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/blog.ptvgroup.com\/en\/user-insights\/urban-mobility-microsimulation-in-a-unesco-heritage-city\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Urban Mobility Microsimulation in a UNESCO Heritage City\" \/>\n<meta property=\"og:description\" content=\"See how urban mobility microsimulation supports planning, scenario testing, and decisions in complex UNESCO World Heritage city environments.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/blog.ptvgroup.com\/en\/user-insights\/urban-mobility-microsimulation-in-a-unesco-heritage-city\/\" \/>\n<meta property=\"og:site_name\" content=\"PTV Blog\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/PTVGroupGlobal\/\" \/>\n<meta property=\"article:published_time\" content=\"2026-03-11T07:00:00+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/blog.ptvgroup.com\/wp-content\/uploads\/2026\/02\/Untitled-design-1.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1025\" \/>\n\t<meta property=\"og:image:height\" content=\"577\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Guest author: Yu Chieh Lo\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"_ptadmin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"9 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/blog.ptvgroup.com\\\/en\\\/user-insights\\\/urban-mobility-microsimulation-in-a-unesco-heritage-city\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/blog.ptvgroup.com\\\/en\\\/user-insights\\\/urban-mobility-microsimulation-in-a-unesco-heritage-city\\\/\"},\"author\":{\"name\":\"Guest Author\",\"@id\":\"https:\\\/\\\/blog.ptvgroup.com\\\/en\\\/#\\\/schema\\\/person\\\/ed495d952d445e59d4114dedcd7a86ba\"},\"headline\":\"Urban Mobility Microsimulation in a UNESCO Heritage City\",\"datePublished\":\"2026-03-11T07:00:00+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/blog.ptvgroup.com\\\/en\\\/user-insights\\\/urban-mobility-microsimulation-in-a-unesco-heritage-city\\\/\"},\"wordCount\":1288,\"publisher\":{\"@id\":\"https:\\\/\\\/blog.ptvgroup.com\\\/en\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/blog.ptvgroup.com\\\/en\\\/user-insights\\\/urban-mobility-microsimulation-in-a-unesco-heritage-city\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/blog.ptvgroup.com\\\/wp-content\\\/uploads\\\/2026\\\/02\\\/Untitled-design-1.png\",\"keywords\":[\"Traffic Simulation\",\"Transportation Modeling\",\"Urban Mobility\"],\"articleSection\":[\"Modeling &amp; 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