# Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model
## 3 Models' Development and Evaluation  
The authors of this study present the results of the SVM\({}_{\text{AK}}\) model as well as other classification models, including the standard SVM model, BN, ANN model, and some SVM-based models in literature proposed for handling the imbalance data. It is a challenging task to determine the most suitable classification model for handling imbalance data issues. The travel mode choice dataset was taken for the empirical investigation. Figure 2 shows the nine classes plotted on the \(x\)-axis, and the size of samples in each class plotted on the \(y\)-axis. As can be seen in this figure, it is obvious that the NHTS dataset includes uneven category distribution; practically, it is called imbalance. Hence, it grows to be more complicated to manage such a circumstance through regular classification techniques. The category-wise distributions of the dataset based on the sample size are: The car category contains 108,885 samples, the pickup truck category consists of 22,188 samples, the SUV class contains 12,582 samples, the van category consists of 2998 samples, the public or commuter bus class includes 2454 samples, the bicycle class category comprises 2091 samples, walk contains 345 samples, the private/charter/tour/shuttle bus category consists of 40 samples, and finally the motorcycle/moped category holds 14 samples. The class imbalance ratio of the dataset is 777.5. Figure 2: Category wise distribution of NHTS dataset. The principal intention of this study is to discover the most competent classification technique that can examine the imbalanced class. Periodically, various scholars had proposed useful techniques to handle class imbalance issue. The majority of the proposed methods were for binary category imbalance issues, which were not fit for the multi-category imbalance issue. These shortcomings urged authors to adjust the algorithm which can effectively handle binary category and multi-category imbalance issues without jeopardizing the performance of algorithms. The classification mentioned above will further assist in attaining the desirable answer toward urban transport planning and prediction of travel mode choice. This study employed four renowned conventional classification techniques and SVM-based models proposed in the literature along with the SVM\({}_{\text{AK}}\) model suggested in this study for the empirical assessment. The model applied in this study was compared with other models to ascertain effectiveness, fitness, and precision. This study evaluated the performance of the models developed using six criteria. Moreover, the authors employed a 10-fold cross-validation procedure as the validation scheme. Four criteria, including accuracy, F1 score, precision, and recall were used to evaluate the outcomes of the classification algorithms applied in this study. The authors validated the classification techniques using the accuracy of classification. As is known, the NHTS dataset includes imbalanced category distribution, which may influence the performance of classification techniques. The overall performance of the models developed in this part of the study is shown in Table 4. All models achieved an overall accuracy above 80%. However, the SVM\({}_{\text{AK}}\) outperformed other models. The worst model was BN. Regarding other evaluation criteria, SVM\({}_{\text{AK}}\) again had the best values. It is worth mentioning that the SVM\({}_{\text{AK}}\) improved the performance of the SVM\({}_{\text{S}}\) model, which shows the capability of the proposed model of this study to handle the imbalanced mode choice data and enhance the performance of the typical SVM model for dealing with such data. An evaluation of the models developed by each class also is provided in Figure 3. For the class of car, which had the largest sample size, the SVM\({}_{\text{AK}}\) improved the prediction accuracy from 82.33% (BN model) to 99.81%. Concerning the category of motorcycle/moped, which had the smallest sample size, the models developed yielded almost a similar accuracy.