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

References

• Axsen, J., J. Bailey, and M. A. Castro. 2015. “Preference and Lifestyle Heterogeneity among Potential Plug-in Electric Vehicle Buyers.” Energy Economics 50: 190–201. doi:10.1016/j.eneco.2015.05.003.
   Web of Science ®Google Scholar
• Bas, J., C. Cirillo, and E. Cherchi. 2021a. “Classification of Potential Electric Vehicle Purchasers: A Machine Learning Approach.” Technological Forecasting and Social Change 168. doi:10.1016/j.techfore.2021.120759.
   Web of Science ®Google Scholar
• Bas, J., C. Cirillo, and E. Cherchi (2021b). A stated choice experiment for considering social conformity in the adoption of electric vehicles (Working Paper).
   Google Scholar
• Burghard, U., and D. Elisabeth. 2019. “Who Wants Shared Mobility? Lessons from Early Adopters and Mainstream Drivers on Electric Carsharing in Germany.” Transportation Research Part D 71: 96–109. doi:10.1016/j.trd.2018.11.011.
   Web of Science ®Google Scholar
• Canals Casals, L., E. Martinez-Laserna, B. Amante García, and N. Nieto. 2016. “Sustainability Analysis of the Electric Vehicle Use in Europe for Co2 Emissions Reduction.” Journal of Cleaner Production 127: 425–437. doi:10.1016/j.jclepro.2016.03.120.
   Web of Science ®Google Scholar
• Carley, S., S. Siddiki, and S. Nicholson-Crotty. 2019. “Evolution of Plug-in Electric Vehicle Demand: Assessing Consumer Perceptions and Intent to Purchase over Time.” Transportation Research Part D 70: 94–111. doi:10.1016/j.trd.2019.04.002.
   Web of Science ®Google Scholar
• Chen, X., M. Zahiri, and S. Zhang. 2017. “Understanding Ridesplitting Behavior of On-demand Ride Services: An Ensemble Learning Approach.” Transportation Research Part C: Emerging Technologies 76: 51–70. doi:10.1016/j.trc.2016.12.018.
   Web of Science ®Google Scholar
• Fukushima, A., T. Yano, S. Imahara, H. Aisu, Y. Shimokawa, and Y. Shibata. 2018. “Prediction of Energy Consumption for New Electric Vehicle Models by Machine Learning.” IET Intelligent Transport Systems 12 (9): 1174–1180. doi:10.1049/iet-its.2018.5169.
   Web of Science ®Google Scholar
• Greene, D., and P. Cunningham. 2006. “Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering.“ In ICML ‘06: Proceedings of the 23rd International Conference on Machine Learning, Vol. 2006, 373–384. Pittsburgh, PA. doi:10.1145/1143844.1143892.
   Google Scholar
• H2O.ai. (2020a) H2o: R Interface for H2O. R Package Version 3.30.0.6. Available online: https://github.com/h2oai/h2o-3
   Google Scholar
• H2O.ai (2020). AutoML: Automatic Machine Learning. Published Online. Available online: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html
   Google Scholar
• Hardman, S. 2019. “Understanding the Impact of Reoccurring and Non-financial Incentives on Plug-in Electric Vehicle Adoption - a Review.” Transportation Research Part A 119: 1–14. doi:10.1016/j.tra.2018.11.002.
   Google Scholar
• He, Z., Y. Zhou, X. Chen, J. Wang, W. Shen, M. Wang, and W. Li. 2021. “Examining the Spatial Mode in the Early Market for Electric Vehicles Adoption: Evidence from 41 Cities in China.” Transportation Letters 1–11. published online:. doi:10.1080/19427867.2021.1917217.
   Google Scholar
• Henderson, J. 2020. “Evs are Not the Answer: A Mobility Justice Critique of Electric Vehicle Transitions.” Annals of the American Association of Geographers 110 (6): 1993–2010. doi:10.1080/24694452.2020.1744422.
   Web of Science ®Google Scholar
• Huang, X., Y. Tan, and X. He. 2011. “An Intelligent Multifeature Statistical Approach for the Discrimination of Driving Conditions of a Hybrid Electric Vehicle.” IEEE Transactions on Intelligent Transportation Systems 12 (2): 453–465. doi:10.1109/TITS.2010.2093129.
   Web of Science ®Google Scholar
• Hughes, S., S. Moreno, W. F. Yushimito, and G. Huerta-Cánepa. 2019. “Evaluation of Machine Learning Methodologies to Predict Stop Delivery Times from GPS Data.” Transportation Research Part C: Emerging Technologies 109: 289–304. doi:10.1016/j.trc.2019.10.018.
   Web of Science ®Google Scholar
• Jahangiri, A., and H. A. Rakha. 2015. “Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data.” IEEE Transactions on Intelligent Transportation Systems 16 (5): 2406–2417. doi:10.1109/TITS.2015.2405759.
   Web of Science ®Google Scholar
• Javid, R. J., and A. Nejat. ”A Comprehensive Model of Regional Electric Vehicle Adoption and Penetration.” Transport Policy 54 (2017): 30–42.
   Web of Science ®Google Scholar
• Jenn, A., K. Laberteaux, and R. Clewlow. 2018. “New Mobility Service Users’ Perceptions on Electric Vehicle Adoption.” International Journal of Sustainable Transportation 12 (7): 526–540. doi:10.1080/15568318.2017.1402973.
   Web of Science ®Google Scholar
• Jensen, A. F., E. Cherchi, and S. L. Mabit. 2013. “On the Stability of Preferences and Attitudes before and after Experiencing an Electric Vehicle.” Transportation Research Part D 25: 24–32. doi:10.1016/j.trd.2013.07.006.
   Web of Science ®Google Scholar
• Jiang, J. (2019). More Americans are using ride-hailing apps. Published on 4 January 2019. Available at: https://www.pewresearch.org/fact-tank/2019/01/04/more-americans-are-using-ride-hailing-apps/
   Google Scholar
• Kim, J., S. Rasouli, and H. Timmermans. 2014. “Expanding Scope of Hybrid Choice Models Allowing for Mixture of Social Influences and Latent Attitudes: Application to Intended Purchase of Electric Cars.” Transportation Research. Part A: Policy and Practice 69: 71–85. doi:10.1016/j.tra.2014.08.016.
   Web of Science ®Google Scholar
• Lakkaraju, H., E. Kamar, R. Caruana, and J. Leskovec (2017). “Interpretable & Explorable Approximations of Black Box Models.” ArXiv: 1707.01154 [Cs]. http://arxiv.org/abs/1707.01154
   Google Scholar
• Lee, D., J. Mulrow, C. J. Haboucha, S. Derrible, and Y. Shiftan. 2019. “Attitudes on Autonomous Vehicle Adoption Using Interpretable Gradient Boosting Machine.” Transportation Research Record: Journal of the Transportation Research Board 2673 (11): 865–878. doi:10.1177/0361198119857953.
   Web of Science ®Google Scholar
• Liao, F., E. Molin, and B. van Wee. 2017. “Consumer Preferences for Electric Vehicles: A Literature Review.” Transport Reviews 37 (3): 252–275. doi:10.1080/01441647.2016.1230794.
   Web of Science ®Google Scholar
• Liu, Z., Y. Liu, Q. Meng, and Q. Cheng. 2019. “A Tailored Machine Learning Approach for Urban Transport Network Flow Estimation.” Transportation Research Part C: Emerging Technologies 108: 130–150. doi:10.1016/j.trc.2019.09.006.
   Web of Science ®Google Scholar
• Maclnnis, B., and J. Krosnick (2020). Climate Insights 2020: Electric Vehicles. Resources for the Future Report. Retrieved online: https://media.rff.org/documents/Climate_Insights_2020_Electric_Vehicles.pdf
   Google Scholar
• Martín-Baos, J. Á., R. García-Ródenas, and L. Rodriguez-Benitez. 2021. “Revisiting Kernel Logistic Regression under the Random Utility Models Perspective. An Interpretable Machine-learning Approach.” Transportation Letters 13 (3): 151–162. doi:10.1080/19427867.2020.1861504.
   Web of Science ®Google Scholar
• Pan, L., G. Liu, X. Mao, H. Li, J. Zhang, H. Liang, and X. Li. 2019. “Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study.” Jmir Medical Informatics 7 (1): e11728. doi:10.2196/11728.
   PubMed Web of Science ®Google Scholar
• Parsa, A. B., R. Shabanpour, A. (Kouros) Mohammadian, J. Auld, and T. Stephens. 2020. “A Data-driven Approach to Characterize the Impact of Connected and Autonomous Vehicles on Traffic Flow.” Transportation Letters. 1–9. doi:10.1080/19427867.2020.1776956.
   PubMedGoogle Scholar
• Rezvani, Z., J. Jansson, and J. Bodin. 2015. “Advances in Consumer Electric Vehicle Adoption Research: A Review and Research Agenda.” Transportation Research Part D 34: 122–136. doi:10.1016/j.trd.2014.10.010.
   Web of Science ®Google Scholar
• Ribeiro, M. T., S. Singh, and C. Guestrin. 2016. “‘Why Should I Trust You?‘: Explaining the Predictions of Any Classifier.“ In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. San Francisco, CA. 10.1145/2939672.2939778.
   Google Scholar
• Sierzchula, W., S. Bakker, K. Maat, and B. van Wee. 2014. “The Influence of Financial Incentives and Other Socio-economic Factors on Electric Vehicle Adoption.” Energy Policy 68: 183–194. doi:10.1016/j.enpol.2014.01.043.
   Web of Science ®Google Scholar
• Sovacool, B. K., J. Kester, L. Noel, and G. Z. de Rubens. 2019. “Income, Political Affiliation, Urbanism and Geography in Stated Preferences for Electric Vehicles (Evs) and Vehicle-to-grid (V2g) Technologies in Northern Europe.” Journal of Transport Geography 78: 214–229. doi:10.1016/j.jtrangeo.2019.06.006.
   Web of Science ®Google Scholar
• Sun, D., F. Leurent, and X. Xie. 2021. “Discovering Vehicle Usage Patterns on the Basis of Daily Mobility Profiles Derived from Floating Car Data.” Transportation Letters 13 (3): 163–171. doi:10.1080/19427867.2020.1861505.
   Web of Science ®Google Scholar
• Sun, S., J. Zhang, J. Bi, Y. Wang, and M. H. Y. Moghaddam. 2019. “A Machine Learning Method for Predicting Driving Range of Battery Electric Vehicles.” Journal of Advanced Transportation 2019. doi:10.1155/2019/4109148.
   Web of Science ®Google Scholar
• Taiebat, M., and M. Xu. 2019. “Synergies of Four Emerging Technologies for Accelerated Adoption of Electric Vehicles: Shared Mobility, Wireless Charging, Vehicle-to-grid, and Vehicle Automation.” Journal of Cleaner Production 230: 794–797. doi:10.1016/j.jclepro.2019.05.142.
   Web of Science ®Google Scholar
• The International Energy Agency (IEA). (2020). Global EV Outlook 2020: Entering the Decade of Electric Drive? Published online in June 2020. Available at: https://www.iea.org/reports/global-ev-outlook-2020
   Google Scholar
• The United States Environmental Protection Agency (EPA). (2021). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2019. Retrieved online: https://www.epa.gov/sites/production/files/2021-04/documents/us-ghg-inventory-2021-main-text.pdf
   Google Scholar
• Vassileva, I., and J. Campillo. 2017. “Adoption Barriers for Electric Vehicles: Experiences from Early Adopters in Sweden.” Energy 120: 632–641. doi:10.1016/j.energy.2016.11.119.
   Web of Science ®Google Scholar
• Wang, X., D. Wang, Z. Yao, B. Xin, B. Wang, C. Lan, Y. Qin, S. Xu, D. He, and Y. Liu. 2019. “Machine Learning Models for Multiparametric Glioma Grading with Quantitative Result Interpretations.” Frontiers in Neuroscience 12: 1046. doi:10.3389/fnins.2018.01046.
   PubMed Web of Science ®Google Scholar
• Yang, S., W. Ma, X. Pi, and S. Qian. 2019. “A Deep Learning Approach to Real-time Parking Occupancy Prediction in Transportation Networks Incorporating Multiple Spatio-temporal Data Sources.” Transportation Research Part C: Emerging Technologies 107: 248–265. doi:10.1016/j.trc.2019.08.010.
   Web of Science ®Google Scholar
• Yi, D., J. Su, C. Liu, M. Quddus, and W.-H. Chen. 2019. “A Machine Learning Based Personalized System for Driving State Recognition.” Transportation Research Part C: Emerging Technologies 105: 241–261. doi:10.1016/j.trc.2019.05.042.
   Web of Science ®Google Scholar
• Zahid, T., K. Xu, W. Li, C. Li, and H. Li. 2018. “State of Charge Estimation for Electric Vehicle Power Battery Using Advanced Machine Learning Algorithm under Diversified Drive Cycles.” Energy 162: 871–882. doi:10.1016/j.energy.2018.08.071.
   Web of Science ®Google Scholar
• Zarazua de Rubens, G., L. Noel, and B. K. Sovacool. 2018. “Dismissive and Deceptive Car Dealerships Create Barriers to Electric Vehicle Adoption at the Point of Sale.” Nature Energy 3 (6): 501–507. doi:10.1038/s41560-018-0152-x.
   Web of Science ®Google Scholar
• Zarazua de Rubens, G. 2019. “Who Will Buy Electric Vehicles after Early Adopters? Using Machine Learning to Identify the Electric Vehicle Mainstream Market.” Energy 172: 243–254. doi:10.1016/j.energy.2019.01.114.
   Web of Science ®Google Scholar
• Zhao, J., J. Ye, M. Xu, and C. Xu. n d. “Practical Model with Strong Interpretability and Predictability: An Explanatory Model for Individuals’ Destination Prediction considering Personal and Crowd Travel Behavior.” Concurrency and Computation-Practice & Experience e6151. doi:10.1002/cpe.6151.
   Web of Science ®Google Scholar
• Zou, Z., and C. Cirillo. 2021. “Does Ridesourcing Impact Driving Decisions: A Survey Weighted Regression Analysis.” Transportation Research Part A 146: 1–12. doi:10.1016/j.tra.2021.02.006.
   Google Scholar