Display journey times with Google Directions and Python

journey times from The Pinnacle

I thought it would be interesting to calculate and display journey times using Google Directions API and Python. After the journey times for each transport modes have been calculated they can be overlayed on a colour coded map, in this case I am using Singapore as an example. To do this I will query Google Directions API and for each destination calculate the journey time for driving, public transport (MRT or Bus) and cycling. Each destination is colour coded with the transport mode that is the quickest.


Just show me the maps!


  1. Define a fixed location where all journeys start from.
  2. Define a bounding box of locations to find directions to.
  3. Run script to query journey times for each point in the bounding box for each transport mode.
  4. Convert results to a javascript array.
  5. Write javascript code to overlay results on a map of Singapore.

Google APIs

The Google Directions API and the Google Geocoding API limit you to 2500 queries per 24 hour period and 10 requests per second. You will need to turn on these service before making any API calls. Go to the API Console and turn on the geocoding and directions services.

Python Script

This script will collect the journey times for sampled locations in the specified bounding box for three transport modes: driving, public transport and walking. I don’t display walking on the map, it is too slow, instead I convert it to cycling by dividing the journey time by 4.

python Script to calculate journey times

Python file: geocode.py Running the script from the command line:

$ python geocode.py --max-lat 1.332 --min-lat 1.306 --min-lng 103.7550 --max-lng 103.765

... Lots of output ...

$ head timings.csv

1.300000    103.825000  597 780 605 3207
1.298750    103.825000  477 780 855 3305
1.297500    103.825000  494 780 932 3209
1.296250    103.825000  477 480 1033    3045
1.295000    103.825000  502 480 1000    3012
1.293750    103.825000  473 240 1054    2776
1.292500    103.825000  506 240 991 2716
1.291250    103.825000  451 240 934 2659
1.290000    103.825000  421 240 788 2570
1.300000    103.826250  527 780 788 3336

The output is a tab delimited file containing the latitude, longitude, driving time, public transport time, public transport walking time (to and from the station/bus) and walking time.

Next the CSV data in converted into a javascript array so that it can easily be used to generate an overlay to Google Maps.

Python file: to_javascript.py  

$ python to_javascript.py
$ head timings.js

var timings = [
[ 1.300000,103.825000,597,780,605,3207 ],
[ 1.298750,103.825000,477,780,855,3305 ],
[ 1.297500,103.825000,494,780,932,3209 ],
[ 1.296250,103.825000,477,480,1033,3045 ],
[ 1.295000,103.825000,502,480,1000,3012 ],
[ 1.293750,103.825000,473,240,1054,2776 ],
[ 1.292500,103.825000,506,240,991,2716 ],
[ 1.291250,103.825000,451,240,934,2659 ],
[ 1.290000,103.825000,421,240,788,2570 ],

HTML page and Javascript code

The javascript shows the map of Singapore using Google Maps and overlays the transport data on top of the map.

html file: transport_modes.html



The yellow star marks the start location of the journey.

  • Red = Driving
  • Green = Public Transport (MRT or Bus)
  • Blue = Cycling

Journey starting from the The Pinnacle @ Duxton.

journey times from The Pinnacle

Journey starting from Pasir Ris.

journey times from Pasir Ris

Transport Modes Map: transport_modes.html


When the origin and destination are far apart driving is the quickest method of transport. For mixed residential and commercial areas cycling can be the best method. Public transport is rarely the best method, this is because of the amount of time required to walk to/from the MRT Station/Bus to the destination.

This can be seen by removing the walking component from the public transport times so that only the time spent on public transport is used. Here is that map after we apply this change:

( Red=Driving, Green=Public Transport, Blue=Cycling )

disregard walking to public transport

Notes about journey times:

I add 25% to the travel time for driving to account for traffic, diversions, congestion etc. For cycling I use the walking route duration divided by four. Some locations map to the sea or lakes, Google will find the nearest point on land when planning the route.

See Also:

If you liked this article please share! Thanks.

  • Rubslopes

    That’s so awesome!! I’ve dreamed to make one of those for my city, but never started to do the coding… Now it’ll be much easier 🙂
    Thanks a lot!

  • Mustansir Aziz

    How do I integrate HTML Java scripts code alongside the Python scripts on Python??