Phd. In Transportation Systems Analysis And Modelling

26 Aug 2023

Job Information

Organisation/Company

Université Gustave Eiffel

Research Field

Engineering

Computer science » Informatics

Mathematics

Researcher Profile

Recognised Researcher (R2)

Leading Researcher (R4)

First Stage Researcher (R1)

Established Researcher (R3)

Country

France

Application Deadline

30 Oct 2023 - 22:00 (UTC)

Type of Contract

Temporary

Job Status

Full-time

Offer Starting Date

1 Oct 2023

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Title: Investigating the relations between transportation systems designs and performances

English Resume:

This PhD proposal is part of a large research project involving four research institutions (IFPEN, ENPC, ENTPE, and Univ Eiffel). The FORBAC project aims to develop a methodology to predict the impact of mobility system decisions on environmental and socio-economic objectives. It also aims to create decision- support tools to design optimal mobility systems according to several criteria. The project aims to develop a system model to analyze causal loops from new policies, technologies, and lifestyle changes. The model will identify all the subsystems' input, output, and state variables and represent their interconnections. The resulting model will include a map of interconnections, equations, and a database to quantify decisions' positive or negative effects at different levels and time scales.

Within the project, this specific PhD proposal aims to investigate the relations between network designs (multimodal network topology, implemented traffic management strategies, public transportation operations, etc.) and network performances. The MFD concept (Geroliminis and Daganzo, 2008; Leclercq et al., 2014; Loder et al. 2017; Paipuri et al., 2021) is a powerful tool to characterize dynamic network performances. It is the foundation of many large-scale multimodal and dynamic simulation tools, e.g., the MnMS platform developed by the LICIT-ECO7 (https: // github.com/licit-lab/MnMS). Calibrating an MFD from actual observations has received much attention in the literature, making it possible to simulate existing networks. However, simulating new network configurations or large deviations in the actual network designs and regulations is challenging mainly because there is no methodological framework to predict the changes in the MFD shape.

This PhD aims to develop a methodological framework considering two main research directions. First, comparing (open) traffic data for multiple cities worldwide and actual network designs, the idea is to unravel relations between the network configurations and performances. The second direction resorts to microsimulation to conduct experiments in a controlled environment. As many configurations are not observable in the field, it would be valuable to complement the initial data analysis by exploring a more comprehensive range of configurations. Machine learning technics will be used to analyze both experimental and simulated data.

References:

Loder, A., Ambühl, L., Menendez, M., Axhausen, K.W., 2017. Empirics of multi- modal traffic networks–using the 3D macroscopic fundamental diagram. Transportation Research Part C:Emerging Technologies 82, 88 – 101.

Paipuri, M., Barmpounakis, E., Geroliminis, N., Leclercq, L., 2021. Empirical Observations of Multi-modal Network-level Models: Insights from the pNEUMA Experiment. Transportation Research part C, 131:103300.

Leclercq, L., Chiabaut, N., Trinquier, B., 2014. Macroscopic Fundamental Diagrams: a Cross-Comparison of Estimation Methods. Transportation Research part B, 62:1-12.

Geroliminis, N., Daganzo, C.F., 2008. Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings. Transportation Research Part B: Methodological 42, 759 – 770.

Mariotte, G., Paipuri, M., Leclercq., L., 2020b. Dynamics of flow merging and diverging in MFD-based systems: Validation versus microsimulation. Frontiers Future in Transportation. 1:3-18.

Mariotte, G., Leclercq, L., Batista, S.F.A., Krug, J., Paipuri, M., 2020a. Calibration and validation of multi-reservoir MFD models: A case study in Lyon. Transportation Research part B. 136:62-86.

Paipuri, M., Xu, Y., Gonzalez, M.C., Leclercq, L., 2020. Estimating MFDs, Trip Lengths and Path Flow Distributions in a Multi-region Setting Using Mobile Phone Data. Transportation Research part C, 118: 102709.

Funding category: Contrat doctoral Contrat doctoral financé dans le cadre d'un projet européen PHD title: Ph.D. in Civil and Computational Engineering PHD Country: France

Requirements

Specific Requirements

Profile of the candidate:

We seek motivated and talented candidates with experience in data analysis, machine learning, modeling, and simulation. Having skills in some of the four previous topics is sufficient to be considered for the position. Knowledge of the transportation domain or multi-agent simulation platforms would be a valuable addition. The candidate must have excellent English language skills (spoken and written).

Other information:

Hosting Laboratory: LICIT-ECO7 (Univ. Gustave Eiffel / ENTPE)

Doctoral school: MEGA – Université de Lyon (Civil and Network Engineering)

PhD supervisor: Prof. Ludovic Leclercq

Starting date: fall 2023

Gross salary: 2044 € / month the first two years, 2240 € / month

Additional Information Work Location(s)

Number of offers available

1

Company/Institute

Université Gustave Eiffel

Country

France

City

Bron

Where to apply

Website

https: // www. abg.asso.fr/fr/candidatOffres/show/idoffre/116294

Contact

Website

https:// www. univ-eiffel.fr

STATUS: EXPIRED