Merge branch 'source' of github.com:tfresource/website into source

.vuepress/config.js

@@ -4,6 +4,12 @@ module.exports = {
   evergreen: true,
   head: [
     ['script', { src: 'https://cdn.jsdelivr.net/pyodide/v0.22.0/full/pyodide.js' }],
+    ['script', { async: true, src: 'https://www.googletagmanager.com/gtag/js?id=G-2DCTYTB7FB' }],
+    [
+      'script',
+      {},
+      "window.dataLayer=window.dataLayer|| []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-2DCTYTB7FB');",
+    ],
     ['link', { rel: 'stylesheet', href: 'https://use.fontawesome.com/releases/v5.2.0/css/all.css' }],
     ['link', { rel: 'icon', href: '/tiny-logo.png' }],
     [
@@ -67,15 +73,15 @@ module.exports = {
         additionalArgs: '--no-merge',
       },
     ],
-    ['vuepress-plugin-minimal-analytics', { ga: 'UA-157455011-1' }],
+    // ['vuepress-plugin-minimal-analytics', { ga: 'G-2DCTYTB7FB' }],
   ],
   title: 'TF Resource',
   themeConfig: {
     repo: 'tfresource/website',
     docsBranch: 'source',
     displayAllHeaders: false,
     editLinks: true,
-    editLinkText: 'Edit This Page »',
+    editLinkText: 'Edit this page',
     lastUpdated: 'Last Updated',
     logo: '/tfr-logo-title.png',
     sidebarDepth: 0,

.vuepress/styles/index.styl

@@ -72,6 +72,19 @@ td
 
   .squish h1 { text-align: center;}
 
+.edit-link {
+  text-transform: uppercase;
+  font-size: 0.8rem;
+  background-color: white;
+  text-decoration-line: none;
+  padding: 4px;
+}
+
+.edit-link:hover {
+  text-decoration-line: none;
+}
+
+
 .page
   .tip,.warning,.danger
     box-shadow: 2px 3px 3px rgba(0,0,0,0.2)

.vuepress/styles/palette.styl

@@ -1,9 +1,9 @@
 // defaults
-$textColor = #223
+$textColor = #181825
 $sideBlue =  #eaeaea
-$pageColor = #f4f4f4
+$pageColor = #f8f8f8
 $mediumZoomZIndex = 10000
-$accentColor = #4a4
+$accentColor = #393
 $contentWidth = 700px
 $codeBgColor = #455
 $navbarHeight = 3.1rem

.vuepress/theme/components/GdprBanner.vue

@@ -24,26 +24,15 @@ export default {
     }
   },
   methods: {
-    gdprNo: function() {
+    gdprNo: function () {
       Vue.$cookies.set('gdpr', 'no')
       this.cookie = 'no'
     },
-    gdprYes: function() {
+    gdprYes: function () {
       Vue.$cookies.set('gdpr', 'ok')
       this.cookie = 'ok'
-
-      // don't miss first page!
-      if (process.env.NODE_ENV === 'production' && GA_ID && typeof window !== 'undefined') {
-        this.sendAnalytics(window, GA_ID, {
-          anonymizeIp: true,
-          colorDepth: true,
-          characterSet: true,
-          screenSize: true,
-          language: true,
-        })
-      }
     },
-    sendAnalytics: function(context, trackingId, options) {
+    sendAnalytics: function (context, trackingId, options) {
       console.log('analytics consent received:', trackingId)
 
       const history = context.history
@@ -60,7 +49,7 @@ export default {
         }
         return storage.cid
       }
-      const serialize = obj => {
+      const serialize = (obj) => {
         var str = []
         for (var p in obj) {
           if (obj.hasOwnProperty(p)) {
@@ -117,7 +106,7 @@ export default {
       }
       const trackEvent = (category, action, label, value) => track('event', category, action, label, value)
       const trackException = (description, fatal) => track(typeException, null, null, null, null, description, fatal)
-      history.pushState = function(state) {
+      history.pushState = function (state) {
         if (typeof history.onpushstate == 'function') {
           history.onpushstate({ state: state })
         }

README.md

@@ -1,5 +1,9 @@
 # Travel Forecasting Resource
 
+> [!IMPORTANT]
+> As of Fall 2024, the Transportation Research Board has discontinued the subcommittee that oversees the development of TF Resource. As a result, active development of this site has stopped.
+> We are grateful for the long-standing contributions of many contributors, and look forward to determining the best way to move forward as a community with or beyond TF Resource.
+
 You've found the "TF Resource" -- the Travel Forecasting Resource website, a collection of best practices for travel demand forecasting and travel survey methods.
 
 This website serves as a community resource for travel forecasting research and practice. We welcome your participation!

topics/Current_Status_of_Statewide_Models.md

@@ -43,7 +43,7 @@ categories:
 | North Dakota   |                   |                               |                                                   |                                                                                                                                 |
 | Ohio           | Yes               | Activity-based                |                                                   | [Ohio modeling website](http://www.dot.state.oh.us/Divisions/Planning/SPR/ModelForecastingUnit/Pages/TravelDemandModeling.aspx) |
 | Oklahoma       | No                | N/A                           | N/A                                               |                                                                                                                                 |
-| Oregon         | Yes               | Activity-based                |                                                   | [Oregon modeling website](http://www.oregon.gov/ODOT/Planning/Pages/Technical-Tools.aspx)                                       |
+| Oregon         | Yes               | Activity-based                |                                                   | [Oregon modeling website](https://github.com/tlumip/tlumip/wiki)                                       |
 | Pennsylvania   | Yes               |                               |                                                   |                                                                                                                                 |
 | Rhode Island   | Yes               | Trip-based                    | No                                                |                                                                                                                                 |
 | South Carolina | Yes               | Trip-based                    |                                                   |                                                                                                                                 |

topics/List_of_Model_User_Groups.md

@@ -73,6 +73,13 @@ Contact: Gene Hicks, MnDOT
 
 [Website](http://www.dot.state.mn.us/traffic/data/mtdmcc.html)
 
+**MWCOG/NCRTPB Travel Forecasting Subcommittee (TFS)**
+----------------------------------------
+
+Contact: Mark Moran, Metropolitan Washington Council of Governments, National Capital Region Transportation Planning Board (MWCOG/NCRTPB)
+
+[Website](https://www.mwcog.org/committees/tpb-travel-forecasting-subcommittee/)
+
 New York Best Practice Model User Group
 ---------------------------------------
 
@@ -127,6 +134,13 @@ Contact: Jonathan Avner, Whitman, Requardt & Associates
 
 [Website](https://www.linkedin.com/in/texas-model-users-group-726718139/)
 
+**Utah Model Users Group (UTAHMUG)**
+-------------------------------
+
+Contact: Natalia Brown, Utah Department of Transportation
+
+[Website](https://utahmug.org)
+
 References
 ==========
 

topics/Statewide_peer_exchange.md

@@ -31,7 +31,7 @@ Employment data at the workplace is always a challenge for travel modelers, sinc
 
 ## Sources of Origin-Destination Data 
 Passenger origin-destination data is not usually a direct input to a travel model, but it can assist model calibration and validation.  Reported sources include Streetlight, CS LOCUS, Local OD surveys, NHTS, CTPP, AirSage, and LBS. 
-Freight origin-destination data may or may not be a direct input to a travel model, depending upon the modelís sophistication, as freight components can vary greatly in complexity or may be omitted.  Mentioned sources include FAF4, Transearch, Streetlight, TREDIS, INRIX, REMI, local ports, commercial truck survey (ON), CFS, and ATRI. 
+Freight origin-destination data may or may not be a direct input to a travel model, depending upon the model's sophistication, as freight components can vary greatly in complexity or may be omitted.  Mentioned sources include FAF4, Transearch, Streetlight, TREDIS, INRIX, REMI, local ports, commercial truck survey (ON), CFS, and ATRI. 
 
 ## Economic and Demographic Forecasts as Inputs 
 For the purpose of testing future scenarios, all statewide models require forecasts of economic activity, demographics, and land use, indirectly.  For the most part, these forecasts originate outside the modeling agency.  Choices of datasets are opportunistic, with modelers looking for least-cost options locally before purchasing commercial products.