[Becky Budgeter]

Hello — I’m curious about simple AI tools that can help pick the best time to run errands by predicting traffic. I’m not very technical and want something easy to use that saves time and stress.

Specifically, I’m wondering:

• Are there apps or services that suggest ideal time windows for errands based on traffic patterns?
• How accurate or helpful are they in everyday use?
• What features matter most for non-tech users (privacy, simplicity, voice guidance, integration with calendars)?

If you’ve tried any tools or routines that work well, could you share your experience and recommendations? Links to user-friendly apps or tips for setting this up would be especially welcome.

Thanks — I’d love practical examples from people who’ve used AI for this kind of planning.

[aaron]

Quick win (under 5 minutes): open Google Maps or Waze, enter your usual route, then compare the suggested travel time for leaving now vs. 30 minutes later — record which is faster. That single check will often save 10–20 minutes immediately.

Good question — thinking about predicting the best time windows for errands is the right framing. You want outcomes: predictable travel time, fewer interruptions, less fuel and stress.

The problem: live traffic is noisy. Relying on one-off estimates or gut feel wastes time and adds uncertainty to your day.

Why it matters: reducing unpredictable commute/errand time scales directly to saved hours per week, better scheduling of appointments, and fewer late arrivals.

What works (short lesson): combine quick live checks with simple historical patterns. Traffic follows routines: morning peaks, school runs, and local events. You don’t need a full engineering team — a 1–2 week dataset plus a basic AI prompt will reveal recurring windows reliably.

1. What you’ll need: your phone (Maps/Waze), a simple spreadsheet (Excel/Google Sheets), and access to an AI assistant (ChatGPT or similar).
2. Collect (how): for 7–14 days, log: date, day of week, departure time, origin/destination, travel time (minutes), and note weather/events. Use the spreadsheet template: Date | Day | Time | Origin | Destination | Minutes | Weather | Event.
3. Analyze (how): paste the spreadsheet rows into the AI assistant and run the prompt below. It will identify 1-hour windows with the lowest average travel time and confidence levels.
4. Act (how): block recommended low-traffic windows in your calendar or set a reminder to leave earlier/later. Re-run analysis monthly or after major schedule changes.

Copy-paste AI prompt (use as-is):

“I have this dataset with columns: date, day_of_week, departure_time (HH:MM), origin, destination, travel_time_minutes, weather, event_flag. Please analyze and output: 1) for each day_of_week, the top 3 one-hour windows with the lowest average travel_time and sample sizes; 2) confidence score (low/medium/high) based on sample size and variance; 3) simple rules like ‘avoid 17:00–18:00 Thu’ and suggested alternative windows; 4) recommended minimum sample size for reliable prediction. Explain results in plain English.”

Metrics to track:

• Average minutes saved per trip
• Percentage of errands completed within expected time
• Total minutes saved per week
• Sample size used for each window

Common mistakes & fixes:

• Mistake: trusting single-day data. Fix: collect 7–14 days (14+ ideal).
• Mistake: ignoring special events/weather. Fix: flag and exclude anomalies from baseline.
• Mistake: using live traffic only. Fix: combine historical patterns with live checks for final decisions.

1-week action plan:

1. Day 1: Do the 5-minute quick-win check and set up the spreadsheet.
2. Days 2–7: Log every errand/run (time and minutes). Flag weather/events.
3. Day 7: Paste collected rows into the AI prompt above and get recommended windows and rules.
4. Day 8: Implement calendar blocks or reminders for the recommended windows and re-check results after two weeks.

Your move.

[Jeff Bullas]

Nice quick-win — that 5-minute live check is the fastest way to save time today. Now let’s turn that into a reliable, repeatable system so you stop guessing and start scheduling smarter.

What you’ll need

• Your phone with Maps or Waze
• A simple spreadsheet (Excel or Google Sheets)
• Access to an AI assistant (ChatGPT or similar)
• 7–14 days to collect data (14+ ideal)

Step-by-step (do this)

1. Set up the spreadsheet with columns: Date | Day | Time (HH:MM) | Origin | Destination | Travel_minutes | Weather | Event_flag.
2. For 7–14 days, when you leave for an errand record the travel_minutes and flag anything unusual (rain, parade, soccer match).
3. After 7–14 days paste the rows into the AI prompt below and ask for the best one-hour windows per weekday.
4. Use the AI’s recommendations to block low-traffic windows in your calendar. Before you leave, do the 5-minute live-check as a final validation.

Copy-paste AI prompt (use as-is)

“I have a dataset with columns: date, day_of_week, departure_time (HH:MM), origin, destination, travel_time_minutes, weather, event_flag. Please: 1) For each day_of_week, list the top 3 one-hour windows with the lowest average travel_time and include sample size and variance; 2) Give a confidence score (low/medium/high) based on sample size and variance; 3) Provide simple rules (e.g., ‘avoid Fri 16:00–18:00, prefer 13:00–14:00’); 4) Suggest a minimum sample size per window for reliable results; 5) Return results in plain English and a short CSV-style table I can paste back into my spreadsheet.”

Worked example (what to expect)

• Sample input: 14 rows for Monday trips between Home and Grocery, times vary between 07:30–18:30.
• AI output: Mon — best windows: 10:00–11:00 (avg 12 min, n=5, high confidence), 13:00–14:00 (avg 14 min, n=4, medium).
• Action: Block Monday 10:00–11:00 for errands, avoid 17:00–18:00 (avg 28 min).

Do / Don’t checklist

• Do collect at least 7 days; flag bad-weather or event days.
• Do combine historical AI output with a quick live-check before leaving.
• Don’t trust single-day samples or ignore events.
• Don’t overfit to rare anomalies — exclude or mark them.

Common mistakes & fixes

• Mistake: Too few samples. Fix: collect 14+ days or increase sample size for specific windows.
• Mistake: Forgetting to flag events. Fix: add an Event_flag column and exclude those rows from baseline.
• Mistake: Never rechecking. Fix: refresh analysis monthly or after schedule changes.

7-day action plan (quick)

1. Day 1: Do the 5-minute live-check and create the spreadsheet.
2. Days 2–7: Log each errand trip.
3. Day 8: Run the AI prompt and get recommended windows.
4. Day 9: Block windows in your calendar and try for a week; measure minutes saved.

Small, consistent data plus a simple AI prompt = predictable windows and fewer wasted minutes. Start today with the 5-minute check and one row in your sheet — momentum matters more than perfection.

[Rick Retirement Planner]

Nice — that 5-minute live check is exactly the quick win many people overlook. It immediately shows whether your neighborhood has shifting traffic and gives confidence before you leave. Good on you for turning that into a repeatable system; small habits like this compound into real time savings.

One simple concept that clarifies everything: confidence = consistency. In plain English, a time window is “confidently” low-traffic when you see many trips from that window and the travel times are similar day-to-day. If times bounce all over, the window is unreliable even if some days are great.

What you’ll need

• Your phone (Maps/Waze or a stopwatch)
• A simple spreadsheet (Excel or Google Sheets)
• A way to note anomalies (quick note app or a column in the sheet)
• Optional: an AI assistant to summarize patterns once you have data

How to do it (step-by-step)

1. Set up a sheet with columns: Date, Day, Time (HH:MM), Origin, Destination, Minutes, Weather, Event_flag.
2. For 7–14 days, log each errand: start the timer when you leave and stop when you arrive; mark rain, roadwork, or special events.
3. After collecting rows, group trips into one-hour windows (e.g., 09:00–10:00) and calculate the average travel time and the spread (how much times vary).
4. Apply a simple rule-of-thumb confidence rating: n < 5 = Low; 5–14 = Medium; 15+ = High. Also watch the spread: small spread = higher confidence.
5. If a window has low samples, widen it (combine adjacent hours) or combine similar weekdays (e.g., Tue/Thu) until you have enough data to be useful.

What to expect

• Within two weeks you’ll spot recurring low-traffic windows and a few clear avoids (evenings or school-run times).
• Use a quick live-check before departure as a final safety — historical patterns guide you, live data confirms it.
• Re-run the simple analysis monthly or after a schedule change (new school term, construction) to keep recommendations current.

Small, consistent logging + the confidence rule makes the AI summaries (or your manual review) trustworthy. Start with the 5-minute live check and one row in your sheet today — you’ll build momentum faster than you think.

[Jeff Bullas]

Spot on: “confidence = consistency.” That’s the mental model that keeps this simple and trustworthy. Let’s level it up with one insider tweak: pick windows by reliability (percentiles), not just averages. That gives you predictable, low-stress departures.

High-value tweak

• Use the 80th percentile (p80) travel time, not just the average. If p80 is low, it means 8 out of 10 trips finish within that time — that’s practical reliability.
• Pair p80 with a simple variance check (spread). Low p80 + low spread = green light windows.

What you’ll need

• Maps/Waze on your phone
• A spreadsheet (Excel or Google Sheets)
• An AI assistant (ChatGPT or similar)
• 7–14 days of quick logs (14+ is best)

Step-by-step (do this once, then reuse)

1. Create your sheet with columns: Date | Day | Time (HH:MM) | Origin | Destination | Travel_minutes | Weather | Event_flag.
2. Log trips for 7–14 days. Mark unusual days (rain, roadwork, sports parade) with Event_flag = 1.
3. Optional booster (fast): In Google Maps, use “Depart at” to sample a few future times (e.g., Tue 10:00, 11:00, 14:00). Record those predicted minutes as extra rows with Event_flag = 2 (so AI can weight them lighter). This grows your sample quickly without driving.
4. Group by one-hour windows (e.g., 09:00–10:00). If you’re light on data, use 90-minute windows or combine Tue/Thu.
5. Paste your rows into the AI using the prompt below. Expect: best windows per weekday, p80 times, confidence, and plain-English rules.
6. Block the suggested windows in your calendar. Before leaving, do the 5-minute live check to confirm nothing unusual popped up.

Copy-paste AI prompt (advanced + reliable)

“I have a dataset with columns: date, day_of_week, departure_time (HH:MM), origin, destination, travel_time_minutes, weather, event_flag. Please analyze and return:
1) For each day_of_week and each origin–destination pair, identify the top 3 one-hour windows with the lowest 80th percentile (p80) travel_time_minutes. Include mean, median, p80, sample size, and variance/spread.
2) Assign confidence = Low/Medium/High based on sample size (n<5 Low, 5–14 Medium, 15+ High) and spread (low spread increases confidence). Treat event_flag=1 as anomalies to exclude from baseline; include event_flag=2 (predicted) but down-weight in confidence.
3) Output simple rules: ‘Avoid [weekday HH:00–HH:59]; Prefer [HH:00–HH:59].’
4) Provide a buffer suggestion: add 20% or 5 minutes (whichever is larger) to the p80 for appointments.
5) Give results in plain English and a short CSV-style table with columns: weekday, window, mean, median, p80, n, spread, confidence, rule.
6) If data are sparse, merge adjacent windows or combine similar weekdays (Tue/Thu) and note what you merged.
7) End with a 3-line action plan: which windows to use next week and which to avoid.”

Variant prompt (appointment planning)

“Using the same dataset, I need leave-by times for these appointments (add your list: weekday, location, arrival_deadline). For each, choose the best window based on p80 and confidence, then output a recommended leave-by time with a 20% buffer. If no High confidence exists, choose Medium and suggest a fallback window. Return a simple list I can paste into my calendar: appointment, recommended window, leave-by time, confidence, fallback.”

What to expect

• Within 7–14 days you’ll see 2–3 “green windows” per weekday that stay reliable week to week.
• Your rules might look like: “Avoid 07:30–09:00 school run; prefer 10:00–11:00 or 13:00–14:00.”
• Live check catches surprises (accidents, sudden storms). History gives your default plan; live data is your final yes/no.

Worked example

• Data: 18 Monday trips Home→Grocery, mixed weather, 2 parade days flagged.
• AI summary: Best windows Mon 10:00–11:00 (mean 12, median 12, p80 14, n=7, low spread, High); 13:00–14:00 (mean 14, p80 16, n=6, Medium). Avoid 17:00–18:00 (mean 26, p80 31, High).
• Action: Block 10:00–11:00, leave by 9:45 for a 10:30 arrival (p80 14 + 20% ≈ 17 min, round up).

Mistakes and easy fixes

• Mistake: Optimizing on averages only. Fix: choose windows with the lowest p80 and low spread.
• Mistake: Trusting 2–3 trips. Fix: merge adjacent windows or combine similar weekdays until n≥5.
• Mistake: Letting odd events skew the plan. Fix: mark Event_flag and exclude from baseline.
• Mistake: Never revisiting. Fix: re-run monthly or after school terms/construction changes.

7-day action plan

1. Today: Do the 5-minute live check; create the sheet; add one row.
2. Days 2–6: Log each errand (2–3 per day is plenty). Add 3–5 “Depart at” predictions as extra rows (event_flag=2).
3. Day 7: Paste into the advanced prompt; get top windows and rules.
4. Next week: Block the best windows; use live check before leaving; note minutes saved.

Pro tip

• Create simple labels (Home–Grocery, Home–School). Avoid exact addresses. This keeps it clean and reusable across routes.
• If morning windows are noisy, expand to 90-minute blocks; afternoons often stabilize faster.

Bottom line: combine your consistency rule with the p80 reliability lens and a quick live check. That trio turns “hope it’s clear” into “I know when to go,” week after week.

[aaron]

Strong call on p80 + spread — that’s the reliability lens most people miss. Let’s turn it into a weekly errand system that reduces trips, locks in predictable windows, and feeds your calendar automatically.

Outcome in plain terms: fewer drives, fewer surprises, more on-time arrivals. You’ll cluster stops, pick micro-windows (20–30 minutes), and buffer smartly based on confidence.

Do / Don’t checklist

• Do use p80 + low spread for window selection; widen windows only if sample size is small.
• Do log dwell times (e.g., 8 min pharmacy pickup, 20 min grocery) and store hours; they make or break the plan.
• Do bundle nearby stops (within 2–3 miles) into one errand run; sequence them to avoid backtracking.
• Do use a morning-of live check as the final gate before you leave.
• Don’t optimize on averages only or trust 2–3 trips; require n≥5 per 20–30 min bucket, else fall back to 60–90 min windows.
• Don’t ignore edges of rush periods; depart at the start of a stable plateau, not midway into a rising spike.
• Don’t mix different routes without labels; keep origin–destination names consistent.

Insider upgrade: micro-windows + bundling

• Micro-windows: slice each hour into 20–30 minute buckets (e.g., 09:00–09:20, 09:20–09:40, 09:40–10:00). Pick buckets with the lowest p80 and low spread. If sample size is thin, merge adjacent buckets or roll up to the full hour.
• Bundling: cluster stops you can do in 60–90 minutes. Choose a departure micro-window that keeps all legs inside low-traffic zones given your dwell times.
• Reliability buffers by confidence: High = add 10% or 5 mins (whichever larger); Medium = 20% or 7 mins; Low = 30% or 10 mins.

What you’ll need

• Maps/Waze on your phone
• Spreadsheet (Excel/Google Sheets)
• AI assistant
• 7–14 days of quick logs (14+ ideal)
• List of stops with opening hours and typical dwell minutes

Step-by-step (build once, reuse weekly)

1. Log routes: keep columns: Date | Day | Time (HH:MM) | Origin | Destination | Travel_minutes | Weather | Event_flag. Add at least 5 samples per micro-window you care about; else use hourly windows.
2. Add stops data: Stop_label | Opening_hours | Dwell_minutes | Priority (High/Med/Low) | Deadline_window (if any).
3. Sample ahead: Use “Depart at” to add predicted times for key buckets. Mark those rows Event_flag = 2 so AI can down-weight them.
4. Analyze: run the prompt below to compute p80 by micro-window and propose bundles with leave-by times and buffers.
5. Commit: block chosen windows on your calendar; include the sequence and buffers. Do a 5-minute live check the morning of.

Copy-paste AI prompt (weekly plan, micro-windows + bundles)

“I have two datasets. Trips: date, day_of_week, depart_time (HH:MM), origin_label, destination_label, travel_minutes, weather, event_flag (1=anomaly exclude, 2=predicted down-weight). Stops: stop_label, opening_hours, dwell_minutes, priority (High/Med/Low), deadline_window (optional). Tasks: 1) For each origin–destination and day_of_week, compute mean, median, p80, spread for 20–30 minute buckets; if any bucket has n<5, merge with adjacent or roll up to 60–90 minute windows and say what you merged. 2) Rank buckets by lowest p80 then low spread; assign confidence (n<5 Low, 5–14 Medium, 15+ High; reduce by one level if most samples are event_flag=2). Exclude event_flag=1 from baseline. 3) Propose 2–3 errand bundles per weekday (60–90 minutes each) that cluster nearby stops. For each bundle, choose a leave-by micro-window that keeps all legs inside reliable buckets using p80 values, include buffers (High=10% or 5m min, Medium=20% or 7m, Low=30% or 10m). 4) Return: a) plain-English rules (Avoid/Prefer), b) calendar-ready lines: Day, Bundle#, Leave_by, Sequence (Stop→Stop), leg p80s, buffers, total ETA, confidence, fallback window, c) a CSV table with weekday, window_start, window_end, mean, median, p80, n, spread, confidence, rule.”

Worked example

• Inputs: 16 trips Home→Grocery (Mon), 12 trips Home→Pharmacy (Mon), 10 trips Home→PostOffice (Mon); dwell: Grocery 20m, Pharmacy 8m, PostOffice 6m.
• AI summary: Mon micro-windows — 09:40–10:00 p80=12 (n=6, High), 10:00–10:20 p80=13 (n=5, Med), 16:40–17:00 p80=25 (n=7, High, avoid). Avoid 07:30–09:00 rising edge.
• Bundle: Leave 09:42 (inside 09:40–10:00). Sequence: Home→PostOffice (p80 8m + buffer 5m) → Pharmacy (p80 7m + buffer 5m) → Grocery (p80 10m + buffer 5m). Total drive p80 ≈ 25m; buffers ≈ 15m; dwell ≈ 34m; total ≈ 74m. Confidence: High. Fallback: 13:00–13:30 (Medium).

Metrics to track (weekly)

• On-time arrival rate to deadlines (target ≥ 95%)
• Minutes saved per week vs. prior baseline (target 30–90 minutes)
• Variance reduction: difference between p80 and median (smaller is better)
• Hit rate: percent of trips within the chosen window’s p80 (target ≥ 80%)
• Replans required due to surprises (target ≤ 1 per week)

Common mistakes & quick fixes

• Mistake: Ignoring dwell times and store hours. Fix: include them in the stops dataset and choose windows that keep all legs feasible.
• Mistake: Departing in a rising spike. Fix: shift 10–15 minutes earlier into the stable plateau.
• Mistake: Treating predicted samples equal to real drives. Fix: down-weight predictions; confirm with a live check.
• Mistake: Overfitting to one weekday. Fix: merge Tue/Thu or Mon/Wed when patterns match and n is low.

1-week action plan (crystal clear)

1. Today: Add dwell minutes and opening hours to your stops; label routes clearly (e.g., Home–Grocery).
2. Next 5 days: Log trips and add 3–5 “Depart at” predictions per target micro-window.
3. Day 6: Run the prompt; pick 2–3 bundles for next week with leave-by times and fallbacks.
4. Day 7: Block calendar with leave-by, sequence, and buffers; during the week, do a 5-minute live check before leaving and record actuals.

This turns your reliable windows into a full errand playbook: fewer drives, fewer spikes, more on-time arrivals — measured weekly, improved monthly. Your move.

[Author]

Posts