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CLC number: U216.3

On-line Access: 2020-08-10

Received: 2019-05-13

Revision Accepted: 2020-01-03

Crosschecked: 2020-06-19

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Jian-li Cong


Yuan Wang


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.8 P.1226-1238


Subway rail transit monitoring by built-in sensor platform of smartphone

Author(s):  Jian-li Cong, Ming-yuan Gao, Yuan Wang, Rong Chen, Ping Wang

Affiliation(s):  School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China; more

Corresponding email(s):   jlcong2019@my.swjtu.edu.cn, gaomingyuan@swu.edu.cn, wangy39@sustech.edu.cn, chenrong@home.swjtu.edu.cn, wping@home.swjtu.edu.cn

Key Words:  Acceleration signals, Smart monitoring, Embedded sensors, Smartphones, Subway

Jian-li Cong, Ming-yuan Gao, Yuan Wang, Rong Chen, Ping Wang. Subway rail transit monitoring by built-in sensor platform of smartphone[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(8): 1226-1238.

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A1 - Jian-li Cong
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Smartphone, as a smart device with multiple built-in sensors, can be used for collecting information (e.g., vibration and location). In this paper, we propose an approach for using the smartphone as a sensing platform to obtain real-time data on vehicle acceleration, velocity, and location through the development of the corresponding application software and thereby achieve the green concept based monitoring of the track condition during subway rail transit. Field tests are conducted to verify the accuracy of smartphones in terms of the obtained data’s standard deviation (SD), Sperling index (SI), and International Organization for Standardization (ISO)-2631 weighted acceleration index (WAI). A vehicle-positioning method, together with the coordinate alignment algorithm for a Global Positioning System (GPS) free tunnel environment, is proposed. Using the time-domain integration method, the relationship between the longitudinal acceleration of a vehicle and the subway location is established, and the distance between adjacent stations of the subway is calculated and compared with the actual values. The effectiveness of the method is verified, and it is confirmed that this approach can be used in the GPS-free subway tunnel environment. It is also found that using the proposed vehicle-positioning method, the integral error of displacement of a single subway section can be controlled to within 5%. This study can make full use of smartphones and offer a smart and eco-friendly approach for human life in the field of intelligent transportation systems.





Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Agapie E, Chen J, Houston D, et al., 2008. Seeing our signals: combining location traces and web-based models for personal discovery. Proc 9th Workshop on Mobile Computing Systems and Applications, p.6-10.

[2]Chellaswamy C, Balaji L, Vanathi A, et al., 2017. IoT based rail track health monitoring and information system. Int Conf on Microelectronic Devices, Circuits and Systems, p.1-6.

[3]Cong JL, Wang Y, Yang CP, et al., 2019. Data preprocessing method of vehicle vibration acceleration by smartphone. J Data Acquis Proc, 34(2):349-357 (in Chinese).

[4]Gao MY, Wang P, Cao Y, et al., 2017. Design and verification of a rail-borne energy harvester for powering wireless sensor networks in the railway industry. IEEE Trans Intell Transp Syst, 18(6):1596-1609.

[5]Gao MY, Wang P, Wang YF, et al., 2018. Self-powered ZigBee wireless sensor nodes for railway condition monitoring. IEEE Trans Intell Transp Syst, 19(3):900-909.

[6]Gao MY, Su CG, Cong JL, et al., 2019. Harvesting thermo-electric energy from railway track. Energy, 180:315-329.

[7]Gao MY, Cong JL, Xiao JL, et al., 2020. Dynamic modeling and experimental investigation of self-powered sensor nodes for freight rail transport. Appl Energy, 257:113969.

[8]Griffin MJ, 2007. Discomfort from feeling vehicle vibration. Veh Syst Dynam, 45(7-8):679-698.

[9]Huang DM, Zhou SX, Yang ZC, 2019. Resonance mechanism of nonlinear vibrational multistable energy harvesters under narrow-band stochastic parametric excitations. Complexity, 2019:1050143.

[10]Huang DM, Zhou SX, Han Q, et al., 2020. Response analysis of the nonlinear vibration energy harvester with an uncertain parameter. Proc Inst Mech Eng K, 234(2):393-407.

[11]International Organization for Standardization, 1997. Mechanical vibration and shock: evaluation of human exposure to whole-body vibration. Part 1, general requirements. ISO 2631-1:1997. International Organization for Standardization.

[12]Jin XS, Wen ZF, Wang KY, et al., 2006. Three-dimensional train–track model for study of rail corrugation. J Sound Vibr, 293(3-5):830-855.

[13]Kaynia AM, Park J, Norén-Cosgriff K, 2017. Effect of track defects on vibration from high speed train. Proc Eng, 199:2681-2686.

[14]Kim YG, Kwon HB, Kim SW, et al., 2003. Correlation of ride comfort evaluation methods for railway vehicles. Proc Inst Mech Eng F, 217(2):73-88.

[15]Lane ND, Miluzzo E, Lu H, et al., 2010. A survey of mobile phone sensing. IEEE Commun Mag, 48(9):140-150.

[16]Mohan P, Padmanabhan VN, Ramjee R, 2008. Nericell: rich monitoring of road and traffic conditions using mobile smartphones. Proc 6th ACM Conf on Embedded Network Sensor Systems, p.323-336.

[17]Molodova M, Li ZL, Núñez A, et al., 2014. Automatic detection of squats in railway infrastructure. IEEE Trans Intell Transp Syst, 15(5):1980-1990.

[18]Mosa ASM, Yoo I, Sheets L, 2012. A systematic review of healthcare applications for smartphones. BMC Med Inform Dec Mak, 12(1):67.

[19]Paddan GS, Griffin MJ, 2002. Evaluation of whole-body vibration in vehicles. J Sound Vibr, 253(1):195-213.

[20]Reddy S, Burke J, Estrin D, et al., 2008. Determining transportation mode on mobile phones. 12th IEEE Int Symp on Wearable Computers, p.25-28.

[21]Ruiz-Zafra A, Orantes-González E, Noguera M, et al., 2015. A comparative study on the suitability of smartphones and IMU for mobile, unsupervised energy expenditure calculi. Sensors, 15(8):18270-18286.

[22]Simonyi E, Fazekas Z, Gáspár P, 2014. Smartphone application for assessing various aspects of urban public transport. Transp Res Proc, 3:185-194.

[23]Tsunashima H, Naganuma Y, Kobayashi T, 2014. Track geometry estimation from car-body vibration. Veh Syst Dynam, 52(S1):207-219.

[24]Wang P, Wang Y, Wang L, et al., 2017. Measurement of carbody vibration in urban rail transit using smartphones. Proc Transportation Research Board 96th Annual Meeting, p.15.

[25]Wang SQ, Chen CF, Ma J, 2010. Accelerometer based transportation mode recognition on mobile phones. Asia- Pacific Conf on Wearable Computing Systems, p.44-46.

[26]Wang Y, Wang P, Wang X, et al., 2018. Position synchronization for track geometry inspection data via big-data fusion and incremental learning. Transp Res Part C Emerg Technol, 93:544-565.

[27]Wang YF, Yang Z, Pang J, 2018. Statistical analysis of urban rail transit lines in 2017 China―express delivery of annual report on urban rail transit V. Urban Mass Trans, 21(1):1-6 (in Chinese).

[28]Wei XK, Jia LM, Liu H, 2013. A comparative study on fault detection methods of rail vehicle suspension systems based on acceleration measurements. Veh Syst Dynam, 51(5):700-720.

[29]Yang K, Wang JL, Yurchenko D, 2019. A double-beam piezo- magneto-elastic wind energy harvester for improving the galloping-based energy harvesting. Appl Phys Lett, 115(19):193901.

[30]Zhao YJ, Deng X, Liu SQ, et al., 2015. Interior noise prediction of high-speed train based on hybrid FE-SEA method. Proc 11th Int Workshop on Railway Noise, p.699-705.

[31]Zhou SX, Zuo L, 2018. Nonlinear dynamic analysis of asymmetric tristable energy harvesters for enhanced energy harvesting. Commun Nonl Sci Numer Simul, 61:271-284.

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