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Resilience management through a cooperative autonomous shuttle-bus as a feeder to conventional public transport operation

ABG-99451 Sujet de Thèse
26/07/2021 Financement public/privé
VEDECOM et l'Univ. Gustave Eiffel
Versailles - Ile-de-France - France
Resilience management through a cooperative autonomous shuttle-bus as a feeder to conventional public transport operation
  • Informatique
  • Sciences de l’ingénieur
Resilience, Cooperative model, optimisation, simulation, autonomous shuttle, public transport

Description du sujet



The ability of mobility system to continue to serve public transport passengers under disruptive states is a resilience characteristic of infrastructure, and traffic management. The research will investigate how public transport operation is affected by potential disruptions and how stakeholders can be prepared and respond to such fluctuation by introducing an intelligent technology with cooperative strategy. In the context of this research, management of operation under potential disruptive states (i.e. physical infrastructure and service-related disruptions) is dependent on the dynamic capacity of the system to function under such fluctuations. Activating adaptive measures (i.e. dynamic optimum resource allocation, dynamic cooperative re-routing, and re-dispatching strategies), through an intelligent technology (i.e.  autonomous shuttle-bus as a feeder service), may potentially prevent or reduce this loss of functionality. Outstanding research questions are (i) the role of autonomous shuttle-bus services for enhancing the resiliency and reliability (i.e. performance of system by introducing cooperative strategies) and (ii) investigating the new service’s adaptive capacity under different disruptive scenarios. In order to develop such decision support tool and guarantee the level of service and the resilience of the network, the research comprises following parts:




Knowing the existing public transport operational profile of the urban area, an autonomous shuttle-bus operation will be designed as a feeder service to traditional public transport. The aim is to develop a dynamic adaptive strategy for the cooperative operation of disruption-responsive services to ensure the overall resilience of the network. This research work will consist of following parts:


  1. Cooperative model for the operation of autonomous shuttle-bus as a feeder to existing transit operation


  • Designing a cooperative model involving synchronization of schedules/timetables between autonomous shuttle-bus and traditional public transport services;
  • Optimizing number and locations of charging stations for feeder autonomous services;
  • Using existing simulation engine to develop a cooperative mobility layer;


Using existing simulation engine, the objective is to develop a re-allocation algorithm (for fleet and passengers) while taking into account the dynamic adaptive capacity of autonomous shuttle-bus and optimal locations of charging stations to maximize the level of service.


  1. Performance of dynamic adaptive capacity of autonomous shuttle-bus, before, during and after different disrupted states


  • Designing disruption scenarios reflecting both physical infrastructure and service fluctuations;


Disruption scenarios reflects different types of capacity fluctuations in system functionality; from both physical infrastructure and service-related fluctuations.


  • Development of re-routing strategies under disruption scenarios to reduce the loss of functionalities and increase the efficiency of the cooperative services;
  • Assessing network performance in terms of reliability and resiliency.


It is necessary to evaluate the resiliency and reliability of the overall service under cooperative operation perspective, before, during and after disruption states.




Complete the dynamic public transport data analysis platform by providing it with a tool capable of:


  • Building a cooperative autonomous service; capable of regulating the network in the event of disruptions.
  • Assessing the adaptability and responsiveness of the autonomous shuttle-bus service to ensure the reliability and resiliency of the network under normal and disrupted operating conditions.





Some Good References:


Resilience and disruption


  • BERCHE, B., VON FERBER, C., HOLOVATCH, T., & HOLOVATCH, Y. (2009). Resilience of public transport networks against attacks. The European Physical Journal. B. 71, 125.
  • Hyun Kim, Changjoo Kim & Yongwan Chun (2016) Network Reliability and Resilience of Rapid Transit Systems, The Professional Geographer, 68:1, 53-65, DOI: 10.1080/00330124.2015.1028299
  • MATTSSON L.-G., & JENELIUS E. (2015). Vulnerability and resilience of transport systems - A discussion of recent research. Transportation Research Part A: Policy and Practice. 81, 16-34.
  • JIN J.G., TANG L.C., SUN L., & LEE D.-H. (2014). Enhancing metro network resilience via localized integration with bus services. Transportation Research Part E: Logistics and Transportation Review. 63, 17-30.
  • CATS, O., & JENELIUS, E. (2015). Planning for the unexpected: The value of reserve capacity for public transport network robustness. Transportation Research Part A: Policy and Practice. 81, 47-61.
  • MAHDAVI M., S. H., BHOURI, N., & SCEMAMA, G. (2020). Dynamic Resilience of Public Transport Network: A Case Study for Fleet-Failure in Bus Transport Operation of New Delhi. Transportation Research Procedia. 47, 672-679.


Integrating Autonomous and public transportation services


  • Chen, T.D., Kockelman, K.M., Hanna, J.P., (2016). Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions. Transp. Research Part A Policy and Practice. 94, 243–254.
  • VOSOOGHI, R., PUCHINGER, J., BISCHOFF, J., JANKOVIC, M., & VOUILLON, A. (2020). Shared autonomous electric vehicle service performance: Assessing the impact of charging infrastructure. Transportation Research Part D. 81.
  • NARAYANAN, S., CHANIOTAKIS, E., & ANTONIOU, C. (2020). Shared autonomous vehicle services: A comprehensive review. Transportation Research Part C. 111, 255-293.
  • SHEN, Y., ZHANG, H., & ZHAO, J. (2018). Integrating shared autonomous vehicle in public transportation system: A supply-side simulation of the first-mile service in Singapore. Transportation Research. Part A, Policy and Practice. 113, 125-136.
  • HU B., FENG S., & NIE C. (2017). Bus transport network of Shenyang considering competitive and cooperative relationship. Physica A: Statistical Mechanics and Its Applications. 466, 259-268.
  • NARAYAN, J., CATS, O., VAN OORT, N., & HOOGENDOORN, S. (2020). Integrated route choice and assignment model for fixed and flexible public transport systems. Transportation Research Part C. 115.
  • PINTO, H. K., HYLAND, M. F., MAHMASSANI, H. S., & VERBAS, I. O. (2020). Joint design of multimodal transit networks and shared autonomous mobility fleets. Transportation Research Part C. 113, 2-20.

Nature du financement

Financement public/privé

Précisions sur le financement

VEDECOM/ Univ. Gustave Eiffel

Présentation établissement et labo d'accueil

VEDECOM et l'Univ. Gustave Eiffel




Intitulé du doctorat


Pays d'obtention du doctorat


Etablissement délivrant le doctorat

Université Gustave Eiffel

Ecole doctorale

MSTIC (mathématiques et des sciences et technologies de l'information et de la communication.

Profil du candidat

Mster 2, mathématiques appliquées ou informatique.

Classé(e) parmi les 3 premiers en Master 2

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