A Novel Method in Light-Rail Condition Monitoring Using Smartphones

Karimpour, Mohammad Hassan; Moridpour, Sara

doi:10.1109/mits.2019.2907680

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…These sensors may be installed in the axlebox [34], bogie [35,36] or cab [37], allowing a wide range of applications when their signals are processed; mainly track monitoring, fault detection and geometry extraction [38]. Track monitoring can be assessed through indices to evaluate its general state [39,40] or by evaluating the received signal [41][42][43][44]. Track fault detection revolves around evaluating data to observe changes associated with faults and, if possible, identify them [45,46] or detect changes before and after maintenance operations [47,48].…”

Section: Track Monitoring and Inspectionmentioning

confidence: 99%

Railway Track and Vehicle Onboard Monitoring: A Review

et al. 2023

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Railway condition monitoring is the compound of activities aimed at the detection and mitigation of faults in both tracks and vehicles. This minimizes damage and disruption, and increases the usability and benefits of railways. Trackside monitoring provides frequent data, but is costly and ineffective for large segments of track. Onboard monitoring using dedicated vehicles provides deep knowledge of the track, but is infrequent and requires allocated resources and track availability. The use of sensors onboard passenger trains overcomes these issues by acquiring signals more frequently and with relatively inexpensive devices, hence the increasing use of these devices. This review presents the current techniques applied for track and train condition monitoring techniques using onboard sensors, as well as the use of trackside sensors.

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Section: Track Monitoring and Inspectionmentioning

confidence: 99%

Railway Track and Vehicle Onboard Monitoring: A Review

et al. 2023

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“…The high maintenance cost of spot detectors has restricted the extensive use of these sensors in the area of measurement [24]. Another alternative method of collecting traffic data is using the data from mobile sensors and CVs [25]. CVs are vehicles equipped with GPS and communication devices, and so they can provide the roadside units with highly accurate data about their position, speed, acceleration and decelerations.…”

Section: Literature Reviewmentioning

confidence: 99%

A neural network algorithm for queue length estimation based on the concept of k-leader connected vehicles

2019

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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.

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“…The authors obtained cross-correlation values above 0.85 between the smartphone measurements and existing track irregularities [4]. Other works developed a standard algorithm for infrastructure supervision [5] or investigated the effects of speed on vibration when measuring with smartphones [6]. Afterall, all these papers consider the smartphones as a black box without studying the quality of the accelerometer data itself.…”

Section: Introductionmentioning

confidence: 99%

Potential of estimating track irregularities from in-service vehicles using smartphones

Leibner¹,

Stillfried²,

Schindler³

Civil-Comp Conferences

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The necessary inspection of railway tracks currently still requires high effort and costs, even though cheap sensors and IoT-capable devices are widely available. Such devices could be installed in regular in-service rail vehicles and potentially monitor and measure track irregularities comparably well. This would enable new possibilities for railway operators as they could provide services that are traditionally executed by network operators. Smartphones are a typical example of these kind of devices. Existing studies already researched upon the usage of smartphones in rail vehicles to potentially measure track irregularities. However, from our knowledge none of the previous studies assessed the quality of smartphone accelerometers. Therefore, we conducted experiments on a shaker test rig with different smartphones to study the frequency response of different devices. This paper presents the results of these experiments and shows that smartphones are in general not suitable to measure track irregularities directly. We show that the quality of the data only allows for monitoring applications with the focus of detecting larger deviations over time. The exact calculation of deviations respectively track irregularities is not viable from our perspective.

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A Novel Method in Light-Rail Condition Monitoring Using Smartphones

Cited by 6 publications

References 26 publications

Railway Track and Vehicle Onboard Monitoring: A Review

Railway Track and Vehicle Onboard Monitoring: A Review

A neural network algorithm for queue length estimation based on the concept of k-leader connected vehicles

Potential of estimating track irregularities from in-service vehicles using smartphones

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