A conditional time-intervals formulation of the real-time Railway Traffic Management Problem

Marlière, Grégory; Richard, Sonia Sobieraj; Pellegrini, Paola; Rodríguez, Joaquín

doi:10.1016/j.conengprac.2022.105430

Cited by 4 publications

(3 citation statements)

References 81 publications

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“…Railway control is another important topic in transportation systems, addressed by Marlière, Richard, Pellegrini, and Rodriguez (2023). The authors of this paper tackle the real-time railway traffic management problem, in which the optimal choices for train schedules and routes must be found to minimize delays due to conflicts.…”

Section: Contents Of the Special Issuementioning

confidence: 99%

Guest Editorial: Innovative control approaches for smart transportation systems

Siri

Pasquale

Bhouri

et al. 2023

Control Engineering Practice

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Section: Contents Of the Special Issuementioning

confidence: 99%

Guest Editorial: Innovative control approaches for smart transportation systems

Siri

Pasquale

Bhouri

et al. 2023

Control Engineering Practice

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“…Finally, the output power of the motor is calculated in (11). (11) Moreover, when the units of , and are , and respectively, the calculation of can be given as follows:…”

Section: A Model Of Electric Truck Dynamicmentioning

confidence: 99%

“…At present, the studies for eliminating traffic conflicts in closed areas are mainly focused on the AGV fleet in container terminals [3], [4] or logistics parks [5]- [8] and train formation in railway networks [9]- [11]. The main solutions for AGV fleets are mainly predicting potential traffic conflicts and thus changing the trajectory of the involved vehicles.…”

Section: Introductionmentioning

confidence: 99%

Sequential Distributed Model Predictive Control for Automated Mining Fleet

Wang,

Yang

2023

IEEE Access

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This work mainly focuses on fleet collaborative control for autonomous mining fleets. In this paper, we first developed a double-layer coupling model including the intersection platoon (IP) in the upper layer and the following platoon (FP) in the lower layer and unified the headway calculation and the structure of the control problem of the two types of platoons. Then, the constant time headway (CTH) spacing policy is implemented to ensure safety during fleet operation. Next, a controller based on the sequential distributed model predictive control (SEQ_DMPC) algorithm is proposed to solve the platoon control problem. To verify the proposed controller, we built a second-order electric truck longitudinal dynamics model as the actuators and verified the accuracy of the model. Next, we assume that all trucks are permitted to implement optimization simultaneously at each time step in the simulation and verify the platoon following stability based on the electric truck dynamic model. Finally, we simulated the fleet production process, and the results show that the proposed controller performs well in all aspects.

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Exploration of Machine Learning-Enabled Prediction and Control Algorithms for Railway Traffic Management

Zheng

2024

Lecture Notes in Networks and Systems

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A conditional time-intervals formulation of the real-time Railway Traffic Management Problem

Cited by 4 publications

References 81 publications

Guest Editorial: Innovative control approaches for smart transportation systems

Guest Editorial: Innovative control approaches for smart transportation systems

Sequential Distributed Model Predictive Control for Automated Mining Fleet

Exploration of Machine Learning-Enabled Prediction and Control Algorithms for Railway Traffic Management

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