International Journal of Sustainable Transportation最新文献

Improving commercial truck fleet composition in emission modeling using 2021 US VIUS data 利用2021年US VIUS数据改进商用卡车车队的排放建模

IF 3.9 3区工程技术 Q2 ENVIRONMENTAL STUDIES

International Journal of Sustainable Transportation

Pub Date : 2025-12-02 DOI: 10.1080/15568318.2025.2566755

Xiaodan Xu , Hung-Chia Yang , Haitam Laarabi , Cristian Poliziani , Alicia Birky , Kyungsoo Jeong , Hongyu Lu , Randall Guensler , C. Anna Spurlock

Commercial trucks are essential elements of the nation’s supply chain system. Meanwhile, intensive truck movements contribute significantly to system externalities, such as energy use and air pollution. However, collecting detailed fleet composition and distribution of operational patterns remains a barrier to accurately accounting for these impacts. The recently released 2021 US Vehicle Inventory and Use Survey (US VIUS) fills a critical gap in understanding commercial truck fleet distributions, their operations, and business constraints at the national scale. This study aims to understand the latest US commercial vehicle fleet composition and operational characteristics using 2021 US VIUS data and calibrate the fleet inputs in regulatory emission models to assess the potential emission implications of the VIUS-derived fleet composition. The emission rates for commercial trucks and default fleet composition are collected from the U.S. EPA’s MOtor Vehicle Emission Simulator (MOVES4). The 2021 US VIUS data is applied to improve fleet characteristics such as the long-haul fraction and the vehicle mileage accumulation rate. The study also investigates potential emission reduction benefits under various forecasted fleet electrification scenarios. The energy consumption and critical air pollutant rates by vehicle types are compared between MOVES4 and US VIUS fleets for both current and future scenarios to provide insights into the latest U.S. commercial vehicle fleet characteristics and their implications on energy and emissions. This study helps policymakers and practitioners advance the commercial fleet generation for emission models. It also deepens the understanding of the emission reduction potential of the commercial fleet under various fleet projections.

商用卡车是国家供应链系统的重要组成部分。与此同时，密集的卡车运输对系统外部性（如能源使用和空气污染）产生了重大影响。然而，收集详细的船队组成和运营模式分布仍然是准确计算这些影响的障碍。最近发布的2021年美国车辆库存和使用调查（US VIUS）填补了在了解全国范围内商用卡车车队分布、运营和业务限制方面的关键空白。本研究旨在利用2021年US VIUS数据了解最新的美国商用车车队组成和运营特征，并校准监管排放模型中的车队输入，以评估由VIUS衍生的车队组成对排放的潜在影响。商用卡车的排放率和默认车队组成是从美国环保署的机动车辆排放模拟器（MOVES4）中收集的。2021年US VIUS数据将用于改善车队特性，如长途分数和车辆里程累积率。该研究还调查了在各种预测的汽车电气化情景下潜在的减排效益。本文比较了MOVES4和US VIUS车队在当前和未来两种情况下的能耗和关键空气污染物率，以深入了解最新的美国商用车车队特征及其对能源和排放的影响。这项研究有助于政策制定者和实践者推进排放模型的商业车队生成。它也加深了对各种船队预测下商业船队减排潜力的理解。

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引用次数: 0

How (not) to incentivize sustainable mobility? Lessons from a swiss mobility competition 如何（不）激励可持续交通？瑞士交通竞赛的经验教训

IF 3.9 3区工程技术 Q2 ENVIRONMENTAL STUDIES

International Journal of Sustainable Transportation

Pub Date : 2025-12-02 DOI: 10.1080/15568318.2025.2559981

Silvio Sticher , Hannes Wallimann , Noah Balthasar

We investigate the impact of a gamified experiment designed to promote sustainable mobility among students and staff members of a Swiss higher-education institution. Despite transportation being a major contributor to domestic CO₂ emissions, achieving behavioral change remains challenging. In our two-month mobility competition, structured as a randomized controlled trial with a 3

\times

3 factorial design, neither monetary incentives nor norm-based nudging significantly influences mobility behavior. Based on 195 competition participants and a total prize sum of CHF 3,150, our (null) results suggest that there is no “gamified quick fix” for making mobility substantially more sustainable. Also, we provide some lessons learned on how not to incentivize sustainable mobility by addressing potential shortcomings of our mobility competition.

我们调查了一个游戏化实验的影响，该实验旨在促进瑞士高等教育机构的学生和工作人员之间的可持续流动性。尽管交通运输是国内二氧化碳排放的主要来源，但实现行为改变仍然具有挑战性。在我们为期两个月的流动性竞争中，我们采用3×3因子设计进行随机对照试验，结果表明，货币激励和基于规范的推动都没有显著影响流动性行为。基于195名参赛者和3,150瑞士法郎的总奖金，我们的（无效）结果表明，没有“游戏化的快速解决方案”可以使交通更加可持续。此外，我们还提供了一些经验教训，说明如何不通过解决交通竞争的潜在缺陷来激励可持续交通。

引用次数: 0

Consumers’ attitudes toward benefits and drawbacks of vehicle-to-grid technology: An agent-based model 消费者对汽车到电网技术利弊的态度：一个基于代理的模型

IF 3.9 3区工程技术 Q2 ENVIRONMENTAL STUDIES

International Journal of Sustainable Transportation

Pub Date : 2025-12-02 DOI: 10.1080/15568318.2025.2546033

Junbei Liu , Chengxiang Zhuge , Justin Hayse Chiwing G. Tang , Meng Meng , Jie Zhang

Vehicle-to-Grid (V2G) is an important technology for Electric Vehicles (EVs) and the power grid. This paper first provided insights into people’s attitudes toward three key benefits and two key drawbacks of V2G, using survey data collected in Beijing in 2020. Further, we incorporated the empirical findings into a spatial agent-based joint model of EV and V2G adoption to explore how changes in people’s attitudes toward the benefits and drawbacks could influence the adoption of V2G. The survey results suggested that people tended to be most concerned about battery degradation and least concerned about grid support. Our diffusion simulation suggests that mitigating BEV owners’ concerns about battery degradation and enhancing public awareness of the cost-saving potential of V2G can significantly increase the number of people will/may adopt V2G with a BEV and a PHEV, respectively. However, these attitudinal improvements do not lead to a notable rise in V2G adopters. Furthermore, V2G tended to diffuse more easily across Plug-in Hybrid EV (PHEV) owners than Battery EV (BEV) owners. The results are expected to be helpful for shaping policies to promote the adoption of V2G.

汽车到电网（V2G）是电动汽车和电网的一项重要技术。本文首先利用2020年在北京收集的调查数据，深入了解了人们对V2G的三个主要优点和两个主要缺点的态度。此外，我们将实证结果纳入基于空间主体的电动汽车和V2G采用联合模型，以探讨人们对V2G利弊态度的变化如何影响V2G的采用。调查结果表明，人们往往最关心电池退化，最不关心电网支持。我们的扩散模拟表明，减轻纯电动汽车车主对电池退化的担忧和提高公众对V2G节省成本潜力的认识，可以显著增加将V2G与纯电动汽车和插电式混合动力汽车分别搭配使用的人数。然而，这些态度上的改善并没有导致V2G采用者的显著增加。此外，V2G在插电式混合动力电动汽车（PHEV）车主中比纯电动汽车（BEV）车主更容易扩散。预计研究结果将有助于制定促进V2G采用的政策。

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引用次数: 0

Investigating the emissions effect of integrating drones into mixed-mode logistics – A case study of a healthcare setting 调查将无人机集成到混合模式物流中的排放影响-以医疗保健环境为例研究

IF 3.9 3区工程技术 Q2 ENVIRONMENTAL STUDIES

International Journal of Sustainable Transportation

Pub Date : 2025-12-02 DOI: 10.1080/15568318.2025.2564851

Matt Grote , Andy Oakey , Aliaksei Pilko , Jakub Krol , Alex Blakesley , Tom Cherrett , James Scanlan , Bani Anvari , Antonio Martinez-Sykora

Interest is growing in the potential of using Uncrewed Aerial Vehicles (UAVs; known as drones) for logistics applications (i.e. last-mile payload delivery). Based on case studies of pathology specimens taken from patients at community clinics and transported to central laboratories for analysis, the effect on greenhouse gas emissions of using drones alongside more traditional transport modes (i.e. electric vans (e-vans) and bicycle couriers) in mixed-mode logistics systems was investigated for networks in locations with contrasting geographic characteristics. Results suggested that reductions in emissions of up to 83% were possible compared to e-van-only solutions. Notably, bicycle couriers made a considerable contribution to these reductions in some cases. In general, serving clinics that were remote and/or isolated tended to be where drones could offer a beneficial effect. Using drones was also associated with decreases in payload transit times (up to 76%) but increases in costs (up to 134%), raising a question regarding the true value of expedited delivery in a medical context.

人们对使用无人驾驶飞行器（uav）进行物流应用（即最后一英里有效载荷交付）的潜力越来越感兴趣。基于从社区诊所的患者身上采集病理标本并将其运送到中央实验室进行分析的案例研究，研究了混合模式物流系统中使用无人机和更传统的运输方式（即电动货车（e-vans）和自行车快递）对温室气体排放的影响，这些网络具有不同的地理特征。结果表明，与纯电动货车解决方案相比，可减少高达83%的排放量。值得注意的是，在某些情况下，自行车信使对这些减少作出了相当大的贡献。一般来说，为偏远和/或孤立的诊所服务往往是无人机可以提供有益效果的地方。使用无人机还与有效载荷运输时间缩短（最多76%）有关，但成本增加（最多134%），这就提出了一个问题，即在医疗环境中快速交付的真正价值。

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引用次数: 0

The role of the new ecological paradigm scale on the willingness to pay and willingness to wait for e-commerce deliveries 新生态范式尺度对支付意愿和等待电子商务配送意愿的作用

IF 3.9 3区工程技术 Q2 ENVIRONMENTAL STUDIES

International Journal of Sustainable Transportation

Pub Date : 2025-12-02 DOI: 10.1080/15568318.2025.2544181

Tanguy Baiwir , Sabine Limbourg , Mario Cools

Amidst the rise of e-commerce, understanding the interplay between consumer behaviors and environmental considerations has become pivotal. This study examines how environmental awareness impacts e-commerce consumers’ preferences for sustainable versus express delivery options. To contribute to the literature in this field, we investigate the behaviors of a sample of 299 e-commerce consumers, particularly in light of growing environmental concerns. Leveraging the New Ecological Paradigm Scale (NEPS), we compute an ecological score, offering a comprehensive insight into its influence on varied consumer decisions. Principal Component Analysis of the NEPS items reveals that the first four components account for nearly 50% of the variance, highlighting significant dimensions of environmental perspectives. Additionally, Cronbach’s alpha analysis indicates that the NEPS scale is reliable and has good internal consistency, justifying the use of a summated scale to reflect overall ecological positioning. We then contrast two primary delivery types: sustainable and express. The key metrics under scrutiny include the willingness to wait and the willingness to pay for sustainable delivery and willingness to pay for express delivery. Our findings affirm that NEPS affects positively willingness to pay and willingness to wait for sustainable delivery and negatively WTP for express delivery.

随着电子商务的兴起，了解消费者行为与环境考虑之间的相互作用变得至关重要。本研究考察了环境意识如何影响电子商务消费者对可持续与快递选择的偏好。为了对这一领域的文献做出贡献，我们调查了299名电子商务消费者的行为样本，特别是考虑到日益增长的环境问题。利用新生态范式量表（NEPS），我们计算了一个生态评分，提供了一个全面的洞察其对各种消费者决策的影响。NEPS项目的主成分分析显示，前四个成分占方差的近50%，突出了环境视角的重要维度。此外，Cronbach 's alpha分析表明，NEPS量表是可靠的，具有良好的内部一致性，证明了使用求和量表来反映整体生态定位是合理的。然后，我们对比了两种主要的交付类型：可持续和快递。审查的关键指标包括等待的意愿、为可持续配送付费的意愿以及为快递付费的意愿。我们的研究结果证实，NEPS正向影响支付意愿和等待可持续交付的意愿，负向影响快递的WTP。

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引用次数: 0

Optimal allocation of electric vehicle charging station integrated with distributed generation using adaptive luminescence moth optimization 结合分布式发电的电动汽车充电站优化配置的自适应发光蛾优化算法

IF 3.9 3区工程技术 Q2 ENVIRONMENTAL STUDIES

International Journal of Sustainable Transportation

Pub Date : 2025-12-02 DOI: 10.1080/15568318.2025.2553903

B. Mohan , Sudhakaran M. , Srikiruthika S.

With the increasing popularity of electric vehicles (EVs) worldwide, the need for efficient and accessible charging infrastructure has become increasingly critical. The rapid expansion of EV adoption and the importance of strategically locating charging stations (CS) to support this transition. Existing research reveals challenges, such as suboptimal placement resulting in uneven distribution, inadequate coverage, and increased energy loss due to inefficient network configurations. This research addresses the challenge of identifying optimal places and dimensions for EVCS and Distributed Generation (DG) to enhance accessibility and minimize power loss in electrical distribution networks. To overcome the conventional optimization method challenges, the adaptive luminescence moth optimization (ALMO) is utilized to identify optimal CS and DG locations with the best sizes of EVCS and DG concerning the network reconfiguration. The optimal place and dimension chosen for the placement of the EVCS and DG should show minimum voltage deviation and maximum voltage stability. To find the losses and voltage profile fast computing with less memory Backward Forward Sweep (BFS) Load Flow Analysis is considered. The provided approach aims to maximize coverage, minimize power loss and voltage deviation, and improve overall network efficiency. By considering various factors, such as voltage stability, voltage deviation, power loss, and cost analysis ALMO model ensures robust and effective placement and capacity decisions. The simulation results with the analysis based on IEEE33 and IEEE69 bus systems demonstrate the efficiency of the proposed model outperforming the other existing techniques.

随着电动汽车在全球范围内的日益普及，对高效、便捷的充电基础设施的需求变得越来越重要。电动汽车的快速普及以及战略性地定位充电站（CS）以支持这一转变的重要性。现有的研究也揭示了一些挑战，如不理想的布局导致分布不均匀、覆盖范围不足以及由于低效的网络配置而增加的能量损失。本研究解决了确定EVCS和分布式发电（DG）的最佳位置和尺寸的挑战，以提高配电网络的可达性并最大限度地减少功率损耗。为了克服传统优化方法的挑战，利用自适应发光蛾优化（ALMO）方法，在网络重构中选取最优的EVCS和DG尺寸，确定最优的CS和DG位置。EVCS和DG放置的最佳位置和尺寸应具有最小电压偏差和最大电压稳定性。为了在更小的内存下快速计算出损耗和电压分布，考虑了反向前向扫描（BFS）的潮流分析方法。该方法旨在最大限度地提高网络的覆盖范围，最大限度地降低网络的功率损耗和电压偏差，提高网络的整体效率。通过考虑各种因素，如电压稳定性、电压偏差、功率损耗和成本分析，ALMO模型确保了鲁棒和有效的布局和容量决策。基于IEEE33和IEEE69总线系统的仿真结果表明，该模型的有效性优于其他现有技术。

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引用次数: 0

Can E-scooters connect first and last-mile of public rail transit? Lessons learned from intercept user survey in Utah 电动滑板车能否连接公共轨道交通的第一英里和最后一英里？犹他州拦截用户调查的经验教训

IF 3.9 3区工程技术 Q2 ENVIRONMENTAL STUDIES

International Journal of Sustainable Transportation

Pub Date : 2025-12-02 DOI: 10.1080/15568318.2025.2546038

Hannaneh Abdollahzadeh Kalantari , Wookjae Yang , Reid Ewing

Urban transportation systems face challenges in providing connectivity, particularly for the first/last-miles of commuters’ journeys. Micromobility services, such as e-scooters, have emerged as potential solutions to bridge this gap and enhance the efficiency and accessibility of public transit. As transit ridership demonstrates declining trends in the US, the integration of micromobility options with existing transit infrastructure presents a promising solution. This study aims to investigate the role of e-scooter services in enhancing first/last-mile connectivity within public transit systems, focusing on factors influencing adoption, barriers to integration, and potential policy interventions by studying Salt Lake County area. Methodologically, intercept surveys were conducted to gather demographic and behavioral insights from both e-scooter users and transit riders. Descriptive statistics, chi-square tests, and thematic qualitative analysis were employed. We found that the promise of e-scooters as conduits for transit connectivity remains largely unmet. Despite bustling transit stations, the number of e-scooter users was extremely low, and walking is the preferred way to connect to transit. Disparities in first/last-mile connectivity patterns between e-scooters and traditional modes of transport among transit riders further highlight the need for targeted interventions. The findings also revealed a strong preference for e-scooters among younger demographics, driven by factors such as convenience and enjoyment. However, challenges related to cost, accessibility, safety, and lack of familiarity hinder widespread adoption. While e-scooter services offer opportunities to enhance transit connectivity, addressing barriers requires efforts from policymakers and authorities.

城市交通系统在提供连通性方面面临挑战，尤其是通勤者旅程的头一英里/最后一英里。微型交通服务，如电动滑板车，已经成为弥合这一差距、提高公共交通效率和可达性的潜在解决方案。由于美国的公共交通客流量呈下降趋势，将微型交通选择与现有的公共交通基础设施相结合是一个很有前景的解决方案。本研究旨在探讨电动滑板车服务在提高公共交通系统第一/最后一英里连通性方面的作用，重点研究影响采用的因素、整合的障碍和潜在的政策干预。在方法上，进行了拦截调查，以收集电动滑板车用户和公共交通乘客的人口统计和行为见解。采用描述性统计、卡方检验和专题定性分析。我们发现，电动滑板车作为交通连接管道的承诺在很大程度上仍未实现。尽管交通站点熙熙攘攘，但电动滑板车用户的数量极低，步行是换乘的首选方式。电动滑板车与传统交通工具之间第一/最后一英里连接模式的差异进一步凸显了有针对性干预的必要性。调查结果还显示，由于方便和享受等因素，年轻人对电动滑板车有强烈的偏好。然而，与成本、可访问性、安全性和缺乏熟悉性相关的挑战阻碍了广泛采用。虽然电动滑板车服务提供了加强交通连通性的机会，但解决障碍需要政策制定者和当局的努力。

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引用次数: 0

Exploring the non-linear relationship between driving behavior and vehicle fuel consumption using long short-term memory 利用长短期记忆研究驾驶行为与车辆油耗的非线性关系

IF 3.9 3区工程技术 Q2 ENVIRONMENTAL STUDIES

International Journal of Sustainable Transportation

Pub Date : 2025-12-02 DOI: 10.1080/15568318.2025.2542293

Aamir Hussain , Shuyan Chen , Sajan Shaikh , Irfan Ullah , Ghim Ping Ong

Modern data acquisition techniques and the growing concern about climate change have significantly contributed to research on fuel consumption. Thus, approaches for predicting fuel consumption, which is heavily determined by driving behavior, continue to evolve. Driving behavior also defines the characteristics of driving styles, which significantly influence fuel consumption. Previous studies lacked the investigation of the non-linear relationship between driving behavior, driving styles, and fuel consumption. To address this problem, our study employs a deep learning approach, specifically Long Short-Term Memory (LSTM), to investigate the actual non-linear relationship between driving behavior and fuel consumption using real-world driving trajectory data, namely the Vehicle Energy Dataset (VED) from Ann Arbor, Michigan, USA. The time series data consists of driving records from which data on internal combustion engine (ICE) vehicles is filtered to align with the scope of study. The LSTM model, which is also known for capturing complex non-linear patterns and temporal dependencies, is employed to predict fuel consumption. The performance of the proposed model is compared with Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU). The results revealed that the LSTM model outperformed RNN and GRU in predicting fuel consumption. Additionally, a feature importance analysis was conducted using the permutation feature importance score to understand the factors influencing fuel consumption in driving behavior, as reflected in the model’s predictive results. The findings from this study can also inform ICE drivers about the development of personalized strategies to optimize fuel efficiency and contribute to eco-driving.

现代数据采集技术和对气候变化的日益关注极大地促进了燃料消耗的研究。因此，预测燃料消耗的方法继续发展，这在很大程度上取决于驾驶行为。驾驶行为也决定了驾驶风格的特征，而驾驶风格对油耗有显著影响。以往的研究缺乏对驾驶行为、驾驶风格和油耗之间非线性关系的调查。为了解决这个问题，我们的研究采用了深度学习方法，特别是长短期记忆（LSTM），利用真实驾驶轨迹数据（即来自美国密歇根州安娜堡的车辆能量数据集（VED））来研究驾驶行为与油耗之间的实际非线性关系。时间序列数据由驾驶记录组成，从中过滤内燃机（ICE）车辆的数据以符合研究范围。LSTM模型也以捕获复杂的非线性模式和时间依赖性而闻名，它被用来预测燃料消耗。将该模型的性能与递归神经网络（RNN）和门控递归单元（GRU）进行了比较。结果表明，LSTM模型在油耗预测方面优于RNN和GRU模型。此外，使用排列特征重要性评分进行特征重要性分析，以了解影响驾驶行为油耗的因素，并反映在模型的预测结果中。这项研究的结果也可以为ICE司机提供个性化策略的发展信息，以优化燃油效率并促进生态驾驶。

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引用次数: 0

Enhancing vehicular emissions monitoring: A raw data processing and imputation model for heavy-duty diesel vehicles using remote OBD systems 加强车辆排放监测：采用远程OBD系统的重型柴油车辆原始数据处理和输入模型

IF 3.9 3区工程技术 Q2 ENVIRONMENTAL STUDIES

International Journal of Sustainable Transportation

Pub Date : 2025-11-02 DOI: 10.1080/15568318.2025.2538685

Tao Li , Xin Lou , Zhuoqian Yang , Jing Zhang , Guoquan Xie , Baoli Gong , Danqi Wang , Kui Wang , Yong Peng

Currently, the raw data exported from the remote on-board diagnostics (OBD) monitoring systems of heavy-duty diesel vehicles exhibit significant issues with missing values in key items such as NOx concentration, posing challenges to effective emission regulation. This study proposes a systematic approach to raw data processing, provides a detailed analysis of NOx data missing patterns, and develops a weighted prediction model based on the AutoRegressive Moving Average with eXogenous variables- Long Short-Term Memory (ARMAX-LSTM) for missing data imputation. The ARMAX-LSTM model combines the capability of LSTM to capture nonlinear patterns with ARMAX’s ability to describe linear data, enhanced by dynamic weighting coefficients to improve prediction accuracy. Using Spearman rank correlation analysis, nine key parameters and the NOx downstream concentration itself were selected as input features for predicting and imputing missing values. Experimental results demonstrate that the proposed model reduces the mean squared error by 26.99% compared to the ARMAX baseline model and by 14.08% compared to the LSTM baseline model for randomly missing data. For naturally missing data segments, the model produced imputed curves with good continuity and stability, meeting engineering application requirements. This study provides technical support for improving OBD data quality and identifying high-emission vehicles, while also highlighting the model’s limitations in handling scenarios with all data items missing over continuous time periods. These findings offer directions for future model optimization.

目前，重型柴油车远程车载诊断（OBD）监测系统输出的原始数据存在严重问题，如氮氧化物浓度等关键项目的值缺失，给有效的排放监管带来了挑战。本研究提出了一种系统的原始数据处理方法，提供了氮氧化物数据缺失模式的详细分析，并开发了一个基于外生变量的自回归移动平均-长短期记忆（ARMAX-LSTM）的加权预测模型，用于缺失数据的输入。ARMAX-LSTM模型结合了LSTM捕获非线性模式的能力和ARMAX描述线性数据的能力，并通过动态加权系数增强了预测精度。利用Spearman秩相关分析，选择9个关键参数和NOx下游浓度本身作为预测和输入缺失值的输入特征。实验结果表明，对于随机缺失数据，该模型比ARMAX基线模型降低了26.99%的均方误差，比LSTM基线模型降低了14.08%。对于自然缺失的数据段，模型生成的输入曲线具有良好的连续性和稳定性，满足工程应用要求。该研究为提高OBD数据质量和识别高排放车辆提供了技术支持，同时也强调了该模型在处理连续时间段内所有数据项缺失的情况时的局限性。这些发现为未来的模型优化提供了方向。

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引用次数: 0

Forecasting the consumption evolution of battery electric vehicles under dynamic market conditions: The case study of Jiangsu Province, China 动态市场条件下纯电动汽车消费演变预测——以江苏省为例

IF 3.9 3区工程技术 Q2 ENVIRONMENTAL STUDIES

International Journal of Sustainable Transportation

Pub Date : 2025-11-02 DOI: 10.1080/15568318.2025.2545449

Boshuai Qiao , Sigal Kaplan , Jie He

Predicting the future market size of battery electric vehicles (BEVs) and their market share is essential for analyzing transport externalizations and optimizing charging infrastructure deployment. Current smooth-curve models, the system dynamics, and agent-based models for BEV market forecasting are usually static functions or rely on market interactions. Still, they hardly quantify the influencing effects and changes of covariates under dynamic market conditions. Given the above-mentioned, the BEV cumulative sales are forecasted under dynamic market conditions using the artificial neural network and the bidirectional short- and long-term memory models. The samples of five covariates are derived from available data about BEV sales, price changes, fuel-to-electricity ratio, charging piles, driving range, and incentive effects from the priorities of BEV license plates in Jiangsu province. Different evolutionary analyses are set the three future scenarios of the BEV sale market based on the Time-Series Multi-Layer Perceptron model, and the marginal effect of a single covariate was further analyzed. Finally, our results show the advantages of machine-learning methods over smooth-curve models used to generate market predictions, further providing insights on covariates effects for market managers to promote the BEV sale market.

预测纯电动汽车（bev）的未来市场规模及其市场份额对于分析交通外部化和优化充电基础设施部署至关重要。目前用于纯电动汽车市场预测的平滑曲线模型、系统动力学模型和基于主体的模型通常是静态函数或依赖于市场相互作用。然而，他们很难量化协变量在动态市场条件下的影响作用和变化。在此基础上，利用人工神经网络和双向长短期记忆模型对动态市场条件下的纯电动汽车累计销量进行了预测。五个协变量的样本来源于江苏省纯电动汽车销量、价格变化、燃料电比、充电桩、续驶里程和纯电动汽车车牌优先级激励效应的现有数据。基于时间序列多层感知器模型对纯电动汽车销售市场的三种未来情景进行了不同的演化分析，并进一步分析了单个协变量的边际效应。最后，我们的研究结果显示了机器学习方法相对于用于生成市场预测的平滑曲线模型的优势，进一步为市场管理者提供了有关协变量效应的见解，以促进纯电动汽车销售市场。

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引用次数: 0