Figure 6. All of the data collected in Long Marston not synchronized with each other , recorded at 17 mph. The lack of major vibrations in the PCB chart between 1, and 2, s was caused by the cable coiling and coming into contact with another piece of electric equipment effectively preventing any meaningful data from being recorded during that time. The HTC Sense chart is shorter than the others because the phone itself crashed on two occasions during the field trial due to unknown reasons.
In Figure 7 , the differences in measurements from the accelerometer and the two phones are observable. The measurement starts as the shunter just started moving around the 60 s mark the shunter starts braking and goes to a full stop 15 s later.
It then starts moving again at around the s mark. The accelerometer has a lot less noise, and the spikes that occur from the air brakes being used are clearly visible. The HTC Sense struggles to record many of the greater vibration, probably due to possessing an older and lower quality accelerometer, but also possibly because it does not have the processing power to keep up with the speed at which the measurements are being recorded.
Figure 7. A 4-min and s sample of the data that were collected in Long Marston, recorded at 17 mph. Figure 10 shows what percentage of the journey would be considered comfortable or uncomfortable according to Tables 1 and 2. Most of the spikes in vibration amplitude were caused due to the driver of the shunter being quite inexperienced with the vehicle and using the air brakes a lot to regulate the speed.
From Figures 7 and 8 , it is quite clear that both phones give a very good representation of ride comfort when compared to the PCB accelerometer. Many of the figures indicate that there are some issues with the noise levels of the phones, especially the Nexus 4, while the HTC Sense might have some issues with its sensitivity.
Figure Pie charts showing the percentage of different comfort zones in a 4-min journey. Power spectral density charts were produced and compared, which can be seen in Figure The spectral charts can be used to understand where many of the vibrations are coming from as generally actions in the lower frequency domain relate to the wheel—track interaction, while actions in the higher frequency domain mainly concern the train itself Oostermeijer and Kok, ; Schulte-Werning et al.
Changes to the profile of the spectral chart can indicate changes to the condition of either the track or train. Power spectral density charts for each device made using fast Fourier transformation. It should also be noted that both phones that were used, were relatively old. Mobile technology develops at a high rate, and the smartphones of today and in the future will be equipped with higher quality accelerometers compared to the ones used in this experiment.
Based on the results of this project, the technology used in modern smartphones is more than enough to measure ride comfort aboard trains. However, it is not the quality of the sensors inside the phones that will hinder this type of technology from being implemented. Roadroid in Sweden is very successful because the application itself is part of a GPS program.
Many drivers in Sweden use their phones for their GPS functionality and so getting people to use roadroid is exceedingly simple, just package it with the most popular GPS software. However, for the app that was developed for this project, it would much more difficult to get passengers to use it. There exists no reason to start up a specific app aboard a train and let it lay relatively still while collecting data.
The greatest challenge in employ this type of technology is to make passengers themselves implement it in their daily lives. For roadroid, it was relatively simple as drivers were already implementing the use of phone GPS in their lives.
For this app to be successful, there needs to be an incentive for passengers to use and implement it. Providing an incentive could be done in a variety of ways, the app could be packaged with another app popularly used onboard trains if such an app exists. Rail companies could provide Wi-Fi onboard their trains while making it so that the app has to be running in order for the passenger to access the Wi-Fi network.
Another idea might see that railway companies provide discounts to passengers who voluntarily use the app and collect valuable data. Perhaps, passengers themselves are curious about the ride comfort and end up implementing the app in their travels on their own. At this stage of the technology, how it will be implemented is mostly speculation. Unfortunately, during the field trials, the carriage only traveled on straight track, and so it was not possible to collect any data on how well the phones can measure swaying and other vibrations that are not in the z -axis perpendicular to the floor.
Due to the HTC Sense being an older phone, the processor was not very powerful and so the sample rate varied quite a lot, dropping down to as low as 18 Hz at certain point in the data collecting. It is entirely possible for it to have missed ride comfort significant vibrations as the phone is forced to buffer the recording of the data due to the lack of processing power.
Designing the application in such a way that it is more efficient in the use of processing power might be a way to mitigate this type of problem in the future. It should be noted that the HTC Sense has a broken screen due to being exposed heavy pressure; it is unknown how this has impacted on the sensors.
The Nexus 4 has had its screen replaced on two separate occasions, once due to scratches caused by sand and another time due to falling, shattering the screen. Both times the screen was replaced by an amateur, and therefore, the instruments on the phone may or may not have suffered.
There were many other researchers onboard the carriage, and due to the lack of time, it was very hectic, and even though the accelerometer and the phones were placed out of the way as possible, people may have stepped near the phones or inadvertently touched the accelerometer or its wiring, introducing errors to the data.
In this study, we have investigated how well the accelerometers inside smartphones could measure ride comfort compared with a more sophisticated accelerometer. Although the phones show some inconsistency in being able to properly detect lower levels of discomfort, they show similarities when it comes to finding more uncomfortable levels of vibrations. After reviewing the results of the field trials, it is evident that there is merit in using smartphones for the purpose of measuring ride comfort aboard trains.
Every year more advanced accelerometers are being used in phones, in the future smartphones will be able measure ride comfort with even more accuracy than they currently can.
The inherent differences in the different phones sampling rate and quality of data could be seen in Figure 5. These differences between the phones can make it difficult to standardize the accuracy of the ride comfort that the application records. A database that keeps track of the inherent differences between the phones would need to be made if this app is to be put to use. Due to there being many possible error sources during the field testing of the developed app, it is recommended that more testing be done to get less error-riddled data.
Testing should preferably be done with a three-dimensional accelerometer to better test the phones ability to measure ride comfort in curves and turnouts.
Newer phones should be used as if and when this technology gets implemented the phones that are new at the time of development will be the standard in the future. Based on our market research, we found that it is highly likely that passengers will install the app to better understand the ride quality. Adding a functionality to the app allowing passengers to give feedbacks on subjective causes of discomfort in trains such as smells, temperature, etc. The extension can attract crowdsourcing data that could truly improve customer experiences.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors deeply appreciated technical support and constructive advice during field measurement campaigns from Drs. In addition, they are grateful to both the reviewers for their constructive comments.
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Dynamic analysis of train-bridge system and riding comfort of trains with rail irregularities. Keywords: railway infrastructure, ride quality, operations monitoring, artificial neural networks, mobile application. Built Environ. The use, distribution or reproduction in other forums is permitted, provided the original author s or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Introduction Every day, many million train journeys take place and passengers traveling on these trains are exposed to the vibrations, noises, temperatures, etc. This part of ISO defines methods for the measurement of periodic, random and transient whole-body vibration.
It indicates the principal factors that combine to determine the degree to which a vibration exposure will be acceptable. Informative annexes indicate current opinion and provide guidance on the possible effects of vibration on health, comfort and perception and motion sickness. The frequency range considered is. Although the potential effects on human performance are not covered, most of the guidance on Whole-body vibration measurement also applies to this area. This part of ISO also defines the principles of preferred methods of mounting transducers for determining human exposure.
It does not apply to the evaluation of extreme-magnitude single shocks such as occur in vehicle accidents. This part of ISO is applicable to motions transmitted to the human body as a whole through the supporting surfaces: the feet of a standing person, the buttocks, back and feet of a seated person or the supporting area of a recumbent person.
This type of vibration is found in vehicles, in machinery, in buildings and in the vicinity of working machinery.
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