Data analysis for multicriteria analysis of the insertion of a shuttle into freeway traffic

Outline

  • Level of qualifications required: Master/PhD degree in data science, statistics, mathematics, civil engineering, mechanical engineering and preferably experience in transportation.
  • Starting date: January 2025
  • Duration of contract: 18 months
  • Deadline to apply: applications will be reviewed until the position is filled
  • Main location: Lyon, France (LICIT-Eco7, UNIVERSITÉ GUSTAVE EIFFEL/ENTPE)
  • Project team:
    • Christine Buisson (Pr.), LICIT-Eco7, Univ. Eiffel, ENTPE, Univ. Lyon, F-69675, LYON
    • Pierre-Antoine Laharotte (Dr.), LICIT-Eco7, Univ. Eiffel, ENTPE, Univ. Lyon, F-69675, LYON
    • Silvia Varotto (Dr.), LICIT-Eco7, ENTPE, Univ. Eiffel, Univ. Lyon, F-69675, LYON
    • Nour-Eddin EL FAOUZI (Pr.), LICIT-Eco7, Univ. Eiffel, ENTPE, Univ. Lyon, F-69675, LYON

Highlight

If you are interested in the following topics, do not hesitate to apply:

  • Motorway safety assessment in the presence of an autonomous vehicle;
  • Assessment of the effects on freeway traffic conditions of an autonomous shuttle service complementing a regular bus service;
  • Pollution assessment of a variable of an autonomous shuttle service.

If you already have some of the following skills and are motivated to add to your skills in the following areas:

  • Statistical analysis
  • Safety assessment of motorway traffic
  • Traffic condition observation and quantification (cumulative vehicle counts, OD matrix estimation)
  • Pollution modeling (Phem, Copert)
  • Traffic microscopic simulation in the presence of an automated vehicle (SUMO)

Please note that we’ll only consider and reply to applications in alignment with the proposed topic and the expected skills.

Context

We are looking for an enthusiastic research engineer to conduct and perform research in the context of the project Mob Auto2, funded by BPI France for 3 years starting in June 2023.

The project aims to enable an autonomous shuttle to ensure Public Transport serving 3 stops along the A10 freeway (France) between the train station of Massy and a multimodal hub in Longvilliers. The shuttle has to drive autonomously on the right lane of the A10 freeway with a maximal speed of 90 km/h. The shuttle is supposed to interact with other vehicles safely.

The project is split into six work packages. The current position is related mainly to task 5.1 (representing about two-thirds of the total time) and secondarily to task 1.4.

Main Activities

Task 5.1 is devoted to safety, traffic, and pollution assessment of introducing an automated shuttle (with a speed up to 90 km/h) into heavy commuting traffic on a 3-lane, 30 km-long motorway. To provide this assessment, the following activities will be deployed:

  • Data curation of the various sources used to feed the data analysis: shuttle data (speed, acceleration, distance with leader and follower, speeds of leader and follower); loop detector data; regular bus service characteristics; origin-destination matrixes.
  • Transfer of the safety assessment KPI developed for previous research, development of new ones (based on a literature review), computation of the results, analysis.
  • On-site traffic data analysis and use for extrapolation in the use case: an automated shuttle mobility service.
  • Pollution emissions prediction using a set of pollution emission models (Copert, Phem).

Task 1.4 focuses on a priori safety evaluation of the shuttle’s impact on traffic operations. Task 1.4 is currently composed of a team of two research engineers. The main expected contributions for this task are the following:

  • To contribute to the development of the network description in the microscopic traffic simulator called SUMO;
  • To contribute to the calibration of the microscopic traffic model based on available traffic data;
  • To contribute to the data processing of the collected data (shuttle data, traffic data, simulation outputs, etc.);
  • To implement post-processing codes to calculate safety Key Performance Indicators (KPI) such as Time-to-Collision, DRAC, etc.;
  • To draw a comprehensive comparison between reference scenarios (without autonomous shuttles) and scenarios involving the autonomous shuttle;
  • To validate and evaluate the data resulting from the microscopic traffic simulator.

The teams of tasks 5.1 and 1.4 will define a common and realistic timeline for the various tasks. For both tasks, the candidate will be involved in data curation and analysis, simulation (with various tools), analytical extrapolation of the observed scenarios with realistic definitions of prospective situations, assessment criteria definition and programming, and deliverables writing. The publication of the results not only in deliverables but also in peer-reviewed journals is a collective goal.

Required skills

We seek strongly motivated candidates with a solid background in data science, statistics, mathematics, civil engineering, and mechanical engineering.

Experience in the domain of transportation is preferable.

Good programming and data-driven modelling skills are prerequisites.

Experience with the following tools is desirable: SUMO / SymuVia (microscopic traffic simulator developed by the LICITEco7 Lab) and CARLA.

Programming skills with Python are desired.

Proven written and verbal communication skills in English are required.

Collaboration opportunities

The candidate will closely interact with members of the LICIT-Eco7, the French Department of Transportation, and the collaborators within the MOBAUTO2 project, like MILLA, who built the autonomous shuttle. International collaborations will also be encouraged, with a focus on safety assessment using detailed trajectory data, particularly in the context of automated vehicles.

Salary

The wage is set according to the civil servant grid, which takes into consideration the work experience as follows:

  • 2,100-2,900€ (gross monthly salary, depending on years of work experience)
  • 1,700-2,300€ (net salary, depending on years of work experience)

Contacts

  • Prof. Christine Buisson – christine.buisson@entpe.fr
  • Dr Pierre-Antoine LAHAROTTE – pierre-antoine.laharotte@univ-eiffel.fr
  • Dr Silvia VAROTTO – SilviaFrancesca.VAROTTO@entpe.fr
  • Prof. Nour-Eddin EL FAOUZI – nour-eddin.elfaouzi@univ-eiffel.fr

Required documents

  • A curriculum vitae;
  • The complete record of master grades (academic scores for French applicants);
  • The master or PhD thesis manuscript;
  • A short (2-page max) motivation letter discussing how the candidate’s background and research interest relate to the proposed subject and bibliographic references.

About us, the LICIT-Eco7 Lab – Université Gustave Eiffel & ENTPE

The Transport and Traffic Engineering Laboratory (LICIT) is a Joint Research Unit under the dual administrative supervision of the UNIVERSITÉ GUSTAVE EIFFEL and the National Post-Graduate School of Public Works (ENTPE). It is recognized for its work in traffic modelling and engineering. The laboratory has already developed many successful applications for traffic information and simulation tools (e.g. SymuVia).

The UNIVERSITÉ GUSTAVE EIFFEL is a state-financed scientific and technological institute under the supervision of the Ministry of Research and the Ministry of Transportation. The Institute’s activities include various fields as acoustics, mechanics, mathematics, computer science, electronics and electrotechnical sciences. The diversity of the approach used to carry out the different research programs gives a multidisciplinary characteristic to the UNIVERSITÉ GUSTAVE EIFFEL research teams.

The UNIVERSITÉ GUSTAVE EIFFEL research program covers many aspects of work involved within this project including driving aids, information, assistance and automation; transport networks and services; sustainability, environment and road safety. UNIVERSITÉ GUSTAVE EIFFEL has been and still is heavily involved in various projects, especially European Commission-supported research like the INTRO project. UNIVERSITÉ GUSTAVE EIFFEL’s experience will be used to assist in achieving the aims of this project.

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