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

Lißner, Sven; Huber, Stefan

doi:10.1186/s12544-020-00462-2

Cited by 13 publications

(4 citation statements)

References 30 publications

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“…They benefit from the increasing accuracy of GNSSs that are based on the use of new satellite systems, including Galileo [11] and the global positioning system (GPS). These systems may also be used for monitoring cycling routes [12]. Further commonly used sensors include accelerometers [13], [14] inside mobile phones [15], gyrometers, and sensors to monitor the heart rate [16] and further physiological functions.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Accelerometric and Cycling Cadence Data for Motion Monitoring

et al. 2021

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Motion pattern analysis uses methods for the recognition of physical activities recorded by wearable sensors, video-cameras, and global navigation satellite systems. This paper presents the motion analysis during cycling, using data from a heart rate monitor, accelerometric signals recorded by a navigation system, and the sensors of a mobile phone. The set of real cycling experiments was recorded in a hilly area with each route about 12 km long. The associated signals were analyzed with appropriate computational tools to find the relationships between geographical and physiological data including the heart rate recovery delay studied as an indicator of physical and nervous condition. The proposed algorithms utilized methods of signal analysis and extraction of body motion features, which were used to study the correspondence of heart rate, route profile, cycling speed, and cycling cadence, both in the time and frequency domains. Data processing included the use of Kohonen networks and supervised two-layer softmax computational models for the classification of motion patterns. The results obtained point to a mean time of 22.7 s for a 50 % decrease of the heart rate after a heavy load detected by a cadence sensor. Further results point to a close correspondence between the signals recorded by the body worn accelerometers and the speed evaluated from the GNSSs data. The accuracy of the classification of downhill and uphill cycling based upon accelerometric data achieved 93.9 % and 95.0 % for the training and testing sets, respectively. The proposed methodology suggests that wearable sensors and artificial intelligence methods form efficient tools for motion monitoring in the assessment of the physiological condition during different sports activities including cycling, running, or skiing. The use of wearable sensors and the proposed methodology finds a wide range of applications in rehabilitation and the diagnostics of neurological disorders as well.INDEX TERMS Multimodal signal analysis, computational intelligence, machine learning, motion monitoring, accelerometer-derived cycling data, classification.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Accelerometric and Cycling Cadence Data for Motion Monitoring

et al. 2021

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“…Aggregated cyclist trip data has been obtained by the annual STADTRADELN campaign over a period of three weeks, in which cyclists record their trips in a gamified, competitive fashion. Associated studies by Lißner, Huber [40] and Harten [41] describe the campaign and data acquisition in more detail. Cyclist trip data and aggregated e-scooter data is spatially joined on a link wise level.…”

Section: Data Processingmentioning

confidence: 99%

Friend or foe? A spatial approach to overlay bicycle and scooter trajectories

Porojkow,

Lißner

2024

Eur. Transp. Res. Rev.

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Dockless e-scooter schemes have seen increasing popularity in 28 German cities. Increasing use on insufficiently dimensioned bicycle infrastructure can lead to conflicts between e-scooter riders and cyclists. A new approach was developed in order to detect potential zones of conflict by overlaying aggregated bicycle and e-scooter trajectories in the City of Dresden, Germany. Bicycle data is being obtained by the annual STADTRADELN campaign where cyclists record and transmit daily trips via GPS for a period of three weeks. Simultaneously, e-scooter API data has been collected over a course of 8 weeks from June to September 2021. Origin/Destination data has been generated and routed over a OSM network in order to obtain aggregate d e-scooter flows. We extrapolated the aggregated bicycle data to match them with the timeframe of the e-scooter data acquisition. Afterwards we spatially joined both: bicycle and e-scooter flows and calculated the link wise proportion of e-scooter trips in relation to bicycle trip volumes. Two important findings emerged: (1) Residential roads have a higher proportion of e-scooter trips. (2) E-scooters are exposed to high bicycle trip volumes on primary roads with bicycle infrastructure. We conclude that this approach can detect possible links of conflict, where overtaking cyclists or insufficient space can lead to dangerous situations. That approach is biased towards a missing route choice model for e-scooter riders or better route data of e-scooters, which needs further research.

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“…The initial dataset covers the entire Copenhagen metropolitan area in the period from the 16 th September 2019 until 31 st May 2021 and consists of 365,813 trips from 10,049 individuals. We applied a combination of steps to process the GPS trajectories based on Lißner and S. Huber (2021) and Schuessler and Axhausen (2009), which we refer to for further details. The data was map-matched to a highly disaggregated bicycle network based on Open Street Map (OSM 3 ) and the choice set was generated by the "categorical" approach from Rasmussen et al (2021).…”

Section: Bicycle Route Choice Model Gps Datamentioning

confidence: 99%

Context-aware Bayesian choice models

Łukawska¹,

Jensen²,

Rodrigues³

2022

Preprint

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Declarations of interest: none Highlights• We propose an approach to model context-dependent intra-respondent heterogeneity.• Contextual information is mapped to additive shifts on preference parameters.• The model allows non-linear interactions between continuous and discrete variables.• Scenario analyses for different context attributes are possible in almost no time.• We estimate a C-MMNL bicycle route choice model on 110,083 trips in 80 minutes.

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

Cited by 13 publications

References 30 publications

Evaluation of Accelerometric and Cycling Cadence Data for Motion Monitoring

Evaluation of Accelerometric and Cycling Cadence Data for Motion Monitoring

Friend or foe? A spatial approach to overlay bicycle and scooter trajectories

Context-aware Bayesian choice models

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