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

Background GPS-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. Results The 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%).

[1]  S Bekhor,et al.  Modelling bicycle route choice using data from a GPS-assisted household survey , 2018 .

[2]  Catherine T. Lawson,et al.  A GPS/GIS method for travel mode detection in New York City , 2012, Comput. Environ. Urban Syst..

[3]  Daniel G. Aliaga,et al.  Urban sensing: Using smartphones for transportation mode classification , 2015, Comput. Environ. Urban Syst..

[4]  Stefan Huber,et al.  GPS-data in bicycle planning: “Which cyclist leaves what kind of traces?” Results of a representative user study in Germany , 2020 .

[5]  Fei Yang,et al.  GPS and Acceleration Data in Multimode Trip Data Recognition Based on Wavelet Transform Modulus Maximum Algorithm , 2015 .

[6]  Eui-Hwan Chung,et al.  A Trip Reconstruction Tool for GPS-based Personal Travel Surveys , 2005 .

[7]  Luis F Miranda-Moreno,et al.  Mapping cyclist activity and injury risk in a network combining smartphone GPS data and bicycle counts. , 2015, Accident; analysis and prevention.

[8]  Kay W. Axhausen,et al.  Identifying trips and activities and their characteristics from GPS raw data without further information , 2008 .

[9]  Peter R. Stopher,et al.  Processing GPS data from travel surveys , 2005 .

[10]  Suely da Penha Sanches,et al.  Analysis of Bicycle Commuter Routes Using GPSs and GIS , 2014 .

[11]  Guangnian Xiao,et al.  Travel Mode Detection Based on Neural Networks and Particle Swarm Optimization , 2015, Inf..

[12]  Steve H. L. Liang,et al.  Real-Time Transportation Mode Detection Using Smartphones and Artificial Neural Networks: Performance Comparisons Between Smartphones and Conventional Global Positioning System Sensors , 2014, J. Intell. Transp. Syst..

[13]  Stefan Schönfelder,et al.  Eighty Weeks of Global Positioning System Traces: Approaches to Enriching Trip Information , 2004 .

[14]  Kevin J. Krizek,et al.  Commuter Bicyclist Behavior and Facility Disruption , 2007 .

[15]  Trisalyn A. Nelson,et al.  Mapping ridership using crowdsourced cycling data , 2016 .

[16]  Tibor Petzoldt,et al.  Are you an ambitious cyclist? Results of the cyclist profile questionnaire in Germany. , 2020, Traffic injury prevention.

[17]  Randall Guensler,et al.  Elimination of the Travel Diary: Experiment to Derive Trip Purpose from Global Positioning System Travel Data , 2001 .

[18]  Kay W. Axhausen,et al.  Route choice of cyclists in Zurich , 2010 .

[19]  Route choice of cyclists in Zurich: GPS-based discrete motion models , 2009 .

[20]  Luciano Bononi,et al.  Custom Dual Transportation Mode Detection By Smartphone Devices Exploiting Sensor Diversity , 2018, 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[21]  Jennifer Dill,et al.  Where do cyclists ride? A route choice model developed with revealed preference GPS data , 2012 .

[22]  Deborah Estrin,et al.  Biketastic: sensing and mapping for better biking , 2010, CHI.

[23]  Philip S. Yu,et al.  Transportation mode detection using mobile phones and GIS information , 2011, GIS.

[24]  Kevin Heaslip,et al.  Inferring transportation modes from GPS trajectories using a convolutional neural network , 2018, ArXiv.

[25]  Billy Charlton,et al.  A GPS-based bicycle route choice model for San Francisco, California , 2011 .

[26]  Yusak O. Susilo,et al.  Transportation mode detection – an in-depth review of applicability and reliability , 2017 .

[27]  Peter R. Stopher,et al.  Review of GPS Travel Survey and GPS Data-Processing Methods , 2014 .

[28]  Filip Biljecki,et al.  Automatic segmentation and classification of movement trajectories for transportation modes , 2010 .

[30]  Serge P. Hoogendoorn,et al.  How Do People Cycle in Amsterdam, Netherlands?: Estimating Cyclists’ Route Choice Determinants with GPS Data from an Urban Area , 2017 .

[31]  Peter R. Stopher,et al.  Deducing mode and purpose from GPS data , 2008 .

[32]  Monika Sester,et al.  Multi-stage approach to travel-mode segmentation and classification of gps traces , 2012 .

[33]  Yu Bai,et al.  Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm , 2015, Inf..

[34]  Filip Biljecki,et al.  Transportation mode-based segmentation and classification of movement trajectories , 2013, Int. J. Geogr. Inf. Sci..

[35]  Kees Maat,et al.  Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands , 2009 .

[36]  Billy Charlton,et al.  Bicycle Route Choice Data Collection using GPS-Enabled Smartphones , 2011 .

[37]  Amer Shalaby,et al.  Enhanced System for Link and Mode Identification for Personal Travel Surveys Based on Global Positioning Systems , 2006 .

[38]  Monika Sester,et al.  Multi-stage approach to travel-mode segmentation and classification of gps traces , 2011 .