DOI: 10.5281/zenodo.1421305
title: Bicycle Trips Collected Using Cyclists Geo-C Geo-Game

description: This is an experimental dataset for the bicycle trips recorded using and geo-game called "Cyclist Geo-C". It contains the geometry of the trips recorded by 60 participants from three European Cities: Münster, Germany; Castelló, Spain; Valletta, Malta. This dataset was collected and analysed for the PhD Thesis "Mobile Services for Green Living" part of the European Joint Doctorate in Geoinformatics and the Geo-C Project. 

The dataset is composed of three subsets.


	There is a point dataset called "<em><strong>trips_od.geojson</strong></em>" which contained the point geometries where each trip started and ended with attributes for latitude, longitude, altitude, and precision coordinates. Each point also had the timestamp which indicates the time when the user started or ended the trip.
	There is a line dataset called "<em><strong>segments.geojson</strong></em>" which contained the geometries of the straight lines connecting two locations of the participant. Each segment started from an initial point "p<sub>i</sub>" recorded at a "t<sub>i</sub>” and ended at the next point recorded by the user "p<sub>f</sub>” at time “t<sub>f</sub>”. The time difference between "t<sub>i</sub>” and “t<sub>f</sub>” was at most five minutes while the length of the segment was at most one kilometre. Each segment also had the participant and trip identifier, and the segment's sequence number within the trip For each of the trip segments, we calculated the distance and speed using the recorded coordinates and timestamps from "p<sub>i</sub>" and "p<sub>f</sub>" points. \(trip\_segment = f(p_i,p_f)\) and \(segment\_speed = \frac{distance(p_i,p_f)}{\Delta time(p_i,p_f)}\). Then we classified the segments according to the calculated distance as: “<em>walking segment</em>” when the calculated speed was less than 5 km/h;  “<em>cycling segment</em>” when the calculated speed was between 5 and 50 km/h; or “<em>non-cycling segment</em>” when the calculated speed was more than 50 Km/h.
	There was another line dataset called “<em><strong>trips_tags.geojson </strong></em>” which contained the geometries of each of the trip paths. A trip was a line (also called polyline by GIS users) defined by the ordered sequence of trip segments. It started from origin point "p<sub>i</sub>" of the trip’s first segment and ended at the destination point "p<sub>f</sub>" of the trip's last segment. Each trip also had the participant's identification, trip's identification, the number of segments, start and end times.


In addition to the experimental dataset recorded by participants, our analysis used a secondary dataset to define a comparable framework for the three cities. The secondary dataset consisted of the existing bicycle paths in the cities of Münster and Castelló as well as the planned bicycle paths around Valletta. For the city of Münster, the source of the bicycle paths was the OpenStreetMap (we downloaded the line elements with the tags “<em>bicycle=yes</em>” and "<em>cycleway=yes</em>”). For the city of Castelló, we obtained the bicycle paths from the city transport authority, including the city of Valletta, we created a digital version of the national bicycle network plan.

We estimated the number of trips "<em><strong>bikepaths_trips.geojson</strong></em>" and the number of segments "<em><strong>bikepaths_segments</strong></em><em><strong>.</strong></em><em><strong>geojson</strong></em>" at each bike path. Also, we provide the areas where participants faced frictions during the experiment which corresponded to low cycling speeds "frictions.geojson".

Finally, we provide a visual reference of the dataset in "<em><strong>frictions_cities.pdf</strong></em>".
description: For further information on this dataset email the authors.