Targeting Sustainable Transportation Development: The Support Vector Machine and the Bayesian Optimization Algorithm for Classifying Household Vehicle Ownership
Abstract
:1. Introduction
2. Literature Review
2.1. Factors Influencing HVO
2.2. Methods Used for HVO Prediction
Study | Study Aim | Model(s) Used | Hyperparameter Optimization |
---|---|---|---|
Chaipanha and Kaewwichian [47] | To provide a way for balancing the data using over- and under-sampling strategies. | kNN, NB, DTs | No |
Manjushree, GH, Swamy and Giridharan [6] | To apply ML models to forecast the household characteristics that influence car ownership. | DTs, RF, MNL | No |
Shao et al. [48] | To evaluate the nonlinear and interaction impacts of the built environment and motorcycles/E-bikes on automobile ownership using GBDT. | GBDT | Grid search |
Pineda-Jaramillo [46] | To investigate the major factors that impact travel behavior among persons with restricted mobility to promote autonomous, healthy lives and healthy active transport modalities. | NB, kNN, DTs, SVM, RF, AdaBoost, NN, GBDT, CatBoost | Random search |
Kaewwichian [14] | To remedy the imbalanced data problem in automobile ownership datasets. | DT, kNN, NB | No |
Abdul Muhsin Zambang, Jiang and Wahab [42] | To approximate automobile ownership in Greater Tamale. | SGD, SVM, DT, RF, NB, kNN, | No |
Wang et al. [49] | To anticipate ownership of electric vehicles. | AdaBoost | No |
Sabouri, Brewer and Ewing [36] | To investigate the association between ride-sourcing services and household vehicle ownership. | DT, RF | No |
Basu and Ferreira [41] | Through a comparison of econometric and machine learning models, to comprehend household automobile ownership in Singapore. | DT, RF, NN, SVM, LR, SGD, OLC | Grid search |
Ha, Asada and Arimura [43] | To identify the variables that affect household car ownership trends in Phnom Penh. | RF, NN, MNL | No |
Tanwanichkul et al. [50] | To approximate automobile ownership using ML methods. | DT, NN, MNL | No |
3. Research Motivations and Aims
4. Methodology
4.1. Data
4.2. Support Vector Machines (SVM) and Bayesian Optimization (BO) Algorithm
4.3. Models’ Performance Assessment
5. Results
5.1. Input Selection
5.2. SVM and SVMBO Models’ Development and Assessment
- Processing and preparing data: randomly dividing the dataset into a training set and a testing set with an appropriate ratio (70:30).
- Assessment of fitness: before optimizing the target parameter value, estimate, and assess the fitness function.
- Adjustment of parameters: update the optimization criteria satisfied by the parameters based on every iteration’s finding.
- Halt condition inspection: once the optimization stop condition is fulfilled, the optimal parameters are determined.
6. Discussions
Implications for Academic and Policy-Making
7. Conclusions
- These two models took 375.55 s on average to train.
- The SVMBO approach outperformed the traditional SVM model in predicting the HVO.
- The BO technique concluded that the Gaussian kernel was the best kernel function for both datasets.
- The BO method enhanced the performance of the SVM#1 model by 4.27% and 5.16%, respectively, throughout the training and testing phases.
- For the SVM#2 model, the performance of this model was improved by 1.20% and 2.14% for the training and testing phases, correspondingly.
- The AUC of the SVM models used to predict the HVO was improved by using the BO technique.
- In this study, the optimized SVM models did better than the other machine learning models that were applied.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Variable | Description | Type |
---|---|---|
Independent variable | ||
HHVEHCNT | The number of household vehicles | Binary (1, >1) |
Dependent variables | ||
HHFAMINC | Income of household ($) | Categorial |
SIZE | Size of household | Continuous |
HOMEOWN | Home ownership | Binary |
NUMADLT | How many adults live in the household? | Continuous |
WRKCOUNT | How many workers does the household have? | Continuous |
YOUNGCHILD | How many children live in the household? | Continuous |
DRVRCNT | How many drivers does a household have? | Continuous |
TRPHHACC | How many household members are on the trip? | Continuous |
TRPHHVEH | Is the household vehicle used for the trip? | Binary |
BIKE_DFR | How inadequate is bicycle infrastructure? | Categorial |
HBPPOPDN | Density of population | Categorial |
URBANSIZE | What is the size of the urban area around the household? | Categorial |
URBRUR | Does the household live in an urban or rural area? | Binary |
WALK_DEF | How inadequate is walking infrastructure? | Categorial |
Dataset | Model | Train | Test | |||||
---|---|---|---|---|---|---|---|---|
Actual | Prediction | Accuracy (%) | Prediction | Accuracy (%) | ||||
1 | 2 | 1 | 2 | |||||
ME dataset | SVM#1 | 1 | 66 | 24 | 91.3 | 30 | 10 | 87.1 |
2 | 12 | 312 | 13 | 125 | ||||
SVMBO#1 | 1 | 78 | 12 | 95.2 | 28 | 12 | 91.6 | |
2 | 8 | 316 | 3 | 135 | ||||
NV dataset | SVM#2 | 1 | 71 | 8 | 91.2 | 28 | 14 | 83.9 |
2 | 15 | 166 | 4 | 66 | ||||
SVMBO#2 | 1 | 69 | 10 | 92.3 | 31 | 11 | 85.7 | |
2 | 10 | 171 | 5 | 65 |
SVM#1 | SVMBO#1 | SVM#2 | SVMBO#2 | |
---|---|---|---|---|
Population size * | 414 | 414 | 260 | 260 |
Kernel function | - | Gaussian | - | Gaussian |
Gamma | - | 5.3103 | - | 4.1209 |
C | - | 53.4787 | - | 9.4636 |
Training time (sec) | 1.2986 | 324.5 | 3.8578 | 424.6 |
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Xu, Z.; Aghaabbasi, M.; Ali, M.; Macioszek, E. Targeting Sustainable Transportation Development: The Support Vector Machine and the Bayesian Optimization Algorithm for Classifying Household Vehicle Ownership. Sustainability 2022, 14, 11094. https://doi.org/10.3390/su141711094
Xu Z, Aghaabbasi M, Ali M, Macioszek E. Targeting Sustainable Transportation Development: The Support Vector Machine and the Bayesian Optimization Algorithm for Classifying Household Vehicle Ownership. Sustainability. 2022; 14(17):11094. https://doi.org/10.3390/su141711094
Chicago/Turabian StyleXu, Zhiqiang, Mahdi Aghaabbasi, Mujahid Ali, and Elżbieta Macioszek. 2022. "Targeting Sustainable Transportation Development: The Support Vector Machine and the Bayesian Optimization Algorithm for Classifying Household Vehicle Ownership" Sustainability 14, no. 17: 11094. https://doi.org/10.3390/su141711094