A new method for estimation of critical speed for railway tracks on soft ground

Norén-Cosgriff, Karin; Berggren, Eric; Kaynia, Amir M.; Dam, Niels Norman; Mortensen, Niels Gylling

doi:10.1080/23248378.2018.1474811

Cited by 16 publications

(6 citation statements)

References 9 publications

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“…In [5], the help of dynamic time warping algorithms, the estimation of rail system vehicle speeds was investigated. In [6], with the proposed new method called ETL (EBER Track Lab), the subject of predicting the critical speed on the soft ground railway line was studied. Vehicle movements are simulated with the element increment method in [7].…”

Section: Figure 2 Graphic Of Vehicles Driving Modesmentioning

confidence: 99%

“…In [4], there was a 0.1% difference between the values measured by the train motion resistance obtained using the data provided by the test method. In study [6], it was explained that the critical speed was estimated with a difference of 15 km / h (250-235). In the study with [7], simulation of the train movement was provided by optimizing the vehicle driving curve.…”

Section: Figure 8 Errors For Random Samplingmentioning

confidence: 99%

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Estimation of Constant Speed Time for Railway Vehicles by Stochastic Gradient Descent Algorithm

Akçay¹

2020

Sakarya University Journal of Computer and Information Sciences

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While the investments in rail transportation systems continue without slowing down, various optimization issues come to the fore in order for the systems to work more efficiently. One of the most important of these issues is the optimization of the vehicle speed profile. Improvement in vehicle speed profile increases efficiency in operating traffic. Vehicle speed profile varies depending on the electrical-characteristic features of the vehicle, the distance between the stations and the line geometry. The vehicle's speed profile consists of several parts, such as acceleration, constant speed travel and braking zones. The constant speed in the constant velocity zone refers to the max operating speed, which is recommended for operation in the restricted area and remains within the limits. This part is critical in creating the speed profile of the vehicle. In this study, the estimation of the value of the constant speed time in the speed profile of the vehicles used in the city metro systems was made by using the Stochastic Gradient Descent method, which is one of the machine learning methods, and compared with various well-known methods. Coefficient of determination (R 2 ) values were calculated as 0.9955 and 0.9951, respectively, with random sampling hold out and cross validation methods.

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Section: Figure 2 Graphic Of Vehicles Driving Modesmentioning

confidence: 99%

Section: Figure 8 Errors For Random Samplingmentioning

confidence: 99%

Estimation of Constant Speed Time for Railway Vehicles by Stochastic Gradient Descent Algorithm

Akçay¹

2020

Sakarya University Journal of Computer and Information Sciences

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“…While estimating the arrival times of vehicles with vehicle speed measurement done in the study [1], the method for calculating vehicle motion resistance with the help of a vehicle data collection device is explained with [2]. In [3], the prediction of critical speed for the vehicle on soft ground is studied, and in [4] a low-speed estimation has been made with the time signal-based warping algorithm. In [5], the simulation of the vehicle movement has been done by the element increment method.…”

Section: Introductionmentioning

confidence: 99%

Calculation of Driving Parameters for GOA4 Signaling System using Machine Learning Methods

Akçay

Akgündoğdu

2022

Sakarya University Journal of Computer and Information Sciences

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Among the electromechanical components of the rail system, the rail system vehicle is one of the most important units that carrying the passenger load. In terms of the efficiency of the signalization system, it is very critical to create the optimum vehicle driving profile. While many parameters of the vehicle are important during the design of the driving profile, determining the acceleration and braking accelerations directly affects this characteristic. The use of programmable devices and software instead of human factors is becoming more and more widespread day by day with the developed technology in rail transportation systems. Among the software used, artificial intelligence and machine learning applications constitute a large share in the general distribution. Especially, in the driverless (GOA4) signaling systems, these softwares become more important. In this study, the estimation of Vehicle Acceleration and Braking Acceleration with travel time has been done by using Machine Learning Methods. The ideal results obtained were given comparatively and interpreted on the graphics.

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“…This approach is efficient from a computational point of view because it doesn't require the computation of the dynamic track response due to moving loads [4,13,14]. More recently Norén-Cosgrif et al [15] proposed a method for critical speed prediction based on experimental data. However, application of this method is only possible for existing railway lines.…”

Section: Introductionmentioning

confidence: 99%

Railway critical speed assessment: A simple experimental-analytical approach

Costa

Soares

Colaço

et al. 2020

Soil Dynamics and Earthquake Engineering

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When constructing a new railway line, its linear nature means there are significant financial implications associated with determining the geodynamic ground properties. Therefore this paper presents recommendations to optimize the efficiency and depth of such a geotechnical site investigation. Firstly a numerical analysis is performed to investigate the effect of soil layering, soil stiffness and track bending stiffness on critical velocity. It is shown that each of these variables play an important role, however for most practical cases, only the top 8m of soil is influential. Track dynamics are rarely affected by soil properties at depths below this, meaning this is the maximum required depth of soil investigation. Using this knowledge, a hybrid experimental-numerical ground investigation methodology is presented, based on a SASW experimental setup to compute the ground dispersion curve and an analytical model to compute the track dispersion curve. The experimental and analytical results are combined directly, to accurately compute the critical velocity. This approach is attractive because: 1) SASW tests are typically accurate to ≈8m (when using a mobile exciter) thus matching the required depth needed for critical velocity computation, 2) soil property uncertainties are inherently accounted for, 3) the uncertainties associated with SASW inversion are avoided. The approach is attractive when constructing new railway lines and upgrading the speed of existing lines because it can potentially yield site investigation cost savings.

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A new method for estimation of critical speed for railway tracks on soft ground

Cited by 16 publications

References 9 publications

Estimation of Constant Speed Time for Railway Vehicles by Stochastic Gradient Descent Algorithm

Estimation of Constant Speed Time for Railway Vehicles by Stochastic Gradient Descent Algorithm

Calculation of Driving Parameters for GOA4 Signaling System using Machine Learning Methods

Railway critical speed assessment: A simple experimental-analytical approach

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