2b:null 2c:["$","title",null,{"children":"Northern Growth Corridor Travel Time Validation | ktringuyen | Observable"}] 31:Ta90,{ // Filter data for selected route let routeData = validation_data.filter((d) => d.routeName == route); // Get unique directions and periods for this route let directions = [...new Set(routeData.map(d => d.direction))]; let periods = [...new Set(routeData.map(d => d.period))]; // Calculate ranges across all facets let x_range = Math.max(...routeData.map(d => d.chainage)); let y_range = Math.max(...routeData.filter(d => d.type != "perc75" && d.type != "perc25").map(d => d.cumulativeTime)) * 1.1; // Filter chainage labels for selected route let labels_data = chainge.filter(d => d.route == route); // Create chainage labels for each period (duplicate for each period facet) let chainage_with_periods = []; labels_data.forEach(label => { periods.forEach(period => { chainage_with_periods.push({...label, period: period}); }); }); return Plot.plot({ width: 1000, height: 500, marginLeft: 50, marginBottom: 45, marginRight: 30, marginTop: 20, style: { fontFamily: "sans-serif", fontSize: 14, }, color: { legend: true, domain: ["avgTime", selected_scenario, "perc75","perc50", "perc25"], tickFormat: d => { if (d === "avgTime") return "Average Travel Time"; if (d === "perc75") return "75th Percentile"; if (d === "perc25") return "25th Percentile"; if (d === "perc50") return "50th Percentile"; return d; }, range: ["#000", "#f00", "#d3d3d3", "#d3d3d3", "#d3d3d3"], }, fx: { label: null, domain: directions }, fy: { label: null, domain: periods }, x: { label: "Chainage (m)", domain: [0, x_range], ticks: 5, labelAnchor: "center", }, y: { label: "Cumulative Travel Time (min)", domain: [0, y_range], ticks: 4, labelAnchor: "center" }, marks: [ Plot.frame(), Plot.line(routeData, { x: "chainage", y: "cumulativeTime", stroke: "type", fx: "direction", fy: "period", tip: true }), Plot.ruleX(chainage_with_periods, { x: "chainage", fx: "direction", fy: "period", stroke: "black", opacity: 0.1 }), Plot.textX(chainage_with_periods, { x: "chainage", y: 0, fx: "direction", fy: "period", rotate: -90, textAnchor: "start", lineAnchor: "bottom", fontSize:"9px", text: "label", fill: "black", stroke: "white", dx: 12, dy: -5 }), Plot.ruleX([0]), Plot.ruleY([0]) ], }); }32:Taa5,{ // Filter data for selected route let routeData = validation_data.filter((d) => d.routeName == clickedRoute); // Get unique directions and periods for this route let directions = [...new Set(routeData.map(d => d.direction))]; let periods = [...new Set(routeData.map(d => d.period))]; // Calculate ranges across all facets let x_range = Math.max(...routeData.map(d => d.chainage)); let y_range = Math.max(...routeData.filter(d => d.type != "perc75" && d.type != "perc25").map(d => d.cumulativeTime)) * 1.1; // Filter chainage labels for selected route let labels_data = chainge.filter(d => d.route == clickedRoute); // Create chainage labels for each period (duplicate for each period facet) let chainage_with_periods = []; labels_data.forEach(label => { periods.forEach(period => { chainage_with_periods.push({...label, period: period}); }); }); return Plot.plot({ title: `${clickedRoute}`, width: 1000, height: 500, marginLeft: 50, marginBottom: 45, marginRight: 30, marginTop: 30, style: { fontFamily: "sans-serif", fontSize: 14, }, color: { legend: true, domain: ["avgTime", selected_scenario, "perc75","perc50", "perc25"], tickFormat: d => { if (d === "avgTime") return "Average Travel Time"; if (d === "perc75") return "75th Percentile"; if (d === "perc25") return "25th Percentile"; if (d === "perc50") return "50th Percentile"; return d; }, range: ["#000", "#f00", "#d3d3d3", "#d3d3d3", "#d3d3d3"], }, fx: { label: null, domain: directions }, fy: { label: null, domain: periods }, x: { label: "Chainage (m)", domain: [0, x_range], ticks: 5, labelAnchor: "center", }, y: { label: "Cumulative Travel Time (min)", domain: [0, y_range], ticks: 4, labelAnchor: "center" }, marks: [ Plot.frame(), Plot.line(routeData, { x: "chainage", y: "cumulativeTime", stroke: "type", fx: "direction", fy: "period", tip: true }), Plot.ruleX(chainage_with_periods, { x: "chainage", fx: "direction", fy: "period", stroke: "black", opacity: 0.1 }), Plot.textX(chainage_with_periods, { x: "chainage", y: 0, fx: "direction", fy: "period", rotate: -90, textAnchor: "start", lineAnchor: "bottom", text: "label", fill: "black", stroke: "white", dx: 12, dy: -5 }), Plot.ruleX([0]), Plot.ruleY([0]) ], }); }33:T10a2,map = { const container = html`

`; yield container; const map = (container.value = new maplibregl.Map({ container, zoom: 10.1, center: [145.141, -37.5], style: "https://basemaps.cartocdn.com/gl/positron-gl-style/style.json", scrollZoom: true })); map.on("load", function () { map.addSource("routes", { type: "geojson", data: all_routes }); map.addLayer({ id: "cables", type: "line", source: "routes", layout: { "line-join": "round", "line-cap": "round" }, paint: { "line-color": [ "case", ["boolean", ["feature-state", "clicked"], false], "#FFEA00", "#000000", ], "line-width": [ "case", ["boolean", ["feature-state", "clicked"], false], 4, 2 ], "line-offset": -1 } }); }); // Variable to track the currently selected feature let clickedFeatureId = null; // Create tooltip const tooltip = document.createElement('div'); tooltip.style.position = 'absolute'; tooltip.style.backgroundColor = 'white'; tooltip.style.padding = '10px'; tooltip.style.borderRadius = '5px'; tooltip.style.pointerEvents = 'none'; tooltip.style.boxShadow = '0 2px 4px rgba(0,0,0,0.2)'; tooltip.style.fontFamily = 'monospace'; tooltip.style.fontSize = '12px'; tooltip.style.maxWidth = '300px'; tooltip.style.zIndex = '1000'; tooltip.style.display = 'none'; container.appendChild(tooltip); // Mousemove event for tooltip map.on("mousemove", 'cables', e => { const features = map.queryRenderedFeatures(e.point, { layers: ['cables'] }); if (features.length > 0) { const feature = features[0]; // Display all properties in tooltip const properties = feature.properties; let tooltipContent = ''; for (const [key, value] of Object.entries(properties)) { tooltipContent += `${key}: ${value}
`; } tooltip.innerHTML = tooltipContent; tooltip.style.display = 'block'; tooltip.style.left = (e.point.x + 10) + 'px'; tooltip.style.top = (e.point.y - 10) + 'px'; // Change cursor to pointer map.getCanvas().style.cursor = 'pointer'; } else { tooltip.style.display = 'none'; map.getCanvas().style.cursor = ''; } }); map.on("mouseleave", 'cables', () => { tooltip.style.display = 'none'; map.getCanvas().style.cursor = ''; }); map.on("click", 'cables', e => { const features = map.queryRenderedFeatures(e.point, { layers: ['cables'] }); console.log("Click detected, features found:", features.length); if (features.length > 0) { const feature = features[0]; const properties = feature.properties; console.log("Feature ID:", feature.id); console.log("Properties:", properties); // Remove highlight from previously clicked feature if (clickedFeatureId !== null) { map.setFeatureState( { source: 'routes', id: clickedFeatureId }, // Fixed: was 'count_sites' { clicked: false } ); console.log("Removed highlight from feature:", clickedFeatureId); } // Highlight the newly clicked feature if (feature.id !== undefined) { clickedFeatureId = feature.id; map.setFeatureState( { source: 'routes', id: clickedFeatureId }, // Fixed: was 'count_sites' { clicked: true } ); console.log("Highlighted feature:", clickedFeatureId); } else { console.error("Feature has no ID! Cannot highlight."); console.log("Your GeoJSON needs to have feature IDs for highlighting to work."); } // Set the mutable variables mutable clickedRoute = properties.routeName.slice(0,properties.routeName.length - 3) || properties.RouteName || ''; mutable clickedDirection = properties.direction; console.log("Set clickedRoute:", mutable clickedRoute); console.log("Set clickedDirection:", mutable clickedDirection); } }); invalidation.then(() => map.remove()); }34:T9b6,{ let routes = [...new Set(validation_data.map(d => d.routeName))]; let direction_map = { "Northern Highway - Watson Street":["EB","WB"], "Camerons Lane":["EB","WB"], "Donnybrook Road":["EB","WB"], "Craigieburn Road":["EB","WB"], "Somerton Road":["EB","WB"], "Camp Road":["EB","WB"], "Old Sydney Road":["NB","SB"], "Mickleham Road":["NB","SB"], "Northern Highway - Hume Freeway":["NB","SB"], "Hume Freeway":["NB","SB"], "Hume Highway":["NB","SB"], "Pascoe Vale Road":["NB","SB"], "Epping Road":["NB","SB"], "Plenty Road":["NB","SB"], "Aitken Boulevard":["NB","SB"], }; // Create legend once let legend = Plot.legend({ color: { domain: ["avgTime", selected_scenario2], range: ["#000", "#f00"], label: "Type", tickFormat: d => d === "avgTime" ? "Average Travel Time" : d, } }); let subplots = routes.map(r => { let direction = direction_map[r][selected_direction]; let data = validation_data.filter((d) => d.routeName == r && d.direction == direction && d.period == selected_period); let x_range = Math.max(...data.map(d => d.chainage)) * 1; let y_range = Math.max(...data.filter(d => d.type != "perc75" && d.type != "perc25").map(d => d.cumulativeTime)) * 1.1; return Plot.plot({ width: 1000, height: 180, marginTop: 20, marginBottom: 40, style: { fontFamily: "sans-serif", fontSize: 14, }, color: { domain: ["avgTime", selected_scenario2], range: ["#000", "#f00"], legend: false, }, x: { label: "Chainage (m)", domain: [0, x_range], ticks: 5, }, y: { label: "Cumulative Travel Time (min)", domain: [0, y_range], ticks: 4, }, marks: [ Plot.frame(), Plot.line(data, { x: "chainage", y: "cumulativeTime", stroke: "type", tip: true }), Plot.text([`${r} - ${direction}`], { frameAnchor: "top-left", dx: 10, dy: 6, fontSize: "12", fontWeight: "bold" }), Plot.ruleX([0]), Plot.ruleY([0]), // ...labels ], }); }); return html`

${legend}

${subplots}

`; }35:T782,compare_table = { const formatMMSS = (value) => { if (!isFinite(value)) return "N/A"; // Handle Infinity/NaN const totalSec = value * 60; // Convert minutes to seconds const sign = value >= 0 ? "" : "-"; const mins = Math.floor(Math.abs(totalSec) / 60); const secs = Math.round(Math.abs(totalSec) % 60); return `${sign}${mins}:${secs.toString().padStart(2, '0')}`; }; const formatDiff = aq.escape(d => formatMMSS(d["Modelled Travel Time"] - d["TomTom Average"])); const formatObserved = aq.escape(d => formatMMSS(d["TomTom Average"])); const formatModelled = aq.escape(d => formatMMSS(d["Modelled Travel Time"])); let modelled_table = aq.from(modelled_data) .groupby("routeName","direction", "period", "type") .rollup({ "Modelled Travel Time": d => aq.op.max(d.cumulativeTime) }) .orderby("routeName", "direction", "period") .objects(); let observed_table = aq.from(observed_data) .groupby("routeName","direction", "period") .rollup({ "TomTom Average": d => aq.op.max(d.cumulativeTime) }) .orderby("routeName", "direction", "period") .objects() return aq.from(modelled_table) .join(aq.from(observed_table), aq.op.equal(["routeName", "direction", "period"]) ) .derive({ "Diff (%)": d => ((d["Modelled Travel Time"] - d["TomTom Average"]) / d["TomTom Average"]) * 100, "Diff (mm:ss)": formatDiff, "Observed": formatObserved, "Modelled": formatModelled, "Route": d=>d.routeName, "Direction" : d=> d.direction, "Period": d=>d.period, "Scenario": d=> d.type }) .select([ "Route", "Direction","Period","Scenario", "Observed","Modelled", "Diff (%)","Diff (mm:ss)" ]) .orderby(["Route", "Direction","Period"]) .objects(); }36:T68f,function tomtomTime(json, timeObj) { const routes = json.routes; let routeData = []; for (let i=0; i ({ routeName: route.routeName.slice(0, -3).replace(",", ""), direction: route.routeName.slice(-3).trim(), distance: d.distance, avgTime: d3.mean( d.segmentTimeResults.filter((r) => period.includes(r.timeSet)), (r) => r.averageTravelTime ), perc25: d3.mean( d.segmentTimeResults.filter((r) => period.includes(r.timeSet)), (r) => r.speedPercentiles[14] / 3.6 ), perc75: d3.mean( d.segmentTimeResults.filter((r) => period.includes(r.timeSet)), (r) => r.speedPercentiles[4] / 3.6 ), period: period_name })); let result = []; const keys = ["avgTime", "perc25", "perc75"]; keys.forEach((key) => { let chainage = 0; let cumulativeTime = 0; result.push( segments.map((d) => { chainage += d.distance; if (key == "avgTime") { cumulativeTime += d[key]; } else { cumulativeTime += d.distance / d[key]; } return { ...d, chainage: chainage, type: key, cumulativeTime: cumulativeTime / 60 }; }) ); }); routeData.push(result.flat()); } } return routeData.flat(); }37:[{"id":"56a0864ef22151f2182cfba72aa1c7a18255e7eca82e33ced2add2fa882df772a4c073d3ac50b5f33f79b92ba838358ccd09717ef538fb2dbe9a34059eb74fb4","href":"https://static.observableusercontent.com/files/56a0864ef22151f2182cfba72aa1c7a18255e7eca82e33ced2add2fa882df772a4c073d3ac50b5f33f79b92ba838358ccd09717ef538fb2dbe9a34059eb74fb4","name":"jobs_7973733_results_Northern_Growth_Corridor__TN__MEL_.json","type":"application/json","lastModified":1761017395910.813,"size":29362456},{"id":"2250b81114db1dafd37544e49b019f331ad01269a1de90842489af6b9c5b2be3db69b03461fa33a4b0e410e5f28d394924e49cdcc91b28d073cc18b0321ec73e","href":"https://static.observableusercontent.com/files/2250b81114db1dafd37544e49b019f331ad01269a1de90842489af6b9c5b2be3db69b03461fa33a4b0e410e5f28d394924e49cdcc91b28d073cc18b0321ec73e","name":"jobs_7973737_results_North_Growth_Corridor__TN__MEL__2@1.json","type":"application/json","lastModified":1761025568067.043,"size":26114533},{"id":"2dae8b49842d0be7108d39fd299479da9594def90cb61c101ce61835c01e3440d213f5da40f4f7d08a860caa9380359f90a8df6c44ee3d739571121b9dacaae0","href":"https://static.observableusercontent.com/files/2dae8b49842d0be7108d39fd299479da9594def90cb61c101ce61835c01e3440d213f5da40f4f7d08a860caa9380359f90a8df6c44ee3d739571121b9dacaae0","name":"all_routes@3.geojson","type":"application/geo+json","lastModified":1761104634707.704,"size":1604016},{"id":"0e5679db80e34f3f60fe8fdde795b4a4fa3d535c5e6053b271e677f7a8d1f275bb1c5ec381f274664c0ab806b71f97bee279a26c80f0de849bf7d205893e6ff0","href":"https://static.observableusercontent.com/files/0e5679db80e34f3f60fe8fdde795b4a4fa3d535c5e6053b271e677f7a8d1f275bb1c5ec381f274664c0ab806b71f97bee279a26c80f0de849bf7d205893e6ff0","name":"SUMMARY_LOADED_NETWORK_Y2025_NGC_S1_01 2.csv","type":"text/csv","lastModified":1761083796425.459,"size":64907},{"id":"24f85f973284199772b0a54eee22082f9b8fd31004349a5736f144e6d967fa2e4b8a12e117fd96d5e2310c1c2c6c6d023d8d7f06732d20eb48d4e176e59f48e3","href":"https://static.observableusercontent.com/files/24f85f973284199772b0a54eee22082f9b8fd31004349a5736f144e6d967fa2e4b8a12e117fd96d5e2310c1c2c6c6d023d8d7f06732d20eb48d4e176e59f48e3","name":"SUMMARY_LOADED_NETWORK_Y2025_NGC_S1_02 2.csv","type":"text/csv","lastModified":1761083806396.22,"size":64907},{"id":"0baef435b00eebfb07f26be26e4cb9ef9b3179b7eba73fe893fdd180c7b513c162ebdbb4cb829d5cb2a7f1e7a4d6c3b0655ce7baa58784bbe03830f1103e2279","href":"https://static.observableusercontent.com/files/0baef435b00eebfb07f26be26e4cb9ef9b3179b7eba73fe893fdd180c7b513c162ebdbb4cb829d5cb2a7f1e7a4d6c3b0655ce7baa58784bbe03830f1103e2279","name":"chainage@1.csv","type":"text/csv","lastModified":1761096960065.586,"size":4983}] 2f:{"id":"13845c9f8ebbd79c","slug":null,"title":"Northern Growth Corridor Travel Time Validation","thumbnail":"58582a40390e453bc44ea39727a7da9253a1b56ce8c8bccd20d0c2b0ee22f30e","access_level":"unlisted","create_time":"$D2025-10-21T03:29:03.001Z","update_time":"$D2026-03-01T23:30:39.749Z","view_time":null,"first_public_time":null,"has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"b8b49653f61623f1","login":"ktringuyen","name":"ktringuyen","avatar_url":"https://avatars-next.observableusercontent.com/d4181af5d64e221c05a65bc1b61eee96abe87d64f0b0839e0757f71641ba33c7","tier":"starter_2024","bio":""},"version":1524,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"eae8b26a5e6eeaf3","login":"kytringuyen","name":"Nguyen Ky Tri","avatar_url":"https://avatars-next.observableusercontent.com/b2a48bc07bad0bedc9d706e8f81618992e650a71116025706a3f296f78c66941","tier":null,"bio":"Engineer specialised in Data Science and Data Visualisation"}],"members":[{"type":"team","id":"b8b49653f61623f1","login":"ktringuyen","name":"ktringuyen","avatar_url":"https://avatars-next.observableusercontent.com/d4181af5d64e221c05a65bc1b61eee96abe87d64f0b0839e0757f71641ba33c7","tier":"starter_2024","role":"editor"},{"type":"individual","id":"eae8b26a5e6eeaf3","login":"kytringuyen","name":"Nguyen Ky Tri","avatar_url":"https://avatars-next.observableusercontent.com/b2a48bc07bad0bedc9d706e8f81618992e650a71116025706a3f296f78c66941","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":2,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Northern Growth Corridor Travel Time Validation","cells":[{"id":0,"value":"# Northern Growth Corridor Travel Time Validation","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1403,"value":"## Route Travel Time Validation\n\nLine chart comparing modelled cumulative travel times against observed average travel times along a route corridor.\n\n**Key features:**\n- X-axis: Chainage (distance along route in metres) | Y-axis: Cumulative travel time (minutes)\n- Black line: Observed average travel time from TomTom data\n- Red line: Modelled travel time from selected scenario\n- Vertical gridlines: Major intersections/landmarks along the route with rotated labels\n- Interactive tooltips: Hover for detailed time and location information\n- Filters applied: Selected route","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1130,"value":"viewof selected_scenario = Inputs.select([...new Set(scenarios.map(d => d.scenario_name))], {label:\"Select Scenario\", value:\"Base Case\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1133,"value":"viewof route = Inputs.select(validation_data.map(d => d.routeName), {label: \"Select route\", value: \"Northern Highway - Watson Street\", unique: true});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1143,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1333,"value":"# Interactive Map","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1433,"value":"## Interactive Route Selection Map\n\nInteractive map displaying travel time validation routes with click-to-select functionality for detailed analysis.\n\n**Key features:**\n- **Route lines**: Black lines showing all validation routes across the network\n- **Click selection**: Click any route to highlight it in yellow and populate detailed charts above\n- **Interactive tooltips**: Hover over routes to view all route properties (name, direction, etc.)\n \n**Usage:** Click on any route line to select it for detailed travel time analysis in the faceted charts below. The selected route remains highlighted until a different route is clicked.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1421,"value":"viewof selected_scenario3 = Inputs.select([...new Set(scenarios.map(d => d.scenario_name))], {label:\"Select Scenario\", value:\"Base Case\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1427,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1335,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1392,"value":"\"\"","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1496,"value":"## Route Travel Time Comparison Table\n\nSummary table comparing end-to-end modelled travel times against observed TomTom GPS data for each route, direction, period, and scenario combination.\n\n**Columns:**\n- **Route**: Route corridor name\n- **Direction**: Travel direction (e.g., EB, WB, NB, SB)\n- **Period**: Time period (e.g., AM, PM)\n- **Scenario**: Model scenario name\n- **Observed**: TomTom average end-to-end travel time (mm:ss format)\n- **Modelled**: Model predicted end-to-end travel time (mm:ss format)\n- **Diff (%)**: Percentage difference between modelled and observed times\n- **Diff (mm:ss)**: Absolute time difference in minutes and seconds (mm:ss format)\n\n**Calculation method:** Travel times represent the maximum cumulative time for each route (i.e., end-to-end journey time from start to finish of the route). Positive differences indicate the model overestimates travel time; negative differences indicate underestimation.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1499,"value":"compare_table","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1474,"value":"## Debug","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1465,"value":"viewof selected_scenario2 = Inputs.select([...new Set(scenarios.map(d => d.scenario_name))], {label:\"Select Scenario\", value:\"Base Case\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1468,"value":"viewof selected_direction = Inputs.select([0,1] , {\"label\": \"Direction\", value: 1})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1471,"value":"viewof selected_period = Inputs.select([\"AM\",\"PM\"],{label:\"Period\",value:\"AM\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1462,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1482,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1455,"value":"## Import maplibre and load route geometry","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1327,"value":"all_routes = await FileAttachment(\"all_routes@3.geojson\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1331,"value":"mutable clickedRoute = \"\";","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1338,"value":"mutable clickedDirection = \"\";","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1329,"value":"maplibregl = require(\"maplibre-gl@2.1.9\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"## Observed data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1222,"value":"chainge = await FileAttachment(\"chainage@1.csv\").csv({typed:true, header:true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1053,"value":"observed_data = {\n let json1 = await FileAttachment(\"jobs_7973733_results_Northern_Growth_Corridor__TN__MEL_.json\").json();\n let json2 = await FileAttachment(\"jobs_7973737_results_North_Growth_Corridor__TN__MEL__2@1.json\").json();\n\n let routeData1 = tomtomTime(json1, getTimeID(json1, time_period));\n let routeData2 = tomtomTime(json2, getTimeID(json2, time_period));\n return routeData1.concat(routeData2);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":375,"value":"## Modelled inputs","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":633,"value":"time_period = ({ \n AM: [\"7:00-8:00\", \"8:00-9:00\"],\n PM: [\"15:00-16:00\",\"16:00-17:00\", \"17:00-18:00\"] \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":614,"value":" Transforming modelled data into json format and append it to observed dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1060,"value":"validation_data = observed_data.concat(modelled_data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1037,"value":"# Modelled data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1449,"value":"Import modelled for each scenario. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1042,"value":"modelled_data = {\n let result = [];\n await Promise.all(\n scenarios.map(async (scenario) => {\n let tmp;\n switch(scenario.file_name) {\n case \"SUMMARY_LOADED_NETWORK_Y2025_NGC_S1_02.csv\":\n tmp = await FileAttachment(\"SUMMARY_LOADED_NETWORK_Y2025_NGC_S1_02 2.csv\").csv({typed: true});\n break;\n case \"SUMMARY_LOADED_NETWORK_Y2025_NGC_S1_01.csv\":\n tmp = await FileAttachment(\"SUMMARY_LOADED_NETWORK_Y2025_NGC_S1_01 2.csv\").csv({typed: true});\n break;\n }\n result.push(...transformVITM(tmp, scenario.scenario_name));\n })\n );\n return result;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1031,"value":"scenarios = [\n {\n file_name: \"SUMMARY_LOADED_NETWORK_Y2025_NGC_S1_02.csv\",\n scenario_name: \"Base Case\"\n },\n { \n file_name: \"SUMMARY_LOADED_NETWORK_Y2025_NGC_S1_01.csv\",\n scenario_name: \"Base Case with SALUP\"\n },\n]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":10,"value":"## Helper functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":12,"value":"function getTimeID(json, times) {\n let timeObj = {};\n \n for (let i = 0; i < Object.keys(times).length; i++) {\n let timeIDs = [];\n \n for (const time of Object.values(times)[i]){\n timeIDs.push(json.timeSets.map(d => d[\"@id\"])[json.timeSets.map(d => d.name).indexOf(time)]);\n }\n \n timeObj[Object.keys(times)[i]] = timeIDs;\n }\n return timeObj;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":30,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":743,"value":"function transformVITM(arr, str) {\n const periods = [\"AM\", \"PM\"];\n const routes = [...new Set(arr.map(d => d.Route))];\n let segments = [];\n\n for (const period of periods) {\n for (const route of routes) {\n let chainage = 0;\n let cumulativeTime = 0;\n\n segments.push(({\n routeName: route.slice(0,-3),\n direction: route.slice(-2),\n chainage: 0,\n cumulativeTime: 0,\n period: period,\n type: str\n }))\n \n segments.push(arr.filter(d => d.Route == route).map(r => {\n chainage += r.DISTANCE*1000,\n cumulativeTime += r[\"TIME_\"+period]\n\n return {\n routeName: r.Route.slice(0,-3),\n direction: r.Route.slice(-2),\n chainage: chainage,\n cumulativeTime: cumulativeTime,\n period: period,\n type: str\n }\n }))\n }\n }\n\n return segments.flat();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W37","stdlib":"1"}} 30:"$2f:body"