Skip to main content
EURAXESS

MSCA-COFUND-CLEAR-Doc-PhD Position#CD22-46: Comparative analysis of synthetic travel demand modeling approaches

19/10/2022

Job Information

Organisation/Company
Université Gustave Eiffel
Department
COSYS-GRETTIA
Research Field
Mathematics
Mathematics » Applied mathematics
Researcher Profile
First Stage Researcher (R1)
Country
France
Application Deadline
Type of Contract
Temporary
Job Status
Full-time
Hours Per Week
35
Is the job funded through the EU Research Framework Programme?
H2020 / Marie Skłodowska-Curie Actions COFUND
Marie Curie Grant Agreement Number
101034248
Is the Job related to staff position within a Research Infrastructure?
No

Offer Description

Traditionally, transport planning has focused on large flows between spatial regions and gave insights on the need for constructing new road or rail infrastructure. Today, the focus has shifted towards making efficient use of the existing infrastructure with limited changes to the built environment. Such an efficient use requires an intelligent and dynamic matching between demand (travelers and their mobility needs) and supply (transport offers and services. Tools for transport planning are moving from flow-based zonal analysis tools to agent-based simulations in which the dynamic interactions between demand and supply can be modeled and studied in detail.

A precondition for performing such simulations are synthetic demand data sets which describe in detail households, persons, and their daily movements in a given territory. To allow for analyses on a high level of detail, sociodemographic attributes of the households and persons need to be congruent with reality, as well as the movements that should follow realistic daily travel patterns. Those can then be used in a detailed transport simulation to explore which services are the most ecological, comfortable and/or cost-efficient in a given territorial context.

Various approaches for population (households and persons) and demand (movements) synthesis have been proposed. For population synthesis, approaches based on sample weighting are most common (Yameogo et al., 2021), while alternative methods based on Bayesian networks (Sun and Erath, 2015), Hierchichal Mixture Models (Sun et al., 2018), Hidden Markov Models (Saadi et al., 2016b) or Deep Neural Networks (Borysov et al., 2019) have been proposed in recent years. To generate the daily movements of a synthetic population, statistical matching approaches are common (Namazi-Rad et al., 2017) that are challenged by novel Bayesian Network-based methods (Joubert and de Waal, 2020) or Hidden Markov Models (Saadi et al., 2016a). Other aspects such as location choice (Yoon et al., 2012) and discretionary activity assignment (Hörl and Axhausen, 2021) are even less covered in literature. To date, there is an evident gap in literature on comparing those competing or complementary approaches with each other and consistent criteria for comparison are missing. In order to allow for a comparative assessment of synthetic travel demand generation methods, (Hörl and Balac, 2021) have developed a consistent pipeline from raw, publicly available open data sets to final synthetic travel demand data sets for France. The approach, hence, provides an ideal test bed for benchmarking and comparing existing methods along the generation process.

The thesis will put in place a comparative analysis of existing population synthesis methods and proposes, where applicable, new extensions to the existing methods or combinations thereof. The comparison should be carried out in a horizontal (various algorithms in either population synthesis, location assignment, trip assignment, etc.) and vertical fashion (reconfiguring individual processing steps along an end-to-end generation pipeline). To that end, evaluation criteria need to be developed and consolidated regarding representativeness of the synthetic data and variability (vs. overfitting) towards future and policy scenarios. Furthermore, a special focus should be put on formalizing the flow of information through the modeling pipeline, allowing to consistently assess which correlations between environmental, infrastructural, social, and travel characteristics are expected to have an impact on each other.

The thesis project will follow a sequence of work activities:

- Literature review on existing algorithms along the synthetic travel demand pipeline, including classification of the existing approaches and identification of algorithmic gaps.

- Development of a benchmarking pipeline for synthetic travel demand. Integration of the required functionality to track correlations between the inputs and outputs of applied models. Integration of a set of relevant algorithms and methods for comparison, especially taking into account methods for the processing of mobility traces.

- Definition of key performance indicators on the quality and responsiveness of synthetic travel demand data sets, also taking into account the correlation structure of the generated data set.

- Benchmarking of the implemented approaches in terms pf the defined indicators and in terms of data granularity. Provision of a roadmap for future developments on synthetic travel demand modeling, taking into account the obtained results and insights.

References:

- Borysov, S.S., Rich, J., Pereira, F.C., 2019. How to generate micro-agents? A deep generative modeling approach to population synthesis. Transp. Res. Part C Emerg. Technol. 106, 73–97.

- Hörl, S., Axhausen, K.W., 2021. Relaxation–discretization algorithm for spatially constrained secondary location assignment. Transp. Transp. Sci. 1–20.

- Hörl, S., Balac, M., 2021. Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data. Transp. Res. Part C Emerg. Technol. 130, 103291.

- Joubert, J.W., de Waal, A., 2020. Activity-based travel demand generation using Bayesian networks. Transp. Res. Part C Emerg. Technol. 120, 102804.

- Namazi-Rad, M.-R., Tanton, R., Steel, D., Mokhtarian, P., Das, S., 2017. An unconstrained statistical matching algorithm for combining individual and household level geo-specific census and survey data. Comput. Environ. Urban Syst. 63, 3–14.

- Saadi, I., Mustafa, A., Teller, J., Cools, M., 2016a. Forecasting travel behavior using Markov Chains-based approaches. Transp. Res. Part C Emerg. Technol. 69, 402–417.

- Saadi, I., Mustafa, A., Teller, J., Farooq, B., Cools, M., 2016b. Hidden Markov Model-based population synthesis. Transp. Res. Part B Methodol. 90, 1–21.

- Sun, L., Erath, A., 2015. A Bayesian network approach for population synthesis. Transp. Res. Part C Emerg. Technol. 61, 49–62.

- Sun, L., Erath, A., Cai, M., 2018. A hierarchical mixture modeling framework for population synthesis. Transp. Res. Part B Methodol. 114, 199–212.

- Yameogo, B.F., Vandanjon, P.-O., Gastineau, P., Hankach, P., 2021. Generating a Two-Layered Synthetic Population for French Municipalities: Results and Evaluation of Four Synthetic Reconstruction Methods. J. Artif. Soc. Soc. Simul. 24, 5.

- Yoon, S.Y., Deutsch, K., Chen, Y., Goulias, K.G., 2012. Feasibility of using time–space prism to represent available opportunities and choice sets for destination choice models in the context of dynamic urban environments. Transportation 39, 807–823.

Requirements

Research Field
Mathematics
Education Level
Bachelor Degree or equivalent
Skills/Qualifications
  • At the time of the deadline, applicants must be in possession or finalizing their Master’s degree or equivalent/postgraduate degree.
  • At the time of recruitment, applicants must be in possession of their Master’s degree or equivalent/postgraduate degree which would formally entitle to embark on a doctorate.
Languages
FRENCH
Level
Basic
Languages
ENGLISH
Level
Good

Additional Information

Benefits
  • High-quality doctoral training rewarded by a PhD degree, delivered by Université Gustave Eiffel
  • Access to cutting-edge infrastructures for research & innovation.
  • Appointment for a period of 36 months based on a salary of 2 700 € (gross salary per month).
  • Job contract under the French labour legislation in force, respecting health and safety, and social security: 35 hours per week contract, 25 days of annual leave per year.
  • International mobility will be mandatory
  • An international environment supported by the adherence to the European Charter & Code.
  • Access to dedicated CLEAR-Doc trainings with a strong interdisciplinary focus, together with a Career development Plan.
Eligibility criteria

Applicants must fulfil the following eligibility criteria:

  • At the time of the deadline, applicants must be in possession or finalizing their Master’s degree or equivalent/postgraduate degree.
  • At the time of recruitment, applicants must be in possession of their Master’s degree or equivalent/postgraduate degree which would formally entitle to embark on a doctorate.
  • At the time of the deadline, applicants must be in the first four years (full-time equivalent research experience) of their research career (career breaks excluded) and not yet been awarded a doctoral degree. Career breaks refer to periods of time where the candidate was not active in research, regardless of his/her employment status (sick leave, maternity leave etc). Short stays such as holidays and/or compulsory national service are not taken into account.
  • At the time of the deadline, applicants must fulfil the transnational mobility rule: incoming applicants must not have resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the 3 previous years.
  • One application per call per year is allowed.
  • Applicants must be available full-time to start the programme on schedule (November 1st 2023).
  • Application rules are enforced by the French doctoral system which specifies a standard duration of 3 years for a full-time PhD together with the MSCA standards and the OTM-R European rules as follows.
  • Citizens of any nationality may apply to the programme.
  • There is no age limit.
Selection process

Please refer to the Guide for Applicants available on the CLEAR-Doc website: https://clear-doc.univ-gustave-eiffel.fr/how-to-apply/useful-documents/

Additional comments
  • The First step before applying is contacting the PhD supervisor. You will not be able to apply without an acceptation letter from the PhD supervisor.
  • International mobility planned: A 6 month mobility is planned at Mac Gill University (Canada).
  • Please contact the PhD supervisor for any additional detail on job offer.
  • There are no restrictions concerning the age, gender or nationality of the candidates. Applicants with career breaks or variations in the chronological sequence of their career, with mobility experience or with interdisciplinary background or private sector experience are welcome to apply.
  • Support service is available during every step of the application process by email: clear-doc@univ-eiffel.fr
Website for additional job details

Work Location(s)

Number of offers available
1
Company/Institute
Université Gustave Eiffel
Country
France
City
Marne-La-Vallée
Postal Code
77454
Street
5, Boulevard Descartes
Geofield

Contact

City
Marne-La-Vallée
Website
Street
5, Boulevard Descartes
Postal Code
77454
E-Mail
latifa.oukhellou@univ-eiffel.fr
sebastian.horl@irt-systemx.fr