Automatic Train Operation Using Autonomic Prediction of Train Runs

Asuka, Masashi; Kataoka, K.; Komaya, Kiyotoshi; Nishida, Syogo

doi:10.1541/ieejias.128.1365

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…In 1987, Japan developed an intelligent train automatic driving system based on fuzzy predictive control and successfully applied it to the Sendai subway in Japan. This method combined with the predictive control method and solved the shortcoming of low-accuracy fuzzy control, which became a model of the predictive control method in ATO systems [12]. Australian scholars theoretically proved that the best maneuvering strategies for train operation on an ideal line are: maximum force traction, cruise, idle and maximum force braking.…”

Section: Introductionmentioning

confidence: 99%

A Modeling Method for the Driving Process of Heavy-Haul Train Based on Multi-Mass Model

2020

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Unlike the locomotive, heavy-haul trains have large carrying capacity and consist of hundreds of carriages; the longitudinal impulse in the train has significant coupled nonlinear characteristics during its operation. With the continuous increase in traction weight, problems with the existing control models gradually appear, vehicle decoupling and derailment occur, and the trains often fail to operate on schedule. Using the coupler characteristic curve, this paper comprehensively considers uncertain factors such as the train loading weight, line ramps, vehicle dynamic performance differences, and changeable operating environment to accurately model the heavy-haul train coupling force and train longitudinal dynamic multiple points. We optimize the target curve of the ideal running speed of the train and design the corresponding running tracking control strategy. The simulation results show that the ideal target speed curve optimization method designed in this paper can effectively improve key indicators such as safety, punctuality and energy saving in train operation to make trains more safely and efficiently run.

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Section: Introductionmentioning

confidence: 99%

A Modeling Method for the Driving Process of Heavy-Haul Train Based on Multi-Mass Model

2020

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Errors in Measuring the Distance to an Obstacle by Technical Vision Means and in Forecasting Braking Distance in Driverless Train Control Systems. World of Transport and Transportation

et al. 2022

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Technical vision systems are sources of information about an obstacle on the track in the case of driverless train control. Based on the information received, the traffic control system decides to turn on the braking mode to prevent a colliosni with an obstacle. In accordance with international and domestic expertise and standard ratings, it is necessary to ensure the probability of a dangerous failure, in this case, the probability of hitting an obstacle, not more than 10-8 with a confidence probability of 0,95 according to SIL-4 ([Russian state standard] GOST-R61508). Considering the presence of an error in measuring the distance to an obstacle by the technical vision system and an error in calculating the stopping distance, it is required to determine the coordinate of the braking start point when an object is detected on the track in such a way as to ensure that the train stops before the obstacle with a probability determined in accordance with SIL-4.A feature of the problem being solved for estimating the errors in measuring the distance to an obstacle and calculating the stopping distance implies the need to determine the estimates of their maximum values and to develop an algorithm for using these estimates in such a way that the collision probability does not exceed the normalised value.A technique is described for determining the maximum value of the error in measuring the distance to the obstacle, the probability of exceeding which is quite small (from 10-2 to 10-6). A proposed algorithm for multiple measurements of the distance to an obstacle allows choosing the minimum measurement result for deciding on the start of braking, which ensures meeting standard indicator of a probability of a train colliding with an obstacle according to SIL-4. A method for estimating the error in calculating the stopping distance has been developed, which, together with the algorithm of multiple measurements by the technical vision system of the distance to the obstacle, provides the standard indicator according to SIL-4. The need for the second channel of technical vision due to the presence of curves along the route is shown. The necessity of using algorithms for multiple measurements to an obstacle through the second channel located outside the train is also substantiated. It is noted that the methods described in this article for choosing the maximum values of random errors in measurements and calculations, the values of which can be exceeded with a very low probability, can be used to solve various applied problems of traffic control in transportation processes.

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Automatic Train Operation Using Autonomic Prediction of Train Runs

Cited by 2 publications

References 2 publications

A Modeling Method for the Driving Process of Heavy-Haul Train Based on Multi-Mass Model

A Modeling Method for the Driving Process of Heavy-Haul Train Based on Multi-Mass Model

Errors in Measuring the Distance to an Obstacle by Technical Vision Means and in Forecasting Braking Distance in Driverless Train Control Systems. World of Transport and Transportation

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