An interpretable machine learning approach to understanding the impacts of attitudinal and ridesourcing factors on electric vehicle adoption

Bas, Javier; Zou, Zhenpeng; Cirillo, Cinzia

doi:10.1080/19427867.2021.2009098

Cited by 13 publications

(9 citation statements)

References 42 publications

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“…It can, therefore, be postulated that concern about climate change plays a crucial role in the intention to buy EVs. Bas et al ( 14 ) investigated the effects of different variables on electric vehicle adoption, and the results showed that awareness of environmental protection was one of the most important variables in explaining the high willingness to adopt EVs. Moreover, previous studies demonstrated that people with different levels of CC-SoC are statistically different in willingness to buy EVs ( 29 , 30 ).…”

Section: Resultsmentioning

confidence: 99%

“…EV to ICEV purchase price ratio is the second significant parameter of vehicle choice since its contribution to the likelihood of buying an EV is roughly 9%. It has been demonstrated that monetary incentives, such as income tax deduction for EVs and vehicle price, significantly influence individuals’ choice when selecting between an ICEV and an EV ( 14 , 30 ). The results mentioned are consistent with the outcomes of the current research.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, machine learning techniques have been applied to predict who prefers EVs and determine which parameters highly contribute to electric vehicle adoption. For instance, Bas et al ( 14 ) developed a model to predict individuals’ electric vehicle adoption. As the survey was done in only one state of the USA, it is not known how generalizable the results are.…”

mentioning

confidence: 99%

“…Similarly, Bas et al ( 15 ) proposed a classification model to predict potential electric vehicle purchasers in the United States. The same survey (i.e., the survey used in Bas et al [ 14 ]) was used as above and taken into account. The results of the leading model found that attitudes toward EVs, household income, and environmental concerns were the top factors.…”

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confidence: 99%

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Interpretable Machine Learning Approach to Predicting Electric Vehicle Buying Decisions

Naseri

Waygood

Wang

et al. 2023

Transportation Research Record: Journal of the Transportation R

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To address the problem of climate change emissions from the transport sector, many countries are promoting electric vehicles (EVs). To support such efforts, it is essential to know what influences the choice of an EV over a traditional internal combustion engine vehicle (ICEV). To study this, a discrete choice experiment was developed, and 2,015 valid responses were gathered from Canadian adults with a driver’s license. In place of a more traditional analysis, a machine learning approach, XGBoost, was applied. However, two key issues were addressed with respect to its application. First, a practical question related to how best to split the training and testing data was examined. A new technique based on the Coyote optimization algorithm (COA) is developed that automatically determines the split that leads to the greatest prediction accuracy. The policy-relevant results of the analysis found that an individual’s Climate Change-Stage of Change (CC-SoC) and the price ratio of EVs to ICEVs are the most important direct influences. The interaction effect of the first two (CC-SoC and price ratio) is also influential. However, this leads to the second key issue: interpretability. Although high prediction accuracy (87.1%) was achieved, the black-box nature of the approach limits its policy relevance. As such, this research applied a technique, Accumulated Local Effects (ALE), that can determine the strength and direction of influence of the variable. This research demonstrates how machine learning can be applied to a policy-relevant question and provide information that is useful to policy decision makers.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Interpretable Machine Learning Approach to Predicting Electric Vehicle Buying Decisions

Naseri

Waygood

Wang

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…Accordingly, after detecting the input variables with the highest relative influence on the response variable, different methods, such as accumulated local effects [ 59 ], Shapley additive explanations (SHAP) [ 60 ], partial dependence plot (PDP) [ 61 ], and local interpretable model agnostic explanations (LIME) [ 62 ], can be applied to represent in which the direction (positively, negatively, linearly, quadratically, etc.) of the top input variables impacts the response variable.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Naseri

Waygood

Wang

et al. 2022

IJERPH

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Travel mode choice (TMC) prediction is crucial for transportation planning. Most previous studies have focused on TMC in adults, whereas predicting TMC in children has received less attention. On the other hand, previous children’s TMC prediction studies have generally focused on home-to-school TMC. Hence, LIGHT GRADIENT BOOSTING MACHINE (LGBM), as a robust machine learning method, is applied to predict children’s TMC and detect its determinants since it can present the relative influence of variables on children’s TMC. Nonetheless, the use of machine learning introduces its own challenges. First, these methods and their performance are highly dependent on the choice of “hyperparameters”. To solve this issue, a novel technique, called multi-objective hyperparameter tuning (MOHPT), is proposed to select hyperparameters using a multi-objective metaheuristic optimization framework. The performance of the proposed technique is compared with conventional hyperparameters tuning methods, including random search, grid search, and “Hyperopt”. Second, machine learning methods are black-box tools and hard to interpret. To overcome this deficiency, the most influential parameters on children’s TMC are determined by LGBM, and logistic regression is employed to investigate how these parameters influence children’s TMC. The results suggest that MOHPT outperforms conventional methods in tuning hyperparameters on the basis of prediction accuracy and computational cost. Trip distance, “walkability” and “bikeability” of the origin location, age, and household income are principal determinants of child mode choice. Furthermore, older children, those who live in walkable and bikeable areas, those belonging low-income groups, and short-distance travelers are more likely to travel by sustainable transportation modes.

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Comparing the Performance of Classification Algorithms for Predicting Electric Vehicle Adoption

AlRashdi,

AlHassani,

Haile

et al. 2024

Lecture Notes in Operations Research

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An interpretable machine learning approach to understanding the impacts of attitudinal and ridesourcing factors on electric vehicle adoption

Cited by 13 publications

References 42 publications

Interpretable Machine Learning Approach to Predicting Electric Vehicle Buying Decisions

Interpretable Machine Learning Approach to Predicting Electric Vehicle Buying Decisions

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters

Comparing the Performance of Classification Algorithms for Predicting Electric Vehicle Adoption

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