projectmapsfrontend

Make a 'Where to Eat' Route Planner Using Maps APIs (Micro‑App Project)

ccodeacademy

2026-01-26

10 min read

Build a compact web micro‑app that recommends restaurants and optimizes meetup routes using Maps APIs and simple preference matching.

Stop the group chat ping-pong: build a tiny web app that recommends restaurants and plans the best meetup route

Deciding where to eat for a group is a classic friction point: long chat threads, conflicting preferences, and everyone late because no one picked a place. In 2026 the solution is often a micro‑app — a focused web tool you can build in a weekend that uses Maps APIs, simple preference matching, and route optimization to suggest restaurants and pick meetup spots that minimize total travel time.

Why this micro‑app matters now (2026 context)

Micro‑apps and “vibe coding” took off in 2024–2025 and in 2026 they’re mainstream. With readily available Maps APIs, better SDK pricing tiers, and more AI toolchains to scaffold UI and matching logic, building a personal utility app is easier than ever. At the same time, users expect instant, context-aware suggestions: live traffic, preference weighting, and a clean UX that closes the loop from recommendation to navigation.

"A small app that solves a single recurring pain — like 'where to eat' — is more valuable than a big unfinished project."

Project overview: what you’ll build

This walkthrough shows you how to build a Where to Eat micro‑app that:

Collects a small group’s locations (or approximated neighborhoods)
Fetches nearby restaurant candidates from Maps APIs (Google Places, Mapbox, or OSM-based services)
Scores restaurants by group preferences (cuisine, budget, rating, distance)
Computes travel times for each participant and selects the best meetup restaurant(s)
Provides a link to navigation (Google Maps deep link, Waze deep link) and a sharable summary

High‑level architecture

Frontend: React (or Svelte/Vue) for UI, location inputs, and map preview.
Backend: Node.js/Express (or Serverless functions) to call Maps APIs and run scoring/optimization.
Maps & Data: Google Maps Platform (Places, Directions, Distance Matrix) or alternatives (Mapbox, OpenRouteService, TomTom, or OSM).
Optional AI layer: lightweight preference matching (no heavy LLM needed) or small models to interpret free-text preferences.

Step 1 — Choose APIs and plan for costs & privacy

2026 note: Maps providers improved free tiers and offer granular pay-as-you-go pricing, but traffic and Directions calls still add up. Plan for caching and batch requests.

Core API options

Google Maps Platform: Places API for restaurant search, Distance Matrix to get travel times, Directions API for route links. Excellent data and live traffic. (Most examples below use Google.)
Waze: Use Waze deep links for in-app navigation or Waze Transport SDK for integrated experiences; Waze doesn’t expose a direct Places search like Google but offers great traffic routing.
Mapbox: Strong alternative for map rendering and geocoding; Directions API and Matrix API comparable. Good for privacy-focused apps using Mapbox tiles.
OpenRouteService / OSM: Fully open, useful for prototypes and lower cost, but place coverage varies by region.

Only collect what you need: let users enter a neighborhood or share approximate location. For formal consent flows see consent capture playbooks.
Show an explicit consent dialog if you use precise device location.
Cache results for short durations (e.g., 5–10 minutes) to reduce API calls and avoid unnecessary tracking; for operational data patterns, review guides on operationalizing secure collaboration and data workflows.

Step 2 — Data model and scoring formula

Keep the model simple but expressive. For each restaurant candidate compute a composite score from several weighted factors.

Minimal data model

// Restaurant candidate
{
  id: 'place123',
  name: 'Tasty Tacos',
  lat: 41.88,
  lng: -87.62,
  rating: 4.5,
  price_level: 2, // 0-4 (Google)
  types: ['restaurant','mexican'],
  distance_m: 1200
}

// Participant
{
  id: 'u1',
  lat: 41.88,
  lng: -87.63,
  mobility: 'drive' // drives/walk/transit
}

Scoring formula (example)

Compute a score for each restaurant as a weighted sum:

score = w_pref * pref_score + w_rating * normalized_rating - w_time * avg_travel_time

Where:

pref_score measures how well the restaurant matches group preferences (cuisine, price)
normalized_rating rescales ratings to 0–1 (e.g., rating/5)
avg_travel_time is the mean travel time (seconds or minutes) from participants to the restaurant

Weights are tunable: for commuting-heavy groups increase w_time; for foodies increase w_pref.

Step 3 — Implementation details

Backend flow (Node.js / Express)

Receive participants’ approximate locations and preferences.
Compute a central search point (geometric median or simple midpoint).
Call the Places API around that point to get candidate restaurants.
For each candidate, call Distance Matrix to compute travel times from each participant (batch requests where possible).
Score candidates and return top N to the frontend.

// Simplified pseudo-code
app.post('/suggest', async (req, res) => {
  const {participants, preferences} = req.body;
  const center = computeMidpoint(participants);
  const places = await fetchPlacesAround(center, preferences);
  const timesMatrix = await getDistanceMatrix(participants, places);
  const scored = scorePlaces(places, timesMatrix, preferences);
  res.json(scored.slice(0, 10));
});

Efficient API usage

Batch Distance Matrix requests (Google allows many origins/destinations per request but check limits). For API-cost forecasting and quota planning see forecasting platform reviews.
Cache Places responses by (center, radius, preference_hash) for ~5–15 minutes.
Use a simple rate limiter on the backend to avoid spikes if multiple groups use the app concurrently; implement security and fraud detection patterns referenced in fraud prevention guides.

Example: call Google Places and Distance Matrix

// Using node-fetch or axios
const PLACES_URL = `https://maps.googleapis.com/maps/api/place/nearbysearch/json`;
const MATRIX_URL = `https://maps.googleapis.com/maps/api/distancematrix/json`;

async function fetchPlacesAround(center, radius=2000, type='restaurant'){
  const url = `${PLACES_URL}?location=${center.lat},${center.lng}&radius=${radius}&type=${type}&key=${process.env.GMAPS_KEY}`;
  const r = await fetch(url);
  return r.json();
}

async function getDistanceMatrix(origins, destinations, mode='driving'){
  const origStr = origins.map(o => `${o.lat},${o.lng}`).join('|');
  const destStr = destinations.map(d => `${d.lat},${d.lng}`).join('|');
  const url = `${MATRIX_URL}?origins=${origStr}&destinations=${destStr}&mode=${mode}&key=${process.env.GMAPS_KEY}`;
  const r = await fetch(url);
  return r.json();
}

Step 4 — Basic route optimization strategies

For this micro‑app you don't need a full TSP solver. The goal is picking a restaurant that minimizes group travel time. Strategies:

Minimize average travel time: compute mean travel time to each candidate and pick the lowest.
Minimize maximum travel time: pick the candidate that minimizes the slowest participant’s time (fairness).
Weighted fairness: weight travel times by participant preferences or accessibility needs (e.g., someone who can’t walk receives higher weight).
Multi-stop meetup: if participants want to meet halfway first (coffee) then go to dinner, run a multi-leg optimization with Directions API.

Example: compute both avg_time and max_time and present both to users so the group can choose.

Step 5 — Frontend UX & map interactions

Design for fast decisions:

Show top 3 choices with thumbnails, rating, price level, and travel times per person.
Allow toggles: prioritize food quality vs shortest travel time.
Offer direct navigation buttons: "Open in Google Maps" and "Open in Waze". For deep-link patterns and cross-app flows, read notes on deep-link discovery channels to understand app-to-app UX.
Support quick voting — everyone can thumbs-up a candidate and the app auto‑finalizes if there's a clear winner.

Deep link examples

// Google Maps link for directions to place
const googleDirections = `https://www.google.com/maps/dir/?api=1&destination=${lat},${lng}&travelmode=driving`;

// Waze deep link (opens Waze app)
const wazeLink = `https://www.waze.com/ul?ll=${lat}%2C${lng}&navigate=yes`;

Advanced strategies & 2026 trends

As of 2026, small apps benefit from a few advanced but practical techniques:

AI for preference parsing: let users type freeform like "cheap sushi or ramen, not spicy" and use a lightweight LLM or rule-based extractor to produce structured filters. This approach echoes broader AI-driven matching patterns described in AI-driven deal matching.
Personalization & memory: store simple taste profiles (cuisine favorites) to speed matching across future meetups; small-state persistence patterns are covered in remote-productivity notes like Mongoose.Cloud.
Contextual ranking: incorporate time-of-day (lunch vs dinner), day-of-week (brunch spots on weekends), and live reservations availability by integrating OpenTable or provider APIs.
Vector search for fuzzy matching: embed menu texts or descriptions to match free-text preferences — useful if you have a small dataset of local restaurants.
Live traffic and predictions: leverage traffic-prediction endpoints offered by major providers for better ETA estimates during rush hours (improves fairness in optimization). For latency and edge compute patterns that improve ETA responsiveness, see edge compute discussions.

Testing, metrics, and UX experiments

To iterate fast, track a few simple metrics after launch:

Conversion: % of sessions where a group selects a restaurant
Time to decision: time between app open and final choice
User satisfaction: quick thumbs-up after the meal to tune weights
API costs: calls per session and cost per session — use forecasting and cost-monitoring playbooks like forecasting platform reviews.

Run quick A/B tests: does showing avg travel time first increase picks? Does fairness weighting reduce complaints? Operational tips and collaboration workflows are summarized in data workflow guides.

Common pitfalls and how to avoid them

Too many API calls: Batch requests, cache results, and limit client-side polling. For caching and short-term persistence patterns see operationalizing secure workflows.
Assuming precise locations: Allow approximate input (neighborhoods) to respect privacy and simplify onboarding.
Ignoring edge cases: Closed restaurants, limited hours, or walk-only attendees — include filters for opening_hours and access constraints.
UX friction: People won’t input long preference forms. Provide defaults and one-click options.

Mini case study: How a student built this in a weekend (real pattern)

Inspired by the 2024–2025 rise of micro‑apps, many learners build a production‑light version in 48–72 hours:

Day 1: Wireframe UI, implement participant inputs and midpoint calculation.
Day 2: Hook up Places API and basic scoring, render results on a map.
Day 3: Add Distance Matrix integration, optimize calls, and add share/vote feature.

Outcome: A working tool for friends to pick dinner spots. Key to success: scope narrow, iterate quickly, swap in mock data for offline work. If you’re building on a laptop or travel setup, check tips like Digital Nomad Desk.

Code snippets you can copy

Compute geographic midpoint (simple)

function computeMidpoint(points){
  const avgLat = points.reduce((s,p)=>s+p.lat,0)/points.length;
  const avgLng = points.reduce((s,p)=>s+p.lng,0)/points.length;
  return {lat: avgLat, lng: avgLng};
}

Score function (example)

function scorePlace(place, times, preferences){
  const prefScore = computePreferenceScore(place, preferences); // 0-1
  const rating = (place.rating || 3)/5; // normalize
  const avgTime = times.reduce((a,b)=>a+b,0)/times.length; // minutes
  // weights
  const wPref = 0.5, wRating = 0.3, wTime = 0.2;
  return wPref*prefScore + wRating*rating - wTime*(avgTime/60); // scale time
}

Where to go next (extensions)

Add live reservation checks and book a table if group picks a place.
Integrate calendar invites with ETA pinned to the event location.
Offer dietary filters (vegan, halal) using structured Place metadata.
Make a mobile PWA with offline caching for transient meetup planning; mobile-first build notes are covered in creator carry kit resources.

Final checklist before shipping

API key security: keep keys on server side or use restricted API keys. For security and fraud patterns see fraud prevention guides.
Graceful errors: show fallback options when API limits or responses fail.
Localization: currency and measurement units, plus language support.
Accessibility: keyboard navigation, readable fonts, color contrast. For hybrid team and accessibility workflows, review co-working and hybrid office tool notes like co-working & hybrid office tools.

Conclusion — why this project is a perfect micro‑app

This micro‑app hits the sweet spot: narrowly scoped, solves a recurring social pain, and integrates well-known APIs. In 2026, rapid prototyping tools and cheaper Maps data make it practical for students and hobbyists to build a polished tool in a weekend. Focus on clear UX, efficient API usage, and a transparent scoring model that your group can tweak.

Actionable takeaways

Start with a simple midpoint + Places search, then add Distance Matrix for travel times.
Tune your scoring formula for your group’s priorities (speed vs taste).
Cache aggressively and prefer batch API calls to reduce costs; for micro-payment and cost patterns see microcash & microgigs.
Offer navigation deep links for both Google Maps and Waze to respect user preference.

Call to action

Ready to ship your own Where to Eat micro‑app? Clone a starter repo, wire your Maps API key, and build the core flow in a weekend. If you want, share your repo in the CodeAcademy.site community — I’ll review and give feedback on scoring, UX, and cost optimization.

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