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A 100% personal route optimizer in a known environment based on experience

Home Page: https://eumiro.github.io

License: MIT License

Jupyter Notebook 9.47% Python 90.53%

gps-track route-planner python navigation network-analysis commuting traffic-analysis

pygohome's Introduction

pygohome: Python, Let's Go Home. Quickly.

pygohome is a 100% personal route optimizer in a known environment based on experience.

You walk/ride/drive frequently between known locations (home, work, school, shops, family, friends, …) using different routes, but would like to know the optimal route, that should take you the least time possible? pygohome uses your recorded GPS tracks to build a route network of your world with estimation on how long you need to get from A to B using the mean of transport of your choice.

How it works

You track all your trips

A simple GPS track with 1 or 2 seconds interval works well. Just walk/ride/drive as you are used to, stop at lights, don't speed. You may start tracking before you leave and stop it after you arrive.

You identify your points of interest (and crossroads)

pygohome does not use any map data, so you'll have to help it. First, you identify all points of interest (home, work, school, shop, family, friends, pub, club, beach, …) and name them.

In the current version, you'll also have to identify all forks and crossroads where your individual GPS tracks cross, split, or join.

You let pygohome build your world

It will build a route network with your nodes (named POIs and identified intersections) and edges (automatically generated lists of timedeltas you needed to get between the nodes).

You can find the fastest route from A to B

You can choose anywhere between “I'm feeling lucky” (i.e. Sunday 7am, sunny) and “I'd like to make sure I get there in time” (i.e. Friday 5pm, blizzard).

pygohome's People

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obestwalter betatim idf0830

pygohome's Issues

GPX Upload limited to 10MB

If I upload GPX files whose total is over 10MB, it says nothing and they are not added to the world.

Looks like this will be solved with the next version of ipywidgets: jupyter-widgets/ipywidgets#2522

Plot the whole `world` with some statistics

Show segments and how many tracks are they covered with.
Show segments that have not been observed in both directions (a config file would be needed to ignore those that are one-way anyway).

… more ideas in further comments?

I've had success with gpxpy (and I think now the slow datetime recognition is improved).

Basic usage:

with open(filename, 'rb') as gpx_file:
	gpx = gpxpy.parse(gpx_file, parser='lxml')
	#gpx = gpxpy.parse(gpx_file, parser='minidom')

# loop through gpx file and extract each point
for track in gpx.tracks:
	for segment in track.segments:
		for p in segment.points:
                # do something here

To convert lat/long to UTM grid, you can use this package: https://github.com/Turbo87/utm

import utm
utm.from_latlon(51.2, 7.5)

my opinion: rather than convert to a table/csv, I'd just convert the gpx to an array of dicts (one for each gps point), using the above tools. Then use pandas.from_dict().