Using Digital Footprints for a City-Scale Traffic Simulation

McArdle, Gavin; Furey, Eoghan; Lawlor, Aonghus; Pozdnoukhov, Alexei

doi:10.1145/2517028

Cited by 18 publications

(10 citation statements)

References 25 publications

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“…Previous work using such data sources has addressed a diversity of urban-related issues. Among them, attention has been given to the identification of points and places of interest for users (Huang et al 2015;Van Canneyt et al 2012), smell and sound perception (Aiello et al 2016;Martínez et al 2014;Quercia et al 2015), infrastructure planning (Morandi et al 2014), and the analysis of tourism consumption and mobility (Cerrone 2015;Mcardle et al 2014).…”

Section: The Potential Of Geolocated Social Media Data For Urban Analmentioning

confidence: 99%

Social Activity in Gothenburg’s Intermediate City: Mapping Third Places through Social Media Data

Adelfio

Serrano-Estrada

Ciriquián

et al. 2020

Appl. Spatial Analysis

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This research focuses on the intermediate city, composed of urban areas located right outside the city center typically maintaining an in-between urban/suburban character. It aims to explore the degree to which this segment of the city exhibits urban activity and social life through the identification of activity areas in the so-called Third Places. Four intermediate city neighborhoods in Gothenburg, Sweden are adopted as case areas and are analyzed using a twofold approach. First, socioeconomic statistics provide a quantitative understanding of the case areas and, second, geolocated Social Media Data (SMD) from Foursquare, Google Places and Twitter makes it possible to identify the intermediate city's urban activity areas and socially preferred urban spaces. The findings suggest that a) the four analyzed intermediate city areas of Gothenburg all have a degree of social activity, especially where economic activities are clustered together; b) Third Places in more affluent areas tend to be linked to commodified consumption of urban space while neighborhoods with lower income levels and higher ethnic diversity seem to emphasize open public space as Third Places; and c) nowadays the typology of Third Places has evolved from the types identified in previous decades to include additional types of places, such as those you pass on the way to something else (e.g. gas and bus stations). The study has verified the value of SMD for studies of urban social life but also identified a number of topics for further research. Additional sources of SMD should be identified to secure a just representation of Third Places across diverse social groups. Furthermore, new methods for effective cross validation of SMD with other types of data are crucial, including e.g. statistics, on-site observations and surveys/interviews, not least to identify Third Places that are not frequently present (or are misrepresented) in SMD.

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Section: The Potential Of Geolocated Social Media Data For Urban Analmentioning

confidence: 99%

Social Activity in Gothenburg’s Intermediate City: Mapping Third Places through Social Media Data

Adelfio

Serrano-Estrada

Ciriquián

et al. 2020

Appl. Spatial Analysis

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“…In the future, the research will be expanded by considering a multimodal transport network and scaling the scenario from an urban simulation to a national one. Full details of the Dublin scenario can be found in McArdle et al (2012) and McArdle et al (2014). …”

Section: Associated Projects and Where To Find Morementioning

confidence: 99%

“…This approach is justi ed both by the need to reduce computational expenses in the re-planning stage, as well as evidence of spatial hierarchies in human spatial cognition and decision making. A spatial choice model for the secondary home-and work-based trips is calibrated from the CDRs, using the McArdle et al (2014) approach. A key parameter set in this model is the attractiveness of agent venues, which is assumed to be proportional to the number of peers who also visit the venue.…”

Section: Social In Uence In Destination Choicementioning

confidence: 99%

The Multi-Agent Transport Simulation MATSim

Axhausen¹

2016

499

123

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PrefaceDeveloping complex so ware for over a decade with a heterogeneous group of engineers and scientists, each with widely di erent skill levels and expertise across multiple locations around the world, requires dedication and mechanisms unusual for a university environment. This book is one of these mechanisms. It allows us, collectively, to take stock and present a coherent state-of-the-system: for us and anyone interested in this approach. It highlights basics for the student who wants to undertake a small rst research project as part of his or her degree, provides a description of the main functionalities, in detail, for the engineer setting up MATSim (MultiAgent Transport Simulation) to conduct a policy analysis and, nally, ts the approach into the theoretical background of complex systems in computer science and physics.The choice of the additional e-book format is an advantage, as it allows us to keep the book upto-date with future chapters, revisions and, if necessary, errata. Equally importantly it allows you, the readers, to select those sections relevant to your needs.The book comes at an important time for the system; for most of the rst decade, its use was limited to the original developers and users in Berlin and Zürich. It is now much more widely consulted around the world, as we document in the chapter summarizing contributions on scenarios so far.Scenario: This term will occur again and again. In MATSim context, it is de ned as the combination of speci c agent populations, their initial plans and activity locations (home, work, education), the network and facilities where, and on which, they compete in time-space for their slots and modules, i.e., behavioral dimensions, which they can adjust during their search for equilibrium. Within these scenarios, the user can experiment and explore with behavioral utility function parameters, with the sampling rate of the population between 1 % and 100 %, with algorithm parameters, e.g., the share of the sample engaged in replanning in any iteration, or behavioral dimensions or exact settings necessary to avoid gridlock due to the tra c ow dynamics. The creation of a scenario is a substantial e ort, and the framework makes a number of tools available to accelerate it: population synthesizers, network editors, network converters between popular formats and the MATSim representation, e.g., OSM (OpenStreetMap) or GTFS (General Transit Feed Speci cation), semi-automatic network matching to join information, among others.A large group of colleagues has been involved and many of them are contributors to this book; this is a list of those involved, other than ourselves, in Berlin, Singapore and Zürich. Additional contributors are mentioned as authors of their respective chapters in this book. We hope to acknowledge the contributions of more colleagues from other groups in future versions of this book and in the so ware.Special thanks go to a number of people who greatly helped improving this book beyond their own chapters. Benjamin Kickhöfer's deep knowledge of ...

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“…For the Dublin scenario, 300 iterations were required which took several days to complete. Full details of the Dublin Scenario, including details of how non-commuting trips were included in the simulation can be found in McArdle et al (2014b).…”

Section: Building a Traffic Simulation For Dublinmentioning

confidence: 99%