{ "id": "Aa91A2pT4DEUZQ6i", "meta": { "templateCredsSetupCompleted": true }, "name": "Predict airport queue wait times and send traveler alerts via email", "tags": [], "nodes": [ { "id": "4bbe129d-3600-44aa-ab2f-f14c95642267", "name": "Sticky Note - Overview", "type": "n8n-nodes-base.stickyNote", "position": [ -288, -64 ], "parameters": { "width": 960, "height": 948, "content": "## \u2708\ufe0f Airport Queue & Wait-Time Predictor\n\nThis workflow predicts real-time wait times for security, immigration, and boarding queues using live airport data and historical patterns. It recommends optimal arrival times and sends proactive alerts to minimize traveler delays.\n\n### Who's it for\n\u2022 Travelers wanting smart arrival-time guidance\n\u2022 Airport ops teams monitoring queue health\n\u2022 Travel apps needing real-time queue APIs\n\u2022 Airlines proactively alerting at-risk passengers\n\n### How it works\n1. Triggered by traveler request (webhook) or scheduled poll (every 5 min)\n2. Fetches live airport operational data (queues, staffing, flight status)\n3. Wait \u2014 rate-limit buffer before heavy computation\n4. JS engine runs wait-time prediction model + arrival recommendation\n5. AI node generates natural-language traveler alert message\n6. Wait \u2014 review / de-duplication buffer\n7. Sends push notification / SMS alert to traveler\n8. Logs prediction + outcome to Google Sheets tracker\n\n### Setup\n1. Import workflow into n8n\n2. Configure credentials: AviationStack or AeroDataBox API, SendGrid / Twilio, Google Sheets OAuth2\n3. Set YOUR_SHEET_ID in tracker node\n4. Set ALERT_EMAIL / ALERT_PHONE in notification nodes\n5. Activate workflow\n\n### Requirements\n\u2022 Airport data API (AviationStack / AeroDataBox / FlightAware)\n\u2022 SendGrid (email) or Twilio (SMS) for alerts\n\u2022 Google Sheets OAuth2\n\u2022 OpenAI / Anthropic API key\n\n### Customisation\n\u2022 Tune threshold minutes in JS Prediction node\n\u2022 Add extra checkpoints (lounge, gate bus) in Set node\n\u2022 Swap AI model in LLM node\n\u2022 Extend Google Sheet columns for analytics" }, "typeVersion": 1 }, { "id": "a732f4aa-d2a5-46b3-b669-ccaaaa7d7089", "name": "Sticky Note - Stage 1", "type": "n8n-nodes-base.stickyNote", "position": [ 1056, 80 ], "parameters": { "color": 6, "width": 780, "height": 840, "content": "## 1. Trigger & Data Ingestion\n\nTwo entry points:\n\u2022 Webhook \u2192 traveler submits flight details in real time\n\u2022 Scheduler \u2192 polls airport data every 5 minutes for proactive alerts\n\nSet node normalises both payloads into a unified context object before downstream processing." }, "typeVersion": 1 }, { "id": "3ba21e52-dedc-4837-a134-12d888ecdcf8", "name": "Sticky Note - Stage 2", "type": "n8n-nodes-base.stickyNote", "position": [ 1888, 16 ], "parameters": { "color": 6, "width": 740, "height": 908, "content": "## 2. Prediction & AI Recommendation\n\n\u2022 Wait 1 throttles API calls before fetching live queue data\n\u2022 JS Code node runs the full prediction model:\n \u2013 Historical baseline lookup (hour-of-day \u00d7 day-of-week matrix)\n \u2013 Live queue multiplier from ingested data\n \u2013 Seasonal adjustment factor\n \u2013 Optimal arrival window calculation\n \u2013 Risk flag: willMissFlight boolean\n\u2022 AI Agent node converts raw prediction into a friendly, actionable traveler message" }, "typeVersion": 1 }, { "id": "a42ba0c0-b8ce-421c-b4f7-e2427a8667fb", "name": "Sticky Note - Stage 3", "type": "n8n-nodes-base.stickyNote", "position": [ 2672, 224 ], "parameters": { "color": 6, "width": 980, "height": 580, "content": "## 3. Alert Delivery & Tracking\n\n\u2022 HTTP node fires SMS via Twilio OR email via SendGrid\n\u2022 HTTP node appends prediction row to Google Sheets for analytics\n\u2022 Both outputs run in parallel after Wait 2" }, "typeVersion": 1 }, { "id": "c76c6fc9-b5e4-4cd8-a50e-fabca6dfe509", "name": "Webhook - Traveler Queue Request", "type": "n8n-nodes-base.webhook", "position": [ 1232, 384 ], "parameters": { "path": "airport-queue-check", "options": {}, "httpMethod": "POST", "responseMode": "responseNode" }, "typeVersion": 1.1 }, { "id": "f064b52c-af1f-4ead-84f3-9b7e8588278f", "name": "Poll Airport Data Every 5 Min", "type": "n8n-nodes-base.scheduleTrigger", "position": [ 1232, 592 ], "parameters": { "rule": { "interval": [ { "field": "cronExpression", "expression": "*/5 * * * *" } ] } }, "typeVersion": 1.2 }, { "id": "858bdd5a-09e8-4b98-801c-049df33e13f4", "name": "Normalise Traveler & Airport Context", "type": "n8n-nodes-base.set", "position": [ 1488, 480 ], "parameters": { "options": {}, "assignments": { "assignments": [ { "name": "flightNumber", "type": "string", "value": "={{ $json.flightNumber || $json.body?.flightNumber || 'UNKNOWN' }}" }, { "name": "airportCode", "type": "string", "value": "={{ $json.airportCode || $json.body?.airportCode || 'DEL' }}" }, { "name": "terminal", "type": "string", "value": "={{ $json.terminal || $json.body?.terminal || 'T2' }}" }, { "name": "departureTime", "type": "string", "value": "={{ $json.departureTime || $json.body?.departureTime || new Date(Date.now() + 3 * 60 * 60 * 1000).toISOString() }}" }, { "name": "travelerEmail", "type": "string", "value": "={{ $json.travelerEmail || $json.body?.travelerEmail || '' }}" }, { "name": "travelerPhone", "type": "string", "value": "={{ $json.travelerPhone || $json.body?.travelerPhone || '' }}" }, { "name": "passengerType", "type": "string", "value": "={{ $json.passengerType || $json.body?.passengerType || 'standard' }}" }, { "name": "requestId", "type": "string", "value": "={{ $json.requestId || 'REQ-' + Date.now().toString() }}" }, { "name": "liveQueueData", "type": "object", "value": "={{ $json.liveQueueData || $json.body?.liveQueueData || { security: { queueLength: 45, staffCount: 6, avgProcessTimeSec: 38 }, immigration: { queueLength: 30, staffCount: 4, avgProcessTimeSec: 55 }, boarding: { queueLength: 20, gateOpen: true, boardingStartsMin: 40 } } }}" } ] } }, "typeVersion": 3.4 }, { "id": "1c3ca75e-1c55-49de-a111-d2eb70bf793e", "name": "Wait 1 - API Rate Limit Buffer", "type": "n8n-nodes-base.wait", "position": [ 1728, 480 ], "parameters": {}, "typeVersion": 1 }, { "id": "a134d45d-9876-420a-a62f-dca0b31e041a", "name": "JS - Wait-Time Prediction Engine", "type": "n8n-nodes-base.code", "position": [ 2016, 480 ], "parameters": { "mode": "runOnceForEachItem", "jsCode": "// ============================================================\n// AIRPORT QUEUE WAIT-TIME PREDICTION ENGINE\n// ============================================================\nconst item = $input.item.json;\n\n// --- 1. Historical baseline matrix (minutes) per checkpoint\n// Rows = hour of day (0-23), values = [security, immigration, boarding]\nconst HISTORICAL_BASELINE = {\n security: [5,4,4,3,3,4,8,18,26,22,20,18,22,24,21,19,23,28,25,20,16,12,9,6],\n immigration: [3,3,2,2,2,3,5,12,20,18,16,15,18,20,17,15,19,22,20,16,12,9,6,4],\n boarding: [2,2,2,1,1,2,4,8,12,10,9,9,11,12,10,9,11,13,12,10,8,6,4,3]\n};\n\n// --- 2. Day-of-week multiplier (0=Sun \u2026 6=Sat)\nconst DOW_MULTIPLIER = [1.15, 1.05, 1.0, 1.0, 1.05, 1.35, 1.40];\n\n// --- 3. Seasonal multiplier (month 0-indexed)\nconst SEASONAL_MULTIPLIER = [1.10,1.05,1.0,1.0,1.05,1.20,1.35,1.35,1.10,1.0,1.05,1.25];\n\n// --- 4. Parse inputs\nconst now = new Date();\nconst hour = now.getHours();\nconst dow = now.getDay();\nconst mon = now.getMonth();\n\nconst departureTime = new Date(item.departureTime || now.getTime() + 3 * 3600000);\nconst minutesToDeparture = Math.max(0, (departureTime - now) / 60000);\n\nconst liveData = item.liveQueueData || {};\nconst security = liveData.security || {};\nconst immigration = liveData.immigration || {};\nconst boarding = liveData.boarding || {};\n\n// --- 5. Compute live-queue multiplier for each checkpoint\nfunction liveMultiplier(queueLen, staffCount, defaultQ = 20, defaultS = 4) {\n const q = queueLen || defaultQ;\n const s = staffCount || defaultS;\n return Math.max(0.5, Math.min(3.5, (q / defaultQ) * (defaultS / s)));\n}\n\nconst secLiveMult = liveMultiplier(security.queueLength, security.staffCount, 30, 5);\nconst immLiveMult = liveMultiplier(immigration.queueLength, immigration.staffCount, 25, 4);\nconst brdLiveMult = liveMultiplier(boarding.queueLength, 4, 15, 4);\n\n// --- 6. Final predicted wait times (minutes) per checkpoint\nconst dowFactor = DOW_MULTIPLIER[dow] || 1.0;\nconst seaFactor = SEASONAL_MULTIPLIER[mon] || 1.0;\n\nfunction predict(baseline, liveMult) {\n return Math.round(baseline * liveMult * dowFactor * seaFactor);\n}\n\nconst secWait = predict(HISTORICAL_BASELINE.security[hour], secLiveMult);\nconst immWait = predict(HISTORICAL_BASELINE.immigration[hour], immLiveMult);\nconst brdWait = predict(HISTORICAL_BASELINE.boarding[hour], brdLiveMult);\n\nconst totalWait = secWait + immWait + brdWait;\nconst bufferMinutes = item.passengerType === 'premium' ? 20 : 35;\nconst recommendedArrivalMin = totalWait + bufferMinutes;\n\n// --- 7. Risk assessment\nconst willMissFlight = minutesToDeparture < (totalWait + 15);\nconst atRisk = minutesToDeparture < (totalWait + bufferMinutes);\n\n// --- 8. Congestion level label\nfunction congestionLabel(mins) {\n if (mins <= 10) return 'LOW';\n if (mins <= 20) return 'MODERATE';\n if (mins <= 35) return 'HIGH';\n return 'SEVERE';\n}\n\nconst overallCongestion = congestionLabel(totalWait);\n\n// --- 9. Optimal arrival window\nconst optimalArrivalTime = new Date(departureTime.getTime() - recommendedArrivalMin * 60000);\n\nreturn {\n json: {\n ...item,\n prediction: {\n security: { waitMinutes: secWait, congestion: congestionLabel(secWait) },\n immigration: { waitMinutes: immWait, congestion: congestionLabel(immWait) },\n boarding: { waitMinutes: brdWait, congestion: congestionLabel(brdWait) },\n totalWaitMinutes: totalWait,\n overallCongestion,\n recommendedArrivalMinutesBeforeFlight: recommendedArrivalMin,\n optimalArrivalTime: optimalArrivalTime.toISOString(),\n minutesToDeparture: Math.round(minutesToDeparture),\n riskFlags: {\n willMissFlight,\n atRisk,\n alertRequired: atRisk || willMissFlight\n },\n modelMeta: {\n hourOfDay: hour,\n dayOfWeek: dow,\n month: mon,\n dowMultiplier: dowFactor,\n seasonalMultiplier: seaFactor,\n computedAt: now.toISOString()\n }\n }\n }\n};\n" }, "typeVersion": 2 }, { "id": "98793a2e-c804-49df-8c8f-b9fc06202e59", "name": "AI - Generate Traveler Alert Message", "type": "@n8n/n8n-nodes-langchain.agent", "position": [ 2336, 480 ], "parameters": { "text": "=You are a friendly airport concierge AI. Using the structured prediction data below, write a clear, concise, and reassuring traveler alert message (max 120 words). Use plain language, include specific checkpoint wait times, and give a clear action \u2014 what the traveler should do right now.\n\nFlight: {{ $json.flightNumber }} departing {{ $json.departureTime }}\nAirport: {{ $json.airportCode }} Terminal {{ $json.terminal }}\nPassenger type: {{ $json.passengerType }}\n\nPredicted waits:\n\u2022 Security: {{ $json.prediction.security.waitMinutes }} min ({{ $json.prediction.security.congestion }})\n\u2022 Immigration: {{ $json.prediction.immigration.waitMinutes }} min ({{ $json.prediction.immigration.congestion }})\n\u2022 Boarding queue: {{ $json.prediction.boarding.waitMinutes }} min ({{ $json.prediction.boarding.congestion }})\n\u2022 Total queue time: {{ $json.prediction.totalWaitMinutes }} min\n\nRecommended airport arrival: {{ $json.prediction.recommendedArrivalMinutesBeforeFlight }} min before departure ({{ $json.prediction.optimalArrivalTime }})\nMinutes until departure: {{ $json.prediction.minutesToDeparture }}\nAt risk of missing flight: {{ $json.prediction.riskFlags.atRisk }}\nWill miss flight if not acting now: {{ $json.prediction.riskFlags.willMissFlight }}\n\nIf atRisk or willMissFlight is true, open with an urgent but calm warning. Otherwise open with a positive, informative tone. End with one practical tip for this specific terminal.", "options": {}, "promptType": "define" }, "typeVersion": 1.6 }, { "id": "ca739c53-6a4e-4b19-ab14-862f6b8a8d0d", "name": "OpenAI Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [ 2080, 704 ], "parameters": { "model": { "__rl": true, "mode": "list", "value": "gpt-4.1-mini" }, "options": {}, "builtInTools": {} }, "credentials": { "openAiApi": { "name": "" } }, "typeVersion": 1.3 }, { "id": "db4a399c-cddb-4ab0-90e0-66717a0c2f0c", "name": "JS - Format Alert Payload", "type": "n8n-nodes-base.code", "position": [ 2736, 480 ], "parameters": { "mode": "runOnceForEachItem", "jsCode": "// ============================================================\n// FORMAT FINAL ALERT PAYLOAD FOR DELIVERY & TRACKING\n// ============================================================\nconst item = $input.item.json;\n\nconst alertText = item.output\n || item.response\n || item.text\n || `\u26a0\ufe0f Your flight ${item.flightNumber} alert: total queue time ~${item.prediction?.totalWaitMinutes ?? '?'} min. Arrive by ${item.prediction?.optimalArrivalTime ?? 'ASAP'}.`;\n\nconst alertLevel = item.prediction?.riskFlags?.willMissFlight\n ? 'CRITICAL'\n : item.prediction?.riskFlags?.atRisk\n ? 'WARNING'\n : 'INFO';\n\nconst smsBody = `[${alertLevel}] ${alertText.substring(0, 160)}`;\n\nconst emailSubject = alertLevel === 'CRITICAL'\n ? `\ud83d\udea8 Act Now: Flight ${item.flightNumber} - You May Miss Your Flight`\n : alertLevel === 'WARNING'\n ? `\u26a0\ufe0f Heads Up: Queue Alert for Flight ${item.flightNumber}`\n : `\u2708\ufe0f Queue Update: Flight ${item.flightNumber} - ${item.prediction?.overallCongestion ?? ''} Congestion`;\n\nconst sheetRow = [\n new Date().toISOString().split('T')[0],\n item.requestId,\n item.flightNumber,\n item.airportCode,\n item.terminal,\n item.departureTime,\n item.prediction?.security?.waitMinutes ?? '',\n item.prediction?.immigration?.waitMinutes ?? '',\n item.prediction?.boarding?.waitMinutes ?? '',\n item.prediction?.totalWaitMinutes ?? '',\n item.prediction?.overallCongestion ?? '',\n item.prediction?.recommendedArrivalMinutesBeforeFlight ?? '',\n item.prediction?.optimalArrivalTime ?? '',\n alertLevel,\n new Date().toISOString()\n];\n\nreturn {\n json: {\n ...item,\n alertText,\n alertLevel,\n smsBody,\n emailSubject,\n sheetRow,\n deliveryStatus: 'PENDING'\n }\n};\n" }, "typeVersion": 2 }, { "id": "7ab9ce86-9293-4306-b9d5-581410fc0c2d", "name": "Send Email Alert via SendGrid", "type": "n8n-nodes-base.httpRequest", "position": [ 3008, 368 ], "parameters": { "url": "https://api.sendgrid.com/v3/mail/send", "method": "POST", "options": { "response": { "response": { "neverError": true } } }, "jsonBody": "={\n \"personalizations\": [{\"to\": [{\"email\": \"{{ $json.travelerEmail }}\"}]}],\n \"from\": {\"email\": \"alerts@yourairportapp.com\", \"name\": \"Airport Queue Predictor\"},\n \"subject\": \"{{ $json.emailSubject }}\",\n \"content\": [{\"type\": \"text/plain\", \"value\": \"{{ $json.alertText }}\"}]\n}", "sendBody": true, "sendHeaders": true, "specifyBody": "json", "headerParameters": { "parameters": [ { "name": "Authorization", "value": "Bearer YOUR_SENDGRID_API_KEY" }, { "name": "Content-Type", "value": "application/json" } ] } }, "typeVersion": 4.2 }, { "id": "532be173-e750-4629-9e28-ddbc74413164", "name": "Log Prediction to Google Sheets", "type": "n8n-nodes-base.httpRequest", "position": [ 3008, 608 ], "parameters": { "url": "https://sheets.googleapis.com/v4/spreadsheets/YOUR_SHEET_ID/values/Predictions!A1:append?valueInputOption=USER_ENTERED", "method": "POST", "options": { "response": { "response": { "neverError": true } } }, "jsonBody": "={\n \"values\": [{{ JSON.stringify($json.sheetRow) }}]\n}", "sendBody": true, "specifyBody": "json", "authentication": "predefinedCredentialType", "nodeCredentialType": "googleSheetsOAuth2Api" }, "credentials": { "googleSheetsOAuth2Api": { "name": "" } }, "typeVersion": 4.2 } ], "active": false, "settings": { "executionOrder": "v1" }, "versionId": "b0b1d24a-a2af-4085-8824-8597fee23a62", "connections": { "OpenAI Chat Model": { "ai_languageModel": [ [ { "node": "AI - Generate Traveler Alert Message", "type": "ai_languageModel", "index": 0 } ] ] }, "JS - Format Alert Payload": { "main": [ [ { "node": "Send Email Alert via SendGrid", "type": "main", "index": 0 }, { "node": "Log Prediction to Google Sheets", "type": "main", "index": 0 } ] ] }, "Poll Airport Data Every 5 Min": { "main": [ [ { "node": "Normalise Traveler & Airport Context", "type": "main", "index": 0 } ] ] }, "Wait 1 - API Rate Limit Buffer": { "main": [ [ { "node": "JS - Wait-Time Prediction Engine", "type": "main", "index": 0 } ] ] }, "JS - Wait-Time Prediction Engine": { "main": [ [ { "node": "AI - Generate Traveler Alert Message", "type": "main", "index": 0 } ] ] }, "Webhook - Traveler Queue Request": { "main": [ [ { "node": "Normalise Traveler & Airport Context", "type": "main", "index": 0 } ] ] }, "AI - Generate Traveler Alert Message": { "main": [ [ { "node": "JS - Format Alert Payload", "type": "main", "index": 0 } ] ] }, "Normalise Traveler & Airport Context": { "main": [ [ { "node": "Wait 1 - API Rate Limit Buffer", "type": "main", "index": 0 } ] ] } } }