{"title":"Mechanisms and implications of autonomous vehicle market penetration: Insights from a Markov forecasting model","authors":"","doi":"10.1016/j.tranpol.2024.07.008","DOIUrl":null,"url":null,"abstract":"<div><p>Due to the rapid evolution of autonomous driving technology and the complexity of market penetration mechanisms, establishing a reliable quantitative research approach for measuring autonomous vehicle (AV) penetration and effectively validating forecasted outcomes poses significant challenges. To address this issue, this paper overcomes data limitations by starting from the perspective of Chinese automotive market. It introduces a quantifiable Markov forecasting model that establishes the link between transition probabilities and penetration influencing factors. Through a penetration network, it visually represents the correlation and evolutionary states of AVs. Building upon the model, a framework for data quantification and analysis is formed. By quantifying model indicators with market data such as car performance and historical sales, the network parameters and transition probabilities are continuously updated in real-time. This drives the model to output short-term forecasts for AV penetration in the automotive market. In addition, we devise a two-stage simulation algorithm to accomplish parameter calibration and model validation. Through validation and comparative analysis, it is observed that, compared to direct learning from historical data, our model can more accurately forecast real market penetration trends. Furthermore, sensitivity analysis experiments on market strategies indicate that, compared to technical investment, the market exhibits a higher sensitivity to price adjustments. A strategy combination of increased technical investment in high-level vehicles and judiciously raising prices proves more advantageous for intelligent transformation in the automotive sector than a singular strategy. Additionally, as the AV market evolves, the sensitivity to favorable strategies will gradually increase. Therefore, the developmental stage of the market is a crucial factor for both car companies and investors to consider. The insights gleaned from this paper offer actionable guidance for policymakers and automotive corporations in shaping future market strategies, thereby fostering the continued growth of autonomous driving technologies within the industry.</p></div>","PeriodicalId":48378,"journal":{"name":"Transport Policy","volume":null,"pages":null},"PeriodicalIF":6.3000,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transport Policy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0967070X2400204X","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
Due to the rapid evolution of autonomous driving technology and the complexity of market penetration mechanisms, establishing a reliable quantitative research approach for measuring autonomous vehicle (AV) penetration and effectively validating forecasted outcomes poses significant challenges. To address this issue, this paper overcomes data limitations by starting from the perspective of Chinese automotive market. It introduces a quantifiable Markov forecasting model that establishes the link between transition probabilities and penetration influencing factors. Through a penetration network, it visually represents the correlation and evolutionary states of AVs. Building upon the model, a framework for data quantification and analysis is formed. By quantifying model indicators with market data such as car performance and historical sales, the network parameters and transition probabilities are continuously updated in real-time. This drives the model to output short-term forecasts for AV penetration in the automotive market. In addition, we devise a two-stage simulation algorithm to accomplish parameter calibration and model validation. Through validation and comparative analysis, it is observed that, compared to direct learning from historical data, our model can more accurately forecast real market penetration trends. Furthermore, sensitivity analysis experiments on market strategies indicate that, compared to technical investment, the market exhibits a higher sensitivity to price adjustments. A strategy combination of increased technical investment in high-level vehicles and judiciously raising prices proves more advantageous for intelligent transformation in the automotive sector than a singular strategy. Additionally, as the AV market evolves, the sensitivity to favorable strategies will gradually increase. Therefore, the developmental stage of the market is a crucial factor for both car companies and investors to consider. The insights gleaned from this paper offer actionable guidance for policymakers and automotive corporations in shaping future market strategies, thereby fostering the continued growth of autonomous driving technologies within the industry.
由于自动驾驶技术的快速发展和市场渗透机制的复杂性,建立一种可靠的定量研究方法来衡量自动驾驶汽车(AV)的渗透率并有效验证预测结果面临巨大挑战。针对这一问题,本文从中国汽车市场的角度出发,克服了数据的局限性。它引入了一个可量化的马尔可夫预测模型,建立了过渡概率与渗透率影响因素之间的联系。通过渗透率网络,该模型直观地呈现了 AV 的相关性和演化状态。在该模型的基础上,形成了一个数据量化和分析框架。通过将模型指标与汽车性能和历史销量等市场数据进行量化,网络参数和过渡概率不断得到实时更新。这推动模型输出汽车市场中 AV 渗透率的短期预测。此外,我们还设计了一种两阶段模拟算法来完成参数校准和模型验证。通过验证和对比分析,我们发现,与直接从历史数据中学习相比,我们的模型能更准确地预测真实的市场渗透趋势。此外,市场策略的敏感性分析实验表明,与技术投资相比,市场对价格调整表现出更高的敏感性。事实证明,增加对高级车辆的技术投资和明智地提高价格相结合的策略比单一策略更有利于汽车行业的智能化转型。此外,随着影音市场的发展,对有利战略的敏感度也会逐渐提高。因此,市场的发展阶段是汽车公司和投资者需要考虑的关键因素。本文的见解为政策制定者和汽车企业制定未来市场战略提供了可操作的指导,从而促进自动驾驶技术在行业内的持续发展。
期刊介绍:
Transport Policy is an international journal aimed at bridging the gap between theory and practice in transport. Its subject areas reflect the concerns of policymakers in government, industry, voluntary organisations and the public at large, providing independent, original and rigorous analysis to understand how policy decisions have been taken, monitor their effects, and suggest how they may be improved. The journal treats the transport sector comprehensively, and in the context of other sectors including energy, housing, industry and planning. All modes are covered: land, sea and air; road and rail; public and private; motorised and non-motorised; passenger and freight.