During the hackathon in Riga our team was working on the analysis of public bus transport system. The data for such analysis was kindly provided by the Riga city hall (https://opendata.riga.lv/satiksme.html) .
The datasets from Riga open data portal which we have used in our work were following:
- Bus runs timetable (Autobusu kustības saraksts)
- Electronic ticket validation data – because of the huge amount of data – on a working day more than 200 000 passengers validate tickets – the data just for one working day (09.02.2016) were downloaded – the assumption is that more or less on each working day trips of a passenger will be similair (E talonu reģistrācijas dati 09.02.2016)
- Data for location and names of bus stops (Sabiedriskā transporta pieturas)
- Description of individual bus runs (Sabiedriskā transporta veidi)
The following datasets are very good from the point of view that analysis of how much people use the bus routes can be conducted.
Our ultimate goal is to be able to evaluate public bus transportation system and eventually propose some optimizations (improvements). The reason behind is that it can be useful – due to the fact that last year Riga public transport company required 93 mil. € subsidies. The analysis can help re-plan the bus routes to minimize subsidies.
At first some visualizations of where and how many people use public buses nowadays were made. For this it was necessary to do the mapping between validations of e-ticket and the bus schedules. In brief all rides where divided according to the bus route number, direction of the ride, start time (for validation interval it was the first validation on that certain bus ride) and end time (for validation interval it was the last validation on that certain bus ride). After this mapping was made – the cartographic visualizations were created and published as either WMS(-T) services or Map Compositions. At the moment they are to be seen at: http://opentnet.eu/web/guest/create-maps in Datasources (WMS(-T) services) and Compositions menus.
Here some examples of such created Map Compositions are shown.
Figure 1. Number of passengers traveled by different bus lines and entering bus at different bus stations by hours during the day of 09.02.2016 in Riga (http://opentnet.eu/web/guest/create-maps?composition=261a62fd-d6f4-4f85-bf8d-3f631a1b810f&hs_panel=layermanager)
Figure 2. Heatmap showing the number of passengers traveled by different bus lines and entering bus at different bus stations by periods of the day of 09.02.2016 in Riga (possible categories: morning, afternoon, early evening and late evening). Available at: http://opentnet.eu/web/guest/create-maps?composition=6271e0ca-3d62-42c9-9644-40f1c46fc54d&hs_panel=layermanager
As well as looking at overall picture and distributions of the passengers (total number of passengers at each stop by hour, total number of passengers at each bus route by hour etc.) – the distribution of the passengers within each bus route but also in different particular parts of the city was visualized.
For this WebGLayer.js library (https://github.com/jezekjan/webglayer ) was used. Further you can see the illustrations from the application.
Figure 3. Overall distribution of the passengers on bus route number 3 on 09.02.2016 in Riga.
Figure 4. Distribution of the passengers on the bus route number 3 on 09.02.2016 in Riga from 5:00 till 7:00 where number of passengers were higher than 40.
Figure 5. Distribution of the passengers on the bus route number 3 on 09.02.2016 in Riga from 5:00 till 7:00 where number of passengers were higher than 180. Identifying the bus stop with the most passengers at the time of the day.
Also with the help of the library it is possible to compare various parts of the city between each other.
Figure 6. Comparing distribution of the passengers on buses in two randomly selected regions on 09.02.2016.
For the WebGLayer application now more attributes are being prepared (the direction of the ride, land use, population density etc.) . The all in some time will be added to our application. And it will be possible to filter by all of them.
As we need very much to find some optimizations the basic evaluation parameters of the public bus transport network need to be assessed. Those are for instance: distance to the closest bus stop, bus network density, identifying overlapping portions of the routes etc.
Figure 6. 100/200/500/1000/2000-meters buffer zones around the bus station. The map composition is available at: http://opentnet.eu/web/guest/create-maps?composition=f910b59b-4e83-4b15-83c7-f2bf8b29e275&hs_panel=layermanager
After looking at the current number and distribution of the passengers and evaluating the bus network, optimizations can be proposed.