SYSTEM UPDATE: We will be performing system maintenace Saturday September 26 at 9pm to midnight Pacific Time

Searching the RRID Resource Information Network

Our searching services are busy right now. Please try again later

  • Register
X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

X

Leaving Community

Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.

No
Yes
Protocol Name
DOI:DOI:10.17504/protocols.io.bjr8km9w RRID Copied  
PDF Report How to cite
Jessica Domingues Lamosa, Lívia R Tomás, Marcos G. Quiles, Luciana R. Londe, Leonardo B L Santos 2020. Topological indexes and community structure for urban mobility networks: variations in a typical day. protocols.io https://dx.doi.org/10.17504/protocols.io.bjr8km9w
Copy Citation Copied
Protocol Information

URL: https://dx.doi.org/DOI:10.17504/protocols.io.bjr8km9w

Authors: Jessica Domingues Lamosa, Lívia R Tomás, Marcos G. Quiles, Luciana R. Londe, Leonardo B L Santos

Summary: This work fo-cused in the problem of Urban mobility, which in an unplanned urban growth sce-nario might generate negative impacts, like traffic jams, air pollution and infras-tructure flaws. Based on real data for the city of São José dos Campos, the mobil-ity of a typical day was represented. These data consist of an Origin-Destinationsurvey: the city was divided into 55 traffic zones and more than 20 thousand peo-ple were asked about the time of departure and arrival of each trip. The devel-opment was divided in 3 steps, pre-processing, processing and post-processing.In preprocessing, an origin destination graph was generated with a 3-dimensionmatrix representation, in language C++, in which each vertex represents a traf-fic zone and the edges are weighted by the flux of people, with 24 time variations,one for each hour of the day. In the processing, in C, the igraph library was usedto calculate the topological properties such as degree (number of connections),clustering coefficient (neighbors redundancy) and diameter (longest distance) ofa network of mobility over a typical day and we also applied the textit walktrapalgorithm for community detection. In the post-processing, using the concept of(geo) graphs, graphs represented with geolocation, the GeoCNet was developed. Itis a tool that allows the creation of a textit shapefile with the topological prop-erties of the graph.

Affiliations: Universidade Federal de São Paulo (UNIFESP), Centro Nacional de Monitoramento e Alertas de Desastres Naturais (Cemaden), Universidade Federal de São Paulo (UNIFESP) at São José dos Campos, Centro Nacional de Monitoramento e Alertas de Desastres Naturais (Cemaden), Centro Nacional de Monitoramento e Alertas de Desastres Naturais (Cemaden), Humboldt University of Berlin

Version: 1

Publication Date: 2020

Expand All
Usage and Citation Metrics

Coming soon.

Checkfor resource mentions.

Collaborator Network

Coming soon.

Data and Source Information

Source: