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How to use AI to analyze responses from citizen survey about traffic congestion

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Adam Sabla

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Aug 22, 2025

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This article will give you tips on how to analyze responses/data from a citizen survey about traffic congestion. Using AI survey analysis tools, you’ll get deeper insights from your data and actually enjoy uncovering what matters.

Choosing the right tools for analyzing survey responses

How you approach survey analysis really comes down to the nature and structure of your data. Here’s the split:

  • Quantitative data: If your survey asked structured questions like “How often do you get stuck in traffic?” with set answer options, tools like Excel or Google Sheets let you quickly add up, chart, and cross-tabulate the results. This gives you reliable counts and percentages at a glance.

  • Qualitative data: For deeper open-ended answers (“Describe your experience with local traffic”), filtering through all those rich stories feels impossible by hand. Since reading them word-for-word isn’t practical (especially with large data sets), this is where AI tools come to the rescue. AI summarizes, categorizes, and extracts meaning from hundreds or thousands of narratives in a fraction of the time it’d take you—or any research team—to do the same manually.

There are two approaches for tooling when dealing with qualitative responses:

ChatGPT or similar GPT tool for AI analysis

You can simply copy all exported survey responses into ChatGPT and start chatting about them. Ask things like, “What are the main issues citizens report about traffic?” and see what it finds.
Pros: Immediately accessible and familiar if you already use AI tools.
Cons: Handling real-world survey data this way can quickly become a hassle. Formatting, data size limits, and context loss mean the process isn’t seamless, especially if your conversations grow beyond a few dozen responses or if you want to drill down by segments. Keeping track of your follow-up requests (and AI’s context) takes effort.

All-in-one tool like Specific

Purpose-built AI tools like Specific combine survey creation with robust AI-driven analysis. Here’s why that matters:
High-quality data: Specific AI-powered interviews probe deeper by auto-generating follow-up questions, so you don’t just get surface-level feedback. As a result, the data is richer and more useful. Automatic AI follow-up questions are a big reason why response quality is so high.
Instant analysis: As soon as responses roll in, Specific summarizes, extracts themes, and flags actionable insights using AI—so lengthy exports and spreadsheets become optional.
Chat with your data: You can interact directly with the data (like with ChatGPT), but it’s organized for survey analysis and enriched by extra context (filters, breakdowns by question, etc). You manage exactly what goes into each AI query, so nothing critical gets ignored. Explore the feature in-depth: AI survey response analysis in Specific.
Unified workflow: No need to bounce between survey platforms and external analysis tools. Your citizen survey on traffic congestion can be fully created, distributed, and analyzed in one place—saving time and reducing errors.

Extra tip: If you haven’t built your survey yet, you can jump-start with the AI survey builder, preset for citizen feedback on traffic congestion. There’s also a step-by-step guide if you want to see exactly how to set up questions that generate actionable data.

Useful prompts that you can use to analyze citizen survey data on traffic congestion

When you use AI (either in Specific or any GPT-powered tool), how you ask matters. Well-crafted prompts unlock truly useful insights—especially with a broad and emotionally charged subject like traffic congestion.

Prompt for core ideas is foundational for condensing lots of open-ended responses. This prompt works right out of the box:

Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.

Output requirements:

- Avoid unnecessary details

- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top

- no suggestions

- no indications

Example output:

1. **Core idea text:** explainer text

2. **Core idea text:** explainer text

3. **Core idea text:** explainer text

AI always delivers better results if you give it extra detail about your survey, the purpose, and your context. For example:

The survey was filled out by Seattle residents. My goal is to understand the biggest traffic congestion pain points and the impact on daily life. Focus on unique perspectives where possible.

Once you have a core idea (say, “commuting delays”), ask follow-up prompts like:

Tell me more about commuting delays. Which groups talk about them most?

Prompt for specific topic: If a major news story pops up or your team is concerned about one intersection, just use:

Did anyone talk about Highway 99? Include quotes.

Super straightforward—and perfect for fact-checking narratives before presenting to stakeholders.

Prompt for personas: Great for identifying different resident types, like daily drivers, bus commuters, cyclists, or people working remotely. Try:

Based on the survey responses, identify and describe a list of distinct personas—similar to how "personas" are used in product management. For each persona, summarize their key characteristics, motivations, goals, and any relevant quotes or patterns observed in the conversations.

Prompt for pain points and challenges: Especially useful when traffic congestion has worsened. For reference, Seattle drivers lost 63 hours to traffic in 2024—up 9% from the previous year, and nationwide, U.S. drivers lost 43 hours and $771 each to congestion in that same time frame [1][2]. Get a lay of the land with:

Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each, and note any patterns or frequency of occurrence.

Prompt for Motivations & Drivers: Underneath complaints, there’s motivation—why are people so vocal, what would improve their commutes, or why do they avoid public transit? Unpack it with:

From the survey conversations, extract the primary motivations, desires, or reasons participants express for their behaviors or choices. Group similar motivations together and provide supporting evidence from the data.

Prompt for suggestions & ideas: Crowdsource actual solutions straight from residents. Use:

Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.

Prompt for unmet needs & opportunities: Find what citizens truly want but aren’t getting—perfect for pitch decks or city planning proposals:

Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.

Want more ideas on constructing questions that lead to richer survey data? Check out our list of best questions for citizen traffic congestion surveys.

How Specific analyzes qualitative answers by question type

One of the most powerful things about Specific is how it summarizes qualitative feedback across different question types—giving you actionable takeaways at every level:

  • Open-ended questions (with or without follow-ups): For each question, Specific gives you a clear summary of all responses, including additional layers surfaced through follow-up probing. You see what people say and why they feel that way, not just surface complaints.

  • Choices with follow-ups: Each answer option gets its own summary, plus detailed breakdowns of follow-up responses. This is invaluable when you want to see, for instance, how commuters who chose “public transit” describe their unique pain points—versus those who drive solo.

  • NPS surveys: For Net Promoter Score, you get summaries by promoter segment (detractors, passives, promoters)—so you immediately see what’s holding people back versus what loyalists appreciate most.

You can accomplish a similar breakdown by carefully engineering your ChatGPT queries and slicing exported data into logical subgroups—but with Specific, it’s handled automatically, meaning much less labor and higher confidence that important themes won’t slip through the cracks. Want to see real examples? Try an interactive traffic survey demo and chat with the data yourself.

Dealing with context limits in AI analysis tools

Every GPT-based tool—ChatGPT or built-in solutions—has a context limit (the amount of text/answers you can analyze at once). With large citizen surveys about traffic congestion (sometimes hundreds or thousands of responses), you’ll hit that wall sooner than expected. Here’s what works:

  • Filtering: Only analyze the conversations where users answered specific questions or chose relevant options. This trims the data set, stays focused, and keeps you inside AI limits. In Specific, filters are built-in to prevent overwhelming the system.

  • Cropping: Limit the analysis to specific questions, sending only those to AI for summary and theme extraction. This way, even with massive survey response sets, your insights stay sharp. These approaches are seamlessly integrated into Specific’s workflow; if replicating in ChatGPT, you’ll manually split/crop files or use scripts.

Find out how this works with the AI survey response analysis feature in Specific.

Collaborative features for analyzing citizen survey responses

Team collaboration is usually the weak link in survey analysis—pasting giant spreadsheets into shared drives, endless back-and-forth with “final-final” versions, and difficulty knowing which insight belongs to whom. It gets even messier with emotionally charged, big-impact issues like city traffic congestion.

Analyze by chatting: With Specific, anyone on your team can explore survey data simply by chatting with AI. No need for coding, manual organizing, or exporting to another tool.

Multiple chats, separate focus: Run several independent conversations at once—for example, one chat analyzing frustrations from commuters, another on suggestions from cyclists. Each “chat view” has custom filters, and you can instantly see who created which line of reasoning—perfect for parallel exploration.

Identity & history: When collaborating, each person’s inputs in the AI chat are clearly labeled with avatars and names. This makes it easy to trace the origin of key findings, build on top of each other’s work, and keep track of next steps for action. It’s designed for research, not just ad-hoc team banter.

Want to get hands-on? Try the AI survey editor for traffic congestion surveys and see how natural teamwork can feel.

Create your citizen survey about traffic congestion now

Don’t miss an opportunity to hear from your community. With AI-powered tools tailored for citizen surveys on traffic congestion, you get fast, actionable insights—while digging deeper than ever before. Start analyzing what citizens really need to improve mobility and daily life.

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Sources

  1. axios.com. Seattle ranked as 10th most congested city in US and 23rd globally in 2024

  2. axios.com. Seattle drivers lost more time to traffic in 2023 than any other US metro area

  3. fhwa.dot.gov. 2005 Traveler Opinions and Attitudes survey on traffic congestion

  4. time.com. 2013 Los Angeles drivers spent 90 hours stuck in traffic

Adam Sabla - Image Avatar

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.