# Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model
###### Abstract  
The investigation of travel mode choice is an essential task in transport planning and policymaking for predicting travel demands. Typically, mode choice datasets are imbalanced and learning from such datasets is challenging. This study deals with imbalanced mode choice data by developing an algorithm (SVM\({}_{\text{AK}}\)) based on a support vector machine model and the theory of adjusting kernel scaling. The kernel function's choice was evaluated by applying the likelihood-ratio chi-square and weighting measures. The empirical assessment was performed on the 2017 National Household Travel Survey-California dataset. The performance of the SVM\({}_{\text{AK}}\) model was compared with several other models, including neural networks, XGBoost, Bayesian Network, standard support vector machine model, and some SVM-based models that were previously developed to handle the imbalanced datasets. The SVM\({}_{\text{AK}}\) model outperformed these models, and in some cases improved the accuracy of the minority class classification. For the majority class, the accuracy improvement was substantial. This algorithm can be applied to other tasks in the transport planning domain that deal with uneven data distribution. imbalanced data; travel mode choice data; hybrid support vector machine-based model +
Footnote †: journal: Applied  
To date, many studies investigated the choice of travel mode to work (Table 1). These methods employed both traditional statistical methods and machine learning (ML) techniques. However, the former is criticized because of its linearity assumptions concerning mode choice data [13; 14; 15]. Thus, the employment of ML techniques has receieved more attention recently [16; 17; 18; 19; 20; 21; 22; 23]. The classification of the new cases established concerning the existing samples is an essential task in ML models. If at least one of the categories comprises a smaller number of samples than other categories, the classification process becomes complex [24]. The class imbalance issue is simply an uneven data distribution amongst the different categories of the target. The precision of the classification algorithms will be unreliable when they are influenced by the majority class. In this case, the new samples are distributed to the majority category since the classification model tends to predict the minority category with less accuracy, which is an undesirable consequence [25]. Support vector machine (SVM) is a renowned ML technique for classification [26]. This algorithm also was used as a base to cope with imbalanced data. [PERSON] and [PERSON] [27] developed the fuzzy SVM model and dealt with imbalanced data in the presence of noises and outliers. [PERSON] and [PERSON] [28] suggested boosting-SVMs with asymmetric cost. Their model runs by adjusting the classifier utilizing cost assignation, though it compensates the bias presented with adjustment through utilizing a combination system that is comparable, in effect, to adjust the distribution of data. [PERSON] and [PERSON] [29] suggested the class-boundary alignment algorithm to augment the SVM model to deal with imbalanced data. They modified the class boundary by converting the kernel function if data is represented in a vector space. This modification can also be performed by adjusting the kernel matrix if the data do not possess a vector-space representation. To enhance forecast performance, [PERSON], et al. [30] suggested consolidating an integrated sampling system, which mixes both over-sampling and under-sampling, with an ensemble of SVM. These studies investigated the binary classification problem based on the SVM model; however, less examinations have been done concerning multiclass imbalanced classification based on this model. Many studies in other domains, including medicine, economy, crash severity, and so on, tried to reduce the issues of imbalanced data, e.g., [31; 32; 33].