Transportation Research Part D-transport and Environment最新文献_第6页

Green wheels in motion: Electric vehicle sales in the path to decarbonization 绿色车轮在运动：电动汽车销售在脱碳的道路上

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-03-25 DOI: 10.1016/j.trd.2025.104704

Mohammad Younus Bhat , Arfat Ahmad Sofi , Javeed Ahmad Ganie

{"title":"Green wheels in motion: Electric vehicle sales in the path to decarbonization","authors":"Mohammad Younus Bhat , Arfat Ahmad Sofi , Javeed Ahmad Ganie","doi":"10.1016/j.trd.2025.104704","DOIUrl":"10.1016/j.trd.2025.104704","url":null,"abstract":"<div><div>The transportation sector is the second-largest carbon emitter after the power sector; achieving decarbonization goals through electric and hybrid vehicles is crucial. This study explores the relationship between electric vehicle sales, economic growth, population, urbanization, and carbon emissions, framed within the Environmental Kuznets Curve and Ecological Modernization Theory. This study adopts the Method of Moments Quantile Regression techneque with fixed effects, analysing data from 15 nations between 2010 and 2023. The spatial analysis reveals notable growth in electric vehicle sales alongside substantial changes in energy consumption trends. Empirical findings indicate that carbon emissions adversely affect electric vehicle sales in the lower quantiles. However, its influence diminishes in upper quantiles as sales rise in these countries. Simultaneously, the expansion of the economy and the rise in renewable energy usage drive electric vehicle sales across all quantiles. These results highlight the need for policy intervention to promote renewable energy, investments, and public awareness to enhance sustainable transportation in these countries.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"142 ","pages":"Article 104704"},"PeriodicalIF":7.3,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The changing maritime transport and its effects on carbon emissions 海上运输的变化及其对碳排放的影响

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-03-24 DOI: 10.1016/j.trd.2025.104715

Pengjun Zhao, Mengzhu Zhang, Adolf K.Y. Ng, Zhijia Tan

引用次数: 0

A novel reservation-based charging strategy considering carbon quota incentives 考虑碳配额激励的预约收费策略

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-03-24 DOI: 10.1016/j.trd.2025.104705

Yi Hu, Zhigang Jin, Zepei Liu, Gen Li

引用次数: 0

A novel model for barriers to electric vehicle adoption in the Middle East 中东电动汽车采用障碍的新模型

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-03-23 DOI: 10.1016/j.trd.2025.104714

Darrick Evensen , Joao M. Uratani , Benjamin K. Sovacool , Steve Griffiths

{"title":"A novel model for barriers to electric vehicle adoption in the Middle East","authors":"Darrick Evensen , Joao M. Uratani , Benjamin K. Sovacool , Steve Griffiths","doi":"10.1016/j.trd.2025.104714","DOIUrl":"10.1016/j.trd.2025.104714","url":null,"abstract":"<div><div>This study presents the first large-scale, representative survey to assess consumer preferences for electric vehicles (EVs) in the Middle East. Leveraging a large-scale data collection of more than 5,000 United Arab Emirates (UAE) respondents, this study employs a rigorous and novel methodology, capturing positive and negative influences on EV adoption, alongside environmental, financial, and normative dimensions. Our survey incorporates 52 specific barriers to EV adoption, covering technical, economic, infrastructural, and psychological factors. Financial cost perceptions were particularly salient; yet, general environmental views have an outsized effect on purchase likelihood, due to a large direct effect, and then notable associations with knowledge/experience of EVs, which themselves notably reduce perceived barriers to adoption. Understanding UAE consumers’ attitudes and beliefs are important for knowing how decision-makers can communicate about and target policy on EVs. We offer both a novel modelling approach, involving theoretically-derived four-stage mediation, and recommendations for government action in the UAE, Middle East and beyond.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"142 ","pages":"Article 104714"},"PeriodicalIF":7.3,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Urban transit decarbonization via stochastic programming and statistical inference under uncertainty 不确定条件下随机规划与统计推理的城市交通脱碳研究

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-03-22 DOI: 10.1016/j.trd.2025.104711

Jing Wang, Majid Gholami Shirkoohi, Rubina Akter, Walter Mérida

{"title":"Urban transit decarbonization via stochastic programming and statistical inference under uncertainty","authors":"Jing Wang, Majid Gholami Shirkoohi, Rubina Akter, Walter Mérida","doi":"10.1016/j.trd.2025.104711","DOIUrl":"10.1016/j.trd.2025.104711","url":null,"abstract":"<div><div>Urban transit decarbonization is integral to achieving a net-zero public transportation systems. This work proposes an optimization model for bus fleet transition planning, involving purchases and allocation to routes, fueling and charging infrastructure, and financing. The model adopts stochastic programming to address decision-making under uncertainty and is formulated as a mixed-integer linear program. A confidence interval estimation method is derived to accommodate diverse decision values and non-uniform scenario probabilities, alongside an efficient scenario construction approach. A case study of the Metro Vancouver regional bus network is conducted to explore transition pathways for adopting battery electric and hydrogen fuel cell buses. Results indicate that shifting to a battery electric fleet is more cost-effective overall, while the hydrogen pathway demands smaller infrastructure investments. The competitiveness of hydrogen could significantly improve if the substantial potential for cost reductions is realized. A mixed fleet can integrate the advantages of both pathways.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"142 ","pages":"Article 104711"},"PeriodicalIF":7.3,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Eco-cooperative adaptive cruise control for platoons in mixed traffic using single-agent and multi-agent reinforcement learning 基于单智能体和多智能体强化学习的混合交通环境下队列生态合作自适应巡航控制

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-03-22 DOI: 10.1016/j.trd.2025.104658

Zhiwei Yang , Zuduo Zheng , Jiwon Kim , Hesham Rakha

{"title":"Eco-cooperative adaptive cruise control for platoons in mixed traffic using single-agent and multi-agent reinforcement learning","authors":"Zhiwei Yang , Zuduo Zheng , Jiwon Kim , Hesham Rakha","doi":"10.1016/j.trd.2025.104658","DOIUrl":"10.1016/j.trd.2025.104658","url":null,"abstract":"<div><div>Signalized arterials create stop-and-go traffic, leading to collisions, delays, wasted energy, and discomfort. Connected Automated Vehicles (CAVs), using Cooperative Adaptive Cruise Control (CACC), can potentially mitigate these issues by optimizing speeds with shared information. However, the traffic environment in CACC research on signalized roads is predominantly generated through simulations. This paper compares various eco-friendly CACC methods based on reinforcement learning (RL) for CAVs operating with Human-driven Vehicles (HVs) on signalized arterials. Methods analyzed include Deep Deterministic Policy Gradient (DDPG), Soft Actor-Critic (SAC), and their multi-agent versions (MADDPG, MASAC), trained and tested on naturalistic data from the pNEUMA dataset. These RL methods are benchmarked against human-driven trajectories and the Intelligent Driver Model (IDM) in mixed platoon scenarios. Results show that DDPG and SAC excel in vehicle performance (safety, efficiency, energy, comfort), while MADDPG and MASAC perform best in platoon stability. Key factors influencing performance include platoon characteristics, vehicle position, and preceding vehicle type.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"142 ","pages":"Article 104658"},"PeriodicalIF":7.3,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding electric vehicle charging network resilience: Resilience curves and interpretable machine learning 理解电动汽车充电网络弹性：弹性曲线和可解释的机器学习

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-03-21 DOI: 10.1016/j.trd.2025.104709

Yuwen Lu , Yan Zhang , Wei Zhai , Guofang Zhai

{"title":"Understanding electric vehicle charging network resilience: Resilience curves and interpretable machine learning","authors":"Yuwen Lu , Yan Zhang , Wei Zhai , Guofang Zhai","doi":"10.1016/j.trd.2025.104709","DOIUrl":"10.1016/j.trd.2025.104709","url":null,"abstract":"<div><div>Understanding and enhancing the resilience of urban infrastructure, particularly electric vehicle (EV) charging networks becomes crucial as extreme weather events intensify. While extensive research exists on EV charging infrastructure planning, the resilience of charging networks under extreme weather remains largely unexplored. This study presents a comprehensive framework combining resilience curves with interpretable machine learning to assess and explain charging network resilience during extreme weather events. Utilizing data from 18,061 public charging piles during Typhoon Chaba in Shenzhen, China, we uncover significant spatial–temporal variability in network resilience. XGBoost modeling with SHAP analysis identifies charging station count, commercial land ratio, and raining as primary determinants of resilience patterns. The integration of traditional resilience assessment with modern data-driven analysis provides new insights into charging network dynamics, offering urban planners quantitative evidence and spatially-explicit strategies for developing climate-resilient charging infrastructure tailored to different urban contexts.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"142 ","pages":"Article 104709"},"PeriodicalIF":7.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A dynamic carbon tax on gasoline 对汽油征收动态碳税

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-03-21 DOI: 10.1016/j.trd.2025.104708

Stefano F. Verde , Valeria Di Cosmo

{"title":"A dynamic carbon tax on gasoline","authors":"Stefano F. Verde , Valeria Di Cosmo","doi":"10.1016/j.trd.2025.104708","DOIUrl":"10.1016/j.trd.2025.104708","url":null,"abstract":"<div><div>We propose a dynamic carbon tax (DCT) that stabilises gasoline prices by adjusting inversely to crude oil prices. By virtue of reducing gasoline price uncertainty and thus promoting the purchase of more fuel efficient vehicles, the DCT is expected to be more effective in cutting CO<sub>2</sub> emissions than an equivalent ordinary fixed-rate carbon tax. By virtue of preventing or limiting gasoline price spikes, the DCT is also expected to receive greater public support. The analysis is structured into three parts. First, we show how any policy that reduces uncertainty about future gasoline prices improves the expected utility of more fuel efficient vehicles relative to that of less efficient ones. Second, we show how the DCT could be designed to stabilise gasoline prices and thereby reduce gasoline price uncertainty. Third, we test whether gasoline price volatility, taken as a proxy for gasoline price uncertainty, negatively affects the fuel efficiency of light-duty vehicles bought by US households. Using micro-data from the 2017 National Household Transport Survey, we find a negative correlation as expected despite limited volatility of gasoline prices in the study period.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"142 ","pages":"Article 104708"},"PeriodicalIF":7.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Electrifying choices: How electric bicycles impact on mode choice and CO2 emissions 电气化选择：电动自行车如何影响模式选择和二氧化碳排放

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-03-20 DOI: 10.1016/j.trd.2025.104682

Thomas Hagedorn, Marlena Meier, Jan Wessel

{"title":"Electrifying choices: How electric bicycles impact on mode choice and CO2 emissions","authors":"Thomas Hagedorn, Marlena Meier, Jan Wessel","doi":"10.1016/j.trd.2025.104682","DOIUrl":"10.1016/j.trd.2025.104682","url":null,"abstract":"<div><div>This paper analyzes (i) the influence of electric bicycle (“e-bike”) ownership on transport mode choice and (ii) how a change in e-bike ownership affects carbon dioxide (CO<sub>2</sub>) emissions in Germany. Using longitudinal data from household surveys from 2016 to 2022, we first conduct a trip-level analysis with a mixed multinomial logit model (MMNL model) to estimate mode choice probabilities. The results show that the change in e-bike ownership significantly affects travel behavior, by increasing the likelihood of choosing an e-bike as means of transportation by 14.6percentage points (p.p.), while correspondingly decreasing the likelihood of choosing other modes, especially conventional bicycles by 5.6p.p., as well as car and public transportation by about 4p.p. each. Second, by using observed changes in individual distances traveled and transport-mode-specific emissions values, we calculate net emissions savings per person after acquiring an e-bike. These savings amount to 526.9kg CO<sub>2</sub> per person and year.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"142 ","pages":"Article 104682"},"PeriodicalIF":7.3,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AutonomieAI: An efficient and deployable vehicle energy consumption estimation toolkit AutonomieAI：一个高效且可部署的车辆能耗评估工具包

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-03-20 DOI: 10.1016/j.trd.2025.104686

Ayman Moawad, Bokai Xu, Sylvain Pagerit, Daniela Nieto Prada, Ram Vijayagopal, Phil Sharer, Ehsan Islam, Namdoo Kim, Paul Phillips, Aymeric Rousseau

{"title":"AutonomieAI: An efficient and deployable vehicle energy consumption estimation toolkit","authors":"Ayman Moawad, Bokai Xu, Sylvain Pagerit, Daniela Nieto Prada, Ram Vijayagopal, Phil Sharer, Ehsan Islam, Namdoo Kim, Paul Phillips, Aymeric Rousseau","doi":"10.1016/j.trd.2025.104686","DOIUrl":"10.1016/j.trd.2025.104686","url":null,"abstract":"<div><div>This paper presents AutonomieAI, a novel toolkit designed for efficient energy estimation of vehicles across diverse trip scenarios, routes, and drive cycles, applicable to a broad range of vehicle powertrain technologies. It leverages state-of-the-art Machine Learning techniques to deliver real-time energy prediction of vehicles, enabling co-simulation with transportation level system tools and opening doors for large-scale optimization at city, network or national level. Benchmark results show that AutonomieAI achieves high accuracy, with an average percentage error below 2% for most powertrain types, and computational efficiency capable of processing over 10,000 trips per second. Applications of AutonomieAI have potential to offer the flexibility to assist in solving eco-routing problems, optimize for vehicle and powertrain selection, study charging decision behavior, and optimize for charging station placement. AutonomieAI is the result of large neural network based model architectures, trained on very large and unique high fidelity vehicle simulation data. It is lightweight, deployable, efficient and has accuracy comparable to specialized and complex physics based simulation softwares.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"142 ","pages":"Article 104686"},"PeriodicalIF":7.3,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0