Abstract
Urban mobility data are important to areas ranging from traffic engineering to the analysis of outbreaks and disasters. In this paper, we study mobility data from a major Brazilian city from a geographical viewpoint using a Complex Network approach. The case study is based on intra-urban mobility data from the Metropolitan area of Rio de Janeiro (Brazil), presenting more than 480 spatial network nodes. While for the mobility flow data a log-normal distribution outperformed the power law, we also found moderate evidence for scale-free and small word effects in the flow network’s degree distribution. We employ a novel open-source GIS tool to display (geo)graph’s topological properties in maps and observe a strong traffic-topology association and also a fine adjustment for hubs location for different flow threshold networks. In the central commercial area for lower thresholds and in high population residential areas for higher thresholds. This set of results, including statistical, topological and geographical analysis may represent an important tool for policymakers and stakeholders in the urban planning area, especially by the identification of zones with few but strong links in a real data-driven mobility network.
Highlights
Urban mobility data are important to several areas, from traffic engineering to the analysis of outbreaks and disasters
It is important to highlight that 12% of the travels have both origin and destination in the same node (TZ), i.e., are internal travels
In this paper we address some aspects of the urban mobility phenomenon under a geographical point of view using a Complex Network approach
Summary
Urban mobility data are important to several areas, from traffic engineering to the analysis of outbreaks and disasters. Applicability, and limitations on urban mobility (Gonzalez et al 2008; Song et al 2010; Simini et al 2012; Guo et al 2012; Wang et al 2012; Louail et al 2015). Another common thread among these studies is the importance of spatial structure. The spatial structure of a actual data-based mobility complex network is explored. The complex network approach emerges as a natural mechanism to handle mobility data, taking areas as nodes and movements between origins and destinations as edges. A general approach for handling geographical data is needed
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