Passengers Comfort Perception and Demands on Railway Vehicles: A Review

Silva, Patricia; Mendes, Joaquim

doi:10.18502/keg.v5i6.7039

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…The time history of the rail suspension response under sinusoidal track excitation is shown in Figs. (7)(8)(9)(10)(11); RVBA, RVBD, PSA, PSVD, and STD, respectively. Passive represents the vehicle model without an MR damper and depends on a conventional damper.…”

Section: Sinusoidal Track Profilementioning

confidence: 99%

“…Rail vehicles are the backbone of an integrated system because of their capacity and speed; they are becoming one of the most important and popular public transportation worldwide. Lately, statistics have reported that, in the past decade, the number of train passengers has increased by about 40% [10] Many researchers are motivated to minimize the vertical vibration transmitted to train passengers from rail irregularities to improve passenger comfort. The high levels of vibration annoy passengers and significantly affect human health due to the many hours spent riding trains.…”

Section: Introductionmentioning

confidence: 99%

“…the passive suspension. The five performance criteria stated above of semiactive suspensions are compared to the passive suspension in terms of PRI of peak-to-peak (PTP), based on Fig (7)(8)(9)(10)(11),. and root-mean-square (RMS), based on Fig (13)(14)(15)(16)(17),.…”

mentioning

confidence: 99%

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Semi-Active Vibration Control of Train Suspension Using Optimized PID Controller

A. Ali,

Metered,

M. Bassuiny

et al. 2024

Engineering Research Journal

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Magnetorheological (MR) dampers emerge as highly advantageous semi-active mechanisms for vibration control within engineering systems, offering superior reliability and cost-effectiveness compared to active actuators, thus underscoring their significance in practical implementation. This research delves into the analysis of ride comfort in rail vehicles employing semi-active suspension control, examining its impact on the vertical dynamics of the train. The study employs the Harmony Search (HS) algorithm to optimize the gains of a proportional integral derivative (PID) controller, leveraging the self-adaptive global best harmony search method (SGHS) for its efficacy in minimizing tuning time and achieving optimal objective function values. The efficacy of the proposed controller is assessed through simulation of a quarter-rail vehicle model featuring six degrees of freedom (6-DOF) using MATLAB/Simulink software. Analysis of the simulated results demonstrates that the optimized PID controller markedly enhances ride comfort when compared to both passive suspension systems and conventional PID control strategies.

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Section: Sinusoidal Track Profilementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semi-Active Vibration Control of Train Suspension Using Optimized PID Controller

A. Ali,

Metered,

M. Bassuiny

et al. 2024

Engineering Research Journal

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“…Passengers' travel comfort is associated with the departure time and degree of accessibility to trains [61]. Train comfort is defined by multiple factors such as vibration, noise, temperature, humidity, smell, visual stimulation, and design layout [62]. According to the respondents' comments in the questionnaire survey, train comfort includes the train's air conditioning system and information.…”

Section: ) Comfortmentioning

confidence: 99%

Strategies Encouraging Mode Shifting on Rail-Based Transportation: A Case Study in Jakarta

Saroji¹,

Berawi²,

Sari³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Public rail transport infrastructure has been developed to address urban issues in some major cities worldwide, including Jakarta, Indonesia. Despite its development, the rail transit ridership is not optimal yet, since traffic flows on the roads are still heavy and cause congestion. Therefore, the modal shift on rail transit in should be increased. The objective of this study is twofold; determining factors affecting the mode split on rail transit and exploring strategies to improve the modal shifting to rail-based transportation by taking into account the development of properties near the transit stations of Greater Jakarta's commuter line, including hotel, retail, office, and residential. Both quantitative and qualitative methods through questionnaire survey and benchmarking study were used to obtain these objectives. The survey results show that punctuality is the factor that affects the modal shifting to rail the most, followed by train comfort and shorter waiting time. Benchmarking studies were conducted to develop strategies for improving the train performance in terms of those factors, which include utilizing planning and management tool for train scheduling and rail infrastructure control, improving the air conditioning system on board trains, and increasing the information availability by installing real-time information monitors.

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“…The comfort of taking high-speed rail is receiving a lot of attention from passengers; there has been rapid development of the high-speed rail industry and a sharp increase in the number of people who choose to travel by high-speed rail [ 1 , 2 , 3 ]. The change in the environment during the high-speed train driving is a critical ingredient that causes the temperature change in the high-speed train compartment, such as outside temperature, light intensity, speed, and other factors [ 4 ]. Because of its high hysteresis, the air conditioner’s air supply temperature will fluctuate with the temperature of the high-speed train compartment, causing human discomfort and making control difficult.…”

Section: Introductionmentioning

confidence: 99%

An Improved Temporal Fusion Transformers Model for Predicting Supply Air Temperature in High-Speed Railway Carriages

Feng

Zhang

et al. 2022

Entropy

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A key element for reducing energy consumption and improving thermal comfort on high-speed rail is controlling air-conditioning temperature. Accurate prediction of air supply temperature is aimed at improving control effects. Existing studies of supply air temperature prediction models are interdisciplinary, involving heat transfer science and computer science, where the problem is defined as time-series prediction. However, the model is widely accepted as a complex model that is nonlinear and dynamic. That makes it difficult for existing statistical and deep learning methods, e.g., autoregressive integrated moving average model (ARIMA), convolutional neural network (CNN), and long short-term memory network (LSTM), to fully capture the interaction between these variables and provide accurate prediction results. Recent studies have shown the potential of the Transformer to increase the prediction capacity. This paper offers an improved temporal fusion transformers (TFT) prediction model for supply air temperature in high-speed train carriages to tackle these challenges, with two improvements: (i) Double-convolutional residual encoder structure based on dilated causal convolution; (ii) Spatio-temporal double-gated structure based on Gated Linear Units. Moreover, this study designs a loss function suitable for general long sequence time-series forecast tasks for temperature forecasting. Empirical simulations using a high-speed rail air-conditioning operation dataset at a specific location in China show that the temperature prediction of the two units using the improved TFT model improves the MAPE by 21.70% and 11.73%, respectively the original model. Furthermore, experiments demonstrate that the model effectively outperforms seven popular methods on time series computing tasks, and the attention of the prediction problem in the time dimension is analyzed.

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Passengers Comfort Perception and Demands on Railway Vehicles: A Review

Cited by 6 publications

References 15 publications

Semi-Active Vibration Control of Train Suspension Using Optimized PID Controller

Semi-Active Vibration Control of Train Suspension Using Optimized PID Controller

Strategies Encouraging Mode Shifting on Rail-Based Transportation: A Case Study in Jakarta

An Improved Temporal Fusion Transformers Model for Predicting Supply Air Temperature in High-Speed Railway Carriages

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