isaWOW This workflow corresponds to n8n.io template #16836 — we link there as the canonical source.
This workflow follows the Form Trigger → Gmail recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
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{
"id": "sZRsdOerPYjPfRAJ",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "Railway Station Queue Predictor \u2014 Form + Smart Wait Time + ChatGPT AI Advisory + Gmail + Sheets",
"tags": [],
"nodes": [
{
"id": "be16ddb5-255c-4d20-ac28-e8e73fb7f540",
"name": "Overview",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1600,
-80
],
"parameters": {
"color": 4,
"width": 536,
"height": 1372,
"content": "## Railway Station Queue Predictor \u2014 Form + Smart Wait Time + ChatGPT AI Advisory + Gmail + Sheets\n\nFor Indian train travelers who want to know how early to reach the station and how to skip the worst queues. The traveler fills a short form with their train and station details. A built-in prediction algorithm (no external API) estimates crowd level, platform wait, ticket-counter queue, security time, and the ideal arrival time. A ChatGPT AI model then writes a friendly, plain-English advisory with station-specific tips. The traveler gets a colour-coded email alert, and every prediction is logged to Google Sheets.\n\n## What each node does\n- **1. Form \u2014 Traveler Train Details** collects train number, station, travel date and time, class, e-ticket status, and passenger count\n- **2. Code \u2014 Predict Queue Wait Times** runs the rule-based algorithm and returns crowd score, all wait times, alert level, and the recommended arrival time\n- **3. HTTP \u2014 AI Write Advisory** sends the prediction to a ChatGPT AI model and gets back a short traveler advisory\n- **4. Code \u2014 Parse Advisory and Alert Colors** merges the advisory and assigns the colour-coded alert level\n- **5. Code \u2014 Build HTML Email** builds the full colour-coded alert email\n- **6. Sheets \u2014 Log Prediction** appends the prediction to the log (parallel branch)\n- **7. Gmail \u2014 Send Queue Alert to Traveler** emails the alert to the traveler (parallel branch)\n\n## How the prediction works\nThe algorithm in node 2 scores expected crowding from day of week, time of day, festival season, station size, train type, and class of travel. Alert levels: GREEN (light), YELLOW (moderate), ORANGE (busy), RED (very crowded). It is a rule-based estimate, not live station data.\n\n## Google Sheet setup\nCreate a Google Sheet, add a tab named **Predictions**, and put these column headers in row 1:\nDate, Train Number, Train Name, Station, Travel Date, Departure Time, Class, Passengers, Has E-Ticket, Crowd Score, Alert Level, Platform Wait (min), Ticket Queue (min), Security Time (min), Total Buffer (min), Optimal Arrival, Traveler Email, Advisory Sent\n\n## Set up steps\n1. **OpenAI** \u2014 in **3. HTTP \u2014 AI Write Advisory**, connect your OpenAI API credential. A ChatGPT AI model is used for the advisory\n2. **Google Sheets** \u2014 in **6. Sheets \u2014 Log Prediction**, connect Google Sheets OAuth2 and replace `YOUR_QUEUE_LOG_SHEET_ID` with your sheet ID (from the sheet URL after /d/)\n3. **Gmail** \u2014 in **7. Gmail \u2014 Send Queue Alert to Traveler**, connect Gmail OAuth2. The recipient comes from the form\n4. Activate the workflow, open the Form URL, enter train details, and submit. The alert is emailed and logged instantly"
},
"typeVersion": 1
},
{
"id": "e29779ae-ac73-4a1e-bc32-496fb658e411",
"name": "Section \u2014 Form and Prediction Algorithm",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1008,
48
],
"parameters": {
"color": 5,
"width": 484,
"height": 324,
"content": "## Form and Prediction Algorithm\nTraveler fills train details. Code calculates crowd score, platform wait, ticket queue, security time, and the optimal arrival time \u2014 all without any external API."
},
"typeVersion": 1
},
{
"id": "2fba56e1-5ac8-4d3d-994b-60065f1c73f7",
"name": "Section \u2014 AI Advisory and Alert Level",
"type": "n8n-nodes-base.stickyNote",
"position": [
-512,
48
],
"parameters": {
"color": 6,
"width": 452,
"height": 372,
"content": "## AI Advisory and Alert Level\nThe ChatGPT AI model writes a friendly traveler advisory with station tips. Code parses the response and assigns the colour-coded alert level."
},
"typeVersion": 1
},
{
"id": "2d161391-2d4f-4fa4-9962-63fc1151b407",
"name": "Section \u2014 Build Email, Log, and Send",
"type": "n8n-nodes-base.stickyNote",
"position": [
0,
-144
],
"parameters": {
"color": 4,
"width": 660,
"height": 628,
"content": "## Build Email, Log, and Send\nCode builds the colour-coded HTML email. Two parallel branches run: Sheets logs the prediction, and Gmail sends the alert to the traveler."
},
"typeVersion": 1
},
{
"id": "0fb166ca-0a2d-4ba6-bd80-a53080bbfb2f",
"name": "1. Form \u2014 Traveler Train Details",
"type": "n8n-nodes-base.formTrigger",
"position": [
-928,
192
],
"parameters": {
"options": {
"path": "railway-queue-predictor",
"buttonLabel": "Predict My Queue Times",
"appendAttribution": false
},
"formTitle": "Railway Queue Predictor",
"formFields": {
"values": [
{
"fieldLabel": "Your Name",
"placeholder": "e.g. Rahul Kumar",
"requiredField": true
},
{
"fieldLabel": "Your Email",
"placeholder": "your@email.com \u2014 advisory will be sent here",
"requiredField": true
},
{
"fieldLabel": "Train Number",
"placeholder": "e.g. 12301 or 22691",
"requiredField": true
},
{
"fieldLabel": "Train Name",
"placeholder": "e.g. Rajdhani Express, Shatabdi, Duronto, Express",
"requiredField": true
},
{
"fieldLabel": "Departure Station Code",
"placeholder": "e.g. NDLS, CSMT, HWH, MAS, SBC, ADI, PUNE, LKO",
"requiredField": true
},
{
"fieldLabel": "Travel Date",
"placeholder": "YYYY-MM-DD e.g. 2025-07-15",
"requiredField": true
},
{
"fieldLabel": "Train Departure Time",
"placeholder": "HH:MM in 24hr format e.g. 06:30 or 17:45",
"requiredField": true
},
{
"fieldType": "dropdown",
"fieldLabel": "Class of Travel",
"fieldOptions": {
"values": [
{
"option": "SL \u2014 Sleeper Class"
},
{
"option": "3A \u2014 AC 3 Tier"
},
{
"option": "2A \u2014 AC 2 Tier"
},
{
"option": "1A \u2014 AC First Class"
},
{
"option": "CC \u2014 Chair Car"
},
{
"option": "2S \u2014 Second Sitting"
}
]
},
"requiredField": true
},
{
"fieldType": "dropdown",
"fieldLabel": "Do you have IRCTC e-ticket on app?",
"fieldOptions": {
"values": [
{
"option": "Yes \u2014 I have digital ticket on IRCTC app"
},
{
"option": "No \u2014 I need to collect physical ticket or PRS printout"
}
]
},
"requiredField": true
},
{
"fieldLabel": "Number of Passengers",
"placeholder": "e.g. 1, 2, 4",
"requiredField": true
}
]
},
"formDescription": "Enter your train details to get a predicted crowd and wait time report for your departure station. You will receive a personalized advisory email with tips to avoid queues."
},
"typeVersion": 2.2
},
{
"id": "ed593746-0cf1-47df-8bcb-f46871f38d3f",
"name": "2. Code \u2014 Predict Queue Wait Times",
"type": "n8n-nodes-base.code",
"position": [
-688,
192
],
"parameters": {
"jsCode": "// RAILWAY STATION QUEUE PREDICTION ALGORITHM\n// Predicts crowd levels and wait times based on multiple factors\n// No external API needed \u2014 rule-based scoring system\n\nconst row = $json;\n\n// --- Extract form inputs ---\nconst travelerName = (row['Your Name'] || '').trim();\nconst email = (row['Your Email'] || '').trim();\nconst trainNo = (row['Train Number'] || '').trim();\nconst trainName = (row['Train Name'] || '').trim();\nconst stationCode = (row['Departure Station Code'] || '').trim().toUpperCase();\nconst travelDate = (row['Travel Date'] || '').trim();\nconst departureTime = (row['Train Departure Time'] || '').trim();\nconst travelClass = (row['Class of Travel'] || '').trim();\nconst hasETicket = (row['Do you have IRCTC e-ticket on app?'] || '').startsWith('Yes');\nconst passengers = parseInt(row['Number of Passengers']) || 1;\n\nif (!email || !trainNo || !stationCode || !travelDate || !departureTime) {\n throw new Error('Train number, station code, travel date, departure time, and email are all required.');\n}\n\n// --- Parse travel date and time ---\nconst travelDateObj = new Date(travelDate);\nconst dayOfWeek = travelDateObj.getDay(); // 0=Sun, 6=Sat\nconst month = travelDateObj.getMonth() + 1; // 1-12\nconst [depHour, depMin] = departureTime.split(':').map(Number);\nconst depMinutes = (depHour * 60) + (depMin || 0);\n\n// --- FACTOR 1: Day of week multiplier ---\nconst dayMultiplier =\n dayOfWeek === 0 ? 1.35 :\n dayOfWeek === 1 ? 1.25 :\n dayOfWeek === 5 ? 1.20 :\n dayOfWeek === 6 ? 1.30 :\n 1.0;\n\n// --- FACTOR 2: Time of day multiplier ---\nconst timeMultiplier =\n (depMinutes >= 300 && depMinutes < 540) ? 1.35 :\n (depMinutes >= 960 && depMinutes < 1260) ? 1.30 :\n (depMinutes >= 1260 || depMinutes < 300) ? 1.15 :\n 1.0;\n\n// --- FACTOR 3: Season/festival multiplier ---\nconst seasonMultiplier =\n (month === 10 || month === 11) ? 1.40 :\n (month === 12 || month === 1) ? 1.25 :\n (month === 5 || month === 6) ? 1.30 :\n month === 3 ? 1.20 :\n 1.0;\n\n// --- FACTOR 4: Station size multiplier ---\nconst stationMultipliers = {\n 'NDLS': 1.50, 'DLI': 1.40, 'CSMT': 1.45, 'BCT': 1.35,\n 'HWH': 1.40, 'SDAH': 1.25, 'MAS': 1.35, 'SBC': 1.30,\n 'ADI': 1.30, 'PUNE': 1.25, 'LKO': 1.30, 'PNBE': 1.35,\n 'JP': 1.25, 'HYB': 1.30, 'SC': 1.25\n};\nconst stationMult = stationMultipliers[stationCode] || 1.15;\n\n// --- FACTOR 5: Train type multiplier ---\nconst trainNameLower = trainName.toLowerCase();\nconst trainMultiplier =\n (trainNameLower.includes('rajdhani') || trainNameLower.includes('shatabdi') || trainNameLower.includes('tejas') || trainNameLower.includes('vande')) ? 0.75 :\n (trainNameLower.includes('duronto') || trainNameLower.includes('gatimaan')) ? 0.80 :\n (trainNameLower.includes('superfast') || trainNameLower.includes('sf')) ? 1.05 :\n (trainNameLower.includes('express')) ? 1.10 :\n 1.20;\n\n// --- FACTOR 6: Class multiplier ---\nconst classCode = travelClass.split('\u2014')[0].trim();\nconst classMultiplier =\n classCode === 'SL' ? 1.40 :\n classCode === '2S' ? 1.30 :\n classCode === 'CC' ? 1.10 :\n classCode === '3A' ? 0.90 :\n classCode === '2A' ? 0.80 :\n classCode === '1A' ? 0.70 :\n 1.0;\n\n// --- COMBINED CROWD SCORE ---\nconst baseCrowdScore = 50;\nconst rawScore = baseCrowdScore * dayMultiplier * timeMultiplier * seasonMultiplier * stationMult * trainMultiplier * classMultiplier;\nconst crowdScore = Math.min(Math.round(rawScore), 100);\n\n// --- WAIT TIME CALCULATIONS (in minutes) ---\nconst platformWait = Math.round((crowdScore / 100) * 25 + 5);\nconst ticketQueue = hasETicket ? 0 : Math.round((crowdScore / 100) * 35 + 5);\nconst hasSecurityCheck = ['NDLS', 'DLI', 'CSMT', 'HWH', 'MAS', 'SBC'].includes(stationCode);\nconst securityTime = hasSecurityCheck ? Math.round((crowdScore / 100) * 20 + 5) : 0;\nconst platformWalk = stationMult > 1.35 ? 12 : stationMult > 1.25 ? 8 : 5;\nconst totalBuffer = platformWait + ticketQueue + securityTime + platformWalk + (passengers > 3 ? 10 : 0);\n\n// --- Optimal arrival time ---\nconst optimalArrivalMinutes = depMinutes - totalBuffer;\nconst optHour = Math.floor(((optimalArrivalMinutes % 1440) + 1440) % 1440 / 60);\nconst optMin = Math.floor(((optimalArrivalMinutes % 1440) + 1440) % 1440 % 60);\nconst optimalArrival = String(optHour).padStart(2,'0') + ':' + String(optMin).padStart(2,'0');\n\n// --- ALERT LEVEL ---\nconst alertLevel =\n crowdScore >= 80 ? 'RED' :\n crowdScore >= 60 ? 'ORANGE' :\n crowdScore >= 40 ? 'YELLOW' :\n 'GREEN';\n\n// --- BUILD CONTEXT FOR AI ---\nconst predictionContext = [\n 'Traveler: ' + travelerName,\n 'Train: ' + trainNo + ' ' + trainName,\n 'Station: ' + stationCode,\n 'Travel Date: ' + travelDate,\n 'Day of Week: ' + ['Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Saturday'][dayOfWeek],\n 'Departure Time: ' + departureTime,\n 'Class: ' + travelClass,\n 'Passengers: ' + passengers,\n 'Has e-Ticket: ' + (hasETicket ? 'Yes' : 'No'),\n '',\n 'PREDICTION RESULTS:',\n 'Crowd Score: ' + crowdScore + '/100',\n 'Alert Level: ' + alertLevel,\n 'Platform Wait: ' + platformWait + ' min',\n 'Ticket Counter Queue: ' + (hasETicket ? 'Skipped (e-ticket)' : ticketQueue + ' min'),\n 'Security Check: ' + (hasSecurityCheck ? securityTime + ' min' : 'Not required at this station'),\n 'Platform Walk: ' + platformWalk + ' min',\n 'Total Buffer Needed: ' + totalBuffer + ' minutes',\n 'Recommended Arrival: ' + optimalArrival + ' (arrive by this time)',\n '',\n 'Factors driving this prediction:',\n 'Day multiplier: ' + dayMultiplier + 'x (' + ['Sun','Mon','Tue','Wed','Thu','Fri','Sat'][dayOfWeek] + ')',\n 'Time of day: ' + timeMultiplier + 'x (departure at ' + departureTime + ')',\n 'Season: ' + seasonMultiplier + 'x (month ' + month + ')',\n 'Station size: ' + stationMult + 'x (' + stationCode + ')',\n 'Train type: ' + trainMultiplier + 'x (' + trainName + ')',\n 'Class: ' + classMultiplier + 'x (' + classCode + ')'\n].join('\\n');\n\nconst loggedAt = new Date().toISOString();\n\nreturn [{\n json: {\n travelerName, email, trainNo, trainName, stationCode,\n travelDate, departureTime, travelClass, classCode,\n hasETicket, passengers, dayOfWeek,\n crowdScore, alertLevel,\n platformWait, ticketQueue, securityTime,\n platformWalk, totalBuffer, optimalArrival,\n hasSecurityCheck,\n predictionContext,\n loggedAt\n }\n}];"
},
"typeVersion": 2
},
{
"id": "9d90dfa5-5125-4296-91c5-5ebec9534f39",
"name": "3. HTTP \u2014 AI Write Advisory",
"type": "n8n-nodes-base.httpRequest",
"position": [
-448,
192
],
"parameters": {
"url": "https://api.openai.com/v1/chat/completions",
"method": "POST",
"options": {
"timeout": 30000,
"response": {
"response": {
"responseFormat": "json"
}
}
},
"jsonBody": "={\n \"model\": \"gpt-4o-mini\",\n \"temperature\": 0.5,\n \"max_tokens\": 800,\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You are a helpful railway travel assistant for Indian train travelers. Write a friendly, practical travel advisory in simple English. Be specific, actionable, and conversational. Do not use markdown formatting \u2014 plain text paragraphs only.\"\n },\n {\n \"role\": \"user\",\n \"content\": {{ JSON.stringify(\n \"Write a short traveler advisory based on this queue prediction.\\n\\n\" + $json.predictionContext +\n \"\\n\\nInclude these points in your advisory (use plain text, no markdown, no bullets, no headers):\\n\" +\n \"1. One opening sentence mentioning their train and the crowd level prediction.\\n\" +\n \"2. A specific recommendation on what time to arrive and why.\\n\" +\n \"3. One practical tip for their class of travel (\" + $json.classCode + \") at \" + $json.stationCode + \" station to save time.\\n\" +\n \"4. One tip about the ticket counter situation based on whether they have an e-ticket or not.\\n\" +\n \"5. One general safety or comfort tip for travelling with \" + $json.passengers + \" passengers.\\n\" +\n \"Keep the entire advisory under 120 words. Be warm and helpful.\"\n ) }}\"\n }\n ]\n}",
"sendBody": true,
"sendHeaders": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
},
"nodeCredentialType": "openAiApi"
},
"typeVersion": 4.2
},
{
"id": "c10c5399-6ff7-4deb-9141-7593eefacb2a",
"name": "4. Code \u2014 Parse Advisory and Alert Colors",
"type": "n8n-nodes-base.code",
"position": [
-208,
192
],
"parameters": {
"jsCode": "// Extract AI advisory text and merge with prediction data\nconst resp = $input.first().json;\nconst prediction = $('2. Code \u2014 Predict Queue Wait Times').first().json;\n\nconst advisory = resp?.choices?.[0]?.message?.content || 'Please arrive at least ' + prediction.totalBuffer + ' minutes before your train departure. Check the station board for your platform number.';\n\n// Alert level colors for email\nconst alertColor =\n prediction.alertLevel === 'RED' ? '#e74c3c' :\n prediction.alertLevel === 'ORANGE' ? '#e67e22' :\n prediction.alertLevel === 'YELLOW' ? '#f39c12' :\n '#27ae60';\n\nconst alertBg =\n prediction.alertLevel === 'RED' ? '#fdf0f0' :\n prediction.alertLevel === 'ORANGE' ? '#fdf5ec' :\n prediction.alertLevel === 'YELLOW' ? '#fefde7' :\n '#f0fdf4';\n\nconst alertText =\n prediction.alertLevel === 'RED' ? 'Very Crowded \u2014 Arrive 90+ minutes early' :\n prediction.alertLevel === 'ORANGE' ? 'Busy Station \u2014 Arrive 60-90 minutes early' :\n prediction.alertLevel === 'YELLOW' ? 'Moderate Crowd \u2014 Arrive 45-60 minutes early' :\n 'Light Crowd \u2014 Arrive 30 minutes early is sufficient';\n\nreturn [{\n json: {\n ...prediction,\n advisory: advisory.trim(),\n alertColor,\n alertBg,\n alertText\n }\n}];"
},
"typeVersion": 2
},
{
"id": "8185aa7b-1c5a-4e1c-9db0-4a0ee7bd73bc",
"name": "5. Code \u2014 Build HTML Email",
"type": "n8n-nodes-base.code",
"position": [
48,
192
],
"parameters": {
"jsCode": "// Build the complete HTML email for the traveler\nconst d = $input.first().json;\n\nconst fmtMin = (m) => m === 0 ? 'Skipped' : m + ' min';\n\nconst htmlEmail =\n '<!DOCTYPE html><html><head><meta charset=\"utf-8\"><meta name=\"viewport\" content=\"width=device-width,initial-scale=1\"></head>'\n + '<body style=\"font-family:Arial,sans-serif;max-width:640px;margin:0 auto;background:#f5f5f5;\">'\n\n + '<div style=\"background:#1a237e;padding:24px 28px;border-radius:8px 8px 0 0;\">'\n + '<h1 style=\"color:#fff;font-size:19px;margin:0;\">Railway Queue Predictor</h1>'\n + '<p style=\"color:#c5cae9;font-size:13px;margin:6px 0 0;\">' + d.travelerName + ' \u2014 Train ' + d.trainNo + ' ' + d.trainName + '</p>'\n + '</div>'\n\n + '<div style=\"background:' + d.alertColor + ';padding:16px 24px;\">'\n + '<div style=\"color:#fff;font-size:16px;font-weight:700;\">' + d.alertLevel + ' \u2014 ' + d.alertText + '</div>'\n + '<div style=\"color:rgba(255,255,255,0.9);font-size:13px;margin-top:2px;\">Crowd Score: ' + d.crowdScore + '/100</div>'\n + '</div>'\n\n + '<div style=\"background:#283593;padding:10px 24px;display:flex;gap:20px;flex-wrap:wrap;\">'\n + '<span style=\"color:#c5cae9;font-size:13px;\">' + d.travelDate + '</span>'\n + '<span style=\"color:#c5cae9;font-size:13px;\">' + d.stationCode + '</span>'\n + '<span style=\"color:#c5cae9;font-size:13px;\">Departs ' + d.departureTime + '</span>'\n + '<span style=\"color:#c5cae9;font-size:13px;\">' + d.classCode + '</span>'\n + '<span style=\"color:#c5cae9;font-size:13px;\">' + d.passengers + ' passenger(s)</span>'\n + '</div>'\n\n + '<div style=\"background:#fff;padding:20px 24px;\">'\n\n + '<div style=\"background:' + d.alertBg + ';border:2px solid ' + d.alertColor + ';border-radius:8px;padding:16px 20px;text-align:center;margin-bottom:20px;\">'\n + '<div style=\"font-size:12px;color:#666;text-transform:uppercase;letter-spacing:1px;margin-bottom:4px;\">Recommended Arrival Time</div>'\n + '<div style=\"font-size:32px;font-weight:700;color:' + d.alertColor + ';\">' + d.optimalArrival + '</div>'\n + '<div style=\"font-size:13px;color:#555;margin-top:4px;\">Arrive by this time \u2014 total buffer needed: <strong>' + d.totalBuffer + ' minutes</strong></div>'\n + '</div>'\n\n + '<h2 style=\"font-size:15px;color:#1a237e;border-bottom:2px solid #eee;padding-bottom:8px;margin:0 0 14px;\">Queue Time Breakdown</h2>'\n + '<table style=\"width:100%;border-collapse:collapse;font-size:14px;margin-bottom:20px;\">'\n + '<tr style=\"background:#e8eaf6;\"><th style=\"padding:9px 12px;text-align:left;font-size:12px;color:#3f51b5;\">Queue Type</th><th style=\"padding:9px 12px;text-align:right;font-size:12px;color:#3f51b5;\">Predicted Wait</th><th style=\"padding:9px 12px;text-align:right;font-size:12px;color:#3f51b5;\">Status</th></tr>'\n + '<tr><td style=\"padding:9px 12px;border-bottom:1px solid #f0f0f0;\">Platform Walk</td><td style=\"padding:9px 12px;text-align:right;border-bottom:1px solid #f0f0f0;\">' + d.platformWalk + ' min</td><td style=\"padding:9px 12px;text-align:right;border-bottom:1px solid #f0f0f0;font-size:12px;color:#666;\">Always required</td></tr>'\n + '<tr><td style=\"padding:9px 12px;border-bottom:1px solid #f0f0f0;\">Security Check</td><td style=\"padding:9px 12px;text-align:right;border-bottom:1px solid #f0f0f0;\">' + fmtMin(d.securityTime) + '</td><td style=\"padding:9px 12px;text-align:right;border-bottom:1px solid #f0f0f0;font-size:12px;color:#666;\">' + (d.hasSecurityCheck ? 'Required at ' + d.stationCode : 'Not required') + '</td></tr>'\n + '<tr><td style=\"padding:9px 12px;border-bottom:1px solid #f0f0f0;\">Ticket Counter</td><td style=\"padding:9px 12px;text-align:right;border-bottom:1px solid #f0f0f0;\">' + fmtMin(d.ticketQueue) + '</td><td style=\"padding:9px 12px;text-align:right;border-bottom:1px solid #f0f0f0;font-size:12px;color:' + (d.hasETicket ? '#27ae60' : '#e74c3c') + ';\">' + (d.hasETicket ? 'E-ticket \u2014 skip queue' : 'Physical ticket needed') + '</td></tr>'\n + '<tr><td style=\"padding:9px 12px;border-bottom:1px solid #f0f0f0;\">Platform Wait</td><td style=\"padding:9px 12px;text-align:right;border-bottom:1px solid #f0f0f0;\">' + d.platformWait + ' min</td><td style=\"padding:9px 12px;text-align:right;border-bottom:1px solid #f0f0f0;font-size:12px;color:#666;\">Find seat, settle luggage</td></tr>'\n + '<tr style=\"background:#f0f4ff;\"><td style=\"padding:9px 12px;font-weight:700;color:#1a237e;\">Total Buffer</td><td style=\"padding:9px 12px;text-align:right;font-weight:700;color:' + d.alertColor + ';\">' + d.totalBuffer + ' min</td><td style=\"padding:9px 12px;text-align:right;font-size:12px;color:' + d.alertColor + ';font-weight:600;\">' + d.alertLevel + '</td></tr>'\n + '</table>'\n\n + '<div style=\"background:#e8eaf6;border-left:4px solid #3f51b5;padding:14px;border-radius:0 6px 6px 0;margin-bottom:16px;\">'\n + '<h2 style=\"margin:0 0 8px;font-size:14px;color:#1a237e;\">Travel Advisory</h2>'\n + '<p style=\"margin:0;font-size:14px;color:#333;line-height:1.7;\">' + d.advisory + '</p>'\n + '</div>'\n\n + '<div style=\"background:#e8f5e9;border-left:4px solid #43a047;padding:12px 14px;border-radius:0 6px 6px 0;\">'\n + '<h2 style=\"margin:0 0 8px;font-size:13px;color:#1b5e20;\">Quick Tips</h2>'\n + '<p style=\"margin:0 0 4px;font-size:13px;color:#444;\">- Keep your ticket and ID ready before entering the station</p>'\n + '<p style=\"margin:0 0 4px;font-size:13px;color:#444;\">- Check the NTES app for real-time train location and platform number</p>'\n + '<p style=\"margin:0 0 4px;font-size:13px;color:#444;\">- Platform number is usually announced 20-30 minutes before departure</p>'\n + (d.hasETicket ? '<p style=\"margin:0;font-size:13px;color:#27ae60;font-weight:600;\">- You have an e-ticket \u2014 go straight to your platform and skip the counter</p>' : '<p style=\"margin:0;font-size:13px;color:#e74c3c;font-weight:600;\">- You need a physical ticket \u2014 reach early to avoid the counter queue</p>')\n + '</div>'\n\n + '</div>'\n\n + '<div style=\"background:#1a237e;padding:12px 24px;text-align:center;border-radius:0 0 8px 8px;\">'\n + '<p style=\"color:#c5cae9;font-size:11px;margin:0;\">Railway Queue Predictor \u2014 n8n + ChatGPT AI</p>'\n + '<p style=\"color:#7986cb;font-size:11px;margin:4px 0 0;\">Prediction based on general patterns. Always allow extra time for unexpected delays.</p>'\n + '</div>'\n + '</body></html>';\n\nreturn [{\n json: {\n ...d,\n htmlEmail,\n emailSubject: d.alertLevel + ' Queue Alert \u2014 Train ' + d.trainNo + ' | Arrive by ' + d.optimalArrival + ' | ' + d.stationCode\n }\n}];"
},
"typeVersion": 2
},
{
"id": "bc2d4ac8-8376-466d-b475-b174fad1e24d",
"name": "6. Sheets \u2014 Log Prediction",
"type": "n8n-nodes-base.googleSheets",
"position": [
288,
32
],
"parameters": {
"columns": {
"value": {
"Date": "={{ $json.loggedAt.split('T')[0] }}",
"Class": "={{ $json.classCode }}",
"Station": "={{ $json.stationCode }}",
"Passengers": "={{ $json.passengers }}",
"Train Name": "={{ $json.trainName }}",
"Alert Level": "={{ $json.alertLevel }}",
"Crowd Score": "={{ $json.crowdScore }}",
"Travel Date": "={{ $json.travelDate }}",
"Has E-Ticket": "={{ $json.hasETicket ? 'Yes' : 'No' }}",
"Train Number": "={{ $json.trainNo }}",
"Advisory Sent": "Yes",
"Departure Time": "={{ $json.departureTime }}",
"Traveler Email": "={{ $json.email }}",
"Optimal Arrival": "={{ $json.optimalArrival }}",
"Ticket Queue (min)": "={{ $json.ticketQueue }}",
"Total Buffer (min)": "={{ $json.totalBuffer }}",
"Platform Wait (min)": "={{ $json.platformWait }}",
"Security Time (min)": "={{ $json.securityTime }}"
},
"schema": [
{
"id": "Date",
"type": "string",
"display": true,
"required": false,
"displayName": "Date",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Train Number",
"type": "string",
"display": true,
"required": false,
"displayName": "Train Number",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Train Name",
"type": "string",
"display": true,
"required": false,
"displayName": "Train Name",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Station",
"type": "string",
"display": true,
"required": false,
"displayName": "Station",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Travel Date",
"type": "string",
"display": true,
"required": false,
"displayName": "Travel Date",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Departure Time",
"type": "string",
"display": true,
"required": false,
"displayName": "Departure Time",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Class",
"type": "string",
"display": true,
"required": false,
"displayName": "Class",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Passengers",
"type": "string",
"display": true,
"required": false,
"displayName": "Passengers",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Has E-Ticket",
"type": "string",
"display": true,
"required": false,
"displayName": "Has E-Ticket",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Crowd Score",
"type": "string",
"display": true,
"required": false,
"displayName": "Crowd Score",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Alert Level",
"type": "string",
"display": true,
"required": false,
"displayName": "Alert Level",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Platform Wait (min)",
"type": "string",
"display": true,
"required": false,
"displayName": "Platform Wait (min)",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Ticket Queue (min)",
"type": "string",
"display": true,
"required": false,
"displayName": "Ticket Queue (min)",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Security Time (min)",
"type": "string",
"display": true,
"required": false,
"displayName": "Security Time (min)",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Total Buffer (min)",
"type": "string",
"display": true,
"required": false,
"displayName": "Total Buffer (min)",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Optimal Arrival",
"type": "string",
"display": true,
"required": false,
"displayName": "Optimal Arrival",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Traveler Email",
"type": "string",
"display": true,
"required": false,
"displayName": "Traveler Email",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Advisory Sent",
"type": "string",
"display": true,
"required": false,
"displayName": "Advisory Sent",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": true
},
"options": {},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "name",
"value": "Predictions"
},
"documentId": {
"__rl": true,
"mode": "id",
"value": "YOUR_QUEUE_LOG_SHEET_ID"
}
},
"typeVersion": 4.5
},
{
"id": "5c57ee84-93ca-4eb1-81e8-bec30f82408d",
"name": "7. Gmail \u2014 Send Queue Alert to Traveler",
"type": "n8n-nodes-base.gmail",
"position": [
288,
224
],
"parameters": {
"sendTo": "={{ $json.email }}",
"message": "={{ $json.htmlEmail }}",
"options": {
"senderName": "Railway Queue Predictor"
},
"subject": "={{ $json.emailSubject }}"
},
"typeVersion": 2.1
}
],
"active": false,
"settings": {
"binaryMode": "separate",
"executionOrder": "v1"
},
"versionId": "495ddbc1-3420-4ce4-972f-c4f44c63a72f",
"nodeGroups": [],
"connections": {
"5. Code \u2014 Build HTML Email": {
"main": [
[
{
"node": "6. Sheets \u2014 Log Prediction",
"type": "main",
"index": 0
},
{
"node": "7. Gmail \u2014 Send Queue Alert to Traveler",
"type": "main",
"index": 0
}
]
]
},
"3. HTTP \u2014 AI Write Advisory": {
"main": [
[
{
"node": "4. Code \u2014 Parse Advisory and Alert Colors",
"type": "main",
"index": 0
}
]
]
},
"1. Form \u2014 Traveler Train Details": {
"main": [
[
{
"node": "2. Code \u2014 Predict Queue Wait Times",
"type": "main",
"index": 0
}
]
]
},
"2. Code \u2014 Predict Queue Wait Times": {
"main": [
[
{
"node": "3. HTTP \u2014 AI Write Advisory",
"type": "main",
"index": 0
}
]
]
},
"4. Code \u2014 Parse Advisory and Alert Colors": {
"main": [
[
{
"node": "5. Code \u2014 Build HTML Email",
"type": "main",
"index": 0
}
]
]
}
}
}
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About this workflow
This workflow collects train trip details via an n8n Form, predicts station crowding and queue times with a rule-based algorithm, generates a short advisory with OpenAI, then emails a color-coded HTML alert via Gmail and logs each prediction to Google Sheets. Receives a…
Source: https://n8n.io/workflows/16836/ — original creator credit. Request a take-down →
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