Urban Zoning Using Intraday Mobile Phone-Based Commuter Patterns in the City of Brno

Bogomolov, Yuri; Belyi, Alexander; Mikeš, Ondřej; Sobolevsky, Stanislav

doi:10.1007/978-3-031-36808-0_35

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Given the same structure of the clusters, we performed a quantitative comparison of clustering results with the previous approach (Bogomolov et al, 2023 ). We received a Surprisingly, 93.75% of districts belong to the same cluster, while previously considered commute timeline signature vector components correspond to only 25% of the proposed signature vector.…”

Section: Resultsmentioning

confidence: 99%

“…Building on our prior work (Bogomolov et al, 2023 ), where we relied on hourly origin-destination flow data from a mobile phone company covering one week in October 2019 and capturing movements of residents of 48 districts in Brno. It's essential to note that the initial dataset lacked information on suburban commuters.…”

Section: Methodsmentioning

confidence: 99%

“…In our previous work (Bogomolov et al, 2023 ), we proposed an approach harnessing intraday mobility patterns data from mobile phones to establish distinct signatures for urban locations and subsequently apply them to urban zoning. Delineated city zones were spatially cohesive and had distinct commute patterns.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Urban delineation through a prism of intraday commute patterns

Bogomolov,

Belyi,

Sobolevsky

2024

Front. Big Data

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IntroductionUrban mobility patterns are crucial for effective urban and transportation planning. This study investigates the dynamics of urban mobility in Brno, Czech Republic, utilizing the rich dataset provided by passive mobile phone data. Understanding these patterns is essential for optimizing infrastructure and planning strategies.MethodsWe developed a methodological framework that incorporates bidirectional commute flows and integrates both urban and suburban commute networks. This comprehensive approach allows for a detailed representation of Brno's mobility landscape. By employing clustering techniques, we aimed to identify distinct mobility patterns within the city.ResultsOur analysis revealed consistent structural features within Brno's mobility patterns. We identified three distinct clusters: a central business district, residential communities, and an intermediate hybrid cluster. These clusters highlight the diversity of mobility demands across different parts of the city.DiscussionThe study demonstrates the significant potential of passive mobile phone data in enhancing our understanding of urban mobility patterns. The insights gained from intraday mobility data are invaluable for transportation planning decisions, allowing for the optimization of infrastructure utilization. The identification of distinct mobility patterns underscores the practical utility of our methodological advancements in informing more effective and efficient transportation planning strategies.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%