See Source link for R code.

This repository collects the code produced at ODD Zurich 2017 for visualizing the passenger spatial-temporal data on the station level.

It is connected to Issue #8： number of passengers traveling visualization https://github.com/OpenDataDayZurich2016/ideas/issues/8.

A brief prototype of our work is shown here: https://invis.io/ZDAPT1M82.

There are passengers data (in and out) of stations at every stop and we try to analyze the spatial temporal pattern of stations on certain days (Monday-Thursday, Friday, weekend). We try to cluster stations based on their passenger volume change pattern through the day. Using Dynamic Time Warping (DTW) Distances as features allow the analysis of time series data, (Kate, 2016). The hierarchical cluster analysis of data on day type 6 is implemented in R programming. The clusters of example stations are visualization on the map in QGIS.

The idea of this project is to visualize on a map all VBZ vehicles as the move across the city during the day, with special focus on dates of special events that affect the public transportation network.

Play with it yourself here!

ZüriFäscht 2016

The following is a fraction of the 1st July 2016 visualized, the first day of ZüriFäscht 2016, and it is clearly visible how at around 18:35 all vehicles leave the central area of Zürich because the event is starting.

Synopsis

In this project, we fit a simple linear model to predict delays in arrival times of VBZ public transportation vessels using data of 4 weeks. The accompanying shiny app can be found here.

To fit the model, we use the predictors 'weekday', 'vehicle type', 'temperature' and 'precipitation'. 'weekday' and 'vehicle type' are categorical predictors. 'temperature' and 'precipitation' are continuous predictors.

We obtain data for the predictors 'weekday' and 'vehicle type' from Open Data Zurich (https://www.stadt-zuerich.ch/opendata) and data for the predictors 'temperature' and 'precipitation' from http://www.tecson-data.ch/zurich/mythenquai/.

The delay in arrival times, which is the quantity we want to predict, we obtain from Open Data Zurich as well.

The data set we use for fitting the model contains ca. 6 mio data points.

To run our model:

• Clone the project
• Make directory 'raw' in project root directory
• Move data into dir 'raw'. If you have the data on a USB stick 'Stadt Zurich Open Data' move data from USB into directory 'raw'.
  
• Open the RProject in RStudio.
• Hit Ctrl+Shift+B to start the Makefile-based project build.
• Enter remake::create_bindings() in R console to bind to the data object from within R.

The goal was to classify public transport stops by their surrounding landmarks, like universities or coworking_spaces, extracted from OpenStreetMap.

usage

• The tool chain uses make and python 3.6.
• create a data folder or change the Makefile variable.
• add the data-examples/haltepunkt.csv file to data.
• run make.

The Make script will - extract all active stop point coordinates - retrieve OpenStreetMaps landmarks with around these coordinates. - eliminates duplicates. - classifies the landmarks with the given classifiers.

This repository collects the code produced at ODD 2017 for visualizing the delays.

It is connected to - Issue #11 Visualisation of vehicle delays and - Issue #2 Visualization (map) of delay-causing streets