Understanding Spatiotemporal Station and Trip Activity Patterns in the Lisbon Bike-Sharing System

Vitória Albuquerque; Francisco Andrade; João Carlos Ferreira; Miguel Sales Dias

Intelligent Transport Systems, From Research and Development to the Market Uptake. 4th EAI International Conference, INTSYS 2020, Virtual Event, December 3, 2020, Proceedings

Research Article

Understanding Spatiotemporal Station and Trip Activity Patterns in the Lisbon Bike-Sharing System

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@INPROCEEDINGS{10.1007/978-3-030-71454-3_2,
    author={Vit\^{o}ria Albuquerque and Francisco Andrade and Jo\"{a}o Carlos Ferreira and Miguel Sales Dias},
    title={Understanding Spatiotemporal Station and Trip Activity Patterns in the Lisbon Bike-Sharing System},
    proceedings={Intelligent Transport Systems, From Research and Development to the Market Uptake. 4th EAI International Conference, INTSYS 2020, Virtual Event, December 3, 2020, Proceedings},
    proceedings_a={INTSYS},
    year={2021},
    month={7},
    keywords={Bike-sharing system Mobility patterns Statistical analysis Cluster analysis K-means Urban mobility},
    doi={10.1007/978-3-030-71454-3_2}
}

Vitória Albuquerque
Francisco Andrade
João Carlos Ferreira
Miguel Sales Dias
Year: 2021
Understanding Spatiotemporal Station and Trip Activity Patterns in the Lisbon Bike-Sharing System
INTSYS
Springer
DOI: 10.1007/978-3-030-71454-3_2

Vitória Albuquerque¹, Francisco Andrade², João Carlos Ferreira², Miguel Sales Dias¹

1: Universidade Nova de Lisboa
2: Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR

Abstract

The development of the Internet of Things and mobile technology is connecting people and cities and generating large volumes of geolocated and space-time data. This paper identifies patterns in the Lisbon GIRA bike-sharing system (BSS), by analyzing the spatiotemporal distribution of travel distance, speed and duration, and correlating with environmental factors, such as weather conditions. Through cluster analysis the paper finds novel insights in origin-destination BSS stations, regarding spatial patterns and usage frequency. Such findings can inform decision makers and BSS operators towards service optimization, aiming at improving the Lisbon GIRA network planning in the framework of multimodal urban mobility.

Keywords: Bike-sharing system Mobility patterns Statistical analysis Cluster analysis K-means Urban mobility

Published: 2021-07-16
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-030-71454-3_2