Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing

Díaz, Juan José Vinagre; Pozo, Rubén Fernández; González, Ana Belén Rodríguez; Wilby, Mark Richard; Ávila, Carmen Sánchez

doi:10.3390/s20123550

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…We recognized this same behavior on the docking stations activity profile [34], i.e., the net flow of bicycles. Selecting 2 as the number of groups to be generated by the proposed clustering algorithm resulted in two distinct classes of docking stations corresponding to commuter and non-commuter behaviors.…”

Section: Operational Analysissupporting

confidence: 53%

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Data-Driven Analysis of Bicycle Sharing Systems as Public Transport Systems Based on a Trip Index Classification

Wilby

Díaz

Pozo

et al. 2020

Sensors

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Bicycle Sharing Systems (BSSs) are exponentially increasing in the urban mobility sector. They are traditionally conceived as a last-mile complement to the public transport system. In this paper, we demonstrate that BSSs can be seen as a public transport system in their own right. To do so, we build a mathematical framework for the classification of BSS trips. Using trajectory information, we create the trip index, which characterizes the intrinsic purpose of the use of BSS as transport or leisure. The construction of the trip index required a specific analysis of the BSS shortest path, which cannot be directly calculated from the topology of the network given that cyclists can find shortcuts through traffic lights, pedestrian crossings, etc. to reduce the overall traveled distance. Adding a layer of complication to the problem, these shortcuts have a non-trivial existence in terms of being intermittent, or short lived. We applied the proposed methodology to empirical data from BiciMAD, the public BSS in Madrid (Spain). The obtained results show that the trip index correctly determines transport and leisure categories, which exhibit distinct statistical and operational features. Finally, we inferred the underlying BSS public transport network and show the fundamental trajectories traveled by users. Based on this analysis, we conclude that 90.60% of BiciMAD’s use fall in the category of transport, which demonstrates our first statement.

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Section: Operational Analysissupporting

confidence: 53%

“…• User's range of age: 6 intervals [0,16], [17,18], [19][20][21][22][23][24][25][26], [27][28][29][30][31][32][33][34][35][36][37][38][39][40]…”

Section: Datasetmentioning

confidence: 99%

Data-Driven Analysis of Bicycle Sharing Systems as Public Transport Systems Based on a Trip Index Classification

Wilby

Díaz

Pozo

et al. 2020

Sensors

Self Cite

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Blind classification of e-scooter trips according to their relationship with public transport

et al. 2023

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E-scooter services have multiplied worldwide as a form of urban transport. Their use has grown so quickly that policymakers and researchers still need to understand their interrelation with other transport modes. At present, e-scooter services are primarily seen as a first-and-last-mile solution for public transport. However, we demonstrate that $$50\,\%$$ 50 % of e-scooter trips are either substituting it or covering areas with little public transportation infrastructure. To this end, we have developed a novel data-driven methodology that autonomously classifies e-scooter trips according to their relation to public transit. Instead of predefined design criteria, the blind nature of our approach extracts the city’s intrinsic parameters from real data. We applied this methodology to Rome (Italy), and our findings reveal that e-scooters provide specific mobility solutions in areas with particular needs. Thus, we believe that the proposed methodology will contribute to the understanding of e-scooter services as part of shared urban mobility.

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Potentials and Prospects of Bicycle Sharing System in Smart Cities: A Review

Shen

Zhu

et al. 2022

IEEE Sensors J.

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Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing

Cited by 5 publications

References 25 publications

Data-Driven Analysis of Bicycle Sharing Systems as Public Transport Systems Based on a Trip Index Classification

Data-Driven Analysis of Bicycle Sharing Systems as Public Transport Systems Based on a Trip Index Classification

Blind classification of e-scooter trips according to their relationship with public transport

Potentials and Prospects of Bicycle Sharing System in Smart Cities: A Review

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