Spatiotemporal analysis of urban mobility dynamics: a case study of bicycle sharing system

Purnama, Ida Bagus Irawan

doi:10.14264/uql.2018.150

Cited by 1 publication

(2 citation statements)

References 93 publications

(301 reference statements)

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“…On the other hand, they arise from uncertainty and randomness for bike operators [3]. In this case, BSS are on-demand means that need intense monitoring to keep enough resources (bikes and docking slots) in proper locations and times [4]. Conversely, other transportation modes such as buses or trains are mass-based system with fixed schedules and routes so that dynamics resources allocation are not critical [5].…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Mining of BSS Data for Characterising Seasonal Urban Mobility Dynamics

Purnama¹

2018

International Journal on Advanced Science, Engineering and Information Technology

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Digital traces of individual mobility can be revealed from the origin-destination sensing systems of BSS (Bicycle Sharing System). This record enables wide analysis of human mobility traits in urban area including pattern, trend, and anomalies. This study investigates and compares trip history of BSS open data from two cities, London and New York, along a year period with respect to annual weather data as explanatory factors. This aims to get insights about seasonal urban mobility dynamics both temporally andspatially. Results show that, for both cities, there are differences as well as similarities of temporal correlation level between riding behavior of BSS users and hour of the day, day of the week, season, and local weather. Practically, the most correlated factor can be further considered and used as predictive features. Meanwhile, the proposed spatial analysis shows the positive bikes imbalance occurs in the morning, mostly at inner stations because of inward flow, and vice versa. This spatial extent can be used for redistribution purpose, specifically in order to provide enough resources at the highly visited stations before peak time occurs.

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Section: Introductionmentioning

confidence: 99%

“…This makes the likely BSS use could be projected from the mobility record, which is embedded in their trip history. Accordingly, by examining some critical features such as weather, season and peak times, the mobility pattern of BSS users should be able to be understood [4].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Mining of BSS Data for Characterising Seasonal Urban Mobility Dynamics

Purnama¹

2018

International Journal on Advanced Science, Engineering and Information Technology

View full text Add to dashboard Cite

show abstract

Spatiotemporal analysis of urban mobility dynamics: a case study of bicycle sharing system

Cited by 1 publication

References 93 publications

Spatiotemporal Mining of BSS Data for Characterising Seasonal Urban Mobility Dynamics

Spatiotemporal Mining of BSS Data for Characterising Seasonal Urban Mobility Dynamics

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