# Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model
## 5 Conclusions  
In this research, the authors offered a novel method for learning from imbalanced mode choice data by the adjustable kernel based SVM classification model (SVM\({}_{\text{AK}}\)). The likelihood-ratio chi-square test and weighting measures were used in this suggested method for selecting the kernel function. The aforementioned kernel transformation function makes it possible to increase the class limits and offset the irregular class limits. The authors also performed a sensitivity analysis which showed that the reason for not walking (walkmore), the number of drivers in the household (drvcnet), and the count of adult household members no less than 18 years old (numadlt) were the most influential  
Figure 4: Outcomes of sensitivity analysis.  
factors. On the other hand, the lowest scores found for flexibility of work start time (flextime), owning a vehicle longer than a year (vehowned), and gender (r_sex) were the most influential factors on travel mode choice.  
The outcomes of this model were compared with those of various SVM-based models and ML models. The authors employed four criteria, including accuracy, F1 score, precision, and recall to perform this comparison. The results of this study showed that the SVM\({}_{\text{AK}}\) model achieved the best results and outperformed other models. The results also showed that this model improved the classification accuracy of most categories, especially the car class that had the largest samples.  
Prediction of travel mode choice is an essential component of transport planning and traffic engineering. An accurate prediction is viable only if the data are precisely classified. Therefore, precise mode choice classification utilizing the algorithm suggested in this study would be efficacious for enhancing the current transport systems and further boosting the capacities for an efficient response to the worst traffic and transport scenarios.  
The classes of choice of travel mode to work in the NHTS dataset are distinct from other mode choice datasets since this dataset considered the \"SUV\" category as different from the \"car\" category. However, future studies could combine these two classes and create a new class of travel mode to work. In the US, private motorized transport is dominant. Thus, the NHTS and, in turn, the result of this study is affected by this issue. Future studies can employ the method developed in this study to predict choice of travel mode to work in different environments, such as those in which walking, cycling, and public transport are dominant.  
Conceptualization, M.A. ([PERSON]); methodology, M.A. ([PERSON]); software, M.A. ([PERSON]); formal analysis, M.A. ([PERSON]); investigation, M.A. ([PERSON]); resources, Y.Q., M.A. ([PERSON]), M.A. ([PERSON]), B.S., E.E.H.; data curation, M.A. ([PERSON]); writing--original draft preparation, M.A. ([PERSON]) and M.M.; writing--review and editing, M.A. ([PERSON]) and M.A. ([PERSON]); supervision, R.Z.; funding acquisition, Y.Q., M.A. ([PERSON]), M.A. ([PERSON]), B.S., E.E.H. All authors have read and agreed to the published version of the manuscript. This work was funded by the King Saud University, Saudi Arabia, through researchers supporting project number (RSP-2021/145), and the Taif University, Saudi Arabia, through Taif University Researchers Supporting Project, grant number [TURSP-2020/324]. Not applicable. Not applicable. The authors of this paper thank King Saud University, Saudi Arabia for funding this work through researchers supporting project numbers (RSP-2021/145). The authors also would like to acknowledge Taif University Researches Supporting Project number (TURSP-2020/324), Taif University, Taif, Saudi Arabia for supporting this work. The authors declare no conflict of interest.