How comfortable are bike lanes? And how can road surface quality be measured? This project explores the feasability of quantifying the bicycle lane surface roughness using an accelerometer. To guarantee a broad application, this project uses mobile devices for data collection
To acquire data, a mobile device is needs to be placed in a fixed position on a bicycle frame (best handlebar). As of now, the input data is generated with the free Android/iOs application "Physics Toolbox" (version 1.9.3.7). With the 'multireport' feature in the app we need to record 'g-force' and 'position'.
To run the processor on a dataset, use python -W ignore process.py /<input>.csv <outputname> .
What we're left with after processing is the "Happy-Bike-Index" (HBI) exported as geoJSON Line-Features. The data/processed/citytracks folder contains several sample datasets. The processed data is stored with the parameter specifications in the filename, that were applied on the datasets:
- c: cutoff frequency (best 1 Hz)
- s: timeframe size (best 2s)
- t: thresholding value (best 1.5 times g-force)
Due to hardware caused GPS errors of the tracking device, the datasets of tracks 1-6 have flawed Features. These issues (if not to grave) can to be manipulated in a GIS. Track 7 and 8 show promising results.
The data is processed in the following steps:
- Input is generated with Physics tool box with an unknown sample distance of T
- The input is assumed as a Time discrete signal
- A dynamic threshold is applied with timeframe size (s) and threshold (z_t)
- Highpass Butterworth filter with cutofffrequency (omega) generates a highpassed signal
- The floating root mean square with timeframe size (s) caculates average over time
- To get a spatial represantiation, another average is calculated but only over a unique Geotag
- To get an Index, the averages are normalized between 0 (good) and 1 (bad)
- The index is finally exported as a geojson polyline feature
