A survey on usage of smartphone accelerometer sensor in intelligent transportation systems

Document Type : Original Article

Author

Department of Computer Engineering, Malayer University, Iran

Abstract

The numerous capabilities of smartphones have made them suitable alternative to expensive tools and methods in intelligent transportation systems. This study surveys the literature on the role of the accelerometer of smartphones in intelligent transportation applications. At first, the opportunities and challenges of using the accelerometer are stated. Then, the architecture of using this sensor including preprocessing, feature extraction, mode detection, reorientation and applications are explained. Finally, different applications that have used the accelerometer of mobile phones in the intelligent transportation systems have been investigated.

Keywords

Main Subjects


[1] F. K. Afukaar and J. Damsere-Derry, Evaluation of speed humps on pedestrian injuries in ghana, Injury Prevention, 16 (2010), pp. A205–A206.
[2] R. Ahmadian, M. Ghatee, and J. Wahlstrom¨ , Discrete wavelet transform for generative adversarial network to identify drivers using gyroscope and accelerometer sensors, IEEE Sensors Journal, 22 (2022), pp. 6879– 6886.
[3] Y. I. Alatoom and T. I. Obaidat, Measurement of street pavement roughness in urban areas using smarphone, Int. J. Pavement Res. Technol., 15 (2022), pp. 1003–1020.
[4] R. G. Aldunate, O. A. Herrera, and J. P. Cordero, Early vehicle accident detection and notification based on smartphone technology, in Ubiquitous Computing and Ambient Intelligence. Context-Awareness and Context-Driven Interaction, Springer, Cham, 2013, pp. 358–365.
[5] A. Alessandrini, A. Cattivera, F. Filippi, and F. Ortenzi, Driving style influence on car co2 emissions, in 2012 international emission inventory conference, 2012.
[6] J. Almazan, L. M. Bergasa, J. J. Yebes, R. Barea, and R. Arroyo ´ , Full auto-calibration of a smartphone on board a vehicle using imu and gps embedded sensors, in 2013 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2013, pp. 1374–1380.
[7] F. Aloul, I. Zualkernan, R. Abu-Salma, H. Al-Ali, and M. Al-Merri, ibump: Smartphone application to detect car accidents, Computers and Electrical Engineering, 43 (2015), pp. 66–75.
[8] M. S. Amin, M. B. I. Reaz, M. A. S. Bhuiyan, and S. S. Nasir, Kalman filtered gps accelerometer-based accident detection and location system: A low-cost approach, Current Science, (2014), pp. 1548–1554.
[9] V. Astarita, M. V. Caruso, G. Danieli, D. C. Festa, V. P. Giofre, T. Iuele, and R. Vaiana ` , A mobile application for road surface quality control: Uniqualroad, Procedia Soc. Behav. Sci., 54 (2012), pp. 1135– 1144.
[10] E. Beuving, T. De Jonghe, D. Goos, T. Lindahl, and A. Stawiarski, Environmental impacts and fuel efficiency of road pavements, European Roads Review, (2004).
[11] D. M. Bhandari, A. Witayangkurn, R. Shibasaki, and M. M. Rahman, Estimation of origin[1]destination using mobile phone call data: A case study of greater dhaka, bangladesh, in 2018 Thirteenth International Conference on Knowledge, Information and Creativity Support Systems (KICSS), IEEE, 2018, pp. 1–7.
[12] R. Bhoraskar, N. Vankadhara, B. Raman, and P. Kulkarni, Wolverine: Traffic and road condition estimation using smartphone sensors, in 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012), IEEE, 2012, pp. 1–6.
[13] M. R. Carlos, L. C. Gonzalez, F. Mart ´ ´ınez, and R. Cornejo, Evaluating Reorientation Strategies for Accelerometer Data from Smartphones for ITS Applications, Springer International Publishing, 2016, pp. 407– 418.
[14] G. Castignani, R. Frank, and T. Engel, An evaluation study of driver profiling fuzzy algorithms using smartphones, in 2013 21st IEEE International Conference on Network Protocols (ICNP), 2013, pp. 1–6.
[15] P. Chaovalit, C. Saiprasert, and T. Pholprasit, A method for driving event detection using SAX with resource usage exploration on smartphone platform, J. Wireless Com. Network, 2014 (2014), p. 135.
[16] J. Dai, J. Teng, X. Bai, Z. Shen, and D. Xuan, Mobile phone based drunk driving detection, in 2010 4th International Conference on Pervasive Computing Technologies for Healthcare, 2010, pp. 1–8.
[17] S. H. de Frutos and M. Castro, Using smartphones as a very low-cost tool for road inventories, Transp. Res. C: Emerg. Technol., 38 (2014), pp. 136–145.
[18] H. Dong, M. Wu, X. Ding, L. Chu, L. Jia, Y. Qin, and X. Zhou, Traffic zone division based on big data from mobile phone base stations, Transp. Res. Part C Emerg., 58 (2015), pp. 278–291.
[19] H. R. Eftekhari, Smartphone-based system for driver anger scale estimation using neural network on continuous wavelet transformation, AUT J. Math. Comput., 1 (2020), pp. 113–124.
[20] H. R. Eftekhari and M. Ghatee, An inference engine for smartphones to preprocess data and detect stationary and transportation modes, Transp. Res. C: Emerg. Technol., 69 (2016), pp. 313–327.
[21] , Hybrid of discrete wavelet transform and adaptive neuro fuzzy inference system for overall driving behavior recognition, Transp. Res. F: Traffic Psychol. Behav, 58 (2018), pp. 782–796.
[22] H. R. Eftekhari and M. Ghatee, A similarity-based neuro-fuzzy modeling for driving behavior recognition applying fusion of smartphone sensors, J. Intell. Transp. Syst., 23 (2019), pp. 72–83.
[23] A. Efthymiou, E. N. Barmpounakis, D. Efthymiou, and E. I. Vlahogianni, Transportation mode detection from low-power smartphone sensors using tree-based ensembles, J. Big Data Anal. Transp., 1 (2019), pp. 57–69.
[24] J. Eriksson, L. Girod, B. Hull, R. Newton, S. Madden, and H. Balakrishnan, The pothole patrol: using a mobile sensor network for road surface monitoring, in Proceedings of the 6th International Confer[1]ence on Mobile Systems, Applications, and Services, New York, NY, USA, 2008, Association for Computing Machinery, p. 29–39.
[25] J. Eriksson, L. Girod, B. Hull, R. Newton, S. Madden, and H. Balakrishnan, The pothole patrol: using a mobile sensor network for road surface monitoring, in Proceedings of the 6th international conference on Mobile systems, applications, and services, 2008, pp. 29–39.
[26] M. Fazeen, B. Gozick, R. Dantu, M. Bhukhiya, and M. C. Gonzalez ´ , Safe driving using mobile phones, IEEE Trans. Intell. Transp. Syst., 13 (2012), pp. 1462–1468.
[27] M. Fekih, T. Bellemans, Z. Smoreda, P. Bonnel, A. Furno, and S. Galland, A data-driven approach for origin–destination matrix construction from cellular network signalling data: a case study of lyon region (france), Transportation, 48 (2021), pp. 1671–1702.
[28] D. Figo, P. C. Diniz, D. R. Ferreira, and J. M. P. Cardoso, Preprocessing techniques for context recognition from accelerometer data, Personal and Ubiquitous Computing, 14 (2010), pp. 645–662.
[29] S. Garg and P. Singh, A novel approach for vehicle specific road/traffic congestion, PhD thesis, Indraprastha Institute of Information Technology Delhi, 2014.
[30] S. Hemminki, P. Nurmi, and S. Tarkoma, Accelerometer-based transportation mode detection on smartphones, in Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, SenSys ’13, New York, NY, USA, 2013, Association for Computing Machinery.
[31] J. H. Hong, B. Margines, and A. K. Dey, A smartphone-based sensing platform to model aggressive driving behaviors, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2014, pp. 4047–4056.
[32] V. Jain, E. Gupta, M. S. Pillai, P. Bhola, and G. Chaudhary, Accist: Automatic traffic accident detection and notification with smartphones, in Computational Intelligence for Information Retrieval, CRC Press, 2021, pp. 35–46.
[33] V. Jain, E. Gupta, M. S. Pillai, P. Bhola, and G. Chaudhary, Accist: Automatic traffic accident detection and notification with smartphones, in Computational Intelligence for Information Retrieval, CRC Press, 2021, pp. 35–46.
[34] D. A. Johnson and M. M. Trivedi, Driving style recognition using a smartphone as a sensor platform, in 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2011, pp. 1609–1615.
[35] M. Kamalian and P. Ferreira, Fogtmdetector - fog based transport mode detection using smartphones, in 2022 IEEE 6th International Conference on Fog and Edge Computing (ICFEC), 2022, pp. 9–16.
[36] W. Z. Khan, Y. Xiang, M. Y. Aalsalem, and Q. Arshad, Mobile phone sensing systems: A survey, IEEE Communications Surveys & Tutorials, 15 (2012), pp. 402–427.
[37] R. Kujala, T. Aledavood, and J. Saramaki ¨ , Estimation and monitoring of city-to-city travel times using call detail records, EPJ Data Science, 5 (2016), pp. 1–16.
[38] G. L. KV, U. Sait, T. Kumar, R. Bhaumik, S. Shivakumar, and K. Bhalla, Design and development of a smartphone-based application to save lives during accidents and emergencies, Procedia Computer Science, 167 (2020), pp. 2267–2275.
[39] R. Mandal, P. Sonowal, M. Kumar, S. Saha, and S. Nandi, Roadspeedsense: Context-aware speed profiling from smart-phone sensors, EAI Endorsed Transactions on Energy Web, 7 (2020).
[40] V. Manzoni, D. Maniloff, K. Kloeckl, and C. Ratti, Transportation mode identification and real[1]time co2 emission estimation using smartphones, tech. rep., SENSEable City Lab, Massachusetts Institute of Technology, 2010.
[41] A. Mednis, G. Strazdins, R. Zviedris, G. Kanonirs, and L. Selavo, Real time pothole detection using android smartphones with accelerometers, in 2011 International conference on distributed computing in sensor systems and workshops (DCOSS), 2011, pp. 1–6.
[42] P. Mohan, V. N. Padmanabhan, and R. Ramjee, Nericell: rich monitoring of road and traffic conditions using mobile smartphones, in Proceedings of the 6th ACM conference on Embedded network sensor systems, 2008, pp. 323–336.
[43] D. Montoya, S. Abiteboul, and P. Senellart, Hup-me: inferring and reconciling a timeline of user activity from rich smartphone data, in Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2015, p. 62.
[44] M. Nikolic and M. Bierlaire, Review of transportation mode detection approaches based on smartphone data, in 17th Swiss Transport Research Conference, 2017.
[45] Online Data, Global smartphone penetration rate as share of population from 2016 to 2023. https://www. statista.com/statistics/203734/global-smartphone-penetration-per-capita-since-2005/.
[46] Online Tutorial, Sparkfun Electronics: Accelerometer basics. https://learn.sparkfun.com/tutorials/ accelerometer-basics/all. Accessed: 2023-08-27.
[47] M. Perttunen, O. Mazhelis, F. Cong, M. Kauppila, T. Leppanen, J. Kantola, and J. Riekki ¨ , Distributed road surface condition monitoring using mobile phones, in International conference on ubiquitous intelligence and computing, Springer, Berlin, Heidelberg, 2011, pp. 64–78.
[48] S. Poslad, Ubiquitous computing: smart devices, environments and interactions, John Wiley & Sons, 2011.
[49] S. Rauscher, G. Messner, P. Baur, J. Augenstein, K. Digges, E. Perdeck, and O. Pieske, En[1]hanced automatic collision notification system-improved rescue care due to injury prediction-first field experi[1]ence, in The 21st International Technical Conference on the Enhanced Safety of Vehicles Conference (ESV)- International Congress Center Stuttgart, Germany, 2009, pp. 09–49.
[50] S. Reddy, M. Mun, J. Burke, D. Estrin, M. Hansen, and M. Srivastava, Using mobile phones to determine transportation modes, ACM Transactions on Sensor Networks (TOSN), 6 (2010), pp. 1–27.
[51] R. M. K. Sandamal and H. R. Pasindu, Applicability of smartphone-based roughness data for rural road pavement condition evaluation, Int. J. Pavement Eng., 23 (2022), pp. 663–672.
[52] S. R. Shakya, C. Zhang, and Z. Zhou, Comparative study of machine learning and deep learning architecture for human activity recognition using accelerometer data, Int. J. Mach. Learn. Comput., 8 (2018), pp. 577–582.
[53] T. Sonderen, Detection of transportation mode solely using smartphones, tech. rep., University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science, 2016.
[54] V. M. Souza, Asphalt pavement classification using smartphone accelerometer and complexity invariant dis[1]tance, Eng. Appl. Artif. Intell., 74 (2018), pp. 198–211.
[55] M. Staniek, Road pavement condition diagnostics using smartphone-based data crowdsourcing in smart cities, J. Traffic Transp. Eng. (Engl. Ed.), 8 (2021), pp. 554–567.
[56] L. Stenneth, O. Wolfson, P. S. Yu, and B. Xu, Transportation mode detection using mobile phones and gis information, in Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems, 2011, pp. 54–63.
[57] B. Tian, Y. Yuan, H. Zhou, and Z. Yang, Pavement management utilizing mobile crowd sensing, Advances in Civil Engineering, 2020 (2020).
[58] W. Tu, F. Xiao, L. Li, and L. Fu, Estimating traffic flow states with smart phone sensor data, Transp. Res. C: Emerg. Technol., 126 (2021), p. 103062.
[59] R. Vaiana, T. Iuele, V. Astarita, M. V. Caruso, A. Tassitani, C. Zaffino, and V. P. Giofre`, Driving behavior and traffic safety: an acceleration-based safety evaluation procedure for smartphones, Modern Applied Science, 8 (2014), p. 88.
[60] A. Vittorio, V. Rosolino, I. Teresa, C. M. Vittoria, and P. G. Vincenzo, Automated sensing system for monitoring of road surface quality by mobile devices, Procedia Soc. Behav. Sci., 111 (2014), pp. 242–251.
[61] A. Vittorio, V. Rosolino, I. Teresa, C. M. Vittoria, P. G. Vincenzo, et al., Automated sensing system for monitoring of road surface quality by mobile devices, Procedia-Social and Behavioral Sciences, 111 (2014), pp. 242–251.
[62] J. White, C. Thompson, H. Turner, B. Dougherty, and D. C. Schmidt, Wreckwatch: Automatic traffic accident detection and notification with smartphones, Mobile Networks and Applications, 16 (2011), pp. 285–303.
[63] G. Xiao, Q. Cheng, and C. Zhang, Detecting travel modes from smartphone-based travel surveys with continuous hidden markov models, Int. J. Distrib. Sens. Netw., 15 (2019), p. 1550147719844156.