Skip to main content
SpringerLink
Log in

Vehicle Usage Extraction Using Unsupervised Ensemble Approach

  • Conference paper
  • First Online:
Intelligent Systems and Applications (IntelliSys 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 542))

Included in the following conference series:

Abstract

Current heavy vehicles are equipped with hundreds of sensors that are used to continuously collect data in motion. The logged data enables researchers and industries to address three main transportation issues related to performance (e.g. fuel consumption, breakdown), environment (e.g., emission reduction), and safety (e.g. reducing vehicle accidents and incidents during maintenance activities). While according to the American Transportation Research Institute (ATRI), the operational cost of heavy vehicles is around \(59\%\) of overall costs, there are limited studies demonstrating the specific impacts of external factors (e.g. weather and road conditions, driver behavior) on vehicle performance. In this work, vehicle usage modeling was studied based on time to determine the different usage styles of vehicles and how they can affect vehicle performance. An ensemble clustering approach was developed to extract vehicle usage patterns and vehicle performance taking into consideration logged vehicle data (LVD) over time. Analysis results showed a strong correlation between driver behavior and vehicle performance that would require further investigation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Murray, D., Glidewell, S.: An analysis of the operational costs of trucking: 2019 update (2019)

    Google Scholar 

  2. Rastegari, A.: Condition Based Maintenance in the Manufacturing Industry: From Strategy to Implementation. Mälardalen University, Ph.D. diss. (2017)

    Google Scholar 

  3. Prytz, R., Nowaczyk, S., Rögnvaldsson, T., Byttner, S.: Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. Eng. Appl. Artif. Intell. 41, 139–150 (2015)

    Article  Google Scholar 

  4. Correia, A., Água, P.B., Oliveira, N.: Data Envelopment Analysis in the optimization of the vehicle maintenance. In: 2021 16th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–6. IEEE (2021)

    Google Scholar 

  5. Kong, Q., Lu, R., Yin, F., Cui, S.: Privacy-preserving continuous data collection for predictive maintenance in vehicular fog-cloud. IEEE Trans. Intell. Transp. Syst. 22, 5060–5070 (2020)

    Google Scholar 

  6. Cakir, M., Ali Guvenc, M., Mistikoglu, S.: The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system. Comput. Ind. Eng. 151, 106948 (2021)

    Google Scholar 

  7. Girbés-Juan, V., Armesto, L., Hernández-Ferrándiz, D., Dols, J.F., Sala, A.: Asynchronous sensor fusion of GPS, IMU and CAN-based odometry for heavy-duty vehicles. IEEE Trans. Veh. Technol. 70(9), 8617–8626 (2021)

    Article  Google Scholar 

  8. Varella, R.A., Faria, M.V., Mendoza-Villafuerte, P., Baptista, P.C., Sousa, L., Duarte, G.O.: Assessing the influence of boundary conditions, driving behavior and data analysis methods on real driving CO2 and NOx emissions. Sci. Total Environ. 658, 879–894 (2019)

    Article  Google Scholar 

  9. Gao, J., et al.: The effect of after-treatment techniques on the correlations between driving behaviours and NOx emissions of passenger cars. J. Clean. Prod. 288, 125647 (2021)

    Article  Google Scholar 

  10. Prakash, S., Bodisco, T.A.: An investigation into the effect of road gradient and driving style on NOX emissions from a diesel vehicle driven on urban roads. Transp. Res. Part D: Transp. Environ. 72, 220–231 (2019)

    Article  Google Scholar 

  11. Revanur, V., Ayibiowu, A., Rahat, M., Khoshkangini, R.: Embeddings based parallel stacked autoencoder approach for dimensionality reduction and predictive maintenance of vehicles. In: Gama, J., Pashami, S., Bifet, A., Sayed-Mouchawe, M., Fröning, H., Pernkopf, F., Schiele, G., Blott, M. (eds.) ITEM/IoT Streams -2020. CCIS, vol. 1325, pp. 127–141. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66770-2_10

    Chapter  Google Scholar 

  12. Tajgardan, M., et al.: Fault forecasting using two-dimensional optimization approach (TDOA). In; Workshop on AI for Transportation AAAI 2022, Communications in Computer and Information Science (CCIS) (2022)

    Google Scholar 

  13. Choi, E., Kim, E.: Critical aggressive acceleration values and models for fuel consumption when starting and driving a passenger car running on LPG. Int. J. Sustain. Transp. 11(6), 395–405 (2017)

    Article  Google Scholar 

  14. Trindade, N.S., Kronbauer, A.H., Aragão, H.G., Campos, J.: Driver Rating: a mobile application to evaluate driver behavior. South Florida J. Dev. 2(2), 1147–1160 (2021)

    Article  Google Scholar 

  15. Khoshkangini, R., Nowaczyk, S., Pashami, S.: Baysian network for failure prediction in different seasons. In: 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (ESREL2020 PSAM15), 1–5 November 2020, Venice, Italy, pp. 1710-1710 (2020)

    Google Scholar 

  16. Bousonville, T., Dirichs, M., Krüger, T.: Estimating truck fuel consumption with machine learning using telematics, topology and weather data. In: 2019 International Conference on Industrial Engineering and Systems Management (IESM), pp. 1-6. IEEE (2019)

    Google Scholar 

  17. Faria, M.V., Baptista, P.C., Farias, T.L.: Identifying driving behavior patterns and their impacts on fuel use. Transp. Res. Procedia 27, 953–960 (2017)

    Article  Google Scholar 

  18. Hsu, C.-Yu., Lim, S.S., Yang, C.-S.: Data mining for enhanced driving effectiveness: an eco-driving behaviour analysis model for better driving decisions. Int. J. Prod. Res. 55(23), 7096–7109 (2017)

    Google Scholar 

  19. Yuan, C., Yang, H.: Research on K-value selection method of K-means clustering algorithm. J. 2(2), 226-235 (2019)

    Google Scholar 

  20. Kettani, O., Ramdani, F., Tadili, B.: An agglomerative clustering method for large data sets. Int. J. Comput. Appl. 92(14) (2014)

    Google Scholar 

  21. Eboli, L., Mazzulla, G., Pungillo, G.: Combining speed and acceleration to define car users’ safe or unsafe driving behaviour. Transp. Res. Part C: Emerg. Technol. 68, 113–125 (2016)

    Article  Google Scholar 

  22. Lozhkina, O.V., Lozhkin, V.N.: Estimation of nitrogen oxides emissions from petrol and diesel passenger cars by means of on-board monitoring: Effect of vehicle speed, vehicle technology, engine type on emission rates. Transp. Res. Part D: Transp. Environ. 47, 251–264 (2016)

    Article  Google Scholar 

  23. Wang, H., Lixin, F., Zhou, Yu., Li, H.: Modelling of the fuel consumption for passenger cars regarding driving characteristics. Transp. Res. Part D: Transp. Environ. 13(7), 479–482 (2008)

    Article  Google Scholar 

  24. Khoshkangini, R., Ontanón, S., Marconi, A., Zhu, J.: Dynamically extracting play style in educational games. In: EUROSIS Proceedings, GameOn (2018)

    Google Scholar 

  25. Singh, A.K., Mittal, S., Malhotra, P., Srivastava, Y.V.: Clustering evaluation by Davies-Bouldin Index (DBI) in Cereal data using K-Means. In: 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), pp. 306-310. IEEE (2020)

    Google Scholar 

  26. Liaw, A., Wiener, M.: Classification and regression by randomForest. R News 2(3), 18–22 (2002)

    Google Scholar 

  27. Reza, K., et al.: Early prediction of quality issues in automotive modern industry. Information 11(7), 354 (2020)

    Google Scholar 

  28. Khoshkangini, R., Pashami, S., Nowaczyk, S.: Warranty claim rate prediction using logged vehicle data. In: Moura Oliveira, P., Novais, P., Reis, L.P. (eds.) EPIA 2019. LNCS (LNAI), vol. 11804, pp. 663–674. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30241-2_55

    Chapter  Google Scholar 

  29. Shao, J., Tanner, S.W., Thompson, N., Cheatham, T.E.: Clustering molecular dynamics trajectories: 1. Characterizing the performance of different clustering algorithms. J. Chem. Theory Comput. 3(6), 2312–2334 (2007)

    Article  Google Scholar 

  30. Hernandez, W., Mendez, A., Diaz-Marquez, A.M., Zalakeviciute, R.: PM 2.5 concentration measurement analysis by using non-parametric statistical inference. IEEE Sens. J. 20(2), 1084–1094 (2019)

    Google Scholar 

  31. Khoshkangini, R., Orand, A.: Forecasting components failures using ant colony optimization for predictive maintenance. In: 31st European Safety and Reliability Conference (2021)

    Google Scholar 

  32. Mojarad, M., et al.: Consensus function based on clusters clustering and iterative fusion of base clusters. Int. J. Uncertainty, Fuzziness Knowl. Based Syst. 27(01), 97–120 (2019)

    Google Scholar 

  33. Khalili, H., Rabbani, M., Akbari, E.: Clustering ensemble selection based on the extended Jaccard measure. Turkish J. Electr. Eng. Comput. Sci. 29(4), 2215–2231 (2021)

    Article  Google Scholar 

  34. Akhremtsev, Y., et al.: Engineering a direct k-way hypergraph partitioning algorithm. In: 2017 Proceedings of the Ninteenth Workshop on Algorithm Engineering and Experiments (ALENEX). Society for Industrial and Applied Mathematics (2017)

    Google Scholar 

  35. Fahad, M., et al.: Grey wolf optimization based clustering algorithm for vehicular ad-hoc networks. Comput. Electr. Eng. 70, 853–870 (2018)

    Google Scholar 

  36. Punera, K., Ghosh, J.: Consensus-based ensembles of soft clusterings. Appl. Artif. Intell. 22(7–8), 780–810 (2008)

    Article  Google Scholar 

  37. Ghosh, J., Acharya, A.: Cluster ensembles. Wiley interdisciplinary reviews: Data mining and knowledge discovery 1(4), 305–315 (2011)

    Google Scholar 

  38. Hu, X., et al.: Molecular classification reveals the diverse genetic and prognostic features of gastric cancer: a multi-omics consensus ensemble clustering. Biomed. Pharmacotherapy 144, 112222 (2021)

    Google Scholar 

  39. Liu, H., Rodgers, M.O., Liu, F.C., Guensler, R.: Bayesian approach in estimating the road grade impact on vehicle speed and acceleration on freeways. Transportmetrica A: Transp. Sci. 16(3), 602–625 (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Media Technology, Malmö University, Malmö, Sweden

    Reza Khoshkangini

  2. Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, Sweden

    Nidhi Rani Kalia & Sachin Ashwathanarayana

  3. Arriver Software AB, a Qualcomm Company, Linköping, Sweden

    Abbas Orand

  4. Information and Communication Technology, University of Tasmania, Hobart, Tasmania, Australia

    Jamal Maktobian

  5. Faculty of Electrical and Computer Engineering Qom University of Technology, Qom, Iran

    Mohsen Tajgardan

Authors
  1. Reza Khoshkangini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nidhi Rani Kalia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sachin Ashwathanarayana
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abbas Orand
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jamal Maktobian
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohsen Tajgardan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reza Khoshkangini .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Khoshkangini, R., Kalia, N.R., Ashwathanarayana, S., Orand, A., Maktobian, J., Tajgardan, M. (2023). Vehicle Usage Extraction Using Unsupervised Ensemble Approach. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-031-16072-1_43

Download citation

Publish with us

Policies and ethics

Search

Navigation