Spatiotemporal Clustering Analysis of Bicycle Sharing System with Data Mining Approach

Ma, Xinwei; Cao, Rui-Ming; Jin, Yuhui

doi:10.3390/info10050163

Cited by 19 publications

(14 citation statements)

References 38 publications

(54 reference statements)

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“…When using our similarity metric on the situation presented in Figure 4, we obtain the results show in Eqs. (5) and (6)…”

Section: E Cross-temporal Similarity Functionmentioning

confidence: 99%

“…These techniques are useful for classification, especially when new data becomes available and does not yet belong to any cluster, and anomaly detection, when an element does not appear to belong to any cluster. Clustering techniques have been applied to several domains and different types of data, including social networks [4] and transportation [5], or both [6].…”

Section: Introductionmentioning

confidence: 99%

“…c: Great CircleThe Great Circle distance assumes that Earth is a sphere and uses a radius of 6371.009 kilometers, as defined by the International Union of Geodesy and Geophysics (IUGG)5 , in distance calculations. As expected, the results are not as This work is licensed under a Creative Commons Attribution 4.0 License.…”

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confidence: 99%

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A Framework for Spatial-Temporal Trajectory Cluster Analysis Based on Dynamic Relationships

2020

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In spatial-temporal data analysis, location data and its evolution through time are investigated with the goal of uncovering important information to provide novel insights. These insights, for example, may involve congestion identification in transportation, mobility patterns in urban computing, and storm prediction in weather forecasting. Clustering, one data analysis technique, groups spatial-temporal data based on location. Current spatial-temporal data analysis techniques fail to investigate relationships between spatial-temporal clusters, such as splitting from a cluster and merging with another one because of a change of properties over time. These relationships could hold valuable information about the existence of a cluster and its interactions with other clusters and trajectories. In this paper, we introduce a framework to identify, process, and analyze relationships between clusters of spatial-temporal data (e.g. enter, merge, or split). We describe its architecture and components, as well as a proposed clustering technique, the different approaches for distance calculation, and how we calculate cluster similarity of temporally separated clusters. The result of these operations are used in the identification of cluster relationships over space and time. The analysis of these relationships helps uncover hidden values that could support novel approaches to more effective decision-making. We evaluate our framework with two case studies, based on truck and human trajectories. INDEX TERMS Cluster analysis, spatial-temporal cluster relationships, spatial-temporal data, spatialtemporal data analysis

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“…When using our similarity metric on the situation presented in Figure 4, we obtain the results show in Eqs. (5) and (6)…”

Section: E Cross-temporal Similarity Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Framework for Spatial-Temporal Trajectory Cluster Analysis Based on Dynamic Relationships

2020

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“…Because of this, the use of unsupervised machine learning techniques for clustering became a common practice for analysing mobility patterns to optimize the BSS. Some of the explored clustering techniques for analysing BSS are: kmeans (Vogel et al, 2011;Chabchoub and Fricker, 2014;Feng et al, 2017;Ma et al, 2019), Expectation-Maximization (Vogel et al, 2011), sequential information-bottleneck (Vogel et al, 2011) and hierarchical clustering (Feng et al, 2017). These algorithms require a subsequent subjective interpretation of the clusters which usually leads to their labelling with respect to the closest point of interest (e.g.…”

Section: State Of the Artmentioning

confidence: 99%

Bikemi Bike-Sharing Service Exploratory Analysis on Mobility Patterns

Toro

Carrión

Brovelli

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Bike Sharing Systems (BSS) are growing worldwide for the social and environmental benefits that they can provide. Thanks to the increasing popularity of the BSS and the availability of monitoring technologies, there is a continuous production of data that can help to understand bike usage and to improve its design and management. This study aims at exploring BSS users’ mobility patterns habits and the demand for the service. To reach this scope, the available data have been preprocessed in order to allow data mining and data visualization with open source tools based on Python. The study case regards the BikeMi BSS of the City of Milan between June 2015 to December 2018. The suggested approach proceeded, first, with the categorization of the user typology based on the frequency of use of the service; at a second stage, the influence of the day typology on the use of the service has been explored; third, the spatial and temporal patterns of the BSS use among the stations has been analysed; fourth, the influence of meteorological conditions on the use of the service has been considered; at last, the clustering of the stations with similar bikes use activity through K-Means has been performed. As expected, it was observed that the service is extensively used for commuting to work-related activities. Regular users compose a large part of the BSS community making use of the service mostly during weekdays. In addition, it was noted that only 'strong' meteorological conditions can impact the use of the service. Both the identification of the demand for the service and of the external factors that can affect its use support the clustering activities, allowing for the elimination of not relevant information and facilitating the interpretation of the obtained clusters.

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“…Clustering approaches, such as those used to develop urban form typologies, have been used in bicycling research as applied to classifying mode share, 4 bike share schemes, 21 and bicycle crashes. 22 Previous research has also develop bicyclist/population typologies based on individual characteristics, such as the Geller Typology, which characteristics individuals based on their confidence riding in a variety of road/street/path configurations.…”

Section: Introductionmentioning

confidence: 99%

Developing urban biking typologies: quantifying the complex interactions of bicycle ridership, bicycle network and built environment characteristics

Beck¹,

Winters²,

Nelson³

et al. 2021

Preprint

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Background: Extensive research has been conducted exploring associations of built environment characteristics and biking. However, these approaches have often lacked the ability to understanding the interactions of built environment, population and bicycle ridership. To overcome these limitations, this study aimed to develop novel urban biking typologies using unsupervised machine learning methods.Methods: We conducted a retrospective analysis of travel surveys, bicycle infrastructure and population and land use characteristics in the Greater Melbourne region, Australia. To develop the urban biking typology, we used a k-medoids clustering method.Results: Analyses revealed 5 clusters. We highlight areas with high bicycle network density and a high proportion of trips made by bike (Cluster 1; reflecting 12% of the population of Greater Melbourne, but 57% of all bike trips) and areas with high off-road and on-road bicycle network length, but a low proportion of trips made by bike (Cluster 4, reflecting 23% of the population of Greater Melbourne and 13% of all bike trips).Conclusion: Our novel approach to developing an urban biking typology enabled the exploration of the interaction of bicycle ridership, bicycle network, population and land use characteristics. Such approaches are important in advancing our understanding of bicycling behaviour, but further research is required to understand the generalisability of these findings to other settings.

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Spatiotemporal Clustering Analysis of Bicycle Sharing System with Data Mining Approach

Cited by 19 publications

References 38 publications

A Framework for Spatial-Temporal Trajectory Cluster Analysis Based on Dynamic Relationships

A Framework for Spatial-Temporal Trajectory Cluster Analysis Based on Dynamic Relationships

Bikemi Bike-Sharing Service Exploratory Analysis on Mobility Patterns

Developing urban biking typologies: quantifying the complex interactions of bicycle ridership, bicycle network and built environment characteristics

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