PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Crowdsourced Driving Comfort Monitoring

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, the authors are showing a calculation of the road quality index called Simple Road Quality Index (SRQI) using the weight provided by the amateur drivers to best possibly rate their comfort on driving on that road. The index is calculated from acceleration data acquired by the smartphone application and is aggregated in a crowdsourcing system for the classification of road quality using the fuzzy membership function. The paper shows that the proposed index correctly shows road quality changes over time and may be used as a way to mark roads to be avoided or needs to be repaired. The numerical experiment was based on the same street in Lublin, Poland, in 2015-2021 and is correctly showing that the quality of analyzed roads deteriorated over time, especially in the winter season.
Twórcy
  • Department of Computer Science, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, Poland
  • Department of Computer Science, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, Poland
  • Department of Computer Science, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, Poland
Bibliografia
  • 1. Matarazzo T., Vazifeh M., Pakzad S., Santi P., Ratti C. Smartphone data streams for bridge health monitoring. Procedia Eng. 2017;199:966–71.
  • 2. Kong Q., Allen R.M., Schreier L., Kwon Y.W.Y.W. MyShake: A smartphone seismic network for earthquake early warning and beyond. Sci Adv. 2016;2(2):e1501055–e1501055.
  • 3. Singh G., Bansal D., Sofat S., Aggarwal N. Smart patrolling: An efficient road surface monitoring using smartphone sensors and crowdsourcing. Pervasive Mob Comput. 2017;40:71–88.
  • 4. Sattar S., Li S., Chapman M. Road surface monitoring using smartphone sensors: A review. Sensors (Switzerland). 2018;18(11).
  • 5. Mednis A., Strazdins G., Zviedris R., Kanonirs G., Selavo L. Real time pothole detection using Android smartphones with accelerometers. 2011 Int Conf Distrib Comput Sens Syst Work DCOSS’11. 2011.
  • 6. Mukherjee A., Majhi S. Characterisation of road bumps using smartphones. Eur Transp Res Rev. 2016;8(2):1–12.
  • 7. Aksamit P., Szmechta M. Distributed, mobile, social system for road surface defects detection. In: ISCIII 2011 5th International Symposium on Computational Intelligence and Intelligent Informatics. 2011;37–40.
  • 8. Jo Y., Ryu S. Pothole detection system using a blackbox camera. Sensors (Switzerland). 2015;15(11).
  • 9. Szczodrak M., Grabowski D., Czyżewski A. Employing economical methods for pavement defects estimation. MATEC Web Conf. 2018;231(43):01016.
  • 10. Seraj F., Meratnia N., Havinga P.J.M. An aggregation and visualization technique for crowd-sourced continuous monitoring of transport infrastructures. 2017.
  • 11. Li X, Goldberg DW. Toward a mobile crowdsensing system for road surface assessment . Comput Environ Urban Syst. 2018;(March).
  • 12. Kalim F., Jeong J., Crater I.M.U. A Crowd Sensing Application to Estimate Road Conditions. IEEE Access. 2016;PP(99):8317–8326.
  • 13. Johannesson P., Rychlik I. Modelling of road profiles using roughness indicators. Int J Veh Des. 2014;66(4):317.
  • 14. Rajamohan D., Gannu B., Maargha R.K. A Prototype System for Road Condition and Surface Type Estimation by Fusing Multi-Sensor Data. ISPRS Int J Geo-Information. 2015;4(3).
  • 15. Aydın M.M., Yıldırım M.S., Forslof L. The Use of Smart Phones To Estimate Road Roughness: a Case Study in Turkey. E-Journal New World Sci Acad. 2018;13(3):247–257.
  • 16. Badurowicz M., Cieplak T. Real-Time Road Quality Assessment Using Smartphones and Cloud Lambda Architecture. Kulisz M., Szala M., Badurowicz M., Cel W., Chmielewska M., Czyż Z., et al., editors. MATEC Web Conf. 2019 Jan 14;252:03011.
  • 17. Badurowicz M., Montusiewicz J., Karczmarek P. Detection of Road Artefacts Using Fuzzy Adaptive Thresholding. In: 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2020.
  • 18. Kiersztyn A., Karczmarek P., Lopucki R., Pedrycz W., Al E., Kitowski I., et al. Data Imputation in Related Time Series Using Fuzzy Set-Based Techniques. In: 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2020.
  • 19. Karczmarek P., Kiersztyn A., Pedrycz W. Fuzzy SetBased Isolation Forest. In: 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2020.
  • 20. Wajszczak E., Galas D. Egnos Use Of Gps System For Approach Procedures. Adv Sci Technol Res J. 2013;7(17):62–5.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bbb2301d-fb10-4029-b685-53500c008a56
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.