This Winter, Use Google Maps and Waze to Find the Best Fuel Prices in the UK

This Winter, Use Google Maps and Waze to Find the Best Fuel Prices in the UK

Winter is the season when the gap between "the station I always use" and "the cheapest station on my route" becomes worth closing. Cold starts, idling while the windscreen clears, and slower traffic all push fuel consumption in the wrong direction at exactly the moment household budgets are under the most pressure. Google's UK team addressed that directly in a company blog post titled "This winter, use Google Maps and Waze to find the best fuel prices in the UK", published on the Google blog and dated 8 October 2026.

This article treats that post as its only factual source. It explains what the announcement covers, what it does not, and how to build a small, honest workflow around Google Maps and Waze — including a few scripts you can run locally so that comparison and record-keeping take seconds rather than minutes.

What the source says — and what it does not

Verified from the source:

• Google published a UK-focused post advising drivers to use Google Maps and Waze to find the best fuel prices in the UK this winter.
• The post is available at https://blog.google/company-news/inside-google/around-the-globe/google-europe/united-kingdom/google-maps-waze-fuel-prices-uk

Not established by this source, and therefore not claimed here:

• How many UK stations are covered, or which retailers participate.
• Where the price data comes from, how frequently it refreshes, or how stations are ranked.
• Whether prices are supplied by partners, by users, or by a mix of both.
• Platform-specific menu names, which differ between app versions and operating systems.

That distinction matters. The practical value of the announcement is that two apps UK drivers already have on their phones are being positioned as places to compare fuel prices. Everything below is written to work with that fact and to avoid filling the gaps with invented detail.

Why the winter framing is not just marketing

This part is interpretation, not a claim from the source, but the reasoning is straightforward. Winter driving is less efficient driving. A cold engine burns more fuel until it reaches operating temperature, and short winter trips never reach it. Defrosting and demisting add idle time before you move a metre. Darker mornings and evenings mean more time in stop-start traffic with headlights, heaters, and heated screens running.

Add volatile pump prices and the result is a season where a few pence per litre compounds quickly across a month of commuting. Comparing prices before you leave the house costs nothing; discovering the difference after you have filled up costs the whole tank. That is the behaviour the Google post is encouraging, and it is the behaviour this workflow is designed to support.

Requirements

Before you start, make sure you have the following:

• A smartphone with Google Maps or Waze installed, and location services enabled.
• Either a mobile data connection or an offline map downloaded in advance for areas with weak signal.
• A Google account if you want saved places and lists to sync across devices (optional for basic searching).
• For the helper scripts: a computer running macOS, Linux, or Windows, with Python 3.9 or newer, and pip available.
• A terminal application (Terminal, PowerShell, or your preferred shell).

The scripts do not use any private API, key, or undocumented endpoint. They build standard, documented Google Maps URLs and maintain a local CSV file that you own.

Step-by-step installation

The setup below takes about five minutes. Each command is preceded by an explanation of what it does.

1. Confirm your Python installation

Check that Python 3 is present and which version you have. Anything from 3.9 upwards is fine.

python3 --version

If that fails on Windows, try the py launcher instead:

py --version

2. Create a working directory

Keep the project in one place so the scripts and the price log travel together.

mkdir -p ~/fuel-prices && cd ~/fuel-prices

3. Create and activate a virtual environment

A virtual environment isolates any packages you install from the rest of your system.

python3 -m venv .venv

Activate it on macOS or Linux:

source .venv/bin/activate

Activate it on Windows PowerShell:

.venv\Scripts\Activate.ps1

4. Update pip

An up-to-date installer avoids most dependency-resolution surprises.

python -m pip install --upgrade pip

The scripts below rely only on the Python standard library, so no further packages are required. If you later want charts, install matplotlib inside the same virtual environment.

5. Create the Maps link builder

This script turns a place name or postcode into a Google Maps search URL using the documented Maps URLs scheme. Save it as maps_link.py.

#!/usr/bin/env python3
"""Build a Google Maps search URL for fuel prices near a place."""
import sys
from urllib.parse import quote

BASE = "https://www.google.com/maps/search/?api=1&query="


def fuel_search_url(place: str) -> str:
    """Return a Maps URL that searches for fuel prices near `place`."""
    query = f"fuel prices near {place}"
    return BASE + quote(query)


if __name__ == "__main__":
    if len(sys.argv) != 2:
        raise SystemExit("usage: python maps_link.py \"<place or postcode>\"")
    print(fuel_search_url(sys.argv[1]))

Run it with a town, a postcode, or a street name.

python maps_link.py "Sheffield"

6. Create the price logger

This script appends one observation to a CSV file in your home directory. It keeps a dated history you can analyse later. Save it as log_price.py.

#!/usr/bin/env python3
"""Append one fuel-price observation to a local CSV log."""
import argparse
import csv
import datetime
import pathlib

FIELDS = ["date", "station", "fuel", "price_pence", "notes"]
LOG = pathlib.Path.home() / "fuel-prices" / "prices.csv"


def main() -> None:
    parser = argparse.ArgumentParser(description="Record one fuel price.")
    parser.add_argument("station", help="Station name or postcode")
    parser.add_argument("fuel", help="petrol, diesel, e10, e5 or super")
    parser.add_argument(
        "price_pence",
        type=float,
        help="Price per litre in pence, exactly as shown in the app",
    )
    parser.add_argument("--notes", default="", help="Optional note")
    args = parser.parse_args()

    LOG.parent.mkdir(parents=True, exist_ok=True)
    new_file = not LOG.exists()

    with LOG.open("a", newline="") as handle:
        writer = csv.DictWriter(handle, fieldnames=FIELDS)
        if new_file:
            writer.writeheader()
        writer.writerow(
            {
                "date": datetime.date.today().isoformat(),
                "station": args.station,
                "fuel": args.fuel.lower(),
                "price_pence": args.price_pence,
                "notes": args.notes,
            }
        )

    print(f"Logged {args.station} {args.fuel} at {args.price_pence}p")


if __name__ == "__main__":
    main()

7. Create the report script

This reads the CSV and prints the cheapest price you have recorded for each station and fuel combination. Save it as report.py.

#!/usr/bin/env python3
"""Print the lowest recorded price per station and fuel type."""
import csv
import pathlib

LOG = pathlib.Path.home() / "fuel-prices" / "prices.csv"


def main() -> None:
    if not LOG.exists():
        raise SystemExit(f"No log yet at {LOG}")

    best: dict[tuple[str, str], float] = {}
    with LOG.open(newline="") as handle:
        for row in csv.DictReader(handle):
            key = (row["station"], row["fuel"])
            price = float(row["price_pence"])
            if key not in best or price < best[key]:
                best[key] = price

    for (station, fuel), price in sorted(best.items()):
        print(f"{station:<28} {fuel:<8} {price:>7.1f}p")


if __name__ == "__main__":
    main()

Using Google Maps in practice

Open Google Maps and search for a fuel station, either by brand name or by a general term such as "petrol station". Results appear as pins; tapping one opens its place details. Where a price is available for that station in your version of the app, it appears in that panel.

A few habits make this faster in winter:

1. Search before you leave, not while driving. Set up the comparison at home, then navigate to the chosen station. Do not interact with the phone at the wheel.
2. Search around your destination, not just your start point. If you are driving to work or to a supermarket, the cheapest station on that corridor is often not the one nearest your front door.
3. Use your saved lists. Adding two or three regular stations to a list gives you a quick rotation to check rather than a fresh search each time.
4. Check the detour. A station five minutes off-route is rarely worth it for a two-pence difference; see the break-even calculation in the usage examples below.

Exact labels and layouts differ between iOS and Android and change between releases, so treat the above as the shape of the task rather than a pixel-perfect walkthrough.

Using Waze in practice

Waze is covered by the same Google post, and it serves a slightly different purpose: it is built around the drive itself. Open Waze, search for a fuel station or use the fuel-related option in the app's menu, and check whether a price is shown for the stations it returns. If you are already navigating, checking stations along the route is usually more useful than checking stations near your current position, because you are going to pass them anyway.

As with Maps, menu names vary by version and region. If a label in your build does not match what you expect, look for the fuel or petrol category rather than a specific wording.

Usage examples

Example 1: Build and open a comparison search

Generate the URL for your area:

python maps_link.py "M32 0XX"

Open the result directly in your default browser on macOS:

open "$(python maps_link.py 'M32 0XX')"

On Linux, use xdg-open in place of open:

xdg-open "$(python maps_link.py 'M32 0XX')"

On Windows PowerShell:

Start-Process "$(py maps_link.py 'M32 0XX')"

Example 2: Log what you find

Record each observation as you compare stations. Replace the placeholder values with the figures shown in the app.

python log_price.py "Station A" petrol <price> --notes "seen in Maps"
python log_price.py "Station B" diesel <price> --notes "seen in Waze"

Example 3: See your cheapest recorded options

After a few weeks of logging, print a summary:

python report.py

Example 4: Work out whether a detour is worth it

This is a small, explicit calculation. It uses the conversion of 1 imperial gallon = 4.54609 litres, and all inputs come from you. No price figures are assumed.

#!/usr/bin/env python3
"""Decide whether driving further for cheaper fuel pays off."""

LITRES_PER_GALLON = 4.54609


def worth_the_detour(
    extra_miles: float,
    mpg: float,
    price_pence: float,
    saving_pence: float,
    litres_bought: float,
) -> bool:
    """Return True if the saving beats the cost of the extra distance."""
    cost_per_mile = (price_pence / mpg) * LITRES_PER_GALLON
    extra_cost = extra_miles * cost_per_mile
    saving = litres_bought * saving_pence
    return saving > extra_cost


if __name__ == "__main__":
    # Replace every value below with your own figures.
    extra_miles = 4.0
    mpg = 40.0
    price_pence = float(input("Price at the nearer station (pence/litre): "))
    saving_pence = float(input("Saving per litre at the further station: "))
    litres = float(input("Litres you intend to buy: "))

    print("Worth the detour:", worth_the_detour(extra_miles, mpg, price_pence, saving_pence, litres))

The point of this script is not precision to the penny. It is to stop you making a twenty-minute round trip to save less than the trip costs in fuel and time.

Example 5: A weekly shell alias

Add this to ~/.bashrc or ~/.zshrc to check prices with a single short command. Replace the placeholder with your own postcode.

alias fuelcheck='open "$(python3 ~/fuel-prices/maps_link.py "<YOUR_POSTCODE>")"'

Reload your shell configuration:

source ~/.bashrc

A winter routine that holds up

Three habits carry most of the benefit:

• Compare on journeys you were already making. School runs, commutes, and supermarket trips already have fixed corridors. Find the cheapest station on those corridors once, and the decision is made for the season.
• Log as you go. Two seconds per observation builds a record that tells you which local station is genuinely cheapest over a month rather than which one happened to be cheapest on a given Tuesday.
• Confirm at the pump. Any in-app price is a reference point, not a contract. What is on the forecourt sign is what you pay.

Limits and open questions

The source is a Google blog post, not a price-guarantee service. It does not state how current the displayed prices are, how coverage varies across the UK, or how disputes about an inaccurate price are handled. Those remain open questions that only the apps themselves, or Google's own documentation, can answer.

Two further caveats are worth stating plainly. First, the scripts in this article are conveniences built on documented URL behaviour and a local CSV file; they are not Google products, not official integrations, and not affiliated with either app. Second, app interfaces are not stable. A label that exists in your version may move or be renamed in the next update, so follow the function rather than the exact wording.

Conclusion

The winter fuel-price problem is not really an information problem — the information is available in two apps most UK drivers already have installed. It is a routine problem. Comparing prices before you set off, keeping a short record of what you find, and refusing detours that cost more than they save will do more for your fuel budget than any single clever trick. Google Maps and Waze give you the comparison; the small scripts above make sure you actually use it, week after week, until the weather improves.