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ACHILLE: a visual data-driven and network-based tool for transportation planning and simulation

November 2017

DOI:10.13140/RG.2.2.14060.46726

Conference: International Conference on Information and Knowledge Management

Authors:

Luca Pappalardo

Italian National Research Council

Michele Ferretti

King's College London

Gianni Barlacchi

Università degli Studi di Trento

Fondazione Bruno Kessler

The availability of massive data describing human mobility offers the possibility to design simulation tools to control and improve transportation systems. In this perspective, we propose a visual and data-driven simulation tool based on a multiplex network representation of mobility data, where every layer describes people’s movements with a given transportation mode. We then develop a visual application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city. Our application allows the user to visualize changes in the transportation system resulting from the addition or removal of transportation modes, urban zones and single stops. We show how our visual application can be used to explore mobility in Singapore, by using data provided by the CIKM challenge 2017 and mobility data obtained from external sources. The application allows to simulate the reaction to changes in the public transportation system and to assess the resilience of the transportation network to the removal of single subway/bus stops.

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Conference Paper

Full-text available

A visual data-driven and network-based tool for transportation planning and simulation

November 2017

The availability of massive data describing human mobility offers the possibility to design simulation tools to control and improve transportation systems. In this perspective, we propose a visual and data-driven simulation tool based on a multiplex network representation of mobility data, where every layer describes people's movements with a given transportation mode. We then develop a visual ... [Show full abstract] application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city. Our application allows the user to visualize changes in the transportation system resulting from the addition or removal of transportation modes, urban zones and single stops. We show how our visual application can be used to explore mobility in Singapore, by using data provided by the CIKM challenge 2017 and mobility data obtained from external sources. The application allows to simulate the reaction to changes in the public transportation system and to assess the resilience of the transportation network to the removal of single subway/bus stops.

Preprint

Full-text available

Weak nodes detection in urban transport systems: Planning for resilience in Singapore

September 2018

The availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g. floods terroristic attacks, etc...). In this perspective, we propose ACHILLES, an application to model people's movements in a given transport system ... [Show full abstract] mode through a multiplex network representation based on mobility data. ACHILLES is a web-based application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city, as well as to visualize changes in the transport system resulting from the addition or removal of transport modes, urban zones, and single stops. Notably, our application allows the user to assess the overall resilience of the transport network by identifying its weakest node, i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To demonstrate the impact of ACHILLES for humanitarian aid we consider its application to a real-world scenario by exploring human mobility in Singapore in response to flood prevention.

Conference Paper

Weak Nodes Detection in Urban Transport Systems: Planning for Resilience in Singapore

October 2018

Preprint

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A Survey on Deep Learning for Human Mobility

December 2020

The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility. ... [Show full abstract] Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides a taxonomy of mobility tasks, a discussion on the challenges related to each task and how deep learning may overcome the limitations of traditional models, a description of the most relevant solutions to the mobility tasks described above and the relevant challenges for the future. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.