Genetic algorithm and particle swarm optimization algorithm for speed error reduction in railway signaling systems

Sandidzadeh, Mohammad Ali; Heydari, Amir; Khodadadi, Ali

doi:10.1002/acs.2320

Cited by 7 publications

(4 citation statements)

References 6 publications

(4 reference statements)

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“…defect prediction [80] failure prediction [81] defect detection [82] Y: defect prediction [83] defect detection [84], [85] Safety and security Y: train protection [86], speed error reduction [87] Y: accidents [53] disruptions [88] Autonomous driving and control Y: energy optimization [89] intelligent train control [90] Y: intelligent train control [55] Traffic planning and management Y: train timetabling [91], [92] Y: delay analysis [40], train rescheduling [93] train timetabling [63], [94], train shunting [95] Revenue management P: revenue simulation [96] P: overall revenue management [97] inventory control and prediction [98] Transport policy P: energy network policy making [99] U Passenger mobility P: demand forecasting [100] Y: flow prediction [101], [102] and reinforcement learning for optimal train control. Reference [89] proposed a method for energy optimisation of the train movement applying control based on genetic algorithms.…”

Section: Machine Learningmentioning

confidence: 99%

“…This paper also shows that rail traffic can be improved regarding the increase of timetable stability and maximizing capacity subject to safety constraints. More strategically, [87] combined a genetic algorithm, particle swarm optimisation algorithm, and Kalman filtering for determining the best locations of balises in order to minimise speed error of railway vehicles. Passenger mobility.…”

Section: Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications

Bešinović

Donato

Flammini

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Artificial Intelligence (AI) is becoming pervasive in most engineering domains, and railway transport is no exception. However, due to the plethora of different new terms and meanings associated with them, there is a risk that railway practitioners, as several other categories, will get lost in those ambiguities and fuzzy boundaries, and hence fail to catch the real opportunities and potential of machine learning, artificial vision, and big data analytics, just to name a few of the most promising approaches connected to AI. The scope of this paper is to introduce the basic concepts and possible applications of AI to railway academics and practitioners. To that aim, this paper presents a structured taxonomy to guide researchers and practitioners to understand AI techniques, research fields, disciplines, and applications, both in general terms and in close connection with railway applications such as autonomous driving, maintenance, and traffic management. The important aspects of ethics and explainability of AI in railways are also introduced. The connection between AI concepts and railway subdomains has been supported by relevant research addressing existing and planned applications in order to provide some pointers to promising directions.

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Section: Machine Learningmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications

Bešinović

Donato

Flammini

et al. 2022

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…Balises provide information to a train to check the actual train location and to calibrate its odometer. It is mentioned in [9] that proper balise positioning also reduces train headways and corrects speed errors.…”

Section: Introductionmentioning

confidence: 99%

Modeling Moving-Block Railway Systems: A Generalized Batches Petri Net Approach

Durmuş

Takai

2013

SICE Journal of Control, Measurement, and System Integration

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Instead of conventional signaling systems (fixed-block signaling systems), moving-block signaling systems are in use to increase transport capacity by reducing headways on railway lines. A moving-block is considered as the sum of the length of the train and the safe following distance between trains. Communication Based Train Control (CBTC) systems and European Rail Traffic Management System (ERTMS) application level 3 are examples of moving-block signaling systems. In this study, speed control of two consecutive trains as moving-block is realized in two levels: the modeling level and the control level. To cope with both discrete and continuous behavior of the moving-block signaling system, a Generalized Batches Petri Net (GBPN) approach is used for modeling the system whereas a fuzzy logic control method is proposed at the control level. Simulation results are shown in order to demonstrate the accuracy of the proposed approach.

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“…14 These algorithms have been investigated in many articles, and their performances were compared in terms of processing time, convergence speed and quality of results. [15][16][17] These recent items may be considered as a benchmark to compare different EAs for different applications. Although all EAs attempt to achieve the global optimum of a problem, the convergence speed and the quality of results may be different depending on their type, parameters and applications.…”

Section: Introductionmentioning

confidence: 99%

Imperialist competitive algorithm–based adaptive fuzzy control of a pneumatic actuator

Ahmadi¹,

Tavakolpour-Saleh

Yazdani

2013

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article presents an investigation into the application of a constrained imperialist competitive algorithm with a new penalty function to optimize an adaptive fuzzy proportional–integral–derivative controller for a pneumatic actuator. The integral absolute error and the maximum overshoot of the control system are considered as the cost functions. The constrained imperialist competitive algorithm–based optimization scheme is thus conducted to obtain the best structure of the fuzzy proportional–integral–derivative controller involving optimum shape and location of membership functions and suitable value of scale factors. Then, a simulation study based on the identified model of the pneumatic actuator and three control approaches namely conventional proportional–integral–derivative control, genetic algorithm–based adaptive fuzzy proportional–integral–derivative control and the proposed constrained imperialist competitive algorithm–based adaptive fuzzy proportional–integral–derivative control is carried out to evaluate the performance of the proposed controllers. Finally, an experimental rig is developed to verify the simulation outcomes. It is found that the constrained imperialist competitive algorithm–based fuzzy controller converges faster to an optimum solution compared to the genetic algorithm method. Besides, the superiority of the proposed constrained imperialist competitive algorithm–based design over other controllers is demonstrated.

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Genetic algorithm and particle swarm optimization algorithm for speed error reduction in railway signaling systems

Cited by 7 publications

References 6 publications

Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications

Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications

Modeling Moving-Block Railway Systems: A Generalized Batches Petri Net Approach

Imperialist competitive algorithm–based adaptive fuzzy control of a pneumatic actuator

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