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Railway Engineering Science
Number of Followers: 0  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2662-4745 - ISSN (Online) 2662-4753
Published by SpringerOpen Homepage  [263 journals]
  • Running safety assessment of a train traversing a three-tower cable-stayed
           bridge under spatially varying ground motion

    • Abstract: To explore the influence of spatially varying ground motion on the dynamic behavior of a train passing through a three-tower cable-stayed bridge, a 3D train–track–bridge coupled model is established for accurately simulating the train–bridge interaction under earthquake excitation, which is made up of a vehicle model built by multi-body dynamics, a track–bridge finite element model, and a 3D rolling wheel–rail contact model. A conditional simulation method, which takes into consideration the wave passage effect, incoherence effect, and site-response effect, is adopted to simulate the spatially varying ground motion under different soil conditions. The multi-time-step method previously proposed by the authors is also adopted to improve computational efficiency. The dynamic responses of the train running on a three-tower cable-stayed bridge are calculated with differing earthquake excitations and train speeds. The results indicate that (1) the earthquake excitation significantly increases the responses of the train–bridge system, but at a design speed, all the running safety indices meet the code requirements; (2) the incoherence and site-response effects should also be considered in the seismic analysis for long-span bridges though there is no fixed pattern for determining their influences; (3) different train speeds that vary the vibration characteristics of the train–bridge system affect the vibration frequencies of the car body and bridge.
      PubDate: 2020-03-31
       
  • Letter from the editor-in-chief

    • PubDate: 2020-03-01
       
  • Effect of cavity flow control on high-speed train pantograph and roof
           aerodynamic noise

    • Abstract: The pantograph and its recess on the train roof are major aerodynamic noise sources on high-speed trains. Reducing this noise is particularly important because conventional noise barriers usually do not shield the pantograph. However, less attention has been paid to the pantograph recess compared with the pantograph. In this paper, the flow features and noise contribution of two types of noise reduction treatments rounded and chamfered edges are studied for a simplified high-speed train pantograph recess, which is represented as a rectangular cavity and numerically investigated at 1/10 scale. Improved delayed detached-eddy simulations are performed for the near-field turbulent flow simulation, and the Ffowcs Williams and Hawkings aeroacoustic analogy is used for far-field noise prediction. The highly unsteady flow over the cavity is significantly reduced by the cavity edge modifications, and consequently, the noise radiated from the cavity is reduced. Furthermore, effects of the rounded cavity edges on the flow and noise of the pantographs (one raised and one folded) are investigated by comparing the flow features and noise contributions from the cases with and without rounding of the cavity edges. Different train running directions are also considered. Flow analysis shows that the highly unsteady flow within the cavity is reduced by rounding the cavity edges and a slightly lower flow speed occurs around the upper parts of the raised pantograph, whereas the flow velocity in the cavity is slightly increased by the rounding. Higher pressure fluctuations occur on the folded pantograph and the lower parts of the raised pantograph, whereas weaker fluctuations are found on the panhead of the raised pantograph. This study shows that by rounding the cavity edges, a reduction in radiated noise at the side and the top receiver positions can be achieved. Noise reductions in the other directions can also be found.
      PubDate: 2020-03-01
       
  • Train–track coupled dynamics analysis: system spatial variation on
           geometry, physics and mechanics

    • Abstract: This paper aims to clarify the influence of system spatial variability on train–track interaction from perspectives of stochastic analysis and statistics. Considering the spatial randomness of system properties in geometry, physics and mechanics, the primary work is therefore simulating the uncertainties realistically, representatively and efficiently. With regard to the track irregularity simulation, a model is newly developed to obtain random sample sets of track irregularities by transforming its power spectral density function into the equivalent track quality index for representation based on the discrete Parseval theorem, where the correlation between various types of track irregularities is accounted for. To statistically clarify the uncertainty of track properties in physics and mechanics in space, a model combining discrete element method and finite element method is developed to obtain the spatially varied track parametric characteristics, e.g. track stiffness and density, through which the highly expensive experiments in situ can be avoided. Finally a train–track stochastic analysis model is formulated by integrating the system uncertainties into the dynamics model. Numerical examples have validated the accuracy and efficiency of this model and illustrated the effects of system spatial variability on train–track vibrations comprehensively.
      PubDate: 2020-03-01
       
  • Power management in co-phase traction power supply system with super
           capacitor energy storage for electrified railways

    • Abstract: Increasing railway traffic and energy utilization issues prompt electrified railway systems to be more economical, efficient and sustainable. As regenerative braking energy in railway systems has huge potential for optimized utilization, a lot of research has been focusing on how to use the energy efficiently and gain sustainable benefits. The energy storage system is an alternative because it not only deals with regenerative braking energy but also smooths drastic fluctuation of load power profile and optimizes energy management. In this work, we propose a co-phase traction power supply system with super capacitor (CSS_SC) for the purpose of realizing the function of energy management and power quality management in electrified railways. Besides, the coordinated control strategy is presented to match four working modes, including traction, regenerative braking, peak shaving and valley filling. A corresponding simulation model is built in MATLAB/Simulink to verify the feasibility of the proposed system under dynamic working conditions. The results demonstrate that CSS_SC is flexible to deal with four different working conditions and can realize energy saving within the allowable voltage unbalance of 0.008% in simulation in contrast to 1.3% of the standard limit. With such a control strategy, the performance of super capacitor is controlled to comply with efficiency and safety constraints. Finally, a case study demonstrates the improvement in power fluctuation with the valley-to-peak ratio reduced by 20.3% and the daily load factor increased by 17.9%.
      PubDate: 2020-03-01
       
  • Computational fluid dynamics simulation of Hyperloop pod predicting
           laminar–turbulent transition

    • Abstract: Three-dimensional compressible flow simulations were conducted to develop a Hyperloop pod. The novelty is the usage of Gamma transition model, in which the transition from laminar to turbulent flow can be predicted. First, a mesh dependency study was undertaken, showing second-order convergence with respect to the mesh refinement. Second, an aerodynamic analysis for two designs, short and optimized, was conducted with the traveling speed 125 m/s at the system pressure 0.15 bar. The concept of the short model was to delay the transition to decrease the frictional drag; meanwhile that of the optimized design was to minimize the pressure drag by decreasing the frontal area and introduce the transition more toward the front of the pod. The computed results show that the transition of the short model occurred more on the rear side due to the pod shape, which resulted in 8% smaller frictional drag coefficient than that for the optimized model. The pressure drag for the optimized design was 24% smaller than that for the short design, half of which is due to the decrease in the frontal area, and the other half is due to the smoothed rear-end shape. The total drag for the optimized model was 14% smaller than that for the short model. Finally, the influence of the system pressure was investigated. As the system pressure and the Reynolds number increase, the frictional drag coefficient increases, and the transition point moves toward the front, which are the typical phenomena observed in the transition regime.
      PubDate: 2020-03-01
       
  • Train energy simulation with locomotive adhesion model

    • Abstract: Railway train energy simulation is an important and popular research topic. Locomotive traction force simulations are a fundamental part of such research. Conventional energy calculation models are not able to consider locomotive wheel–rail adhesions, traction adhesion control, and locomotive dynamics. This paper has developed two models to fill this research gap. The first model uses a 2D locomotive model with 27 degrees of freedom and a simplified wheel–rail contact model. The second model uses a 3D locomotive model with 54 degrees of freedom and a fully detailed wheel–rail contact model. Both models were integrated into a longitudinal train dynamics model with the consideration of locomotive adhesion control. Energy consumption simulations using a conventional model (1D model) and the two new models (2D and 3D models) were conducted and compared. The results show that, due to the consideration of wheel–rail adhesion model and traction control in the 3D model, it reports less energy consumption than the 1D model. The maximum difference in energy consumption rate between the 3D model and the 1D model was 12.5%. Due to the consideration of multiple wheel–rail contact points in the 3D model, it reports higher energy consumption than the 2D model. An 8.6% maximum difference in energy consumption rate between the 3D model and the 1D model was reported during curve negotiation.
      PubDate: 2020-03-01
       
  • Active suspension in railway vehicles: a literature survey

    • Abstract: Since the concept of active suspensions appeared, its large possible benefits has attracted continuous exploration in the field of railway engineering. With new demands of higher speed, better ride comfort and lower maintenance cost for railway vehicles, active suspensions are very promising technologies. Being the starting point of commercial application of active suspensions in rail vehicles, tilting trains have become a great success in some countries. With increased technical maturity of sensors and actuators, active suspension has unprecedented development opportunities. In this work, the basic concepts are summarized with new theories and solutions that have appeared over the last decade. Experimental studies and the implementation status of different active suspension technologies are described as well. Firstly, tilting trains are briefly described. Thereafter, an in-depth study for active secondary and primary suspensions is performed. For both topics, after an introductory section an explanation of possible solutions existing in the literature is given. The implementation status is reported. Active secondary suspensions are categorized into active and semi-active suspensions. Primary suspensions are instead divided between acting on solid-axle wheelsets and independently rotating wheels. Lastly, a brief summary and outlook is presented in terms of benefits, research status and challenges. The potential for active suspensions in railway applications is outlined.
      PubDate: 2020-03-01
       
  • Model tests for surge height of rock avalanche–debris flows based on
           momentum balance

    • Abstract: Rock avalanche–debris flows triggered by earthquakes commonly take place in mountainous areas. When entering a body of water, due to good fluidity they can move for some time instead of halting in water. In this study, we proposed a method for calculating the surge height of rock avalanche–debris flows based on momentum balance and designed a series of model tests to validate this method. The experimental variables include the initial water depth, landslide velocity, and landslide volume. According to the experimental results, we analyzed the maximum wave height in sliding zone based on momentum balance. In addition, we investigated the surge height and proposed the calculation method in propagating zone and running up zone. In this way, we can find out the surge height in different areas when a rock avalanche–debris flow impacts into the water, which could provide a basis for analyzing the burst of barrier lakes.
      PubDate: 2019-12-01
       
  • A neural network algorithm for queue length estimation based on the
           concept of k -leader connected vehicles

    • Abstract: This paper presents a novel method to estimate queue length at signalised intersections using connected vehicle (CV) data. The proposed queue length estimation method does not depend on any conventional information such as arrival flow rate and parameters pertaining to traffic signal controllers. The model is applicable for real-time applications when there are sufficient training data available to train the estimation model. To this end, we propose the idea of “k-leader CVs” to be able to predict the queue which is propagated after the communication range of dedicated short-range communication (the communication platform used in CV system). The idea of k-leader CVs could reduce the risk of communication failure which is a serious concern in CV ecosystems. Furthermore, a linear regression model is applied to weigh the importance of input variables to be used in a neural network model. Vissim traffic simulator is employed to train and evaluate the effectiveness and robustness of the model under different travel demand conditions, a varying number of CVs (i.e. CVs’ market penetration rate) as well as various traffic signal control scenarios. As it is expected, when the market penetration rate increases, the accuracy of the model enhances consequently. In a congested traffic condition (saturated flow), the proposed model is more accurate compared to the undersaturated condition with the same market penetration rates. Although the proposed method does not depend on information of the arrival pattern and traffic signal control parameters, the results of the queue length estimation are still comparable with the results of the methods that highly depend on such information. The proposed algorithm is also tested using large size data from a CV test bed (i.e. Australian Integrated Multimodal Ecosystem) currently underway in Melbourne, Australia. The simulation results show that the model can perform well irrespective of the intersection layouts, traffic signal plans and arrival patterns of vehicles. Based on the numerical results, 20% penetration rate of CVs is a critical threshold. For penetration rates below 20%, prediction algorithms fail to produce reliable outcomes.
      PubDate: 2019-12-01
       
  • Traffic prediction using a self-adjusted evolutionary neural network

    • Abstract: Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. The aim of this paper is to provide a model based on neural networks (NNs) for multi-step-ahead traffic prediction. NNs’ dependency on parameter setting is the major challenge in using them as a predictor. Given the fact that the best combination of NN parameters results in the minimum error of predicted output, the main problem is NN optimization. So, it is viable to set the best combination of the parameters according to a specific traffic behavior. On the other hand, an automatic method—which is applicable in general cases—is strongly desired to set appropriate parameters for neural networks. This paper defines a self-adjusted NN using the non-dominated sorting genetic algorithm II (NSGA-II) as a multi-objective optimizer for short-term prediction. NSGA-II is used to optimize the number of neurons in the first and second layers of the NN, learning ratio and slope of the activation function. This model addresses the challenge of optimizing a multi-output NN in a self-adjusted way. Performance of the developed network is evaluated by application to both univariate and multivariate traffic flow data from an urban highway. Results are analyzed based on the performance measures, showing that the genetic algorithm tunes the NN as well without any manually pre-adjustment. The achieved prediction accuracy is calculated with multiple measures such as the root mean square error (RMSE), and the RMSE value is 10 and 12 in the best configuration of the proposed model for single and multi-step-ahead traffic flow prediction, respectively.
      PubDate: 2019-12-01
       
  • Pedestrian perception-based level-of-service model at signalized
           intersection crosswalks

    • Abstract: Pedestrian level of service (PLOS) is an important measure of performance in the analysis of existing pedestrian crosswalk conditions. Many researchers have developed PLOS models based on pedestrian delay, turning vehicle effect, etc., using the conventional regression method. However, these factors may not effectively reflect the pedestrians’ perception of safety while crossing the crosswalk. The conventional regression method has failed to estimate accurate PLOS because of the primary assumption of an arbitrary probability distribution and vagueness in the input data. Moreover, PLOS categories in existing studies are based on rigid threshold values and the boundaries that are not well defined. Therefore, it is an important attempt to develop a PLOS model with respect to pedestrian safety, convenience, and efficiency at signalized intersections. For this purpose, a video-graphic and user perception surveys were conducted at selected nine signalized intersections in Mumbai, India. The data such as pedestrian, traffic, and geometric characteristics were extracted, and significant variables were identified using Pearson correlation analysis. A consistent and statistically calibrated PLOS model was developed using fuzzy linear regression analysis. PLOS was categorized into six levels (A–F) based on the predicted user perception score, and threshold values for each level were estimated using the fuzzy c-means clustering technique. The developed PLOS model and threshold values were validated with the field-observed data. Statistical performance tests were conducted and the results provided more accurate and reliable solutions. In conclusion, this study provides a feasible alternative to measure pedestrian perception-based level of service at signalized intersections. The developed PLOS model and threshold values would be useful for planning and designing pedestrian facilities and also in evaluating and improving the existing conditions of pedestrian facilities at signalized intersections.
      PubDate: 2019-12-01
       
  • An application of Bayesian multilevel model to evaluate variations in
           stochastic and dynamic transition of traffic conditions

    • Abstract: This study seeks to investigate the variations associated with lane lateral locations and days of the week in the stochastic and dynamic transition of traffic regimes (DTTR). In the proposed analysis, hierarchical regression models fitted using Bayesian frameworks were used to calibrate the transition probabilities that describe the DTTR. Datasets of two sites on a freeway facility located in Jacksonville, Florida, were selected for the analysis. The traffic speed thresholds to define traffic regimes were estimated using the Gaussian mixture model (GMM). The GMM revealed that two and three regimes were adequate mixture components for estimating the traffic speed distributions for Site 1 and 2 datasets, respectively. The results of hierarchical regression models show that there is considerable evidence that there are heterogeneity characteristics in the DTTR associated with lateral lane locations. In particular, the hierarchical regressions reveal that the breakdown process is more affected by the variations compared to other evaluated transition processes with the estimated intra-class correlation (ICC) of about 73%. The transition from congestion on-set/dissolution (COD) to the congested regime is estimated with the highest ICC of 49.4% in the three-regime model, and the lowest ICC of 1% was observed on the transition from the congested to COD regime. On the other hand, different days of the week are not found to contribute to the variations (the highest ICC was 1.44%) on the DTTR. These findings can be used in developing effective congestion countermeasures, particularly in the application of intelligent transportation systems, such as dynamic lane-management strategies.
      PubDate: 2019-12-01
       
  • Calibrating HCM model for roundabout entry capacity under heterogeneous
           traffic

    • Abstract: Roundabout is a channelized intersection where traffic moves around a central island, clockwise for left-side driving and anti-clockwise for right-side driving. Efficiently designed roundabouts can handle traffic very smoothly without causing any delay. The capacity of roundabouts used to be calculated by the weaving theory in India. However, calculation of the entry capacity in the recent literature is based on critical gaps and follow-up times, and the Highway Capacity Manual of US (HCM 2010) provides an equation to estimate the entry capacity of a roundabout by using the flow in passenger car unit per hour (PCU/h), critical gaps and follow-up times at the entry section. In order to examine whether the HCM equation applies to Indian traffic condition or not, we collected data from five roundabouts in India in this study. Relevant data were extracted/estimated to calibrate parameters of the HCM equation. The PCU for a vehicle was estimated on the basis of lagging headway and width of the vehicle, and the critical gap value for a vehicle was estimated by minimizing the sum of absolute difference in a gap with respect to the highest rejected and accepted gaps. Results show that the critical gap values obtained under heterogeneous traffic conditions are much lower than those given in the literature for homogeneous traffic conditions. In addition, the modified HCM equation based on the critical gap values was verified using the field data taken during the formation of a continuous and stable queue at the entry of a roundabout. It was found that a multiplicative adjustment factor needs to be calculated for different sizes of roundabouts to ensure the adjusted HCM equation represents well the traffic condition prevailing in developing countries like India. A test conducted at another roundabout validated that the entry capacity estimated from the calibrated and adjusted HCM model was consistent with the field entry capacity, and the calibrated and adjusted HCM model could predict the entry capacity of an approach to a roundabout quite accurately.
      PubDate: 2019-12-01
       
  • Influence of tire-derived aggregates mixed with ballast on ground-borne
           vibrations

    • Abstract: In this paper, the use of recycled tire-derived aggregates (TDA) mixed with ballast material is evaluated in order to reduce the train-induced ground-borne vibrations. For this purpose, a series of field vibration measurements has been carried out at an executed pilot track. The prepared ballast layer was mixed with different percentages of TDA in three sections. Moreover, another test section with pure ballast is considered as a reference. The vibrations generated by a motor-powered draisine at two different speeds are then recorded. Records of vibration data are provided using four seismometers placed once longitudinally and once transversely beside different sections. The outputs are then processed in both velocity–time and velocity–frequency domains. To verify the vibration mitigation performance of TDA in real operation conditions, field measurements under the passage of two planned passenger and freight trains are finally arranged. Results show that the best TDA mixture ratio, i.e., 10% by weight, can reduce the transmitted vibrations up to 12 dB for frequencies above 31.5 Hz. According to the obtained efficiency and the very low cost of the recycled materials, this solution can be considered as a competitive vibration countermeasure.
      PubDate: 2019-12-01
       
  • Influence of adverse weather on drivers’ perceived risk during car
           following based on driving simulations

    • Abstract: Adverse weather has a considerable impact on the behavior of drivers, which puts vehicles and drivers in hazardous situations that can easily cause traffic accidents. This research examines how drivers’ perceived risk changes during car following under different adverse weather conditions by using driving simulation experiment. An expressway road scenario was built in a driving simulator. Eleven types of weather conditions, including clear sky, four levels of fog, four levels of rain and two levels of snow, were designed. Furthermore, to simulate the car-following behavior, three car-following situations were designed according to the motion of the lead car. Seven car-following indicators were extracted based on risk homeostasis theory. Then, the entropy weight method was used to integrate the selected indicators into an index to represent the drivers’ perceived risk. Multiple linear regression was applied to measure the influence of adverse weather conditions on perceived risk, and the coefficients were considered as indicators. The results demonstrate that both the weather conditions and road type have significant effects on car-following behavior. Drivers’ perceived risk tends to increase with the worsening weather conditions. Under conditions of extremely poor visibility, such as heavy dense fog, the measured drivers’ perceived risk is low due to the difficulties in vehicle operation and limited visibility.
      PubDate: 2019-12-01
       
  • Data analytics approach for travel time reliability pattern analysis and
           prediction

    • Abstract: Travel time reliability (TTR) is an important measure which has been widely used to represent the traffic conditions on freeways. The objective of this study is to develop a systematic approach to analyzing TTR on roadway segments along a corridor. A case study is conducted to illustrate the TTR patterns using vehicle probe data collected on a freeway corridor in Charlotte, North Carolina. A number of influential factors are considered when analyzing TTR, which include, but are not limited to, time of day, day of week, year, and segment location. A time series model is developed and used to predict the TTR. Numerical results clearly indicate the uniqueness of TTR patterns under each case and under different days of week and weather conditions. The research results can provide insightful and objective information on the traffic conditions along freeway segments, and the developed data-driven models can be used to objectively predict the future TTRs, and thus to help transportation planners make informed decisions.
      PubDate: 2019-12-01
       
  • Case study scenarios in site selection of hazardous material facilities
           based on transportation preferences

    • Abstract: A methodology is proposed to evaluate and rank potential sites for facilities dealing with hazardous materials (HAZMAT). The proposed methodology incorporates HAZMAT route planning into facility siting while considering transportation preferences and challenges. The area of interest is divided into smaller zones representing potential sites for a HAZMAT facility. A multimodal transportation network including railways and roads is considered for transportation of HAZMAT. Each zone is evaluated based on its accessibility from a set of selected points of interests (POIs), which are defined as potential origin/destination points for transportation of HAZMAT. The shortest routes between each POI and potential zones are evaluated based on a cost function which can accommodate multiple criteria to determine the associated disutility for each potential zone. Finally, zones are ranked based on their cumulative disutility scores. The proposed analysis method is quantitative, and at the same time it is adequately flexible to allow inclusion of subjective criteria. Application of the proposed methodology is demonstrated for identifying optimal locations for a HAZMAT facility (e.g., a nuclear facility) using the Canadian province of Saskatchewan as an example. Three scenarios were evaluated including (1) all network segments and POIs were treated equally, (2) network segments were rank ordered based on their functional classification while POIs were treated equally and (3) network segments were rank ordered based on their functional classification with preferences given to specific POI(s).
      PubDate: 2019-12-01
       
  • Correction to: Can a polycentric structure affect travel behaviour' A
           comparison of Melbourne, Australia and Riyadh, Saudi Arabia

    • Abstract: In the original publication the first sentence starting with “The model calibration process…” in Sect. 3.2.3 Model calibration and validation should be amended as,
      PubDate: 2019-09-01
       
  • Using Kalman filter algorithm for short-term traffic flow prediction in a
           connected vehicle environment

    • Abstract: We develop a Kalman filter for predicting traffic flow at urban arterials based on data obtained from connected vehicles. The proposed algorithm is computationally efficient and offers a real-time prediction since it invokes the connected vehicle data just before the prediction period. Moreover, it can predict the traffic flow for various penetration rates of connected vehicles (the ratio of the number of connected vehicles to the total number of vehicles). At first, the Kalman filter equations are calibrated using data derived from Vissim traffic simulator for different penetration rates, different fluctuating arrival rates of vehicles and various signal settings. Then the filter is evaluated for a variety of traffic scenarios generated in Vissim simulator. We evaluate the performance of the algorithm for different penetration rates under several traffic situations using some statistical measures. Although many of the previous prediction methods depend highly on data from fixed sensors (i.e., loop detectors and video cameras), which are associated with huge installation and maintenance costs, this study provides a low-cost mean for short-term flow prediction only based on the connected vehicle data.
      PubDate: 2019-09-01
       
 
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