using skims with find_nearest_accessibility_zone is a bottleneck when working with large number of MAZs #1030

Open
Open
using skims with find_nearest_accessibility_zone is a bottleneck when working with large number of MAZs#1030
Labels
BugSomething isn't working/bug f

Description

@bricegnichols
bricegnichols
opened on Jan 27, 2026

Describe the bug
The annotate_households step takes a very long time to complete (~25 minutes without sharrow in our implementation). The slowdown occurs after household data has been processed and disaggregate accessibilities are being calculated. More specifically, the nearest_skim function in find_nearest_accessibility_zone in disaggregate_accessibility.py is the bottleneck.

To Reproduce
Steps to reproduce the behavior:

  1. Copy input data from here and configs from here. (Note: if you want to truly benchmark the runtime slowness I will upload the full data instead of the test data linked here.)
  2. Clone Activitysim
  3. Open a command prompt, cd to Activitysim, then enter uv sync --locked to create the uv .venv.
  4. Enter .venv\Scripts\activate to activate the uv environment.
  5. Enter activitysim run -c path_to_configs -d path_to_data_test -o path_to_output

Expected behavior
This step takes about 2 minutes running with sharrow and I would expect it to run maybe a little slower (but way faster than 25 minutes) without sharrow.

Additional context
David Hensle worked with us to diagnose the problem and proposed a new method that works much faster. We also tested the centroids alternative method but did not yet get it working. However, the skims approach seems preferable to using MAZ centroids. Our implementation has 66k MAZs so this may not have surfaced as an obvious issue for implementations with fewer MAZs.

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      , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
       blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

      Skip to content
      using skims with find_nearest_accessibility_zone is a bottleneck when working with large number of MAZs #1030
      Open
      Open
      using skims with find_nearest_accessibility_zone is a bottleneck when working with large number of MAZs#1030
      Labels
      BugSomething isn't working/bug f
      Description
      @bricegnichols
      bricegnichols
      opened on Jan 27, 2026
      Describe the bug
      
The annotate_households step takes a very long time to complete (~25 minutes without sharrow in our implementation). The slowdown occurs after household data has been processed and disaggregate accessibilities are being calculated. More specifically, the nearest_skim function in find_nearest_accessibility_zone in disaggregate_accessibility.py is the bottleneck.
      To Reproduce
      
Steps to reproduce the behavior:
      1. Copy input data from here and configs from here. (Note: if you want to truly benchmark the runtime slowness I will upload the full data instead of the test data linked here.)
      2. Clone Activitysim
      3. Open a command prompt, cd to Activitysim, then enter uv sync --locked to create the uv .venv.
      4. Enter .venv\Scripts\activate to activate the uv environment.
      5. Enter activitysim run -c path_to_configs -d path_to_data_test -o path_to_output
      Expected behavior
      
This step takes about 2 minutes running with sharrow and I would expect it to run maybe a little slower (but way faster than 25 minutes) without sharrow.
      Additional context
      
David Hensle worked with us to diagnose the problem and proposed a new method that works much faster. We also tested the centroids alternative method but did not yet get it working. However, the skims approach seems preferable to using MAZ centroids. Our implementation has 66k MAZs so this may not have surfaced as an obvious issue for implementations with fewer MAZs.
      Activity
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          None yet
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

          Skip to content
          using skims with find_nearest_accessibility_zone is a bottleneck when working with large number of MAZs #1030
          Open
          Open
          using skims with find_nearest_accessibility_zone is a bottleneck when working with large number of MAZs#1030
          Labels
          BugSomething isn't working/bug f
          Description
          @bricegnichols
          bricegnichols
          opened on Jan 27, 2026
          Describe the bug
          
The annotate_households step takes a very long time to complete (~25 minutes without sharrow in our implementation). The slowdown occurs after household data has been processed and disaggregate accessibilities are being calculated. More specifically, the nearest_skim function in find_nearest_accessibility_zone in disaggregate_accessibility.py is the bottleneck.
          To Reproduce
          
Steps to reproduce the behavior:
          1. Copy input data from here and configs from here. (Note: if you want to truly benchmark the runtime slowness I will upload the full data instead of the test data linked here.)
          2. Clone Activitysim
          3. Open a command prompt, cd to Activitysim, then enter uv sync --locked to create the uv .venv.
          4. Enter .venv\Scripts\activate to activate the uv environment.
          5. Enter activitysim run -c path_to_configs -d path_to_data_test -o path_to_output
          Expected behavior
          
This step takes about 2 minutes running with sharrow and I would expect it to run maybe a little slower (but way faster than 25 minutes) without sharrow.
          Additional context
          
David Hensle worked with us to diagnose the problem and proposed a new method that works much faster. We also tested the centroids alternative method but did not yet get it working. However, the skims approach seems preferable to using MAZ centroids. Our implementation has 66k MAZs so this may not have surfaced as an obvious issue for implementations with fewer MAZs.
          Activity
          Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
          Metadata
          Metadata
          Assignees
          No one assigned
            Labels
            BugSomething isn't working/bug f
            Type
            No type
            Projects
            No projects
              Milestone
              No milestone
              Relationships
              None yet
              Development
              No branches or pull requests
              Issue actions
              
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

              Skip to content
              using skims with find_nearest_accessibility_zone is a bottleneck when working with large number of MAZs #1030
              Open
              Open
              using skims with find_nearest_accessibility_zone is a bottleneck when working with large number of MAZs#1030
              Labels
              BugSomething isn't working/bug f
              Description
              @bricegnichols
              bricegnichols
              opened on Jan 27, 2026
              Describe the bug
              
The annotate_households step takes a very long time to complete (~25 minutes without sharrow in our implementation). The slowdown occurs after household data has been processed and disaggregate accessibilities are being calculated. More specifically, the nearest_skim function in find_nearest_accessibility_zone in disaggregate_accessibility.py is the bottleneck.
              To Reproduce
              
Steps to reproduce the behavior:
              1. Copy input data from here and configs from here. (Note: if you want to truly benchmark the runtime slowness I will upload the full data instead of the test data linked here.)
              2. Clone Activitysim
              3. Open a command prompt, cd to Activitysim, then enter uv sync --locked to create the uv .venv.
              4. Enter .venv\Scripts\activate to activate the uv environment.
              5. Enter activitysim run -c path_to_configs -d path_to_data_test -o path_to_output
              Expected behavior
              
This step takes about 2 minutes running with sharrow and I would expect it to run maybe a little slower (but way faster than 25 minutes) without sharrow.
              Additional context
              
David Hensle worked with us to diagnose the problem and proposed a new method that works much faster. We also tested the centroids alternative method but did not yet get it working. However, the skims approach seems preferable to using MAZ centroids. Our implementation has 66k MAZs so this may not have surfaced as an obvious issue for implementations with fewer MAZs.
              Activity
              Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
              Metadata
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              No one assigned
                Labels
                BugSomething isn't working/bug f
                Type
                No type
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                No projects
                  Milestone
                  No milestone
                  Relationships
                  None yet
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                  No branches or pull requests
                  Issue actions
                  
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

                  Skip to content
                  using skims with find_nearest_accessibility_zone is a bottleneck when working with large number of MAZs #1030
                  Open
                  Open
                  using skims with find_nearest_accessibility_zone is a bottleneck when working with large number of MAZs#1030
                  Labels
                  BugSomething isn't working/bug f
                  Description
                  @bricegnichols
                  bricegnichols
                  opened on Jan 27, 2026
                  Describe the bug
                  
The annotate_households step takes a very long time to complete (~25 minutes without sharrow in our implementation). The slowdown occurs after household data has been processed and disaggregate accessibilities are being calculated. More specifically, the nearest_skim function in find_nearest_accessibility_zone in disaggregate_accessibility.py is the bottleneck.
                  To Reproduce
                  
Steps to reproduce the behavior:
                  1. Copy input data from here and configs from here. (Note: if you want to truly benchmark the runtime slowness I will upload the full data instead of the test data linked here.)
                  2. Clone Activitysim
                  3. Open a command prompt, cd to Activitysim, then enter uv sync --locked to create the uv .venv.
                  4. Enter .venv\Scripts\activate to activate the uv environment.
                  5. Enter activitysim run -c path_to_configs -d path_to_data_test -o path_to_output
                  Expected behavior
                  
This step takes about 2 minutes running with sharrow and I would expect it to run maybe a little slower (but way faster than 25 minutes) without sharrow.
                  Additional context
                  
David Hensle worked with us to diagnose the problem and proposed a new method that works much faster. We also tested the centroids alternative method but did not yet get it working. However, the skims approach seems preferable to using MAZ centroids. Our implementation has 66k MAZs so this may not have surfaced as an obvious issue for implementations with fewer MAZs.
                  Activity
                  Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
                  Metadata
                  Metadata
                  Assignees
                  No one assigned
                    Labels
                    BugSomething isn't working/bug f
                    Type
                    No type
                    Projects
                    No projects
                      Milestone
                      No milestone
                      Relationships
                      None yet
                      Development
                      No branches or pull requests
                      Issue actions
                      
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

                      Skip to content
                      using skims with find_nearest_accessibility_zone is a bottleneck when working with large number of MAZs #1030
                      Open
                      Open
                      using skims with find_nearest_accessibility_zone is a bottleneck when working with large number of MAZs#1030
                      Labels
                      BugSomething isn't working/bug f
                      Description
                      @bricegnichols
                      bricegnichols
                      opened on Jan 27, 2026
                      Describe the bug
                      
The annotate_households step takes a very long time to complete (~25 minutes without sharrow in our implementation). The slowdown occurs after household data has been processed and disaggregate accessibilities are being calculated. More specifically, the nearest_skim function in find_nearest_accessibility_zone in disaggregate_accessibility.py is the bottleneck.
                      To Reproduce
                      
Steps to reproduce the behavior:
                      1. Copy input data from here and configs from here. (Note: if you want to truly benchmark the runtime slowness I will upload the full data instead of the test data linked here.)
                      2. Clone Activitysim
                      3. Open a command prompt, cd to Activitysim, then enter uv sync --locked to create the uv .venv.
                      4. Enter .venv\Scripts\activate to activate the uv environment.
                      5. Enter activitysim run -c path_to_configs -d path_to_data_test -o path_to_output
                      Expected behavior
                      
This step takes about 2 minutes running with sharrow and I would expect it to run maybe a little slower (but way faster than 25 minutes) without sharrow.
                      Additional context
                      
David Hensle worked with us to diagnose the problem and proposed a new method that works much faster. We also tested the centroids alternative method but did not yet get it working. However, the skims approach seems preferable to using MAZ centroids. Our implementation has 66k MAZs so this may not have surfaced as an obvious issue for implementations with fewer MAZs.
                      Activity
                      Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
                      Metadata
                      Metadata
                      Assignees
                      No one assigned
                        Labels
                        BugSomething isn't working/bug f
                        Type
                        No type
                        Projects
                        No projects
                          Milestone
                          No milestone
                          Relationships
                          None yet
                          Development
                          No branches or pull requests
                          Issue actions
                          
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

                          Skip to content
                          using skims with find_nearest_accessibility_zone is a bottleneck when working with large number of MAZs #1030
                          Open
                          Open
                          using skims with find_nearest_accessibility_zone is a bottleneck when working with large number of MAZs#1030
                          Labels
                          BugSomething isn't working/bug f
                          Description
                          @bricegnichols
                          bricegnichols
                          opened on Jan 27, 2026
                          Describe the bug
                          
The annotate_households step takes a very long time to complete (~25 minutes without sharrow in our implementation). The slowdown occurs after household data has been processed and disaggregate accessibilities are being calculated. More specifically, the nearest_skim function in find_nearest_accessibility_zone in disaggregate_accessibility.py is the bottleneck.
                          To Reproduce
                          
Steps to reproduce the behavior:
                          1. Copy input data from here and configs from here. (Note: if you want to truly benchmark the runtime slowness I will upload the full data instead of the test data linked here.)
                          2. Clone Activitysim
                          3. Open a command prompt, cd to Activitysim, then enter uv sync --locked to create the uv .venv.
                          4. Enter .venv\Scripts\activate to activate the uv environment.
                          5. Enter activitysim run -c path_to_configs -d path_to_data_test -o path_to_output
                          Expected behavior
                          
This step takes about 2 minutes running with sharrow and I would expect it to run maybe a little slower (but way faster than 25 minutes) without sharrow.
                          Additional context
                          
David Hensle worked with us to diagnose the problem and proposed a new method that works much faster. We also tested the centroids alternative method but did not yet get it working. However, the skims approach seems preferable to using MAZ centroids. Our implementation has 66k MAZs so this may not have surfaced as an obvious issue for implementations with fewer MAZs.
                          Activity
                          Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
                          Metadata
                          Metadata
                          Assignees
                          No one assigned
                            Labels
                            BugSomething isn't working/bug f
                            Type
                            No type
                            Projects
                            No projects
                              Milestone
                              No milestone
                              Relationships
                              None yet
                              Development
                              No branches or pull requests
                              Issue actions
                              
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

                              Skip to content
                              using skims with find_nearest_accessibility_zone is a bottleneck when working with large number of MAZs #1030
                              Open
                              Open
                              using skims with find_nearest_accessibility_zone is a bottleneck when working with large number of MAZs#1030
                              Labels
                              BugSomething isn't working/bug f
                              Description
                              @bricegnichols
                              bricegnichols
                              opened on Jan 27, 2026
                              Describe the bug
                              
The annotate_households step takes a very long time to complete (~25 minutes without sharrow in our implementation). The slowdown occurs after household data has been processed and disaggregate accessibilities are being calculated. More specifically, the nearest_skim function in find_nearest_accessibility_zone in disaggregate_accessibility.py is the bottleneck.
                              To Reproduce
                              
Steps to reproduce the behavior:
                              1. Copy input data from here and configs from here. (Note: if you want to truly benchmark the runtime slowness I will upload the full data instead of the test data linked here.)
                              2. Clone Activitysim
                              3. Open a command prompt, cd to Activitysim, then enter uv sync --locked to create the uv .venv.
                              4. Enter .venv\Scripts\activate to activate the uv environment.
                              5. Enter activitysim run -c path_to_configs -d path_to_data_test -o path_to_output
                              Expected behavior
                              
This step takes about 2 minutes running with sharrow and I would expect it to run maybe a little slower (but way faster than 25 minutes) without sharrow.
                              Additional context
                              
David Hensle worked with us to diagnose the problem and proposed a new method that works much faster. We also tested the centroids alternative method but did not yet get it working. However, the skims approach seems preferable to using MAZ centroids. Our implementation has 66k MAZs so this may not have surfaced as an obvious issue for implementations with fewer MAZs.
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