MobilityNet is based on the definition of artificial trips called profiles. These profiles are specified before the collection of the mobility traces and contain the sequence of different transport mode segments of the artificial trip as well as the start and end points of each segment. For example, here is a profile that defines a trip containing car and scooter segments in downtown San Jose, California. If you have a look at the profile you can see that it also contains definitions for the number of iOS and Android phones used together with their role in the data collection and sensing settings (e.g., high accuracy sensing vs. low accuracy sensing).
After the specification of an artificial trip, users can define the exact route with the help of OSRM to fill the missing location points automatically. A visual representation of the return trip of the example timeline is shown below. The blue lines represent the ground truth trajectory (obtained through OSRM), and the purple polygons represent transition areas where we can't determine the ground truth a priori and have to use the reference spatio-temporal trajectories instead.
The other timelines are in the same directory.
Since the raw data contains multiple data streams, we store it as JSON instead
of csv by default. Each JSON object is tagged with a key that represents the
stream that it is part of. The data models for the keys are
here.
Some of the keys - e.g. stats/server_api_time - are not relevant to this
analysis since they are used for instrumenting the performance of the system.
We provide separate JSON files for android and iOS for the following sensing regimes:
- the accuracy control (android, ios),
- the first experiment setting: high accuracy, high frequency, duty cycled = HAHFDC (android, ios)
- the second experiment setting: duty cycled (android, high accuracy, medium frequency, ios, medium accuracy, high frequency)
- the power control, which has only battery data (android, ios)
All these are collected during the same evaluation, from
2019-07-23T08:46:22-07:00 to 2019-07-23T14:31:45-07:00. The iOS accuracy
control has the ground truth temporal transitions (with key
manual/evaluation_transition) included in the data. Since this is the raw
data, it does not include inferred trips or sections.
The rest of this data is stored on a public server, currently http://cardshark.cs.berkeley.edu. Scripts to download and pre-process the data, and to compute the metrics, are in the companion repository https://github.com/MobilityNet/mobilitynet-analysis-scripts
Here is how you can get started:
- The scripts are launchable via binder https://mybinder.org/ for easy browsing:
- You can also clone the mobilitynet-analysis-scripts repository and install the environment using
source setup/setup.sh. You can then start with this notebook to get a quick overview exploring some aspects of the data, or this notebook to plot a timeseries
All contributions are welcome! This includes both issues for clarifications and pull requests for improvements.