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Documents authored by Tanin, Egemen

Document
DOI: 10.4230/DagRep.12.1.1
Mobility Data Science (Dagstuhl Seminar 22021)

Authors: Mohamed Mokbel, Mahmoud Sakr, Li Xiong, Andreas Züfle, Jussara Almeida, Taylor Anderson, Walid Aref, Gennady Andrienko, Natalia Andrienko, Yang Cao, Sanjay Chawla, Reynold Cheng, Panos Chrysanthis, Xiqi Fei, Gabriel Ghinita, Anita Graser, Dimitrios Gunopulos, Christian Jensen, Joon-Sook Kim, Kyoung-Sook Kim, Peer Kröger, John Krumm, Johannes Lauer, Amr Magdy, Mario Nascimento, Siva Ravada, Matthias Renz, Dimitris Sacharidis, Cyrus Shahabi, Flora Salim, Mohamed Sarwat, Maxime Schoemans, Bettina Speckmann, Egemen Tanin, Yannis Theodoridis, Kristian Torp, Goce Trajcevski, Marc van Kreveld, Carola Wenk, Martin Werner, Raymond Wong, Song Wu, Jianqiu Xu, Moustafa Youssef, Demetris Zeinalipour, Mengxuan Zhang, and Esteban Zimányi

Published in: Dagstuhl Reports, Volume 12, Issue 1 (2022)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 22021 "Mobility Data Science". This seminar was held January 9-14, 2022, including 47 participants from industry and academia. The goal of this Dagstuhl Seminar was to create a new research community of mobility data science in which the whole is greater than the sum of its parts by bringing together established leaders as well as promising young researchers from all fields related to mobility data science. Specifically, this report summarizes the main results of the seminar by (1) defining Mobility Data Science as a research domain, (2) by sketching its agenda in the coming years, and by (3) building a mobility data science community. (1) Mobility data science is defined as spatiotemporal data that additionally captures the behavior of moving entities (human, vehicle, animal, etc.). To understand, explain, and predict behavior, we note that a strong collaboration with research in behavioral and social sciences is needed. (2) Future research directions for mobility data science described in this report include a) mobility data acquisition and privacy, b) mobility data management and analysis, and c) applications of mobility data science. (3) We identify opportunities towards building a mobility data science community, towards collaborations between academic and industry, and towards a mobility data science curriculum.
Cite as

Mohamed Mokbel, Mahmoud Sakr, Li Xiong, Andreas Züfle, Jussara Almeida, Taylor Anderson, Walid Aref, Gennady Andrienko, Natalia Andrienko, Yang Cao, Sanjay Chawla, Reynold Cheng, Panos Chrysanthis, Xiqi Fei, Gabriel Ghinita, Anita Graser, Dimitrios Gunopulos, Christian Jensen, Joon-Sook Kim, Kyoung-Sook Kim, Peer Kröger, John Krumm, Johannes Lauer, Amr Magdy, Mario Nascimento, Siva Ravada, Matthias Renz, Dimitris Sacharidis, Cyrus Shahabi, Flora Salim, Mohamed Sarwat, Maxime Schoemans, Bettina Speckmann, Egemen Tanin, Yannis Theodoridis, Kristian Torp, Goce Trajcevski, Marc van Kreveld, Carola Wenk, Martin Werner, Raymond Wong, Song Wu, Jianqiu Xu, Moustafa Youssef, Demetris Zeinalipour, Mengxuan Zhang, and Esteban Zimányi. Mobility Data Science (Dagstuhl Seminar 22021). In Dagstuhl Reports, Volume 12, Issue 1, pp. 1-34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{mokbel_et_al:DagRep.12.1.1,
  author =	{Mokbel, Mohamed and Sakr, Mahmoud and Xiong, Li and Z\"{u}fle, Andreas and Almeida, Jussara and Anderson, Taylor and Aref, Walid and Andrienko, Gennady and Andrienko, Natalia and Cao, Yang and Chawla, Sanjay and Cheng, Reynold and Chrysanthis, Panos and Fei, Xiqi and Ghinita, Gabriel and Graser, Anita and Gunopulos, Dimitrios and Jensen, Christian and Kim, Joon-Sook and Kim, Kyoung-Sook and Kr\"{o}ger, Peer and Krumm, John and Lauer, Johannes and Magdy, Amr and Nascimento, Mario and Ravada, Siva and Renz, Matthias and Sacharidis, Dimitris and Shahabi, Cyrus and Salim, Flora and Sarwat, Mohamed and Schoemans, Maxime and Speckmann, Bettina and Tanin, Egemen and Theodoridis, Yannis and Torp, Kristian and Trajcevski, Goce and van Kreveld, Marc and Wenk, Carola and Werner, Martin and Wong, Raymond and Wu, Song and Xu, Jianqiu and Youssef, Moustafa and Zeinalipour, Demetris and Zhang, Mengxuan and Zim\'{a}nyi, Esteban},
  title =	{{Mobility Data Science (Dagstuhl Seminar 22021)}},
  pages =	{1--34},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{12},
  number =	{1},
  editor =	{Mokbel, Mohamed and Sakr, Mahmoud and Xiong, Li and Z\"{u}fle, Andreas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.1.1},
  URN =		{urn:nbn:de:0030-drops-169190},
  doi =		{10.4230/DagRep.12.1.1},
  annote =	{Keywords: Spatio-temporal, Tracking, Privacy, Behavior, Data cleaning, Data management, Analytics}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2021.I.7
Introducing Diversion Graph for Real-Time Spatial Data Analysis with Location Based Social Networks

Authors: Sameera Kannangara, Hairuo Xie, Egemen Tanin, Aaron Harwood, and Shanika Karunasekera

Published in: LIPIcs, Volume 177, 11th International Conference on Geographic Information Science (GIScience 2021) - Part I (2020)

Abstract
Neighbourhood graphs are useful for inferring the travel network between locations posted in the Location Based Social Networks (LBSNs). Existing neighbourhood graphs, such as the Stepping Stone Graph lack the ability to process a high volume of LBSN data in real time. We propose a neighbourhood graph named Diversion Graph, which uses an efficient edge filtering method from the Delaunay triangulation mechanism for fast processing of LBSN data. This mechanism enables Diversion Graph to achieve a similar accuracy level as Stepping Stone Graph for inferring travel networks, but with a reduction of the execution time of over 90%. Using LBSN data collected from Twitter and Flickr, we show that Diversion Graph is suitable for travel network processing in real time.
Cite as

Sameera Kannangara, Hairuo Xie, Egemen Tanin, Aaron Harwood, and Shanika Karunasekera. Introducing Diversion Graph for Real-Time Spatial Data Analysis with Location Based Social Networks. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 7:1-7:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{kannangara_et_al:LIPIcs.GIScience.2021.I.7,
  author =	{Kannangara, Sameera and Xie, Hairuo and Tanin, Egemen and Harwood, Aaron and Karunasekera, Shanika},
  title =	{{Introducing Diversion Graph for Real-Time Spatial Data Analysis with Location Based Social Networks}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
  pages =	{7:1--7:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-166-5},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{177},
  editor =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.7},
  URN =		{urn:nbn:de:0030-drops-130428},
  doi =		{10.4230/LIPIcs.GIScience.2021.I.7},
  annote =	{Keywords: moving objects, shortest path, graphs}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2021.I.9
Traffic Congestion Aware Route Assignment

Authors: Sadegh Motallebi, Hairuo Xie, Egemen Tanin, and Kotagiri Ramamohanarao

Published in: LIPIcs, Volume 177, 11th International Conference on Geographic Information Science (GIScience 2021) - Part I (2020)

Abstract
Traffic congestion emerges when traffic load exceeds the available capacity of roads. It is challenging to prevent traffic congestion in current transportation systems where vehicles tend to follow the shortest/fastest path to their destinations without considering the potential congestions caused by the concentration of vehicles. With connected autonomous vehicles, the new generation of traffic management systems can optimize traffic by coordinating the routes of all vehicles. As the connected autonomous vehicles can adhere to the routes assigned to them, the traffic management system can predict the change of traffic flow with a high level of accuracy. Based on the accurate traffic prediction and traffic congestion models, routes can be allocated in such a way that helps mitigating traffic congestions effectively. In this regard, we propose a new route assignment algorithm for the era of connected autonomous vehicles. Results show that our algorithm outperforms several baseline methods for traffic congestion mitigation.
Cite as

Sadegh Motallebi, Hairuo Xie, Egemen Tanin, and Kotagiri Ramamohanarao. Traffic Congestion Aware Route Assignment. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 9:1-9:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{motallebi_et_al:LIPIcs.GIScience.2021.I.9,
  author =	{Motallebi, Sadegh and Xie, Hairuo and Tanin, Egemen and Ramamohanarao, Kotagiri},
  title =	{{Traffic Congestion Aware Route Assignment}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
  pages =	{9:1--9:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-166-5},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{177},
  editor =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.9},
  URN =		{urn:nbn:de:0030-drops-130443},
  doi =		{10.4230/LIPIcs.GIScience.2021.I.9},
  annote =	{Keywords: Road Network, Traffic Congestion, Route Assignment, Shortest Path}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.COSIT.2019.2
Smartphone Usability for Emergency Evacuation Applications (Short Paper)

Authors: David Amores, Maria Vasardani, and Egemen Tanin

Published in: LIPIcs, Volume 142, 14th International Conference on Spatial Information Theory (COSIT 2019)

Abstract
Mobile phone ubiquity has allowed the implementation of a number of emergency-related evacuation aids. Yet, these applications still face a number of challenges in human-mobile interaction, namely: (1) lack of widely accepted mobile usability guidelines, (2) people’s limited cognitive capacity when using mobile phones under stress, and (3) difficulty recreating emergency scenarios as experiments for usability testing. This study is intended as an initial view into smartphone usability under emergency evacuations by compiling a list of experimental observations and setting the ground for future research in cognitively-informed spatial algorithms and app design.
Cite as

David Amores, Maria Vasardani, and Egemen Tanin. Smartphone Usability for Emergency Evacuation Applications (Short Paper). In 14th International Conference on Spatial Information Theory (COSIT 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 142, pp. 2:1-2:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{amores_et_al:LIPIcs.COSIT.2019.2,
  author =	{Amores, David and Vasardani, Maria and Tanin, Egemen},
  title =	{{Smartphone Usability for Emergency Evacuation Applications}},
  booktitle =	{14th International Conference on Spatial Information Theory (COSIT 2019)},
  pages =	{2:1--2:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-115-3},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{142},
  editor =	{Timpf, Sabine and Schlieder, Christoph and Kattenbeck, Markus and Ludwig, Bernd and Stewart, Kathleen},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2019.2},
  URN =		{urn:nbn:de:0030-drops-110947},
  doi =		{10.4230/LIPIcs.COSIT.2019.2},
  annote =	{Keywords: cognitive load, smartphone usability, ecological validity, emergency evacuation}
}
Document
DOI: 10.4230/LIPIcs.GISCIENCE.2018.1
Early Detection of Herding Behaviour during Emergency Evacuations

Authors: David Amores, Maria Vasardani, and Egemen Tanin

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
Social scientists have observed a number of irrational behaviours during emergency evacuations, caused by a range of possible cognitive biases. One such behaviour is herding - people following and trusting others to guide them, when they do not know where the nearest exit is. This behaviour may lead to safety under a knowledgeable leader, but can also lead to dead-ends. We present a method for the automatic early detection of herding behaviour to avoid suboptimal evacuations. The method comprises three steps: (i) people clusters identification during evacuation, (ii) collection of clusters' spatio-temporal information to extract features for describing cluster behaviour, and (iii) unsupervised learning classification of clusters' behaviour into 'benign' or 'harmful' herding. Results using a set of different detection scores show accuracies higher than baselines in identifying harmful behaviour; thus, laying the ground for timely irrational behaviour detection to increase the performance of emergency evacuation systems.
Cite as

David Amores, Maria Vasardani, and Egemen Tanin. Early Detection of Herding Behaviour during Emergency Evacuations. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 1:1-1:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{amores_et_al:LIPIcs.GISCIENCE.2018.1,
  author =	{Amores, David and Vasardani, Maria and Tanin, Egemen},
  title =	{{Early Detection of Herding Behaviour during Emergency Evacuations}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{1:1--1:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.1},
  URN =		{urn:nbn:de:0030-drops-93293},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.1},
  annote =	{Keywords: spatio-temporal data, emergency evacuations, herding behaviour}
}
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