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PhD. in Modelling and Optimisation of Transportation Systems

ABG-102349 Sujet de Thèse
14/01/2022 Contrat doctoral
Université Gustave Eiffel
Bron - Auvergne-Rhône-Alpes - France
PhD. in Modelling and Optimisation of Transportation Systems
  • Sciences de l’ingénieur
  • Informatique
  • Mathématiques
computer science, transportation, simulation, Traffic, multimodal

Description du sujet

Title: Dynamic lane allocation strategies fostering social objectives in multimodal transportation systems.

 

English Resume:

Transportation systems are currently experiencing a fundamental transformation of their structures and organization, triggered by new technologies (self-driving cars, on-demand services, artificial intelligence) and the development of the sharing economy. In the past, travel choices usually restrict to private cars or public transportation. Now, we observe an abundance of travel options (biking, car-sharing, car-pooling, on-demand services, regular public transport). While promoting virtuous behaviors like ride-sharing, some options can also lead to increased distance-traveled because of improper management of idle vehicles or large fleet sizes. Competitions between mobility services may also harm the overall mobility and increase congestion, fuel consumption, and emissions. It is the case when ride-hailing services take users from public transportation because they offer more appealing travel options at the individual level. Finally, all mobility systems and services share the same and limited urban space, which give some leverage to local authorities to define how to shape multimodal transportation system and prioritize some mobility offers over other to reach collective goals.

 

The goal of this Ph.D. is to investigate how dynamic lane allocation policies can improve multimodal transportation network functioning and foster mobility options most in favor of collective optimum, i.e., reducing total travel times, fuel consumptions, and emissions. Several successful implementations of dynamic lane allocation have been developed for urban corridors, e.g., intermittent bus lanes (Chiabaut et al., 2012, 2014), or dynamic car-bus corridors (Anderson and Geroliminis, 2020) or pre-signal strategies (He et al, 2016). The idea is to allow a subset of vehicles in the dedicated bus lanes when no buses are circulating or by keeping the interactions at a minimum. Permanent lane allocation strategies are also used to favor some transportation modes, like bikes or taxis in dedicated bus lanes or high-occupancy lanes. In the future, a larger share of urban space might be dynamically segregated for travelers willing to use specific modes of transport. However, the situation is challenging in practice, when the road infrastructure capacity cannot be easily divided or when critical events may change the transportation priorities, infrastructure accessibility, or demand allocation. Some streets could be reserved, but significant effort is needed to (i) define which users have accessed to specific capacity batches to guaranty an optimal usage of the network, from a collective point of view and (ii) create connected subnetworks of dedicated roads that provides access to crucial parts of the city. It should be mentioned that dynamic lane allocation strategies can be framed in different ways like fully time-based, i.e., the capacity is allocated to only one class of vehicles at a time, or hybrid, i.e., both space and mode allowance can change with time. The connections between lane allocation and intersection control schemes should be investigated to guarantee smooth crossing for prioritized users. Actual traffic signals can commonly provide priority to transit vehicles on the main corridor but the problem becomes much more complex if they have to arbitrate between multiple users with different priority levels coming from multiple directions.

 

During this Ph.D., we will address the large-scale implementation of dynamic lane allocation policy considering daily demand profiles and different modes of transportation (cars, public transit, e-hailing services, and bikes). The robustness of the design will be tested considering different daily profiles mimicking the annual variations of urban loading patterns. To perform the simulations, we will resort to a new agent-based simulation framework developed in both ERC MAGnUM and METAFEW (Lamotte and Geroliminis, 2018; Mariotte and Leclercq, 2019; Paipuri and Leclercq., 2020). It permits us to consider all trips individually, whatever the transportation mode is while providing fast calculation, which is paramount for assessing the results of different lane allocation strategies and optimizing their parameters. This framework will be extended to better represent the local congestion dynamics related to the considered actual lane allocation schemes. About test cases, we can resort to realistic simulation environment (realistic demand profiles and network settings) implemented by the LICIT laboratory for the city of Lyon. Particular attention will be paid to designs fostering cooperation between complementary modes like on-demand or ride-sharing services and public transportation. For example, dynamic lane allocation could improve the accessibility of major public transportation hubs by on-demand services while not providing a competitive advantage in urban regions where public transportation is well-developed.

 

References:

 

Anderson, P., Geroliminis, N., 2020. Dynamic lane restrictions on congested arterials, Transportation Research Part A: Policy and Practice, 135, 224-243,

Chiabaut, N., Xie, X. Leclercq, L., 2014. Performance analysis for different designs of a multimodal urban arterial, Transportmetrica B: Transport Dynamics, 2014, 2(3), 229-245.

Chiabaut, N., Xie, X., Leclercq, L., 2012. Road Capacity and travel times with bus lanes with Intermittent Priority activation: analytical investigations, Transportation Research Record, 2315:182-190.

He, H., Guler, I., Menendez, M., 2016. Adaptive control algorithm to provide bus priority with a pre-signal, Transportation Research Part C: Emerging Technologies, 64, 28-44,

Lamotte R, Geroliminis N, 2018. The morning commute in urban areas with heterogeneous trip lengths. Transportation Research Part B: Methodological 117:794–810

Mariotte, G., Leclercq, L., 2019. Flow exchanges in multi-reservoir systems with spillbacks. Transportation Research part B, 122:327-349.

Paipuri, M., Leclercq, L., 2020. Bi-modal Macroscopic Traffic Dynamics in a Single Region. Transportation Research part-B, 130:257-290.

Zheng, N. and Geroliminis, N. ,2013. On the distribution of urban road space for multimodal congested networks. Procedia - Social and Behavioral Sciences, 80:119–138. 20th International Symposium on Transportation and Traffic Theory (ISTTT 2013)

Prise de fonction :

03/10/2022

Nature du financement

Contrat doctoral

Précisions sur le financement

Contrat doctoral financé par l'Université Gustave Eiffel

Présentation établissement et labo d'accueil

Université Gustave Eiffel

The PhD. will be supervised by Prof. Ludovic Leclercq (LICIT, Univ. Gustave Eiffel / ENTPE) and Prof. Nikolas Geroliminis (EPFL, LUTS, Switzerland). The primary location for the thesis is Lyon but several visiting periods (between 12 to 18 months in total) will happen at EPFL.

Intitulé du doctorat

Ph.D. in Civil and Computational Engineering

Pays d'obtention du doctorat

France

Etablissement délivrant le doctorat

Université Gustave Eiffel

Ecole doctorale

MEGA

Profil du candidat

Skills:

We are looking for motivated and talented candidates who have experience in modelling, simulation and optimisation. Knowledge of traffic models and/or multi-agent simulation platforms will be highly appreciated. The candidate must have very good English language skills (spoken and written).

 

Other information:

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

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

PhD supervisor: Prof. Ludovic Leclercq and Prof. Nikolas Geroliminis

Location: Lyon, France

Starting date: 01/11/2022

Gross salary: 1764 € / month the first two years, 2058 € / month the last year (this amount may be updated in 2022)

 

 

11/03/2022
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