Abstract
Railway infrastructures degrade due to numerous factors, leading to malfunctions, track irregularities and other faults that negatively impact the running behavior of trains and the normal railway operation. Railway infrastructure managers traditionally conduct visual inspections and use dedicated inspection vehicles for track monitoring, which are expensive and disruptive to railway traffic. To address this issue and following the recent advances in the capabilities of mobile devices, new approaches for railway track monitoring using low-cost systems have been proposed to assess and characterize the railway infrastructure. Low-cost solutions, such as smart mobile devices with embedded IMUs, can revolutionize railway monitoring and maintenance by reducing costs and improving efficiency. A short literature review on various low-cost and smart mobile applications for railway transport performance assessment and characterization is presented, mainly focusing of the use of low-cost and/or mobile IMUs as condition monitoring systems. A case study is also presented on measurements from a smartphone to evaluate accelerations inside a passenger train on an 11 km stretch. Among other aspects, the results evidence the strong correlation between the vertical accelerations and the longitudinal level of the track, particularly in terms of the standard deviation. These approaches have the potential to complement or replace dedicated inspection vehicles and provide early detection of track faults, leading to cost savings and improved safety and ride comfort for passengers. Such approaches can be integrated in monitoring systems to optimize railway operation and maintenance procedures with the use of big data, IoT applications, and AI techniques.
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References
Spencer BF Jr, Ruiz-Sandoval ME, Kurata N (2004) Smart sensing technology: opportunities and challenges. Struct Control Health Monit 11(4):349–368
Alavi AH, Buttlar WG (2019) An overview of smartphone technology for citizen-centered, real-time and scalable civil infrastructure monitoring. Futur Gener Comput Syst 93:651–672
Morgenthal G, Höpfner H (2012) The application of smartphones to measuring transient structural displacements. J Civ Struct Heal Monit 2(3):149–161
Simonyi E, Fazekas Z, Gáspár P (2014) Smartphone application for assessing various aspects of urban public transport. Transp Res Procedia 3:185–194
Forslöf L, Jones H (2015) Roadroid: continuous road condition monitoring with smart phones. J Civ Eng Architect 9(4):485–496
Amador-Jimenez L, Christopher A (2016) A comfort index for public transportation: case study of Montreal. In: 2016 IEEE international conference on intelligent transportation engineering (ICITE), 20–22 August 2016, pp 3–7
Lidén T (2015) Railway infrastructure maintenance - a survey of planning problems and conducted research. Transp Res Procedia 10:574–583
Zarembski AM (2014) Some examples of big data in railroad engineering. In: 2014 IEEE international conference on big data (big data), 27–30 October 2014, pp 96–102
Singh P, Elmi Z, Krishna MV, Pasha J, Dulebenets MA (2022) Internet of things for sustainable railway transportation: past, present, and future. Clean Logist Supply Chain 4:100065
Tsunashima H, Naganuma Y, Matsumoto A, Mizuma T, Mori H (2012) Condition monitoring of railway track using in-service vehicle. In: Perpinya X (ed) Reliability and safety in railway. IntechOpen, pp 334–356
Li C, Luo S, Cole C, Spiryagin M (2017) An overview: modern techniques for railway vehicle on-board health monitoring systems. Veh Syst Dyn 55(7):1045–1070
Yeo GJ (2017) Monitoring railway track condition using inertial sensors on an in-service vehicle. PhD, University of Birmingham, Birmingham
Bastida CA, Gómez Carmona CD, Pino OJ, de la Cruz SE (2017) Validity of an inertial system to measure sprint time and sport task time: a proposal for the integration of photocells in an inertial system. Int J Perform Anal Sport 17(4):600–608
Ward CP, Weston PF, Stewart EJC, Li H, Goodall RM, Roberts C, Mei TX, Charles G, Dixon R (2011) Condition monitoring opportunities using vehicle-based sensors. Proc IMechE Part F: J Rail Rapid Transit 225(2):202–218
Vinkó Á, Simonek T, Ágh C, Csikós A, Figura B (2023) Feasibility of onboard smartphones for railway track geometry estimation: sensing capabilities and characterization. Period Polytech Civ Eng 67(1):200–210
Jo O, Kim YK, Kim J (2018) Internet of things for smart railway: feasibility and applications. IEEE Internet Things J 5(2):482–490
Seraj F, Meratnia N, Havinga PJM (2017) RoVi: continuous transport infrastructure monitoring framework for preventive maintenance. In: 2017 IEEE international conference on pervasive computing and communications (PerCom), 13–17 March 2017, pp 217–226
Weston P, Roberts C, Yeo G, Stewart E (2015) Perspectives on railway track geometry condition monitoring from in-service railway vehicles. Veh Syst Dyn 53(7):1063–1091
Tsunashima H (2019) Condition monitoring of railway tracks from car-body vibration using a machine learning technique. Appl Sci 9(13)
King S (2004) The UK's fastest track monitoring system as used on the channel tunnel rail link. In: IEEE seminar railway condition monitoring 2004 (Ref. No. 2004/10513), 25–25 February 2004, pp 0_11–32
Ackroyd P, Angelo S, Stevens J (2002) Remote ride quality monitoring of Acela train set performance. In: ASME/IEEE joint railroad conference, 23–25 April 2002, pp 171–178
Tsunashima H, Kojima T, Marumo Y, Matsumoto A, Mizuma T (2008) Condition monitoring of railway track and driver using in-service vehicle. In: 2008 4th IET international conference on railway condition monitoring, 18–20 June 2008, pp 1–6
Ishii H, Fu**o Y, Mizuno Y, Kaito K (2006) The study of train intelligent monitoring system using acceleration of ordinary train. In: Asia-Pacific workshop on structural health monitoring, Keio University, Yokohama, Japan, 4–6 December 2006
Kobayashi T, Naganuma Y, Tsunashima H (2013) Condition monitoring of Shinkansen tracks based on inverse analysis. In: Zio E, Baraldi P (eds) Prognostic and system health management - PHM2013, Milan. AIDIC Servizi S.r.l., pp 703–708
Tsunashima H, Naganuma Y, Kobayashi T (2014) Track geometry estimation from car-body vibration. Veh Syst Dyn 52(sup1):207–219
Odashima M, Azami S, Naganuma Y, Mori H, Tsunashima H Track geometry estimation of a conventional railway from car-body acceleration measurement. Mech Eng J 4(1):16-00498–00416-00498 (2017).
Weston PF, Li P, Ling CS, Goodman CJ, Goodall RM, Roberts C (2006) Track and vehicle condition monitoring during normal operation using reduced sensor sets. HKIE Trans 13(1):47–54
Weston PF, Ling CS, Roberts C, Goodman CJ, Li P, Goodall RM (2007) Monitoring vertical track irregularity from in-service railway vehicles. Proc IMechE, Part F: J Rail Rapid Transit 221(1):75–88
Weston PF, Ling CS, Goodman CJ, Roberts C, Li P, Goodall RM (2007) Monitoring lateral track irregularity from in-service railway vehicles. Proc IMechE Part F: J Rail Rapid Transit 221(1):89–100
Lee JS, Choi S, Kim SS, Kim YG, Kim SW, Park C (2011) Track condition monitoring by in-service trains: a comparison between axle-box and bogie accelerometers. In: 5th IET conference on railway condition monitoring and non-destructive testing (RCM 2011), 29–30 November 2011, pp 1–6
Lee JS, Choi S, Kim SS, Park C, Kim YG (2012) A mixed filtering approach for track condition monitoring using accelerometers on the axle box and bogie. IEEE Trans Instrum Meas 61(3):749–758
Chia L, Bhardwaj B, Lu P, Bridgelall R (2019) Railroad track condition monitoring using inertial sensors and digital signal processing: a review. IEEE Sens J 19(1):25–33
Bragança C, Neto J, Pinto N, Montenegro PA, Ribeiro D, Carvalho H, Calçada R (2022) Calibration and validation of a freight wagon dynamic model in operating conditions based on limited experimental data. Veh Syst Dyn 60(9):3024–3050
Gatin O, L’Henoret B, Isasi A, Neveu S, Vicol T, Schrevere T (2013) Track geometry condition monitoring system for non intrusive measurements on commercial trains wireless sensor networks. In: 10th world congress on railway research 2013, Sydney, Australia, 25–28 November 2013
Morais P, Morais J, Santos C, Paixão A, Fortunato E, Asseiceiro F, Alvarenga P, Gomes L (2019) Continuous monitoring and evaluation of railway tracks: proof of concept. Procedia Struct Integr 17:419–426
Morais J, Santos C, Morais P, Paixão A, Fortunato E, Asseiceiro F, Alvarenga P, Gomes L (2019) Continuous monitoring and evaluation of railway tracks: system description and assessment. Procedia Struct Integr 17:448–455
Núñez A, Popa T, Anghel LE, Hendriks J, Moraal J, Buretea LD, Paragreen J, Miron B, Gheorghe D, Campean M, Dollevoet R, Li Z (2018) Smart technology solutions for the NeTIRail-INFRA case study lines: axle box acceleration and ultra-low cost smartphones. In: 7th transport research arena TRA 2018 (TRA 2018), Viena, Austria, 16–19 April 2018
Sorrentino (2022) Harmotrack: investigating acceleration measurements to monitor track quality. Glob Railw Rev. Russell Publishing Ltd., Kent, UK
Dadié F, Neveu S, Causse J, Sorrentino D, Saussine G (2022) Track geometry monitoring using smartphones on board commercial trains. In: World congress on railway research 2022, Birmingham, UK, 6–10 June 2022
Stübinger L, Stavrianidis K (2022) Track monitoring smartphone app. ETR - Eisenbahntechnische Rundschau, International Edition, September 2022. DVV Media Group GmbH, Hamburg
Rodríguez A, Sañudo R, Miranda M, Gómez A, Benavente J (2021) Smartphones and tablets applications in railways, ride comfort and track quality. Transition zones analysis. Measurement 182:109644
Paixão A, Fortunato E, Calçada R (2019) Smartphone's sensing capabilities for on-board railway track monitoring: structural performance and geometrical degradation assessment. In: Advances in civil engineering 2019
Cong J-L, Gao M-Y, Wang Y, Chen R, Wang P (2020) Subway rail transit monitoring by built-in sensor platform of smartphone. Front Inf Technol & Electron Eng 21(8):1226–1238
de Oliveira RH, Loprencipe G, Vaz de Almeida Filho FG, de Sousa Pissardini R (2022) Experimental investigation on the use of multiple very low-cost inertial-based devices for comfort assessment and rail track monitoring. Measurement 199:111549
Ren Y, Dai Z, Lu P, Ai C, Huang Y, Tolliver D (2022) Rail gage-based risk detection Using iPhone 12 pro. Proc IMechE Part F: J Rail Rapid Transit 09544097221116431
Milne D, Le Pen L, Watson G, Thompson D, Powrie W, Hayward M, Morley S (2016) Proving MEMS technologies for smarter railway infrastructure. Procedia Eng 143:1077–1084
Paixão A, Alves Ribeiro C, Pinto NMP, Fortunato E, Calçada R (2015) On the use of under sleeper pads in transition zones at railway underpasses: experimental field testing. Struct Infrastruct Eng 11(2):112–128
Castellanos-Toro S, Marmolejo M, Marulanda J, Cruz A, Thomson P (2018) Frequencies and dam** ratios of bridges through operational modal analysis using smartphones. Constr Build Mater 188:490–504
Bhattacharya S, Murali KA, Lombardi D, Crewe A, Alexander N (2012) Economic MEMS based 3-axis water proof accelerometer for dynamic geo-engineering applications. Soil Dyn Earthq Eng 36:111–118
Aikawa A (2009) Techniques to measure effects of passing trains on dynamic pressure applied to sleeper bottoms and dynamic behavior of ballast stones. Q Report RTRI 50(2):102–109
Lu P, Bridgelall R, Tolliver D, Chia L, Bhardwaj B (2019) Intelligent transportation systems approach to railroad infrastructure performance evaluation: track surface abnormality identification with smartphone-based app
Pender B, Currie G, Delbosc A, Shiwakoti N (2014) Social media use during unplanned transit network disruptions: a review of literature. Transp Rev 34(4):501–521
Azzoug A, Kaewunruen S (2017) RideComfort: a development of crowdsourcing smartphones in measuring train ride quality. Front Built Environ 3(3)
George TK, Gadhia HM, Sukumar R, Cabibihan JJ (2013) Sensing discomfort of standing passengers in public rail transportation systems using a smart phone. In: 2013 10th IEEE international conference on control and automation (ICCA), 12–14 June 2013, pp 1509–1513
Elhamshary M, Youssef M, Uchiyama A, Hiromori A, Yamaguchi H, Higashino T (2019) CrowdMeter: gauging congestion level in railway stations using smartphones. Pervasive Mob Comput 58:101014
Maekawa Y, Uchiyama A, Yamaguchi H, Higashino T (2014) Car-level congestion and position estimation for railway trips using mobile phones. In: 2014 ACM international joint conference on pervasive and ubiquitous computing, Seattle Washington, USA, 13–17 September 2014
Wang Y, Cong J, Wang P, Liu X, Tang H (2019) A data-fusion approach for speed estimation and location calibration of a metro train based on low-cost sensors in smartphones. IEEE Sens J 19(22):10744–10752
Nitsche P, Widhalm P, Breuss S, Maurer P (2012) A strategy on how to utilize smartphones for automatically reconstructing trips in travel surveys. Procedia Soc Behav Sci 48:1033–1046
Lee HP, Lim KM, Kumar S (2021) Noise assessment of elevated rapid transit railway lines and acoustic performance comparison of different noise barriers for mitigation of elevated railway tracks noise. Appl Acoust 183:108340
Tsunashima H, Mori H, Yanagisawa K, Ogino M, Asano A (2014) Condition monitoring of railway tracks using compact size on-board monitoring device. In: 6th IET conference on railway condition monitoring (RCM 2014), 17–18 September 2014, pp 1–5
Heirich O, Lehner A, Robertson P, Strang T (2011) Measurement and analysis of train motion and railway track characteristics with inertial sensors. In: 2011 14th international IEEE conference on intelligent transportation systems (ITSC), 5–7 October 2011, pp 1995–2000
Kampczyk A, Dybeł K (2021) Integrating surveying railway special grid pins with terrestrial laser scanning targets for monitoring rail transport infrastructure. Measurement 170:108729
Guo F, Qian Y, Shi Y (2021) Real-time railroad track components inspection based on the improved YOLOv4 framework. Autom Constr 125:103596
Guo F, Qian Y, Wu Y, Leng Z, Yu H (2021) Automatic railroad track components inspection using real-time instance segmentation. Comput-Aided Civ Infrastruct Eng 36(3):362–377
Paixão A (2014) Transition zones in railway tracks: An experimental and numerical study on the structural behaviour. University of Porto, Faculty of Engineering, Porto
Paixão A, Varandas JN, Fortunato E, Calçada R (2018) Numerical simulations to improve the use of under sleeper pads at transition zones to railway bridges. Eng Struct 164:169–182
Liu S, Huang H, Qiu T, Gao L (2017) Comparison of laboratory testing using SmartRock and discrete element modeling of ballast particle movement. J Mater Civ Eng 29(3):D6016001
Zeng K, Qiu T, Bian X, **ao M, Huang H (2019) Identification of ballast condition using SmartRock and pattern recognition. Constr Build Mater 221:50–59
Zhang B, Lee Seung J, Qian Y, Tutumluer E, Bhattacharya S (2016) A smartphone-based image analysis technique for ballast aggregates. In: International conference on transportation and development 2016, pp 623–630
van Diggelen F, Enge P (2015) The world’s first GPS MOOC and worldwide laboratory using smartphones. In: Proceedings of the 28th international technical meeting of the satellite division of the institute of navigation (ION GNSS+ 2015), Tampa, Florida, 14–18 September 2015
CEN: European Standard EN 13848-5:2017 railway applications - track - track geometry quality - part 5: geometric quality levels - plain line, switches and crossings. 93.100 - construction of railways. CEN/TC 256 - Railway applications, Comité Européen de Normalisation, Brussels (2017)
Berndt DJ, Clifford J (1994) Using dynamic time war** to find patterns in time series. In: KDD-94, knowledge discovery in databases (AAAI-94), Seattle, WA, pp 359–370
William Anthony A (1988) Speech recognition by machine. In: Computing series, vol 12. Peter Peregrinus Ltd, London
Paixão A, Fortunato E, Calçada R (2016) A contribution for integrated analysis of railway track performance at transition zones and discontinuities. Constr & Build Mater 111(C):699–709
Acknowledgements
This work was partially financed by: Base Funding—UIDB/04708/2020 and Programmatic Funding—UIDP/04708/2020 from the CONSTRUCT Research Unit—Institute of R&D in Structures and Constructions—funded by national funds through FCT/MCTES (PIDDAC).
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Paixão, A., Fortunato, E., Calçada, R. (2024). Applications of Low-Cost and Smart Mobile Devices for Railway Infrastructure Performance Assessment and Characterization. In: Ribeiro, D., Montenegro, P.A., Andersson, A., Martínez-Rodrigo, M.D. (eds) Digital Railway Infrastructure. Digital Innovations in Architecture, Engineering and Construction. Springer, Cham. https://doi.org/10.1007/978-3-031-49589-2_3
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