Marija Nikolic

Journal publications [6]

2019

Nikolic M., Bierlaire M., de Lapparent M., Scarinci R. (2019). Multi-class speed-density relationship for pedestrian traffic. Transportation Science 53 (3):642-664


@Article{NikBieLapSca_2018_TS,
  author = {Marija Nikolic and Michel Bierlaire and Matthieu {de Lapparent} and Riccardo Scarinci},
  title = {Multi-class speed-density relationship for pedestrian traffic},
  journal = {Transportation Science},
  year = {2019},
  volume = {53},
  number = {3},
  pages = {642-664},
  DOI = {10.1287/trsc.2018.0849},
  note = {Accepted on Apr 30, 2018}}}

2018

Nikolic M., Bierlaire M. (2018). Data-driven spatio-temporal discretization for pedestrian flow characterization. Transportation Research Part C: Emerging Technologies 94 :185-202


@Article{NikBie17_DataDrivenDiscretization,
  author = {Marija Nikolic and Michel Bierlaire},
  title = {Data-driven spatio-temporal discretization for pedestrian flow characterization},
  journal = {Transportation Research Part C: Emerging Technologies},
  year = {2018},
  volume = {94},
  pages = {185-202},
  DOI = {10.1016/j.trc.2017.08.026},
  note = {Accepted on Aug 30, 2017}}}

2017

Nikolic M., Bierlaire M. (2017). Data-driven spatio-temporal discretization for pedestrian flow characterization. Transportation Research Procedia 23 :188�207

We propose a novel approach to pedestrian flow characterization. The definitions of density, flow and velocity existing in the literature are extended through a data-driven spatio-temporal discretization framework. The framework is based on three-dimensional Voronoi diagrams. Synthetic data is used to empirically investigate the performance of the approach and to illustrate its advantages. Our approach outperforms the considered approaches from the literature in terms of the robustness with respect to the simulation noise and with respect to the sampling frequency. Additionally, the proposed approach is by design (i) independent from an arbitrarily chosen discretization; (ii) appropriate for the multidirectional composition of pedestrian traffic; (iii) able to reflect the heterogeneity of the pedestrian population; and (iv) applicable to pedestrian trajectories described either analytically or as a sample of points.
```
@Article{NikBier_ISTTT22,
  author = {Marija Nikolic and Michel Bierlaire},
  title = {Data-driven spatio-temporal discretization for pedestrian flow characterization},
  journal = {Transportation Research Procedia},
  year = {2017},
  volume = {23},
  pages = {188{\textendash}207},
  DOI = {10.1016/j.trpro.2017.05.012},
  note = {Accepted on Feb 16, 2017}}}
```

Hänseler F., Lam W., Bierlaire M., Lederrey G., Nikolic M. (2017). A dynamic network loading model for anisotropic and congested pedestrian flows. Transportation Research Part B: Methodological 95 :149-168


@Article{HaenLamBierLedeNiko16,
  author = {Flurin H\"anseler and William Lam and Michel Bierlaire and Gael Lederrey and Marija Nikolic},
  title = {A dynamic network loading model for anisotropic and congested pedestrian flows},
  journal = {Transportation Research Part B: Methodological},
  year = {2017},
  volume = {95},
  pages = {149-168},
  DOI = {10.1016/j.trb.2016.10.017},
  note = {Accepted on Oct 31, 2016}}}

2016

Nikolic M., Bierlaire M., Farooq B., de Lapparent M. (2016). Probabilistic speed-density relationship for pedestrian traffic. Transportation Research Part B: Methodological 89 :58-81


@Article{NikBieFarLap_ProbabilisticSpeedDensity,
  author = {Marija Nikolic and Michel Bierlaire and Bilal Farooq and Matthieu {de Lapparent}},
  title = {Probabilistic speed-density relationship for pedestrian traffic},
  journal = {Transportation Research Part B: Methodological},
  year = {2016},
  volume = {89},
  pages = {58-81},
  DOI = {10.1016/j.trb.2016.04.002},
  note = {Accepted on Apr 02, 2016}}}

2014

Nikolic M., Bierlaire M. (2014). Pedestrian-oriented Flow Characterization. Transportation Research Procedia 2 :359-366

Definitions of flow indicators are derived by adapting Edie's definitions through a stream-based approach and a data-driven spatio-temporal discretization framework. The stream-based approach accounts for the multidirectional nature of pedestrian flows. The data-driven discretization framework, based on three-dimensional Voronoi tessellations, accounts for the pedestrian heterogeneity in the case when only samples of points are available, instead of the analytical description of trajectories. The proposed approach enables the pedestrian flow characterization to be applicable to any space-time domain of interest.
```
@Article{NikoBier14,
  author = {Marija Nikolic and Michel Bierlaire},
  title = {Pedestrian-oriented Flow Characterization},
  journal = {Transportation Research Procedia},
  year = {2014},
  volume = {2},
  pages = {359-366},
  DOI = {10.1016/j.trpro.2014.09.032},
  note = {Accepted on Oct 02, 2014}}}
```

Book chapters [1]

2020

Bierlaire M., Scarinci R., Nikolic M., Oyama Y., Molyneaux N., Wang Z. (2020) Methodologies for understanding and improving pedestrian mobility. In Ole B. Jensen, Claus Lassen, Vincent Kaufmann, Malene Freudendal-Pedersen, Ida Sofie Gøtzsche Lange (ed) Handbook of Urban Mobilities, Routledge [ISBN 9781138482197]


@InProceedings{BierScarNikoOyamMolyWang20,
  author = {Michel Bierlaire and Riccardo Scarinci and Marija Nikolic and Yuki Oyama and Nicholas Molyneaux and Zengchao Wang},
  title = {Methodologies for understanding and improving pedestrian mobility},
  year = {2020},
  booktile = {Handbook of Urban Mobilities},
  editor = {Ole B. Jensen and  Claus Lassen and  Vincent Kaufmann and  Malene Freudendal-Pedersen and  Ida Sofie G{\o}tzsche Lange},
  series = {Routledge International Handbooks},
  publisher = {Routledge}}}

Technical reports [4]

2017

Nikolic M., Bierlaire M., de Lapparent M., Scarinci R. (2017). Multi-class speed-density relationship for pedestrian traffic. Report TRANSP-OR 170115


@TechReport{NikBieLapSca_MCVK_2017,
  author = {Marija Nikolic and Michel Bierlaire and Matthieu {de Lapparent} and Riccardo Scarinci},
  title = {Multi-class speed-density relationship for pedestrian traffic},
  institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne},
  year = {2017},
  type = {Technical Report},
  number = {TRANSP-OR 170115},
  address = {Lausanne, Switzerland}}}

2016

Nikolic M., Bierlaire M. (2016). Data-driven characterization of pedestrian flows. Report TRANSP-OR 160815


@TechReport{NikBie_3DVoronoi_2016,
  author = {Marija Nikolic and Michel Bierlaire},
  title = {Data-driven characterization of pedestrian flows},
  institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne},
  year = {2016},
  type = {Technical Report},
  number = {TRANSP-OR 160815},
  address = {Lausanne, Switzerland}}}

2015

Hänseler F., Lam W., Bierlaire M., Lederrey G., Nikolic M. (2015). A dynamic network loading model for anisotropic and congested pedestrian flows. Report TRANSP-OR 151026


@TechReport{HaLaBiLeNi_2015,
  author = {Flurin H\"anseler and William Lam and Michel Bierlaire and Gael Lederrey and Marija Nikolic},
  title = {A dynamic network loading model for anisotropic and congested pedestrian flows},
  institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne},
  year = {2015},
  type = {Technical Report},
  number = {TRANSP-OR 151026},
  address = {Lausanne, Switzerland}}}

Nikolic M., Bierlaire M., Farooq B. (2015). Probabilistic speed-density relationship for pedestrian traffic: a data-driven approach. Report TRANSP-OR 150411


@TechReport{NikBieFar_PedMixFD_2015,
  author = {Marija Nikolic and Michel Bierlaire and Bilal Farooq},
  title = {Probabilistic speed-density relationship for pedestrian traffic: a data-driven approach},
  institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne},
  year = {2015},
  type = {Technical Report},
  number = {TRANSP-OR 150411},
  address = {Lausanne, Switzerland}}}

Articles in conference proceedings [4]

2017

Nikolic M., Bierlaire M. (2017) Review of transportation mode detection approaches based on smartphone data. Proceedings of the 17th Swiss Transport Research Conference (STRC), May, 17-19, Ascona, Switzerland


@InProceedings{NikBie_STRC2017,
  author = {Marija Nikolic and Michel Bierlaire},
  title = {Review of transportation mode detection approaches based on smartphone data},
  booktime = {Proceedings of the 17th Swiss Transport Research Conference (STRC), May, 17-19},
  year = {2017},
  address = {Ascona, Switzerland}}}

2015

Nikolic M., Bierlaire M. (2015) Pedestrian flow characterization based on spatio-temporal Voronoi tessellations. Proceedings of the 15th Swiss Transportation Research Conference (STRC), 15 -17 April, Ascona, Switzerland


@InProceedings{NikBie_STRC2015,
  author = {Marija Nikolic and Michel Bierlaire},
  title = {Pedestrian flow characterization based on spatio-temporal Voronoi tessellations},
  booktime = {Proceedings of the 15th Swiss Transportation Research Conference (STRC), 15 -17 April},
  year = {2015},
  address = {Ascona, Switzerland}}}

2014

Nikolic M., Bierlaire M., Farooq B. (2014) Probabilistic speed-density relationship for pedestrians based on data driven space and time representation. Proceedings of the Swiss Transportation Research Conference (STRC), 14 - 16 May, Ascona, Switzerland


@InProceedings{NikBieFar_STRC2014,
  author = {Marija Nikolic and Michel Bierlaire and Bilal Farooq},
  title = {Probabilistic speed-density relationship for pedestrians based on data driven space and time representation},
  booktime = {Proceedings of the Swiss Transportation Research Conference (STRC), 14 - 16 May},
  year = {2014},
  address = {Ascona, Switzerland}}}

2013

Nikolic M., Farooq B., Bierlaire M. (2013) Exploratory analysis of pedestrian flow characteristics in mobility hubs using trajectory data. Proceedings of the Swiss Transportation Research Conference (STRC), 24-26 April, Ascona, Switzerland


@InProceedings{Nikolic_Farooq_Bierlaire_STRC_2013,
  author = {Marija Nikolic and Bilal Farooq and Michel Bierlaire},
  title = {Exploratory analysis of pedestrian flow characteristics in mobility hubs using trajectory data},
  booktime = {Proceedings of the Swiss Transportation Research Conference (STRC), 24-26 April},
  year = {2013},
  address = {Ascona, Switzerland}}}

Talks [21]

2018

Nikolic M., Bierlaire M. Modeling of human movement behavior June 14, 2018. 4th Advances in Destination Management - Visitor Data and Decision Making: Challenges and Opportunities, University of St. Gallen, St. Gallen, Switzerland.


@Misc{NikBie_PanelADM2018,
  author = {Marija Nikolic and Michel Bierlaire},
  title = {Modeling of human movement behavior},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/NikBie_PanelADM2018.pdf}},
  year = {2018},
  note = {Presented at 4th Advances in Destination Management - Visitor Data and Decision Making: Challenges and Opportunities, University of St. Gallen, St. Gallen, Switzerland}}}

2017

Nikolic M., Bierlaire M., Markov I., Bondo R. Pedestrian multi-class speed-density relationship: evaluation of integrated and sequential approach September 12, 2017. 6th Symposium of the European Association for Research in Transportation, Faculty of Civil and Environmental Engineering, Technion, Haifa, Israel.


@Misc{NBMK_hEART2017,
  author = {Marija Nikolic and Michel Bierlaire and Iliya Markov and Romain Bondo},
  title = {Pedestrian multi-class speed-density relationship: evaluation of integrated and sequential approach},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/NBMK_hEART2017.pdf}},
  year = {2017},
  note = {Presented at 6th Symposium of the European Association for Research in Transportation, Faculty of Civil and Environmental Engineering, Technion, Haifa, Israel}}}

Nikolic M., Bierlaire M. Data-driven spatio-temporal discretization for pedestrian flow characterization August 23, 2017. Intelligent Traffic Control and Service in Big Data Environment, EPFL, Lausanne, Switzerland.


@Misc{NikBie_EPFL-Tongji,
  author = {Marija Nikolic and Michel Bierlaire},
  title = {Data-driven spatio-temporal discretization

for pedestrian flow characterization},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/NikBie_EPFL-Tongji.pdf}},
  year = {2017},
  note = {Presented at Intelligent Traffic Control and Service in Big Data Environment, EPFL, Lausanne, Switzerland}}}

Nikolic M., Bierlaire M. Data-driven spatio-temporal discretization for pedestrian flow characterization July 24, 2017. 22nd International Symposium on Transportation and Traffic Theory, , Northwestern University, Evanston, USA.


@Misc{NikBie_ISTTT22,
  author = {Marija Nikolic and Michel Bierlaire},
  title = {Data-driven spatio-temporal discretization

for pedestrian flow characterization},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/NikBie_ISTTT22.pdf}},
  year = {2017},
  note = {Presented at 22nd International Symposium on Transportation and Traffic Theory, , Northwestern University, Evanston, USA}}}

Nikolic M., Bierlaire M. Review of transportation mode detection approaches based on smartphone data May 18, 2017. 17th Swiss Transportation Research Conference, Ascona, Switzerland.


@Misc{NikBie_STRC2017,
  author = {Marija Nikolic and Michel Bierlaire},
  title = {Review of transportation mode detection

approaches based on smartphone data},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/NikBie_STRC2017.pdf}},
  year = {2017},
  note = {Presented at 17th Swiss Transportation Research Conference, Ascona, Switzerland}}}

Nikolic M. Data-driven fundamental models for pedestrian movements March 09, 2017. Workshop on Transportation Network and Management, EPFL, Lausanne.


@Misc{Nikolic_09032017,
  author = {Marija Nikolic},
  title = {Data-driven fundamental models for pedestrian movements},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/Nikolic_09032017.pdf}},
  year = {2017},
  note = {Presented at Workshop on Transportation Network and Management, EPFL, Lausanne}}}

2016

Nikolic M., Bierlaire M. Data-driven characterization of pedestrian flows September 14, 2016. hEART 2016, 5th Symposium of the European Association for Research in Transportation, Delft University of Technology, Delft, the Netherlands.


@Misc{hEART2016_3DVoro,
  author = {Marija Nikolic and Michel Bierlaire},
  title = {Data-driven characterization of pedestrian flows},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/hEART2016_3DVoro.pdf}},
  year = {2016},
  note = {Presented at hEART 2016, 5th Symposium of the European Association for Research in Transportation, Delft University of Technology, Delft, the Netherlands}}}

Nikolic M., Bierlaire M., de Lapparent M., Scarinci R. Multi-class speed-density relationship for pedestrian traffic September 14, 2016. hEART 2016, 5th Symposium of the European Association for Research in Transportation, Delft University of Technology, Delft, the Netherlands.


@Misc{hEART2016_MCV-K,
  author = {Marija Nikolic and Michel Bierlaire and Matthieu {de Lapparent} and Riccardo Scarinci},
  title = {Multi-class speed-density

relationship for pedestrian traffic},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/hEART2016_MCV-K.pdf}},
  year = {2016},
  note = {Presented at hEART 2016, 5th Symposium of the European Association for Research in Transportation, Delft University of Technology, Delft, the Netherlands}}}

Nikolic M., Bierlaire M. Data-driven characterization of pedestrian traffic June 14, 2016. Ninth Triennial Symposium on Transportation Analysis (TRISTAN IX), Oranjestad, Aruba.


@Misc{TRISTAN2016_3DVoro,
  author = {Marija Nikolic and Michel Bierlaire},
  title = {Data-driven characterization of

pedestrian traffic},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/TRISTAN2016_3DVoro.pdf}},
  year = {2016},
  note = {Presented at Ninth Triennial Symposium on Transportation Analysis (TRISTAN IX), Oranjestad, Aruba}}}

Nikolic M., Bierlaire M., de Lapparent M., Scarinci R. Multi-class speed-density relationship for pedestrian traffic June 14, 2016. Ninth Triennial Symposium on Transportation Analysis (TRISTAN IX), Oranjestad, Aruba.


@Misc{TRISTAN2016_MCVK,
  author = {Marija Nikolic and Michel Bierlaire and Matthieu {de Lapparent} and Riccardo Scarinci},
  title = {Multi-class speed-density

relationship for pedestrian traffic},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/TRISTAN2016_MCVK.pdf}},
  year = {2016},
  note = {Presented at Ninth Triennial Symposium on Transportation Analysis (TRISTAN IX), Oranjestad, Aruba}}}

Nikolic M., Bierlaire M. Data-driven characterization of pedestrian flows May 18, 2016. 16th Swiss Transportation Research Conference, Ascona, Switzerland.


@Misc{NikBie_STRC2016,
  author = {Marija Nikolic and Michel Bierlaire},
  title = {Data-driven characterization of

pedestrian flows},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/NikBie_STRC2016.pdf}},
  year = {2016},
  note = {Presented at 16th Swiss Transportation Research Conference, Ascona, Switzerland}}}

2015

Nikolic M., Bierlaire M., Hänseler F. Data-driven characterization of multidirectional pedestrian traffic October 28, 2015. TGF '15, TU Delft, Delft, The Netherlands.


@Misc{Nik_TGF2015,
  author = {Marija Nikolic and Michel Bierlaire and Flurin H\"anseler},
  title = {Data-driven characterization of multidirectional pedestrian traffic},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/Nik_TGF2015.pdf}},
  year = {2015},
  note = {Presented at TGF '15, TU Delft, Delft, The Netherlands}}}

Nikolic M., Bierlaire M., Hänseler F. Characterization of multidirectional pedestrian flows based on three-dimensional Voronoi tessellations September 09, 2015. 4th Symposium of the European Association for Research in Transportation, Technical University of Denmark, Copenhagen, Denmark.


@Misc{NikBie_hEART2015,
  author = {Marija Nikolic and Michel Bierlaire and Flurin H\"anseler},
  title = {Characterization of multidirectional pedestrian flows based on three-dimensional Voronoi tessellations},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/NikBie_hEART2015.pdf}},
  year = {2015},
  note = {Presented at 4th Symposium of the European Association for Research in Transportation, Technical University of Denmark, Copenhagen, Denmark}}}

Nikolic M., Bierlaire M. Pedestrian flow characterization based on spatio-temporal Voronoi tessellations April 15, 2015. 15th Swiss Transportation Research Conference, Ascona, Switzerland.


@Misc{NikBie_STRC2015,
  author = {Marija Nikolic and Michel Bierlaire},
  title = {Pedestrian flow characterization based on spatio-temporal Voronoi tessellations},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/NikBie_STRC2015.pdf}},
  year = {2015},
  note = {Presented at 15th Swiss Transportation Research Conference, Ascona, Switzerland}}}

2014

Nikolic M., Bierlaire M. Pedestrian-oriented flow characterization October 24, 2014. Pedestrian and Evacuation Dynamics conference (PED 2014), Delft University of Technology, , Delft, the Netherlands.


@Misc{NikBie_DelftPED2014,
  author = {Marija Nikolic and Michel Bierlaire},
  title = {Pedestrian-oriented flow characterization},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/NikBie_DelftPED2014.pdf}},
  year = {2014},
  note = {Presented at Pedestrian and Evacuation Dynamics conference (PED 2014), Delft University of Technology, , Delft, the Netherlands}}}

Nikolic M., Bierlaire M., Farooq B. Probabilistic speed-density relationship for heterogeneous pedestrian traffic September 11, 2014. 3rd Symposium of the European Association for Research in Transportation, University of Leeds, Leeds, United Kingdom.


@Misc{NikBiehEART2014,
  author = {Marija Nikolic and Michel Bierlaire and Bilal Farooq},
  title = {Probabilistic speed-density relationship for heterogeneous pedestrian traffic},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/NikBiehEART2014.pdf}},
  year = {2014},
  note = {Presented at 3rd Symposium of the European Association for Research in Transportation, University of Leeds, Leeds, United Kingdom}}}

Nikolic M., Bierlaire M., Farooq B. Probabilistic speed-density relationship for pedestrians based on data driven space and time representation May 15, 2014. Swiss Transport Research Conference, Ascona, Switzerland.


@Misc{NikBieFar_STRC2014,
  author = {Marija Nikolic and Michel Bierlaire and Bilal Farooq},
  title = {Probabilistic speed-density relationship for pedestrians based on data driven space and time representation},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/NikBieFar_STRC2014.pdf}},
  year = {2014},
  note = {Presented at Swiss Transport Research Conference, Ascona, Switzerland}}}

Nikolic M., Bierlaire M., Farooq B. Probabilistic speed-density relationship for pedestrians based on data driven space and time representation April 10, 2014. WORKSHOP ON PEDESTRIAN MODELS 2014, EPFL, Lausanne, Switzerland.


@Misc{NikBie_PED2014,
  author = {Marija Nikolic and Michel Bierlaire and Bilal Farooq},
  title = {Probabilistic speed-density relationship for pedestrians based on data driven space and time representation},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/NikBie_PED2014.pdf}},
  year = {2014},
  note = {Presented at WORKSHOP ON PEDESTRIAN MODELS 2014, EPFL, Lausanne, Switzerland}}}

2013

Nikolic M., Bierlaire M., Farooq B. Spatial tessellations of pedestrian dynamics September 05, 2013. 2nd Symposium of the European Association for Research in Transportation, Stockholm, Sweden.


@Misc{Nikolic_hEART_2013,
  author = {Marija Nikolic and Michel Bierlaire and Bilal Farooq},
  title = {Spatial tessellations of pedestrian dynamics},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/Nikolic_hEART_2013.pdf}},
  year = {2013},
  note = {Presented at 2nd Symposium of the European Association for Research in Transportation, Stockholm, Sweden}}}

Nikolic M. Pedestrian flow characteristics based on individual trajectories July 15, 2013. DATA SIM Summer School 2013, Mobility Modeling and Big Data Sources, Hasselt University, Hasselt, Belgium.


@Misc{Nikolic_DATASIM-2013,
  author = {Marija Nikolic},
  title = {Pedestrian flow characteristics based on individual trajectories},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/Nikolic_DATASIM-2013.pdf}},
  year = {2013},
  note = {Presented at DATA SIM Summer School 2013, Mobility Modeling and Big Data Sources, Hasselt University, Hasselt, Belgium}}}

Nikolic M., Farooq B., Bierlaire M. Exploratory analysis of pedestrian flow characteristics in mobility hubs using trajectory data April 25, 2013. Swiss Transportation Research Conference (STRC), Ascona, Switzerland.


@Misc{Nikolic_STRC-2013,
  author = {Marija Nikolic and Bilal Farooq and Michel Bierlaire},
  title = {Exploratory analysis of pedestrian flow characteristics in mobility hubs using trajectory data},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/Nikolic_STRC-2013.pdf}},
  year = {2013},
  note = {Presented at Swiss Transportation Research Conference (STRC), Ascona, Switzerland}}}

Reviewing [7]

Research projects [2]

2017

Leveraging Public and Open Data for Transportation Mode detection

Modern smartphones are more than just calling devices. They harbor a wealth of information that provides the transportation field with a new potential. In this project, researchers will create a transportation mode detection model based on telecommunication data from smartphone users combined with public transport-related data sources. The researchers are provided with anonymized telecommunication traces. As those data remain scarce, additional data will be identified from external sources including network infrastructure, route maps, time schedules in static or real-time form, microcensus, weather, etc. A probabilistic model will be developed to infer the transportation modes from those various data sources. The project lasts one year, and will be carried out by the Transport and Mobility Laboratory, directed by Prof. Michel Bierlaire. It is sponsored by Swisscom.
- Sep 04, 2017 - Sep 04, 2018
- Sponsor: Swisscom
- Principal investigator: Michel Bierlaire
- Project managers: Marija Nikolic, Riccardo Scarinci

2012

Pedestrian dynamics: flows and behavior

This project aims at developing mathematical models of pedestrian dynamics, both at aggregate and disaggregate levels. Integrated and holistic mathematical models will handle the complexity of this unique transport mode and help us to answer open research questions. in the future, such foundations will also allow to create the tools society needs to better understand pedestrian dynamics.
- Apr 01, 2012 - Mar 31, 2015
- Sponsor: Swiss National Science Foundation
- Principal investigator: Michel Bierlaire
- Project managers: Bilal Farooq, Riccardo Scarinci
- Collaborators: Antonin Danalet, Flurin H�nseler, Marija Nikolic

EPFL courses [5]

Spring 2017

Optimization and Simulation.
- Level: PhD
- Section: Doctoral program in Civil and Environmental Engineering
- Lecturers: Michel Bierlaire, Riccardo Scarinci
- Teaching assistants: Marija Nikolic, Stefan Binder
- Webpage: http://transp-or.epfl.ch/courses/OptSim2017/

Spring 2016

Optimization and Simulation.
- Level: PhD
- Section: Doctoral program in Civil and Environmental Engineering
- Lecturers: Michel Bierlaire, Riccardo Scarinci, Yousef Maknoon
- Teaching assistants: Iliya Markov, Marija Nikolic
- Webpage: http://transp-or.epfl.ch/courses/OptSim2016/

Spring 2015

Optimization and Simulation.
- Level: PhD
- Section: Doctoral program in Civil and Environmental Engineering
- Lecturers: Michel Bierlaire, Riccardo Scarinci, Yousef Maknoon
- Teaching assistants: Tom�s Robenek, Marija Nikolic
- Webpage: http://transp-or.epfl.ch/courses/OptSim2015/

Spring 2014

Optimization and Simulation.
- Level: PhD
- Section: Doctoral program in Civil and Environmental Engineering
- Lecturers: Michel Bierlaire, Riccardo Scarinci, Jianghang Chen
- Teaching assistants: Tom�s Robenek, Marija Nikolic
- Webpage: http://transp-or.epfl.ch/courses/OptSim2014/

Fall 2012

Analyse III.
- Level: Bachelor
- Section: Civil Engineering
- Lecturer: None None
- Teaching assistant: Marija Nikolic

MSc theses [4]

2017

Integrating Bicycle Travel Demand to a Land-Use and Transport Model, Marc-Edouard Schultheiss (Civil Engineering)
- Supervision: Matthieu de Lapparent, Marija Nikolic, Michel Bierlaire
- External expert: Christopher Zegras

2016-2017

Information system for passenger discretionary activities in transport terminals, Federico Orsini (None)
- Supervision: Riccardo Scarinci, Marija Nikolic, Michel Bierlaire
- External expert: Riccardo Rossi

2014

A multi-class framework for a pedestrian cell transmission model accounting for population heterogeneity, Guy Cooper (Mathematics)
- Supervision: Flurin Hänseler, Marija Nikolic
- External expert:
Mode Choice Analysis Using Smartphone Data, Michael Friederich (Civil Engineering)
- Supervision: Evanthia Kazagli, Marija Nikolic, Anna Fernandez Antolin
- External expert:

Semester projects [14]

2017

Identifying the objectives of car drivers route choice behavior, Nicola Marco Ortelli

Supervision: Evanthia Kazagli, Marija Nikolic, Michel Bierlaire
The goal of this project is to shed light on the underlying objectives of car drivers' behavior --- pertaining to their route choices --- when traveling from one location of a transportation network to another. The student will use clustering methods, defined on the basis of different measures of similarity, that are associated with potential objectives such as the minimization of the travel time, the length or the complexity of a route. The results of the clustering will be analyzed to draw insights into the use of specific objectives depending on the features of the trip, such as the origin and destination points, and the departure time. The project involves an initial stage of data processing. The student needs to have good programming skills (Matlab or equivalent) and good knowledge of statistics. Familiarity with SQL is a plus.

Analysis of pedestrian group behavior based on tracking data and pattern recognition methods, Montesinos Ferrer Martí

Supervision: Marija Nikolic, Evanthia Kazagli, Zhengchao Wang
The objective of this project is to analyze the group behavior among pedestrians based on individual trajectory data. The data is collected in Lausanne train station, where a large-scale network of smart sensors has been used to track pedestrians. The project aims to improve the understanding of group behavior among pedestrians and its impact on pedestrian dynamics. It involves the following steps: (i) Development/selection of suitable methods (e.g. data mining/pattern recognition) for identification of individuals walking together, based on pedestrian trajectory data, and their implementation. (ii) Analysis focused on the behavior of identified groups; (iii) Comparison of the findings with the existing empirical basis, as well as with the proposed theories and models that take group dynamics into account. The student needs to have good programming skills (Scala/Matlab), and knowledge of statistical analysis.

Planning of feeding station installment for a full electric large capacity urban bus system, Adrien Ruault (Computer science)

Supervision: Virginie Lurkin, Marija Nikolic, Michel Bierlaire
The integration of customer behavioural models in optimization provides a better understanding of the preferences of clients (the demand) to policy makers while planning for their systems (the supply). These preferences are formalized with discrete choice models, and the corresponding optimization models where supply and demand closely interact are associated with (mixed) integer optimization problems. One concrete application of this integration consists of an operator selling services to a market, each service at a given price to a finite number of customers, called the capacity of the service. We are interested in finding the best strategy in terms of pricing and capacity allocation in order to maximize the revenues of the operator. The project objectives can be tailored for both Master or Semester project students.

Models for pedestrian movements based on integrated and sequential clustering, Konde Romain Olivier Bondo

Supervision: Marija Nikolic, Iliya Markov, Michel Bierlaire
The focus of this study is the modeling of speed-density relationship for pedestrian movements using the potential of available data. The data set considered in this project contains pedestrian trajectories collected in the train station in Lausanne. To track pedestrians, a large-scale network of tracking sensors is installed in the station (Alahi et al., 2014). The analysis we have performed reveals a high scatter in the data. To characterize the observed scatter we have developed a multi-class model of the speed-density relationship based on the latent class modeling approach (Nikolic et al., 2017). The latent class approach allows for modeling the segmentation in the population and the movement behavior simultaneously. The aim of this project is the derivation of the model representing speed-density relationship based on a two-stage (sequential) approach. The first stage involves segmentation of pedestrian trajectory data, by using machine learning techniques (clustering). In the second stage, a separate speed-density model is to be estimated for each cluster discovered in the first stage. The performance of the approach will be tested using real data, and compared to the more integrated, latent class approach.

2016

Accounting for dynamics in pedestrian multi-class speed-density relationship, Marc-Edouard Schultheiss (Civil Engineering)

Supervision: Marija Nikolic, Matthieu de Lapparent, Michel Bierlaire
The relationship between speed and density plays an important role in modeling of pedestrian traffic. It is useful for planning and design of pedestrian facilities, and it is also a required input or calibration criterion for models of pedestrian dynamics. The relationship is specified under the assumption that the traffic system is at equilibrium (stationary and homogenous). The analysis we have performed, based on the data collected in the Lausanne train station, rules out the use of a unique equilibrium relationship due to a high scatter in the data. This scatter may be explained by the violation of the equilibrium assumptions, as documented in the literature. To characterize the observed scatter we have developed a multi-class model of the speed-density relationship based on the latent class modeling approach. The model is derived by relaxing the homogeneity assumption of equilibrium relationships. It is assumed that pedestrian population is heterogeneous (e.g. different trip purpose, different time to departure, etc.) and that this heterogeneity leads to the existence of multiple pedestrian classes that are characterized by different behavior. There are two specification issues related to the panel data set (data collected over multiple time periods for the same sample of individuals) that we use in our analysis. The first is serial correlation across the observations of the same individual due to unobserved individual factors that persist over time. The second is related to dynamics, meaning that the speed in one period may depend on the speed values in the past. We have addressed the first issue by introducing an agent effect in the model that captures individual related unobserved factors. We term this model the static model with agent effect. The aim of this project will be to deal with the second issue, or dynamics. We will start with the simplified assumption that the speed at time t is influenced by the speed at time t - 1 only. We term such a model a dynamic model with agent effect. The performance of the approach will be tested using real data.

2015-2016

Specification testing of fundamental diagrams for an anisotropic pedestrian network loading model, Joel Mateus Fonseca (Communication Systems)

Supervision: Flurin Hänseler, Marija Nikolic, Michel Bierlaire

2015

Development of a novel pedestrian walking model applicable to congested flows, Gael LEDERREY

Supervision: Flurin Hänseler, Marija Nikolic, Michel Bierlaire
We are in the process of developing a novel pedestrian walking model that can describe multi-directional and congested pedestrian flow. Given a certain demand, the model is supposed to predict travel times and density levels as accurately as possible. The basic idea thereby consists in discretizing walkable space into cells and links, and to compute for each link a speed based on an empirical density-speed relation. The goal of this project, which can be carried out as a Bachelor's, semester, or Master's thesis, is (i) to understand and improve the existing mathematical model, (ii) to accordingly update the computational model (written in Java), and (iii) to consider a real-world case study involving either a Swiss railway station, a Dutch bottleneck experiment or a pedestrian crossing in Hong Kong, China. In the long term, this model will be useful for real-time crowd monitoring and control, as well as for infrastructure dimensioning of e.g. a train station. Skills in object-oriented programming and a basic knowledge of statistical mathematics are required; knowledge of parallel computing is a plus. Semester: Spring 2015. If interested, please contact us at {flurin.haenseler,marija.nikolic}@epfl.ch. We're looking forward to hearing from you.

Pedestrian movement in train stations: modeling speed-density relationship for different classes of passengers, Laure Emma Rosine

Supervision: Marija Nikolic, Riccardo Scarinci, Michel Bierlaire
The increased number of passengers in train stations is causing congestion not only on trains, but also on platforms and underpasses. To better design and manage these infrastructures, pedestrian movement should be understood and modeled accurately. Fundamental Diagram (FD) plays an important role in the representation of pedestrian dynamics. FD models the relationship between density of pedestrians and the speed at which they are able to move. This macroscopic model represents the average behavior and does not take into account differences among pedestrians. They can differ in terms of their purpose of the trip (e.g. business/leisure), personal characteristics (e.g. age, gender), presence of luggage, etc. This heterogeneity can lead to different walking behavior of pedestrians and should be adequately modeled by the FD. This project aims to develop a Multi-Class Fundamental Diagram (MC-FD) assuming the existence of different classes of passengers in a train station. As a case study we will use the Lausanne train station where detailed pedestrian trajectories have been collected. The key elements of the project are: (i) literature review of MC-FD for both pedestrians and vehicles; (ii) development of the modeling assumptions employing empirical, qualitative and sociological considerations; (iii) model specification; (iv) sensitivity analysis, calibration and validation of the proposed model based on empirical data. Knowledge of statistical mathematics and familiarity with programming (MATLAB) are required.

2013-2014

Mode choice analysis from a large smartphone dataset, Mikael Nicolas Xavier Friederich (Civil Engineering)

Supervision: Evanthia Kazagli, Marija Nikolic
Smartphone is a powerful and convenient tool for collecting a variety of data (location, social interaction, and more) that can be useful for individual mobility analysis. One of the advantages of smartphone data is the ability to collect data over longer periods of time (panel data) without burdening the respondent. Having such resolution of information enables us to gain better insight into the general mobility style of people than it would be possible by means of traditional one- or two-day travel surveys. It becomes clear that smartphone data have great potential with respect to analysing travel- ers� profiles and disclose systematic mobility (e.g habit, morning or afternoon routines etc.) and switching behavior patterns (departure time, chosen route, mode). This project focuses on such higher-order mobility styles in order to shed light on travel behavior.

Exploration of smartphone users trip data to investigate travel behavior, Mikael Nicolas Xavier Friederich (Civil Engineering)

Supervision: Evanthia Kazagli, Marija Nikolic
Smartphone is a powerful and convenient tool for collecting a variety of data (location, social interaction, and more) that can be useful for individual mobility analysis. One of the advantages of smartphone data is the ability to collect data over longer periods of time (panel data) without burdening the respondent. Having such resolution of information enables us to gain better insight into the general mobility style of people than it would be possible by means of traditional one- or two-day travel surveys. This project aims at unveiling frequent behaviours in terms of space, i.e., the regions of space traversed during movements and understand the processes generating them. The work elaborated in the project is expected to support our effort in incorporating travelers� mental/ spatial represen- tation items (MRI) in route choice modeling. More specifically, the goal of this bigger project is to develop a modeling framework where the route choice decisions will take place in a higher/ concep- tual level. Path alternatives will be constructed as �replaced by� sequences of mental geo-marked items �akin to the concept of anchor points as elements of travelers� mental maps in cognitive science. We are interested in car route choice, hence the scope of this semester project will also focus on car trips. In this context, processing and analysing the trips of the smartphone users can reveal the most frequently traversed points or segments of the network (both in an aggregate but also in an individual level) that can signify such items in the network.

Exploring pedestrian mobility using video tracking data in Lausanne train station, Babel Hugo Louis

Supervision: Marija Nikolic, Evanthia Kazagli
Pedestrians, contrary to the other modes, do not have defined network and they do not follow strict constraints. A characteristic feature of pedestrian route choice is that routes are continuous trajectories in time and space - pedestrians choose a route from an infinite set of alternatives. As a consequence of these facts we have observed the distribution of walked distances and travel times for specific origin-destination pairs at the train station in Laussane, during the morning rush hour. The goal of this project is to analyse the distribution of walked distances and corresponding travel times and speed values based on pedestrian trajectories collected at the train station in Lausanne. Here we would like to answer the questions such as: 1) Is the motion of pedestrians random or does it follow a specific pattern? How can we characterize it? 2) What is the impact of congestion on the mentioned observables? 3) Do familiarity with the place, the attractiveness of shops and signs found along the corridors affect pedestrian motion behaviour and how? What are the other factors that cause deviations from a straight line motion? Programming skills and knowledge of statistical mathematics are required.

2012-2013

Mobility learning from smartphone WIFI data, Amélie Buisson (Civil Engineering)

Supervision: Jingmin Chen, Evanthia Kazagli, Marija Nikolic
Smartphone is a powerful and convenient platform for collecting location data for mobility behavior research. Most of the research relies on GPS to get accurate location, or GSM to get massively available but imprecise location. This project investigates the feasibility of using WIFI data for mobility behavior research. The dataset is collected from 200 smartphones over 2 years. The data collection application (EPFLScope) records all nearby WIFI access points (AP) every 3-5 minutes. Some of the WIFI access points are associated with location information. Therefore, it is possible to identify the location of the smartphone according to the nearby WIFI AP. With this location information, we can further understand spatial and temporal mobility patterns.

Movement patterns of pedestrians on platforms prior to/after train departures/arrivals in Gare de Lausanne: Exploitation of pedestrian tracking data, Isabel Tovar

Supervision: Flurin Hänseler, Marija Nikolic
In order to better understand how people access and leave a train station by means of trains, the arrival and departure pattern of pedestrians on platform is of interest. In particular, the embarkation/disembarkation behavior of train passengers is an important factor for congestion in pedestrian facilities. The departure pattern of pedestrians awaiting to board a train is expected to follow a beta distribution. In contrast to that, arriving passengers usually enter a train station as a very dense crowd, causing a theoretical inflow that exceeds the capacity of pedestrian facilities. This saturation effect needs to be taken into account when modeling arrival patterns. The goal of this work is to mathematically describe the arrival and departure patterns caused by single trains as well as for platforms on which multiple trains follow each other in close succession. Case studies for RER, RE and ICE/EC trains will be considered in order to better understand the influence of train size and type on these patterns.

Pedestrian flow simulation in Lausanne train station, Nicolas de Lamberterie

Supervision: Bilal Farooq, Marija Nikolic
This project is about modelling, simulating, and analyzing the future expansion scenarios, in the context of pedestrian flows within Lausanne Gare (train station). We have already developed the modelling and simulation test-bed in a pedestrian flow simulator, called VisWalk. Here we would like to use more detailed data for existing and future demand in our models and analysis. A considerable amount of time will be spent to perform the calibration of various parameters using aggregate level speed and density related data. If time permits, we would also be interested in doing small infrastructure changes, specifically tapering of the intersections between PIs and ramps in order to evaluate their effects of the pedestrian flows. Ideal student should have some experience in using CAD software (AutoCAD, Microstation); should have some knowledge of databases; and be able to do basic programming. The student working on the project will have a great and unique opportunity to work directly with SBB CFF FFS, a major transportation software company called PTV AG, and a rapidly growing startup from EPFL called VisioSafe.