Vulnerability Analysis of the Urban Transport System in the Context of Smart Cities

Large cities seek to incorporate intelligent systems into their infrastructure, industrial, educational, and social activities to improve the quality of service provided to citizens and make all processes more efficient. To this end, these cities use technologies that involve the areas of the Internet of Things, Big Data, and Governance to make decisions by governments and authorities. In the field of Urban Computing, it is possible to use open data in the GTFS format to solve the problems of the urban transport system using complex network metrics that make it possible to model complex interactions between objects in high dimensionality. Stops and routes are modeled as a graph, characterized by complex network metrics. According to the literature, if there is an interruption of the urban transport system, we make an evaluation of the degree of vulnerability of the system in the search for solutions. The main goal of this paper is to propose an approach to analyze the vulnerability of the urban transport system using complex network metrics along with GTFS data in scenarios targeted failures. As a contribution, the proposed approach uses the concept of skewness in the methodology established in the literature. The skewness enables a better understanding and differential the failure conditions from the evaluation of the vulnerability the urban transport system, through a quantitative indication of which local metrics most influence the decay of network metrics. The analysis of the results obtained in the proposed approach serves as a subsidy for studies in the search for solutions to the problems of the urban transport system, providing a tool to the set of technological options for the planning of urban transport systems of smart cities available to governments and authorities. The proposed approach can be applied in different cities, regardless of their size and location, as long as you have access to open geographic data in the GTFS format.

[1]  Aric Hagberg,et al.  Exploring Network Structure, Dynamics, and Function using NetworkX , 2008, Proceedings of the Python in Science Conference.

[2]  Juan Carlos García-Palomares,et al.  Measuring the vulnerability of public transport networks , 2014 .

[3]  Yasir Mehmood,et al.  Internet-of-Things-Based Smart Cities: Recent Advances and Challenges , 2017, IEEE Communications Magazine.

[4]  Liya Yao,et al.  Vulnerability assessment of urban rail transit based on multi-static weighted method in Beijing, China , 2018 .

[5]  Ricardo Luders,et al.  Combining K-means method and complex network analysis to evaluate city mobility , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[6]  Catherine T. Lawson,et al.  General Transit Feed Specification data visualization , 2014, 2014 22nd International Conference on Geoinformatics.

[7]  Yurij Holovatch,et al.  A tale of two cities. Vulnerabilities of the London and Paris transit networks , 2012, ArXiv.

[8]  Francesco Archetti,et al.  Vulnerability of public transportation networks against directed attacks and cascading failures , 2019, Public Transport.

[9]  Juan M. Corchado,et al.  Tendencies of Technologies and Platforms in Smart Cities: A State-of-the-Art Review , 2018, Wirel. Commun. Mob. Comput..

[10]  Sybil Derrible,et al.  The complexity and robustness of metro networks , 2010 .

[11]  Thiago H. Silva,et al.  Computação Urbana: Técnicas para o Estudo de Sociedades com Redes de Sensoriamento Participativo , 2016 .

[12]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[13]  D. Joanes,et al.  Comparing measures of sample skewness and kurtosis , 1998 .

[14]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[15]  J. C. G. Palomares,et al.  Measuring the vulnerability of public transport networks , 2014 .

[16]  B. Berche,et al.  Resilience of public transport networks against attacks , 2009, 0905.1638.

[17]  Tamás Németh,et al.  Complex network analysis of public transportation networks: A comprehensive study , 2015, 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).

[18]  E. Todeva.  Networks , 2007 .

[19]  Licia Capra,et al.  Urban Computing: Concepts, Methodologies, and Applications , 2014, TIST.

[20]  A. Vespignani,et al.  The architecture of complex weighted networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Santi Phithakkitnukoon,et al.  GTFS-Viz: tool for preprocessing and visualizing GTFS data , 2017, UbiComp/ISWC Adjunct.

[22]  Nour-Eddin El Faouzi,et al.  A Graph-Based Framework for Real-Time Vulnerability Assessment of Road Networks , 2018, 2018 IEEE International Conference on Smart Computing (SMARTCOMP).

[23]  Jianjun Wu,et al.  Application of Complex Networks Theory in Urban Traffic Network Researches , 2019, Networks and Spatial Economics.

[24]  V. Latora,et al.  A measure of centrality based on network efficiency , 2004, cond-mat/0402050.