# Accelerometer-Based Pavement Classification for Vehicle Dynamics Analysis Using Neural Networks
## 5 Conclusions  
1. The experimental results indicated that both classifiers, the neural network and multinomial logistic regression, executed the tasks well. Both models successfully classified the majority of surfaces, attaining a high degree of accuracy and demonstrating a dependable capacity to differentiate among various pavement kinds.
2. The neural network classifier successfully classified all surface types, attaining 100% accuracy on the test set. This illustrates the neural network's exceptional capability to distinguish between pavement kinds based on the generated vibration signals, without any false positives or negatives.  
Figure 12: ROC curve for classifier based on multinomial logistic regression.  
3. The results of the research indicate that the neural network achieved a classification accuracy of 100%, signifying that all surface types were accurately classified without mistake. Simultaneously, the logistic regression model attained an accuracy of 97.14%, although it misidentified certain surfaces, including potholes and uneven pavement. This disparity indicates that the neural network is a superior approach for identifying intricate surfaces, shown by its reduced error rate and a specificity and sensitivity of 1.0000 across all categories.
4. The multinomial logistic regression classifier struggled to accurately identify the 'pothole' cover type, resulting in several misclassifications, especially with the 'graters' class. This indicates that certain surfaces may exhibit similar vibration patterns, complicating precise differentiation.
5. The neural network did not have the same issues as the logistic regression model in accurately categorizing all the \"pit\" and \"grate\" surface samples. This reaffirms the advantage of the neural network in classifying these kinds of data.
6. Furthermore, ROC curve research indicated that the AUC (Area Under the Curve) of the neural network was 1.0000 across all surface categories, signifying that the model proficiently differentiated between positive and negative situations. Concurrently, the logistic regression model attained marginally reduced AUC values in some categories, particularly in the 'pit' surfaces, where the AUC reached a mere 0.9021.
7. This categorization may serve as a foundation for the development of sophisticated adaptive suspension controls in vehicles, enabling automated adaptation to varying road conditions, and hence, enhancing driving comfort and safety.  
Conceptualization, V.S., E.K., J.M., P.G., A.L. and A.K.; methodology, V.S., E.K., J.M. and P.G.; software, E.K., J.M., P.G., A.L. and A.K.; validation, V.S., E.K., J.M., P.G., A.L. and A.K.; formal analysis, P.G., A.L. and A.K.; investigation, V.S., E.K. and J.M.; resources, P.G., A.L. and A.K.; data curation, V.S., E.K., J.M., P.G., A.L. and A.K.; writing--original draft preparation, V.S., E.K., J.M., P.G., A.L. and A.K.; writing--review and editing, V.S., E.K., J.M., P.G., A.L. and A.K.; visualization, V.S., E.K. and J.M.; supervision, E.K. and J.M.; project administration, J.M.; funding acquisition, E.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are available after request.  
Research was supported by the Joint Research Collaborative Seed Grant Program between National Taipei University of Technology and Vilnius Gediminas Technical University (Grant No: TAIPEI TECH-VGTU-2023-01). This article is the result of [PERSON]'s scientific internship that took place at the Vilnius Tech Mechanical Science Institute on 5-28 August 2024.  
The authors declare no conflicts of interest.