Tim Hillel

Journal publications [7]

2023

Rezvany N., Bierlaire M., Hillel T. (2023). Simulating intra-household interactions for in- and out-of-home activity scheduling. Transportation Research Part C: Emerging Technologies 157 (104362)

Various interactions, time arrangements, and constraints exist for individuals scheduling their day as a member of a household, which affect their in-home as well as out-of-home activity schedule. However, the existing activity-based models are mostly based on the individual decision-making process, which are limited in their demonstration of behaviour. We simulate multiple intra-household interaction dimensions within the same framework and capture the coordination of the activity scheduling decisions among all household members. Our approach adopts the Optimisation-based Activity Scheduling Integrating Simultaneous choice dimensions (OASIS) framework, which is at the level of isolated individuals and focuses on out-of-home activity schedules. We jointly simulate in- and out-of-home activities and incorporate interactions into the framework. Our framework contributes to the state-of-the-art in activity-based modelling by explicitly capturing multiple interactions within the same model, such as the allocation of the private vehicle to household members, dividing household maintenance responsibilities, escorting, joint activity participation, and sharing rides. We operationalise the model using time-use-survey data from the United Kingdom. The simulation results demon- strate the ability of the framework to capture complex intra-household interactions. We then demonstrate how these interactions can cause individuals to deviate from their schedules planned in isolation. This is a general framework applicable to different household compositions and available resources.
```
@Article{RezBieHil23,
  author = {Negar Rezvany and Michel Bierlaire and Tim Hillel},
  title = {Simulating intra-household interactions for in- and out-of-home activity scheduling},
  journal = {Transportation Research Part C: Emerging Technologies},
  year = {2023},
  volume = {157},
  number = {104362},
  DOI = {10.1016/j.trc.2023.104362},
  note = {Accepted on Sep 26, 2023}}}
```

Pougala J., Hillel T., Bierlaire M. (2023). OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions. Transportation Research Part C: Emerging Technologies 155


@Article{PougHillBier23,
  author = {Janody Pougala and Tim Hillel and Michel Bierlaire},
  title = {OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions},
  journal = {Transportation Research Part C: Emerging Technologies},
  year = {2023},
  volume = {155},
  DOI = {10.1016/j.trc.2023.104291

}}}

2022

Manser P., Haering T., Hillel T., Pougala J., Krueger R., Bierlaire M. (2022). Estimating flexibility preferences to resolve temporal scheduling conflicts in activity-based modelling. Transportation

This paper presents a novel activity-based demand model that combines an optimisation framework for continuous temporal scheduling decisions (i.e. activity timings and durations) with traditional discrete choice models for non-temporal choice dimensions (i.e. activity participation, number and type of tours, and destinations). The central idea of our approach is that individuals resolve temporal scheduling conflicts that arise from overlapping activities, e.g. needing to work and desiring to shop at the same time, in order to maximise their daily utility. Flexibility parameters capture behavioural preferences that penalise deviations from desired timings. This framework has three advantages over existing activity-based modelling approaches: (i) the time conflicts between different temporal scheduling decisions including the activity sequence are treated jointly; (ii) flexibility parameters follow a utility maximisation approach; and (iii) the framework can be used to estimate and simulate a city-scale case study in reasonable time. We introduce an estimation routine that allows flexibility parameters to be estimated using real-world observations as well as a simulation routine to efficiently resolve temporal conflicts using an optimisation model. The framework is applied to the full-time workers of a synthetic population for the city of Lausanne, Switzerland. We validate the model results against reported schedules. The results demonstrate the capabilities of our approach to reproduce empirical observations in a real-world case study.
```
@Article{MansHaerHillPougKrueBier22,
  author = {Patrick Manser and Tom Haering and Tim Hillel and Janody Pougala and Rico Krueger and Michel Bierlaire},
  title = {Estimating flexibility preferences to resolve temporal scheduling conflicts in activity-based modelling},
  journal = {Transportation},
  year = {2022},
  DOI = {10.1007/s11116-022-10330-8},
  note = {Accepted on Aug 22, 2022}}}
```
Pougala J., Hillel T., Bierlaire M. (2022). Capturing trade-offs between daily scheduling choices. Journal of Choice Modelling 43 (100354)

We propose a new modelling approach for daily activity scheduling which integrates the different daily scheduling choice dimensions (activity participation, location, schedule, duration and transportation mode) into a single optimisation problem. The fundamental behavioural principle behind our approach is that individuals schedule their day to maximise their overall derived utility from the activities they complete, according to their individual needs, constraints, and preferences. By combining mul- tiple choices into a single optimisation problem, our framework is able to capture the complex trade-offs between scheduling decisions for multiple activities. These trade-offs could include how spending longer in one activity will reduce the time- availability for other activities or how the order of activities determines the travel- times. The implemented framework takes as input a set of considered activities, with associated locations and travel modes, and uses these to produce empirical distri- butions of individual schedules from which different daily schedules can be drawn. The model is illustrated using historic trip diary data from the Swiss Mobility and Transport Microcensus. The results demonstrate the ability of the proposed frame- work to generate complex and realistic distributions of starting time and duration for different activities within the tight time constraints. The generated schedules are then compared to the aggregate distributions from the historical data to demonstrate the feasibility and flexibility of our approach.
```
@Article{PougHillBier22,
  author = {Janody Pougala and Tim Hillel and Michel Bierlaire},
  title = {Capturing trade-offs between daily scheduling choices},
  journal = {Journal of Choice Modelling},
  year = {2022},
  volume = {43},
  number = {100354},
  DOI = {10.1016/j.jocm.2022.100354},
  note = {Accepted on Mar 19, 2022}}}
```

2021

Ortelli N., Hillel T., Pereira F., de Lapparent M., Bierlaire M. (2021). Assisted Specification of Discrete Choice Models. Journal of Choice Modelling 39 (100285)

Determining appropriate utility specifications for discrete choice models is time-consuming and prone to errors. As the number of possible functions exponentially grows with the vari- ables under consideration, analysts need to spend increasing amounts of time on searching for good specifications through trial-and-error and expert knowledge. This paper proposes a data-driven algorithm that aims at assisting modelers in their search. Our approach trans- lates the task into a multi-objective combinatorial optimization problem and makes use of a variant of the variable neighborhood search algorithm to generate solutions. We apply the algorithm to a real mode choice dataset as a proof of concept. The results demonstrate its ability to generate high-quality specifications in reasonable amounts of time.
```
@Article{OrteHillCamadeLaBier21,
  author = {Nicola Ortelli and Tim Hillel and Francisco Pereira and Matthieu {de Lapparent} and Michel Bierlaire},
  title = {Assisted Specification of Discrete Choice Models},
  journal = {Journal of Choice Modelling},
  year = {2021},
  volume = {39},
  number = {100285},
  DOI = {10.1016/j.jocm.2021.100285},
  note = {Accepted on Mar 16, 2021}}}
```
Lederrey G., Lurkin V., Hillel T., Bierlaire M. (2021). Estimation of Discrete Choice Models with Hybrid Stochastic Adaptive Batch Size Algorithms. Journal of Choice Modelling 38 (100226)

The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the estimation of statistical Discrete Choice Models based on the random utility framework. In this article, we provide new ways of dealing with large datasets in the context of Discrete Choice Models. We achieve this by proposing new efficient stochastic optimization algorithms and extensively testing them alongside existing approaches. We develop these algorithms based on three main contributions: the use of a stochastic Hessian, the modification of the batch size, and a change of optimization algorithm depending on the batch size. A comprehensive experimental comparison of fifteen optimization algorithms is conducted across ten benchmark Discrete Choice Model cases. The results indicate that the HAMABS algorithm, a hybrid adaptive batch size stochastic method, is the best performing algorithm across the optimization benchmarks. This algorithm speeds up the optimization time by a factor of 23 on the largest model compared to existing algorithms used in practice. The integration of the new algorithms in Discrete Choice Models estimation software will significantly reduce the time required for model estimation and therefore enable researchers and practitioners to explore new approaches for the specification of choice models.
```
@Article{LedeLurkHillBier21,
  author = {Gael Lederrey and Virginie Lurkin and Tim Hillel and Michel Bierlaire},
  title = {Estimation of Discrete Choice Models with Hybrid Stochastic Adaptive Batch Size Algorithms},
  journal = {Journal of Choice Modelling},
  year = {2021},
  volume = {38},
  number = {100226},
  DOI = {10.1016/j.jocm.2020.100226},
  note = {Accepted on May 20, 2020}}}
```
Hillel T., Bierlaire M., Elshafie M., Jin Y. (2021). A systematic review of machine learning classification methodologies for modelling passenger mode choice. Journal of Choice Modelling 38 (100221)

Machine Learning (ML) approaches are increasingly being investigated as an alternative to Random Utility Models (RUMs) for modelling passenger mode choice. These approaches have the potential to provide valuable insights into choice modelling research questions. However, the research and the methodologies used are fragmented. Whilst systematic reviews on RUMs for mode choice prediction have long existed and the methods have been well scrutinised for mode choice prediction, the same is not true for ML models. To address this need, this paper conducts a systematic review of ML methodologies for modelling passenger mode choice. The review analyses the methodologies employed within each study to (a) establish the state-of-research frameworks for ML mode choice modelling and (b) identify and quantify the prevalence of methodological limitations in previous studies. A comprehensive search methodology across the three largest online publication databases is used to identify 574 unique records. These are screened for relevance, leaving 70 peer-reviewed articles containing 73 primary studies for data extraction. The studies are reviewed in detail to extract 17 attributes covering five research questions, concerning (i) classification techniques, (ii) datasets, (iii) performance estimation, (iv) hyper-parameter selection, and (v) model analysis. The review identifies ten common methodological limitations. Five are determined to be methodological pitfalls, which are likely to introduce bias in the estimation of model performance. The remaining five are identified as areas for improvement, which may limit the achieved performance of the models considered. A further six general limitations are identified, which highlight gaps in knowledge for future work.
```
@Article{HillBierElshJin21,
  author = {Tim Hillel and Michel Bierlaire and Mohammed Elshafie and Ying Jin},
  title = {A systematic review of machine learning classification methodologies for modelling passenger mode choice},
  journal = {Journal of Choice Modelling},
  year = {2021},
  volume = {38},
  number = {100221},
  DOI = {10.1016/j.jocm.2020.100221},
  note = {Accepted on May 10, 2020}}}
```

Book chapters [1]

2024

Bierlaire M., Frejinger E., Hillel T. (2024) Dynamic choice models. In S. Hess and A. Daly (ed) Handbook of Choice Modelling, Edward Elgar Publishing , Cheltenham, UK [ISBN 9781800375628]


@InProceedings{BierFrejHill24,
  author = {Michel Bierlaire and Emma Frejinger and Tim Hillel},
  title = {Dynamic choice models},
  year = {2024},
  booktile = {Handbook of Choice Modelling},
  editor = {S. Hess and A. Daly},
  publisher = {Edward Elgar Publishing},
  address = {Cheltenham, UK},
  pages = {568-593}}}

Technical reports [12]

2024

Rezvany N., Hillel T., Bierlaire M. (2024). Household-level choice-set generation and parameter estimation in activity-based models. Report


@TechReport{RezHilBie2024,
  author = {Negar Rezvany and Tim Hillel and Michel Bierlaire},
  title = {Household-level choice-set generation and parameter estimation in activity-based models},
  institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne},
  year = {2024},
  type = {Technical Report},
  address = {Lausanne, Switzerland}}}

2023

Rezvany N., Bierlaire M., Hillel T. (2023). Simulating intra-household interactions for in- and out-of-home activity scheduling. Report


@TechReport{RezHilBie2023,
  author = {Negar Rezvany and Michel Bierlaire and Tim Hillel},
  title = {Simulating intra-household interactions for in- and out-of-home

activity scheduling},
  institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne},
  year = {2023},
  type = {Technical Report},
  address = {Lausanne, Switzerland}}}

2022

Pougala J., Hillel T., Bierlaire M. (2022). OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions. Report TRANSP-OR 221124


@TechReport{PougHillBier22,
  author = {Janody Pougala and Tim Hillel and Michel Bierlaire},
  title = {OASIS: Optimisation-based Activity Scheduling

with Integrated Simultaneous choice dimensions},
  institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne},
  year = {2022},
  type = {Technical Report},
  number = {TRANSP-OR 221124},
  address = {Lausanne, Switzerland}}}

2021

Manser P., Haering T., Hillel T., Pougala J., Krueger R., Bierlaire M. (2021). Resolving temporal scheduling conflicts in activity-based modelling. Report TRANSP-OR 211209


@TechReport{ManserEtAl2021,
  author = {Patrick Manser and Tom Haering and Tim Hillel and Janody Pougala and Rico Krueger and Michel Bierlaire},
  title = {Resolving temporal scheduling conflicts in activity-based modelling},
  institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne},
  year = {2021},
  type = {Technical Report},
  number = {TRANSP-OR 211209},
  address = {Lausanne, Switzerland}}}

Bierlaire M., Frejinger E., Hillel T. (2021). Dynamic choice models. Report TRANSP-OR 210305


@TechReport{BierFrejHill21,
  author = {Michel Bierlaire and Emma Frejinger and Tim Hillel},
  title = {Dynamic choice models},
  institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne},
  year = {2021},
  type = {Technical Report},
  number = {TRANSP-OR 210305},
  address = {Lausanne, Switzerland}}}

Pougala J., Hillel T., Bierlaire M. (2021). Capturing trade-offs between daily scheduling choices. Report TRANSP-OR 210101


@TechReport{PougHillBier21,
  author = {Janody Pougala and Tim Hillel and Michel Bierlaire},
  title = {Capturing trade-offs between daily scheduling choices},
  institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne},
  year = {2021},
  type = {Technical Report},
  number = {TRANSP-OR 210101},
  address = {Lausanne, Switzerland}}}

Xie S., Hillel T., Jin Y. (2021). An Early Stopping Bayesian Data Assimilation Approach for Mixed-Logit Estimation. Report arXiv:2101.11159


@TechReport{XieHillelJinEarly2021,
  author = {Shanshan Xie and Tim Hillel and Ying Jin},
  title = {An Early Stopping Bayesian Data Assimilation Approach for Mixed-Logit Estimation},
  institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne},
  year = {2021},
  type = {Technical Report},
  number = {arXiv:2101.11159},
  address = {Lausanne, Switzerland}}}

Rezvany N., Hillel T., Bierlaire M. (2021). Integrated models of transport and energy demand: A literature review and framework. Report


@TechReport{RezHilBie2021,
  author = {Negar Rezvany and Tim Hillel and Michel Bierlaire},
  title = {Integrated models of transport and energy demand: A literature review and framework},
  institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne},
  year = {2021},
  type = {Technical Report},
  address = {Lausanne, Switzerland}}}

2020

Ortelli N., Hillel T., Pereira F., de Lapparent M., Bierlaire M. (2020). Assisted Specification of Discrete Choice Models. Report TRANSP-OR 200708


@TechReport{OrtHilPerLapBie2020,
  author = {Nicola Ortelli and Tim Hillel and Francisco Pereira and Matthieu {de Lapparent} and Michel Bierlaire},
  title = {Assisted Specification of Discrete Choice Models},
  institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne},
  year = {2020},
  type = {Technical Report},
  number = {TRANSP-OR 200708},
  address = {Lausanne, Switzerland}}}

Hillel T. (2020). New perspectives on the performance of machine learning classifiers for mode-choice prediction. Report TRANSP-OR 200704


@TechReport{HillelNew2020,
  author = {Tim Hillel},
  title = {New perspectives on the performance of machine learning classifiers for mode-choice prediction},
  institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne},
  year = {2020},
  type = {Technical Report},
  number = {TRANSP-OR 200704},
  address = {Lausanne, Switzerland}}}

2019

Lederrey G., Lurkin V., Hillel T., Bierlaire M. (2019). Estimation of Discrete Choice Models with Hybrid Stochastic Adaptive Batch Size Algorithms. Report TRANSP-OR 191213


@TechReport{LedLurHilBie19,
  author = {Gael Lederrey and Virginie Lurkin and Tim Hillel and Michel Bierlaire},
  title = {Estimation of Discrete Choice Models with Hybrid Stochastic Adaptive Batch Size Algorithms},
  institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne},
  year = {2019},
  type = {Technical Report},
  number = {TRANSP-OR 191213},
  address = {Lausanne, Switzerland}}}

Hillel T., Bierlaire M., Jin Y. (2019). A systematic review of machine learning methodologies for modelling passenger mode choice. Report TRANSP-OR 191025


@TechReport{HillelBierlaireJinReview2019,
  author = {Tim Hillel and Michel Bierlaire and Ying Jin},
  title = {A systematic review of machine learning methodologies for modelling passenger mode choice},
  institution = {Transport and Mobility Laboratory, Ecole Polytechnique F\'ed\'erale de Lausanne},
  year = {2019},
  type = {Technical Report},
  number = {TRANSP-OR 191025},
  address = {Lausanne, Switzerland}}}

Articles in conference proceedings [20]

2024

Rezvany N., Hillel T., Bierlaire M. (2024) Household-level choice-set generation and parameter estimation in activity-based models. Proceedings of the 12th symposium of the European Association for Research in Transportation (hEART), June 18-20, 2024, Espoo, Finland


@InProceedings{RezHilBie_hEART2024,
  author = {Negar Rezvany and Tim Hillel and Michel Bierlaire},
  title = {Household-level choice-set generation and parameter estimation in activity-based models},
  booktime = {Proceedings of the 12th symposium of the European Association for Research in Transportation  (hEART), June 18-20, 2024},
  year = {2024},
  address = {Espoo, Finland}}}

Rezvany N., Hillel T., Bierlaire M. (2024) Integration of group decision-mechanisms into activity-based models. Proceedings of the 17th International Conference on Travel Behavior Research (IATBR), July 14-18, 2024, Vienna, Austria


@InProceedings{RezHilBie_IATBR2024,
  author = {Negar Rezvany and Tim Hillel and Michel Bierlaire},
  title = {Integration of group decision-mechanisms into activity-based models},
  booktime = {Proceedings of the 17th International Conference on Travel Behavior Research

 (IATBR), July 14-18, 2024},
  year = {2024},
  address = {Vienna, Austria}}}

2023

Rezvany N., Hillel T., Bierlaire M. (2023) From domestic energy demand to household activity patterns. Proceedings of the 23rd Swiss Transport Research Conference (STRC)10-12 May, Ascona, Switzerland


@InProceedings{RezHilBie_STRC2023,
  author = {Negar Rezvany and Tim Hillel and Michel Bierlaire},
  title = {From domestic energy demand to household activity patterns},
  booktime = {Proceedings of the 23rd Swiss Transport Research Conference (STRC), 10-12 May},
  year = {2023},
  address = {Ascona, Switzerland}}}

Pougala J., Hillel T., Bierlaire M. (2023) From one-day to multiday activity scheduling: extending the OASIS framework. Proceedings of the 23rd Swiss Transport Research Conference (STRC), 10-12 May, Ascona, Switzerland


@InProceedings{PoHiBi_STRC2023,
  author = {Janody Pougala and Tim Hillel and Michel Bierlaire},
  title = {From one-day to multiday activity scheduling: extending the OASIS framework},
  booktime = {Proceedings of the 23rd Swiss Transport Research Conference (STRC), 10-12 May},
  year = {2023},
  address = {Ascona, Switzerland}}}

2022

Rezvany N., Hillel T., Bierlaire M. (2022) A utility optimization-based framework for joint in- and out-of-home scheduling. Proceedings of the 10th symposium of the European Association for Research in Transportation (hEART) (hEART), 1-3 June 2022, Leuven, Belgium


@InProceedings{RezHilBie_hEART2022,
  author = {Negar Rezvany and Tim Hillel and Michel Bierlaire},
  title = {A utility optimization-based framework for joint in- and out-of-home scheduling},
  booktime = {Proceedings of the 10th symposium of the European Association for Research in Transportation (hEART) (hEART), 1-3 June 2022},
  year = {2022},
  address = {Leuven, Belgium}}}

Pougala J., Hillel T., Bierlaire M. (2022) Parameter estimation for activity-based models. Proceedings of the 22nd Swiss Transport Research Conference (STRC), 18-20 May, Ascona, Switzerland


@InProceedings{PoHiBi_STRC2022,
  author = {Janody Pougala and Tim Hillel and Michel Bierlaire},
  title = {Parameter estimation for activity-based models},
  booktime = {Proceedings of the 22nd Swiss Transport Research Conference (STRC), 18-20 May},
  year = {2022},
  address = {Ascona, Switzerland}}}

Rezvany N., Hillel T., Bierlaire M. (2022) Integrated in- and out-of-home scheduling framework: A utility optimization-based approach. Proceedings of the 22nd Swiss Transport Research Conference (STRC ), 18-20 May, Ascona, Switzerland


@InProceedings{RezHilBie_STRC2022,
  author = {Negar Rezvany and Tim Hillel and Michel Bierlaire},
  title = {Integrated in- and out-of-home scheduling framework: A utility optimization-based approach},
  booktime = {Proceedings of the 22nd Swiss Transport Research Conference (STRC ), 18-20 May},
  year = {2022},
  address = {Ascona, Switzerland}}}

Salvadé N., Hillel T., Pougala J., Haering T., Bierlaire M. (2022) Representing location choice within activity-based models. Proceedings of the 22nd Swiss Transport Research Conference (STRC), 18-20 May, Ascona, Switzerland


@InProceedings{Salvade_STRC2022,
  author = {Nicolas Salvad\'e and Tim Hillel and Janody Pougala and Tom Haering and Michel Bierlaire},
  title = {Representing location choice within activity-based models},
  booktime = {Proceedings of the 22nd Swiss Transport Research Conference (STRC), 18-20 May},
  year = {2022},
  address = {Ascona, Switzerland}}}

2021

Haering T., Manser P., Hillel T., Pougala J., Krueger R., Bierlaire M. (2021) Resolving time conflicts in activity-based scheduling: A case study of Lausanne. Proceedings of the 21st Swiss Transport Research Conference (STRC), 12-14 September, Ascona, Switzerland


@InProceedings{Haering_STRC2021,
  author = {Tom Haering and Patrick Manser and Tim Hillel and Janody Pougala and Rico Krueger and Michel Bierlaire},
  title = {Resolving time conflicts in activity-based scheduling: A case study of Lausanne},
  booktime = {Proceedings of the 21st Swiss Transport Research Conference (STRC), 12-14 September},
  year = {2021},
  address = {Ascona, Switzerland}}}

Pougala J., Hillel T., Bierlaire M. (2021) Choice set generation for activity-based models. Proceedings of the 21st Swiss Transport Research Conference (STRC), 12-14 September, Ascona, Switzerland


@InProceedings{PoHiBi_STRC2021,
  author = {Janody Pougala and Tim Hillel and Michel Bierlaire},
  title = {Choice set generation for activity-based models},
  booktime = {Proceedings of the 21st Swiss Transport Research Conference (STRC), 12-14 September},
  year = {2021},
  address = {Ascona, Switzerland}}}

Rezvany N., Hillel T., Bierlaire M. (2021) Integrated models of transport and energy demand: A literature review and framework. Proceedings of the 21st Swiss Transport Research Conference (STRC ), 12-14 September, Ascona, Switzerland


@InProceedings{RezHilBie_STRC2021,
  author = {Negar Rezvany and Tim Hillel and Michel Bierlaire},
  title = {Integrated models of transport and energy demand: A literature review and framework},
  booktime = {Proceedings of the 21st Swiss Transport Research Conference (STRC ), 12-14 September},
  year = {2021},
  address = {Ascona, Switzerland}}}

2020

Hillel T., Pougala J., Manser P., Luethi R., Scherr W., Bierlaire M. (2020) Modelling mobility tool availability at a household and individual level: A case study of Switzerland. Proceedings of the 9th Symposium of the European Association for Research in Transportation (HEART), 3-4 February 2021, Lyon, France


@InProceedings{HilPouManLueSchBie_HEART2020,
  author = {Tim Hillel and Janody Pougala and Patrick Manser and Raphael Luethi and Wolfgang Scherr and Michel Bierlaire},
  title = {Modelling mobility tool availability at a household and individual level: A case study of Switzerland},
  booktime = {Proceedings of the 9th Symposium of the European Association for Research in Transportation (HEART), 3-4 February 2021},
  year = {2020},
  address = {Lyon, France}}}

Ortelli N., Hillel T., Pereira F., de Lapparent M., Bierlaire M. (2020) Variable Neighborhood Search for Assisted Utility Specification in Discrete Choice Models. Proceedings of the 9th Symposium of the European Association for Research in Transportation (HEART), 3-4 February 2021, Lyon, France


@InProceedings{OrtHilPerLapBie_HEART2020,
  author = {Nicola Ortelli and Tim Hillel and Francisco Pereira and Matthieu {de Lapparent} and Michel Bierlaire},
  title = {Variable Neighborhood Search for Assisted Utility Specification in Discrete Choice Models},
  booktime = {Proceedings of the 9th Symposium of the European Association for Research in Transportation (HEART), 3-4 February 2021},
  year = {2020},
  address = {Lyon, France}}}

Pougala J., Hillel T., Bierlaire M. (2020) An optimization framework for daily activity schedules. Proceedings of the 9th Symposium of the European Association for Research in Transportation (HEART), 3-4 February 2021, Lyon, France


@InProceedings{PouHilBie_HEART2020,
  author = {Janody Pougala and Tim Hillel and Michel Bierlaire},
  title = {An optimization framework for daily activity schedules},
  booktime = {Proceedings of the 9th Symposium of the European Association for Research in Transportation (HEART), 3-4 February 2021},
  year = {2020},
  address = {Lyon, France}}}

Xie S., Hillel T., Jin Y. (2020) An Early Stopping Bayesian Data Assimilation Approach for improved Mixed Multinomial Logit transferability. Proceedings of the 9th Symposium of the European Association for Research in Transportation (HEART), 3-4 February 2021, Lyon, France


@InProceedings{XieHilJin_HEART2020,
  author = {Shanshan Xie and Tim Hillel and Ying Jin},
  title = {An Early Stopping Bayesian Data Assimilation Approach for improved Mixed Multinomial Logit transferability},
  booktime = {Proceedings of the 9th Symposium of the European Association for Research in Transportation (HEART), 3-4 February 2021},
  year = {2020},
  address = {Lyon, France}}}

Pougala J., Hillel T., Bierlaire M. (2020) Scheduling of daily activities: an optimization approach. Proceedings of the 20th Swiss Transport Research Conference (STRC), 13-14 May, Ascona, Switzerland


@InProceedings{PouHilBie_STRC2020,
  author = {Janody Pougala and Tim Hillel and Michel Bierlaire},
  title = {Scheduling of daily activities: an optimization approach},
  booktime = {Proceedings of the 20th Swiss Transport Research Conference (STRC), 13-14 May},
  year = {2020},
  address = {Ascona, Switzerland}}}

2019

Hillel T., Jin Y., Elshafie M., Bierlaire M. (2019) Weak teachers: Assisted specification of discrete choice models using ensemble learning. Proceedings of the 8th Symposium of the European Association for Research in Transportation (HEART), 4-6 September, Budapest, Hungary


@InProceedings{HilBieElsJin_HEART2019,
  author = {Tim Hillel and Ying Jin and Mohammed Elshafie and Michel Bierlaire},
  title = {Weak teachers: Assisted specification of discrete choice models using ensemble learning},
  booktime = {Proceedings of the 8th Symposium of the European Association for Research in Transportation (HEART), 4-6 September},
  year = {2019},
  address = {Budapest, Hungary}}}

Lederrey G., Lurkin V., Hillel T., Bierlaire M. (2019) Stochastic optimization with adaptive batch size: Discrete choice models as a case study. Proceedings of the 8th Symposium of the European Association for Research in Transportation (HEART), 4-6 September, Budapest, Hungary


@InProceedings{LEDLURHILBIER_HEART19,
  author = {Gael Lederrey and Virginie Lurkin and Tim Hillel and Michel Bierlaire},
  title = {Stochastic optimization with adaptive

batch size: Discrete choice models as a case study},
  booktime = {Proceedings of the 8th Symposium of the European

Association for Research in Transportation (HEART), 4-6 September},
  year = {2019},
  address = {Budapest, Hungary}}}

Lederrey G., Lurkin V., Hillel T., Bierlaire M. (2019) Stochastic optimization with adaptive batch size: Discrete choice models as a case study. Proceedings of the 19th Swiss Transport Research Conference (STRC), 15-17 May, Ascona, Switzerland


@InProceedings{LEDLURHILBIER_STRC019,
  author = {Gael Lederrey and Virginie Lurkin and Tim Hillel and Michel Bierlaire},
  title = {Stochastic optimization with adaptive batch size: Discrete choice models as a case study},
  booktime = {Proceedings of the 19th Swiss Transport Research Conference (STRC), 15-17 May},
  year = {2019},
  address = {Ascona, Switzerland}}}

2018

Hillel T., Bierlaire M., Elshafie M., Jin Y. (2018) Validation of probabilistic classifiers. Proceedings of the 18th Swiss Transport Research Conference (STRC), 16-18 May, Ascona, Switzerland


@InProceedings{HilBieElsJin_STRC2018,
  author = {Tim Hillel and Michel Bierlaire and Mohammed Elshafie and Ying Jin},
  title = {Validation of probabilistic classifiers},
  booktime = {Proceedings of the 18th Swiss Transport Research Conference (STRC), 16-18 May},
  year = {2018},
  address = {Ascona, Switzerland}}}

Talks [9]

2021

Hillel T., Pougala J., Bierlaire M., Manser P., Haering T., Luethi R. Modelling mobility tool availability at a household and individual level: A case study of Switzerland February 03, 2021. hEART 2020, 9th Symposium of the European Association for Research in Transportation, Lyon, France.


@Misc{Hillel2021_heart,
  author = {Tim Hillel and Janody Pougala and Michel Bierlaire and Patrick Manser and Tom Haering and Raphael Luethi},
  title = {Modelling mobility tool availability at a household and individual level: A case study of Switzerland},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/Hillel2021_heart.pdf}},
  year = {2021},
  note = {Presented at hEART 2020, 9th Symposium of the European Association for Research in Transportation, Lyon, France}}}

Hillel T., Pougala J., Bierlaire M., Scherr W., Manser P. Validating disaggregate models at an aggregate scale: A case study of mobility tool ownership in Switzerland January 28, 2021. Applied Urban Modelling 2020: Modelling the New Urban World, Cambridge, UK.


@Misc{Hillel2021_AUM,
  author = {Tim Hillel and Janody Pougala and Michel Bierlaire and Wolfgang Scherr and Patrick Manser},
  title = {Validating disaggregate models at an aggregate scale: A case study of mobility tool ownership in Switzerland},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/Hillel2021_AUM.pdf}},
  year = {2021},
  note = {Presented at Applied Urban Modelling 2020: Modelling the New Urban World, Cambridge, UK}}}

2019

Hillel T., Bierlaire M., Jin Y. Weak teachers: A machine learning approach for assisted specification of Discrete Choice Models September 18, 2019. Applied Urban Modelling Workshop, University of Cambridge, Cambridge, UK.


@Misc{Hillel2019_AUM,
  author = {Tim Hillel and Michel Bierlaire and Ying Jin},
  title = {Weak teachers: A machine learning approach for assisted specification of Discrete Choice Models},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/Hillel2019_AUM.pdf}},
  year = {2019},
  note = {Presented at Applied Urban Modelling Workshop, University of Cambridge, Cambridge, UK}}}

Hillel T., Pougala J., Bierlaire M., Manser P., Scherr W. Activity-based Travel Demand Forecasting: Extensions to the SBB Nationwide Model September 13, 2019. nextRail19, International Rail and Mobility Conference, Swiss Federal Institute of Technology - ETH Zurich, Zurich, Switzerland.


@Misc{Hillel2019_nextRail,
  author = {Tim Hillel and Janody Pougala and Michel Bierlaire and Patrick Manser and Wolfgang Scherr},
  title = {Activity-based Travel Demand Forecasting: Extensions to the SBB Nationwide Model},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/Hillel2019_nextRail.pdf}},
  year = {2019},
  note = {Presented at nextRail19, International Rail and Mobility Conference, Swiss Federal Institute of Technology - ETH Zurich, Zurich, Switzerland}}}

Hillel T., Bierlaire M., Elshafie M., Jin Y. Weak teachers: Assisted specification of discrete choice models using ensemble learning September 04, 2019. hEART 2019, 8th Symposium of the European Association for Research in Transportation, Budapest University of Technology and Economics, Budapest, Hungary.


@Misc{Hillel2019_hEART,
  author = {Tim Hillel and Michel Bierlaire and Mohammed Elshafie and Ying Jin},
  title = {Weak teachers: Assisted specification of discrete choice models using ensemble learning},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/Hillel2019_hEART.pdf}},
  year = {2019},
  note = {Presented at hEART 2019, 8th Symposium of the European Association for Research in Transportation, Budapest University of Technology and Economics, Budapest, Hungary}}}

Hillel T. Weak teachers: assisted specification of discrete choice models using ensemble learning algorithms April 26, 2019. Workshop on Discrete Choice Models 2019, EPFL, Lausanne, Switzerland.


@Misc{Hillel_WDCM_2019,
  author = {Tim Hillel},
  title = {

Weak teachers: assisted specification of discrete choice models using ensemble learning algorithms},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/Hillel_WDCM_2019.pdf}},
  year = {2019},
  note = {Presented at Workshop on Discrete Choice Models 2019, EPFL, Lausanne, Switzerland}}}

2018

Hillel T., Bierlaire M., Elshafie M., Jin Y. A new framework for assessing classification algorithms for mode choice prediction September 05, 2018. hEART 2018, 7th Symposium of the European Association for Research in Transportation, National Technical University of Athens, Athens, Greece.


@Misc{Hillel_hEART_2018,
  author = {Tim Hillel and Michel Bierlaire and Mohammed Elshafie and Ying Jin},
  title = {A new framework for assessing classification algorithms for mode choice prediction},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/Hillel_hEART_2018.pdf}},
  year = {2018},
  note = {Presented at hEART 2018, 7th Symposium of the European Association for Research in Transportation, National Technical University of Athens, Athens, Greece}}}

Hillel T., Bierlaire M., Elshafie M., Jin Y. Validation of probabilistic classifiers May 18, 2018. 18th Swiss Transport Research Conference (STRC), Monte Verità, Ascona, Switzerland.


@Misc{Hill_STRC2018,
  author = {Tim Hillel and Michel Bierlaire and Mohammed Elshafie and Ying Jin},
  title = {Validation of probabilistic classifiers},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/Hill_STRC2018.pdf}},
  year = {2018},
  note = {Presented at 18th Swiss Transport Research Conference (STRC), Monte Verit\à, Ascona, Switzerland}}}

2017

Hillel T., Elshafie M., Jin Y. A comparison of classification methods for modelling urban mode choice June 22, 2017. Workshop on Discrete Choice Models 2017, EPFL, Lausanne, Switzerland.


@Misc{Hillel_WDCM_2017,
  author = {Tim Hillel and Mohammed Elshafie and Ying Jin},
  title = {A comparison of classification methods for modelling urban mode choice},
  howpublished = {\url{https://transp-or.epfl.ch/documents/talks/Hillel_WDCM_2017.pdf}},
  year = {2017},
  note = {Presented at Workshop on Discrete Choice Models 2017, EPFL, Lausanne, Switzerland}}}

Reviewing [7]

Research projects [3]

2020

Intelligent digital twins for assessing and predicting bridge road traffic demands

Road bridges are a vital part of transportation networks, forming crucial links in natural bottleneck locations and enabling the continual flow of people and goods into, out of, and across cities. However, the analysis used for design and maintenance planning of this community-critical infrastructure is typically carried out using static models and assuming generalized traffic patterns. This analysis represents only peak loading scenarios and does not reflect the spatial and temporal variations in real-world traffic loads. The resulting uncertainty in load prediction can lead to in overengineering in bridge design as well as sub-optimal maintenance planning. Furthermore, as current analysis techniques model only maximal loads, they cannot be used to predict the maintenance condition of bridges due to fatigue from repeated loading and unloading of the bridge over time. This research aims to address these limitations by developing intelligent digital twins which can simulate the response of a bridge to realistic traffic loading scenarios. These digital twin models combine two primary elements: (i) a traffic simulation model which exploits detailed traffic count and weigh-in-motion data to generate time-dependent traffic loadings, and (ii) a detailed structural model which predicts the compliance and maintenance condition of a bridge for different maximal and cyclic loading patterns. The intelligent digital twin is intended to be generalizable to any bridge or network of bridges for which relevant data exists. This will enable these models to be used within an integrated approach to study infrastructure vulnerability and multi-hazard risk management.
- Oct 01, 2020 - Apr 01, 2022
- Sponsor: School of Architecture, Civil and Environmental Engineering (ENAC), École polytechnique fédérale de Lausanne
- Principal investigator: Tim Hillel
- Project manager: Tim Hillel
Optimization of individual mobility plans to simulate future travel in Switzerland

This project, joint with Swiss Federal Railways (SBB) will develop a new activity-based modelling approach based on optimization of individual daily mobility plans. This approach will be implemented within SBB's existing nationwide model for Switzerland for investment and service planning decisions for future transportation.
- Sep 01, 2020 - Mar 01, 2022
- Sponsor: Innosuisse (Swiss Innovation Agency)
- Principal investigator: Michel Bierlaire
- Project manager: Tim Hillel
- Collaborators: Janody Pougala, Rico Krueger

2019

Activity based travel demand forecasting

This research project aims to update and improve the microscopic activity-based demand model developed and maintained by SBB. Specifically the research intends to address the following questions: 1. Ownership of mobility instruments: Which metrics and specifications can be added to the current model, in order to improve its ability to forecast mid-and long-term ownership of mobility instruments? More specifically, how can the notion of accessibility be integrated to the current model to capture more complex mode interactions? 2. Mode choice model: Can a tour-based approach be used to model mode choice? In addition, how can the processes to estimate destination and mode choice (currently nested) be combined to generate results that are consistent with observed mobility behaviors at different time horizons (short, mid, and long-term)? 3. Tour and activity generation: How can the generation of tours and activity patterns be combined to allow modelling of joint decisions?
- Mar 01, 2019 - Mar 01, 2020
- Sponsor: Swiss Federal Railways (SBB)
- Principal investigator: Michel Bierlaire
- Project manager: Tim Hillel
- Collaborator: Janody Pougala

EPFL courses [5]

Spring 2022

Decision-aid methodologies in transportation.
- Level: Master
- Section: Civil Engineering
- Lecturers: Tim Hillel, Nour Dougui
- Teaching assistants: Selin Atac, Janody Pougala, Marija Kukic, Negar Rezvany, Tom Haering

Spring 2021

Decision-aid methodologies in transportation.
- Level: Master
- Section: Civil Engineering
- Lecturers: Tim Hillel, Nour Dougui
- Teaching assistants: Selin Atac, Janody Pougala, Marija Kukic, Negar Rezvany, Nicola Ortelli, Stefano Bortolomiol
- Webpage: https://transp-or.epfl.ch/courses/decisionAid2020/index.php

Spring 2020

Decision-aid methodologies in transportation.
- Level: Master
- Section: Civil Engineering
- Lecturers: Tim Hillel, Nour Dougui
- Teaching assistants: Stefano Bortolomiol, Gael Lederrey, Selin Atac, Janody Pougala
- Webpage: https://transp-or.epfl.ch/courses/decisionAid2020/index.php

Fall 2019

Mathematical modeling of behavior.
- Level: Master
- Sections: Mathematics, Master in Financial Engineering
- Lecturer: Michel Bierlaire
- Teaching assistants: Tim Hillel, Meritxell Pacheco, Janody Pougala, Nicola Ortelli
- Webpage: https://moodle.epfl.ch/course/view.php?id=1001

Spring 2019

Decision-aid methodologies in transportation.
- Level: Master
- Section: Civil Engineering
- Lecturers: Nikola Obrenovic, Tim Hillel
- Teaching assistants: Stefano Bortolomiol, Gael Lederrey, Selin Atac
- Webpage: http://transp-or.epfl.ch/courses/decisionAid2019/index.php

Lectures [7]

2021

Bayesian Estimation; Individual Prediction.
- Discrete Choice Analysis: Predicting Individual Behavior and Market Demand, EPFL, Lausanne, Switzerland
- Feb 11, 2021
- Lecturer: Rico Krueger
- Assistant: Tim Hillel
Introduction; Logit Estimation and Testing.
- Discrete Choice Analysis: Predicting Individual Behavior and Market Demand, EPFL, Lausanne, Switzerland
- Feb 08, 2021
- Lecturer: Tim Hillel
- Assistant: Cloe Cortes Balcells

2019

Hybrid Choice Models.
- Discrete Choice Analysis: Predicting Individual Behavior and Market Demand, EPFL, Lausanne, Switzerland
- Mar 28, 2019
- Lecturer: Yuki Oyama
- Assistant: Tim Hillel
Bayesian Estimation; Individual Prediction.
- Discrete Choice Analysis: Predicting Individual Behavior and Market Demand, EPFL, Lausanne, Switzerland
- Mar 27, 2019
- Lecturer: Rico Krueger
- Assistants: Tim Hillel, Selin Atac, Meritxell Pacheco
Logit Mixtures; Combining RP and SP data.
- Discrete Choice Analysis: Predicting Individual Behavior and Market Demand, EPFL, Lausanne, Switzerland
- Mar 26, 2019
- Lecturer: Meritxell Pacheco
- Assistants: Tim Hillel, Rico Krueger
Nested Logit; Aggregate Forecasting.
- Discrete Choice Analysis: Predicting Individual Behavior and Market Demand, EPFL, Lausanne, Switzerland
- Mar 25, 2019
- Lecturer: Selin Atac
- Assistants: Meritxell Pacheco, Yuki Oyama, Tim Hillel
Introduction; Logit Estimation; Specification Testing.
- Discrete Choice Analysis: Predicting Individual Behavior and Market Demand, EPFL, Lausanne, Switzerland
- Mar 24, 2019
- Lecturer: Tim Hillel
- Assistants: Meritxell Pacheco, Yuki Oyama, Selin Atac

PhD supervision [3]

Janody Pougala, OASIS : An integrated optimisation framework for activity scheduling.
Gael Lederrey, Bridging the gap between model-driven and data-driven methods in the era of Big Data.
Negar Rezvany, Urban energy demand.

MSc theses [4]

2021-2022

Representing destination choice sets within activity-based models, Nicolas Salvadé (Civil Engineering)
- Supervision: Janody Pougala, Tom Haering, Tim Hillel
- External expert: Patrick Manser

2020-2021

Simulating realistic traffic flows to predict bridge loadings, Isabelle Pumford (Computer science)
- Supervision: Tim Hillel, Michel Bierlaire
- External expert: Dimitrios Papastergiou, ASTRA

2020

Generating daily activity schedules using machine learning, Sergej Gasparovich (Civil Engineering)
- Supervision: Janody Pougala, Tim Hillel, Michel Bierlaire
- External expert: Antonin Danalet

2019

Evaluation of Bootstrap methods, Jessica Hopkins (Mathematics)
- Supervision: Tim Hillel, Gael Lederrey, Michel Bierlaire
- External expert: Damon Wischik

Semester projects [6]

2020-2021

Generating choice sets of destinations for activity based applications, Nicolas Salvadé (Civil Engineering)

Supervision: Janody Pougala, Tim Hillel, Michel Bierlaire
Understanding human mobility behavior is essential to estimate and forecast transport demand. Activity-based models (ABM) are an increasingly popular approach to integrate complex behavioral dimensions influencing mobility choices, such as habits or social interactions. A signicant challenge in ABM research is the choice set for each individual (consisting of the type of activities, the schedules, the locations and the modes to reach them), which is highly combinatorial and thus impossible to fully enumerate. This pre-study will specically investigate the choice set of locations for individuals' daily activities, guided by (but not limited to) the following research questions: (1) How can we generate a plausible set of locations for an individual to consider, for each of their activities? (2) Which attributes can be used to characterize a location or a space (e.g. opening hours, public transport level of service, job density...) ? (3) What is the required granularity or scale to yield consistent results in terms of activity-based estimation, while maintaining a computational tractability ? (4) What are the data requirements for these tasks? What methods or tools can be used to augment datasets which lack this essential information ? The objective is to develop a methodology to generate a choice set of locations, by means of theoretical and data-driven approaches. The choice of location itself is not included in the scope of this project.

Generating choice sets of transport modes for activity based applications, Benoit Pahud (Civil Engineering)

Supervision: Janody Pougala, Tim Hillel, Michel Bierlaire
Understanding human mobility behavior is essential to estimate and forecast transport demand. Activity-based models (ABM) are an increasingly popular approach to integrate complex behav- ioral dimensions influencing mobility choices, such as habits or social interactions. A signicant challenge in ABM research is the choice set for each individual (consisting of the type of activities, the schedules, the locations and the modes to reach them), which is highlycombinatorial and thus impossible to fully enumerate. This pre-study will specically investigate the choice set of transport modes for individuals' daily activities, guided by (but not limited to) the following research questions: (1) How can we generate a plausible set of modes for an individual to consider, for each of their activities? (2) What are the main determinants for including/excluding a mode in the choice set? (3) What are the data requirements for these tasks? What methods or tools can be used to augment datasets which lack this essential information? The objective is to develop a methodology to generate a choice set of modes, by means of theoretical and data-driven approaches. The mode choice itself is not included in the scope of this project.

2019-2020

Tour-based mode choice modelling, Adrien Nicolet (Civil Engineering)

Supervision: Tim Hillel, Janody Pougala, Michel Bierlaire
This project will involve working with real world trip-diary data for London and/or Switzerland to develop sequential tour-based mode-choice models (i.e. where the choice of mode for a trip-leg is dependent on the full tour). The project will involve: � Extracting tour itineraries from the trip-diary data, � Investigating different tour patterns, e.g. primary return-tours, subtours, complex multilegged tours, � Defining different tour-based mode-choice situations which are able to fully represent the tour patterns existing in the data, � Developing appropriate sequential choice models for each mode-choice situation using Discrete Choice Models and/or machine learning.This project will involve working with real world trip-diary data for London and/or Switzerland to develop sequential tour-based mode-choice models (i.e. where the choice of mode for a trip-leg is dependent on the full tour). The project will involve: -Extracting tour itineraries from the trip-diary data, -Investigating different tour patterns, e.g. primary return-tours, subtours, complex multilegged tours, -Defining different tour-based mode-choice situations which are able to fully represent the tour patterns existing in the data, -Developing appropriate sequential choice models for each mode-choice situation using Discrete Choice Models and/or machine learning.

Investigating daily activity patterns, Sergey Gasparovich (Civil Engineering)

Supervision: Janody Pougala, Tim Hillel, Michel Bierlaire
This project will involve working with real world trip-diary data for Switzerland (2015 Mobility and Transport microcensus) to investigate how people schedule activities (e.g. work, lunch, sport, shopping etc) in their day. The project will involve: -Extracting and standardising daily activity schedules from the data, including representing the activity types, locations, start times, and durations with appropriate precision and/or scale, -Establishing a metric to define the similarity (or distance) between different activity schedules, -Using unsupervised machine learning (clustering) algorithms to identify prototypical activity schedules.

2019

Demand forecasting for a novel transportation mode, Denis Steffen (Mathematics)

Supervision: Selin Atac, Tim Hillel, Michel Bierlaire
The vehicle sharing systems (VSSs) are becoming more and more popular due to both economic and environmental effects. However, these systems mainly focus on two specific types of vehicles: bicycles and cars. Recently, some other types of vehicles (e.g. light electric vehicles) with some specific properties that distinguish them from cars and bicycles are introduced for use in such kind of systems. Therefore, they lack research in each component of the framework. The aim of this project is to forecast the demand for a novel transportation mode. The student should survey different types of approaches, such as ARIMA, to forecast demand in similar systems and compare them by identifying performance measures. Since the data for this specific transportation mode does not exist yet the student will be directed to use the taxi data, which shares many properties with the system of interest.

2018

Automatic utility specification using machine learning techniques, Nicola Ortelli

Supervision: Gael Lederrey, Tim Hillel, Virginie Lurkin
The objective of this project is to help building a utility function using different Data Analysis functions. It is well known that modeler spend a lot of time creating their utility function for DCMs. The goal of this project is to build an automated procedure to generate Utility functions based on the data. The tools used are Data Analysis and Machine Learning (Decision Tree, Random Forest, Linear Regression, Clustering, etc.). The utility functions found using these tools will then be compared to standard and more advanced utility functions. Another direction that we can take is using Machine Learning to extract the possible nests for a Nested Logit Model. This direction, however, requires a deeper knowledge in Discrete Choice Modelling. We are open to the discussion concerning the available directions.