Ifsttar PhD subject

French version
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Title : Traffic flows as autonomic systems: application to railway traffic management

Main host Laboratory - Referent Advisor   -

Director of the main host Laboratory   -

PhD Speciality Informatique, Intelligence artificielle, Recherche opérationnelle

Axis of the performance contract 1 - COP2017 - Efficient transport and safe travel

Main location Lille-Villeneuve d'Ascq

Doctoral affiliation ECOLE CENTRALE LILLE

PhD school SCIENCES POUR L'INGENIEUR (SPI)

Planned PhD supervisor RODRIGUEZ Joaquin  -  Université Gustave Eiffel  -  COSYS - ESTAS

Planned financing Contrat doctoral  - Ifsttar

Abstract

An autonomic system is a collection of autonomic elements. They are functionally equivalent and they manage their behavior as a function of politics defined by the system administrator. They do the same for managing the relations with each other and with the environment. The global behavior of the system corresponds to the emergent behavior which results from the actions and interactions of the elements. The applications of the autonomic systems appeared in the literature strictly concern the informatics research field (Agarwal et al., 2003 ; Quiroz et al., 2009). In this thesis, the use of the concept of autonomic system will be extended to model traffic flows in a transport system.
Indeed, the main transport systems are subjected to the development of more and more “intelligent” vehicles. These vehicles are often capable of analyzing their own internal state, of communicating with each other and with the environment, and even of making decisions on their behavior. Hence, these types of intelligent vehicles can be modeled as autonomic elements, making choices which are both autonomous and coherent, based on the observation of their neighborhood. Moreover, these choices must be coherent with the politics defined by the system regulator. To allow these choices to be efficient from both the individual and the collective point of view, it is crucial to design algorithms for decision making in a dynamic environment. Moreover, approaches for the negotiation among vehicles will have to be put in place to harmonize the choices in case of potential conflict.
The application considered in this thesis will concern railway traffic. The improvement of the reliability of the railway system is a very important social concern, and it necessarily requires the deployment of a system to efficiently manage traffic. The study of optimization methods for solving the traffic management problem in real time has been object of several works at the European level (Cacchiani et al., 2014). In particular, IFSTTAR actively contributes to these works (Rodriguez, 2007; Pellegrini et al., 2014, 2015), also thanks to the participation to national and international research projects. The existing methods deal with traffic management in a centralized way, following the current European practice. However, this centralized management suffers from issues as the fact of limiting the market opening to an actual competition.
Some of these issues may be overcome through the modeling of traffic as an autonomic system. This corresponds to a radical change of approach, introducing a decentralized traffic management system, based on the direct and indirect interaction among trains, and on the their autonomous decision making. Through a negotiation phase, the decisions of different trains will be finalized to achieve an effective traffic management at the system-level. In this context, the trains will represent the autonomic systems, which will choose their speed as a function of their state as well as on the state of the whole traffic. These choices will be optimized thanks to adaptive algorithms for decision making in a dynamic environment, and they will be subject to negotiation in case of potential conflict.

M. Agarwal, V. Bhat, H. Liu, V. Matossian, V. Putty, C. Schmidt, G. Zhang, L. Zhen, M. Parashar, B. Khargharia et S. Hariri (2003). AutoMate/ enabling autonomic applications on the grid. In: Autonomic Computing Workshop: 48-57.
Cacchiani, V., Huisman, D., Kidd, M., Kroon, L., Toth, P., Veelenturf, L., Wagenaar, J. (2014). An overview of recovery models and algorithms for real-time railway rescheduling. Transportation Research Part B, 63: 15-37.
Pellegrini, P., Marlière, G., Pesenti, R., and Rodriguez, J. (2015). RECIFE-MILP: an effective MILP-based heuristic for the real-time railway trafic management problem. Intelligent Transportation Systems, IEEE Transactions on, 16(5): 2609-2619.
Pellegrini, P., Marlière, G., and Rodriguez, J. (2014). Optimal train routing and scheduling for managing traffic perturbations in complex junctions. Transportation Research Part B, 59: 58-80.
A. Quiroz, K. Hyunjoo, M. Parashar, N. Gnanasambandam et N. Sharma (2009). Towards autonomic workload provisioning for entreprise grids and clounds. In: 10th IEEE/ACM International Conference on Grid Computing: 50-57.
Rodriguez, J. (2007). A constraint programming model for real-time train scheduling at junctions, Transportation Research Part B, 41: 231–245.

Keywords : Autonomic system, railway transport, traffic management, optimization, adaptive algorithms

Main host Laboratory - Referent Advisor	-
Director of the main host Laboratory	-
PhD Speciality	Informatique, Intelligence artificielle, Recherche opérationnelle
Axis of the performance contract	1 - COP2017 - Efficient transport and safe travel
Main location	Lille-Villeneuve d'Ascq
Doctoral affiliation	ECOLE CENTRALE LILLE
PhD school	SCIENCES POUR L'INGENIEUR (SPI)
Planned PhD supervisor	RODRIGUEZ Joaquin - Université Gustave Eiffel - COSYS - ESTAS
Planned financing	Contrat doctoral - Ifsttar
Abstract An autonomic system is a collection of autonomic elements. They are functionally equivalent and they manage their behavior as a function of politics defined by the system administrator. They do the same for managing the relations with each other and with the environment. The global behavior of the system corresponds to the emergent behavior which results from the actions and interactions of the elements. The applications of the autonomic systems appeared in the literature strictly concern the informatics research field (Agarwal et al., 2003 ; Quiroz et al., 2009). In this thesis, the use of the concept of autonomic system will be extended to model traffic flows in a transport system. Indeed, the main transport systems are subjected to the development of more and more “intelligent” vehicles. These vehicles are often capable of analyzing their own internal state, of communicating with each other and with the environment, and even of making decisions on their behavior. Hence, these types of intelligent vehicles can be modeled as autonomic elements, making choices which are both autonomous and coherent, based on the observation of their neighborhood. Moreover, these choices must be coherent with the politics defined by the system regulator. To allow these choices to be efficient from both the individual and the collective point of view, it is crucial to design algorithms for decision making in a dynamic environment. Moreover, approaches for the negotiation among vehicles will have to be put in place to harmonize the choices in case of potential conflict. The application considered in this thesis will concern railway traffic. The improvement of the reliability of the railway system is a very important social concern, and it necessarily requires the deployment of a system to efficiently manage traffic. The study of optimization methods for solving the traffic management problem in real time has been object of several works at the European level (Cacchiani et al., 2014). In particular, IFSTTAR actively contributes to these works (Rodriguez, 2007; Pellegrini et al., 2014, 2015), also thanks to the participation to national and international research projects. The existing methods deal with traffic management in a centralized way, following the current European practice. However, this centralized management suffers from issues as the fact of limiting the market opening to an actual competition. Some of these issues may be overcome through the modeling of traffic as an autonomic system. This corresponds to a radical change of approach, introducing a decentralized traffic management system, based on the direct and indirect interaction among trains, and on the their autonomous decision making. Through a negotiation phase, the decisions of different trains will be finalized to achieve an effective traffic management at the system-level. In this context, the trains will represent the autonomic systems, which will choose their speed as a function of their state as well as on the state of the whole traffic. These choices will be optimized thanks to adaptive algorithms for decision making in a dynamic environment, and they will be subject to negotiation in case of potential conflict. M. Agarwal, V. Bhat, H. Liu, V. Matossian, V. Putty, C. Schmidt, G. Zhang, L. Zhen, M. Parashar, B. Khargharia et S. Hariri (2003). AutoMate/ enabling autonomic applications on the grid. In: Autonomic Computing Workshop: 48-57. Cacchiani, V., Huisman, D., Kidd, M., Kroon, L., Toth, P., Veelenturf, L., Wagenaar, J. (2014). An overview of recovery models and algorithms for real-time railway rescheduling. Transportation Research Part B, 63: 15-37. Pellegrini, P., Marlière, G., Pesenti, R., and Rodriguez, J. (2015). RECIFE-MILP: an effective MILP-based heuristic for the real-time railway trafic management problem. Intelligent Transportation Systems, IEEE Transactions on, 16(5): 2609-2619. Pellegrini, P., Marlière, G., and Rodriguez, J. (2014). Optimal train routing and scheduling for managing traffic perturbations in complex junctions. Transportation Research Part B, 59: 58-80. A. Quiroz, K. Hyunjoo, M. Parashar, N. Gnanasambandam et N. Sharma (2009). Towards autonomic workload provisioning for entreprise grids and clounds. In: 10th IEEE/ACM International Conference on Grid Computing: 50-57. Rodriguez, J. (2007). A constraint programming model for real-time train scheduling at junctions, Transportation Research Part B, 41: 231–245.
Keywords :	Autonomic system, railway transport, traffic management, optimization, adaptive algorithms

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