Facing the needs for clean bicycle data \u2013 a bicycle-specific approach of GPS data processing

Sven Lißner,Stefan Huber

doi:10.1186/s12544-020-00462-2

Abstract

BackgroundGPS-based cycling data are increasingly available for traffic planning these days. However, the recorded data often contain more information than simply bicycle trips. GPS tracks resulting from tracking while using other modes of transport than bike or long periods at working locations while people are still tracking are only some examples. Thus, collected bicycle GPS data need to be processed adequately to use them for transportation planning.ResultsThe article presents a multi-level approach towards bicycle-specific data processing. The data processing model contains different steps of processing (data filtering, smoothing, trip segmentation, transport mode recognition, driving mode detection) to finally obtain a correct data set that contains bicycle trips, only. The validation reveals a sound accuracy of the model at its’ current state (82–88%).

Highlights

Area-wide cycling data are still hardly available and rarely used for bicycle specific traffic planning these days
3 Methodology To overcome the major shortcomings described in the previous section, we present an approach for bicycle-specific data processing of smartphone-based and crowd-sourced GPS track data
We secondly applied the method to a data set containing more than 8900 GPS tracks that have been recorded within the scope of a large bicycle research project

Summary

Introduction

Area-wide cycling data are still hardly available and rarely used for bicycle specific traffic planning these days. Tracking cyclists routes using smartphone applications can help to fill this data gap. A big amount of data can be collected within a very short period using crowdsourcing approaches that cover hundreds or thousands of cyclists using their smartphones to track their rides. This type of data collection is not new to scientists. GPS-based cycling data are increasingly available for traffic planning these days. Collected bicycle GPS data need to be processed adequately to use them for transportation planning

Methods

Results

Discussion

Conclusion