Transportation Research Part D-transport and Environment最新文献

{"title":"Decarbonizing last-mile delivery: A life cycle comparison of low-altitude and traditional logistics","authors":"Jun-jie Dong ,&nbsp;Yuan-kai Huang","doi":"10.1016/j.trd.2025.105176","DOIUrl":"10.1016/j.trd.2025.105176","url":null,"abstract":"<div><div>With growing environmental concerns in e-commerce logistics, this research employs life cycle assessment to evaluate the carbon footprint of last-mile delivery options by comparing multi-rotor drones, electric tricycles, and fuel-powered minivans. Results show electric tricycles offer the best environmental performance under current conditions. While drones outperform conventional minivans, their manufacturing embodied carbon exceeds that of tricycles, highlighting limitations in operational emission reductions alone. Sensitivity analysis identifies payload capacity, service life, and daily operational frequency as key influencing factors. The research proposes extending vehicle lifespan, improving operational efficiency, and optimizing payload design to reduce emissions. These findings provide valuable insights for logistics operators in vehicle selection, manufacturers in eco-design, and policymakers in sustainable urban planning, contributing to the development of low-carbon delivery systems.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"152 ","pages":"Article 105176"},"PeriodicalIF":7.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796040","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}

{"title":"Resilience analysis of electric vehicle charging infrastructure: a Bayesian network approach","authors":"Ning Guo ,&nbsp;Mengying Li ,&nbsp;Zhe George Zhang ,&nbsp;Xi Chen ,&nbsp;Bingfeng Xie","doi":"10.1016/j.trd.2025.105178","DOIUrl":"10.1016/j.trd.2025.105178","url":null,"abstract":"<div><div>With the widespread adoption of electric vehicles, charging infrastructure has become indispensable to transportation systems. However, various unavoidable disruptive events, such as natural hazards and cyber threats, severely impact the operation of charging infrastructure. To better handle these risks, charging infrastructure should be maintained for high resilience—sustaining service under disturbance and recovering quickly. This study, therefore, conducts a comprehensive resilience analysis of charging infrastructure. First, the study identifies key drivers of charging infrastructure resilience, organized along three inherent capacity dimensions—absorptive, adaptive, and restorative. A Bayesian network is then employed to model charging infrastructure resilience. Furthermore, resilience quantification is analyzed using advanced methods: sensitivity analysis and both forward and backward inference. Lastly, the insights drawn from study will benefit practitioners who wish to improve the charging infrastructure resilience.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"152 ","pages":"Article 105178"},"PeriodicalIF":7.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823198","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}

{"title":"Beyond adoption: Adaptation and usage patterns among electric vehicle drivers","authors":"Anne Voigt ,&nbsp;Christina Kurpiers ,&nbsp;Stefan Brandenburg","doi":"10.1016/j.trd.2025.105175","DOIUrl":"10.1016/j.trd.2025.105175","url":null,"abstract":"<div><div>Battery electric vehicle (BEV) technology continues to advance, offering longer driving ranges. As the market shifts from early adopters to mainstream consumers, who differ in their characteristics, understanding their adaptation process becomes crucial. With advanced BEVs and more diverse users, questions arise about how current BEV drivers use their vehicles, adapt to them, and whether past issues like range anxiety remain significant. To explore these questions, we conducted qualitative interviews with <em>N</em> = 24 BEV owners. Findings reveal diverse usage patterns, from multi-car households using BEVs for short commutes to single-car households relying on them for long trips. Despite improved technical capabilities, concerns about range and charging persist, even as actual range meets daily needs for most drivers. The adaptation process of the drivers involves developing new habits and behaviours, with gained experience reducing initial concerns. Understanding these individual adaptation processes becomes increasingly important to facilitate widespread BEV adoption.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"152 ","pages":"Article 105175"},"PeriodicalIF":7.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823204","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}

{"title":"Collaborative prediction of bike-sharing demand around metro stations using spatiotemporal neural network","authors":"Wenxiang Li ,&nbsp;Xingguang Zhang ,&nbsp;Guojian Zou ,&nbsp;Weiwei Liu","doi":"10.1016/j.trd.2025.105188","DOIUrl":"10.1016/j.trd.2025.105188","url":null,"abstract":"<div><div>Accurate prediction of bike-sharing demand around metro stations is essential for enhancing multimodal integration. However, existing approaches often model bike-sharing demand in isolation, neglecting its strong spatiotemporal interdependence with metro ridership. To address this gap, this study proposes a spatiotemporal neural network that fuses multi-source data of bike-sharing trips, metro ridership, built environment characteristics, and weather conditions. The model combines graph convolutional networks to capture station-level spatial correlations with long term memory networks to learn temporal dynamics, while explicitly incorporating metro entry and exit flows to represent cross-modal dependencies. Using large-scale datasets from bike-sharing and metro systems in Shanghai, empirical analyses show that the proposed model substantially outperforms seven baseline models across multiple temporal granularities. Moreover, the model effectively captures peak-period demand surges, spatial clustering patterns, and imbalances between unlock and lock activities of bike-sharing. These findings provide practical insights for optimizing bike-sharing redistribution and enhancing metro–bike integration in urban environments.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"152 ","pages":"Article 105188"},"PeriodicalIF":7.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885882","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}

{"title":"A three-stage evaluation of airline low-carbon competitiveness","authors":"Mengyuan Lu ,&nbsp;Jinglei Yu ,&nbsp;Fei Li ,&nbsp;Yue Zhang ,&nbsp;Quanxing Jia","doi":"10.1016/j.trd.2025.105173","DOIUrl":"10.1016/j.trd.2025.105173","url":null,"abstract":"<div><div>Achieving deep decarbonisation in aviation requires a systematic understanding of how airlines differ in their carbon efficiency and what operational or technological factors drive these differences. This study proposes an integrated three-stage analytical framework that combines a non-oriented SBM-DDF model, the Global Malmquist–Luenberger (GML) productivity index, Random Forest regression and a cloud-model heterogeneity assessment to evaluate the low-carbon competitiveness of 20 major global airlines from 2018 to 2022. The SBM-DDF model benchmarks multi-input–multi-output environmental efficiency, while the GML index captures dynamic productivity changes and decomposes them into efficiency change and technical change. Random Forest analysis identifies the key operational determinants of carbon efficiency, and the cloud model characterises heterogeneity in performance level, volatility and uncertainty.</div><div>Results reveal pronounced cross-airline and intertemporal heterogeneity. Mean efficiency declined markedly during 2020–2021 and rebounded in 2021–2022, although absolute CO₂-slack reductions lagged behind relative technical efficiency improvements. GML analysis shows that productivity changes were modest and mainly driven by managerial and operational efficiency rather than technological progress, indicating the limited short-run impact of fleet renewal on low-carbon competitiveness. Airline-level patterns demonstrate consistently strong and stable performance among Singapore Airlines, EasyJet and Cathay Pacific. Random Forest results identify revenue tonne-kilometres (RTK), employment scale and fleet size as dominant drivers of emission efficiency, while the cloud-model heterogeneity typology reveals four distinct groups ranging from efficient-stable to inefficient-volatile. Policy implications emphasise the need for performance-sensitive benchmarks in ETS and SAF-crediting schemes, and for managerial efficiency improvements to complement long-run technological transitions.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"152 ","pages":"Article 105173"},"PeriodicalIF":7.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885885","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}

{"title":"Shifting gears? The CO2-emission impact of Austria’s car purchase and use policies","authors":"Tobias Eibinger ,&nbsp;Hans Manner ,&nbsp;Karl W. Steininger","doi":"10.1016/j.trd.2025.105160","DOIUrl":"10.1016/j.trd.2025.105160","url":null,"abstract":"<div><div>Policy makers likely have to resort to a mix of complementary policies to achieve global carbon-neutrality targets. To support effective policy design, we analyse the effect of environmental policies targeting vehicle use and purchase decisions on CO₂ emissions from passenger cars in Austria from 1965 to 2019. First, we propose a novel environmental policy stringency index tailored to the Austrian transport sector. Second, we analyse the effect of policies on transport-related CO₂ emissions using a structural vector autoregressive model to account for interdependencies between variables. We find that policies targeting the investment decision to buy new cars reduce emissions in Austria in the long run, but their effect materializes with a delay. In terms of stringency levels implemented, the annual engine-related tax shows the strongest impact, while the new vehicle registration tax shows a weaker effect. In contrast, policies targeting car usage produce immediate effects that fade over time.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"152 ","pages":"Article 105160"},"PeriodicalIF":7.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796027","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}

{"title":"Technical performance evolution of BEVs: range, consumption and weight projections to 2050","authors":"Abdullah Isilti,&nbsp;James E. Tate","doi":"10.1016/j.trd.2025.105172","DOIUrl":"10.1016/j.trd.2025.105172","url":null,"abstract":"<div><div>Battery Electric Vehicles (BEVs) are assuming a pivotal role in the road electrification, with continuous advancements in technology. This UK-focused study analyses the evolution of the technical parameters of BEVs between 2011 and 2024 and forecasts their development to 2050 for a range of scenarios (pessimistic, realistic and optimistic) focusing on range, energy consumption and vehicle weight. The research is underpinned by a data set of the technical specifications of 575 BEV models. The relationships between the parameters were revealed by machine learning (Random Forest and SHAP), with the forecast performed using the Prophet. The driving range of a typical C segment Sports Utility Vehicle (SUV) is forecast to increase by between 60 % and 180 %, vehicle weight and energy consumption varied between 1051–2000 kg and 267–286 Wh/mile, respectively, depending on the scenarios and underlying assumptions. The findings provide evidence-based insights for automotive technology planning and weight-based fiscal policy responses.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"152 ","pages":"Article 105172"},"PeriodicalIF":7.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145771986","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}

{"title":"Economic and environmental analysis for electric bus system with dynamic wireless charging","authors":"Jinhua Ji ,&nbsp;Linhong Wang ,&nbsp;Yiming Bie ,&nbsp;Shiwu Li","doi":"10.1016/j.trd.2025.105159","DOIUrl":"10.1016/j.trd.2025.105159","url":null,"abstract":"<div><div>Dynamic wireless charging (DWC) enables real-time energy replenishment for electric buses (EBs) while in motion, substantially reducing onboard battery capacity requirements, vehicle curb mass, and associated procurement and electricity expenditures. However, charging infrastructure entails significant upfront capital investment, and economic viability and decarbonization potential remain insufficiently quantified, which hinders its widespread adoption. To analyze the advantages and challenges of DWC in EB systems, this paper constructs a life cycle assessment framework. First, the system boundary is defined, and the life cycle duration is determined. Then, the operating costs and greenhouse gas emissions are calculated to analyze the economic and environmental impacts of the system. A case study is carried out on an actual route, and results show that DWC reduces emissions compared with plug-in charging. Economically, DWC lowers operating costs under the flat-rate tariff policy, but increases them under the time-of-use pricing policy.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"151 ","pages":"Article 105159"},"PeriodicalIF":7.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732506","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}

{"title":"Explicit consideration of human exposure to minimize freight routing impacts","authors":"Sarah Dennis-Bauer ,&nbsp;Anmol Pahwa ,&nbsp;Miguel Jaller","doi":"10.1016/j.trd.2025.105133","DOIUrl":"10.1016/j.trd.2025.105133","url":null,"abstract":"<div><div>As freight demand grows, so do its negative externalities, such as health and environmental impacts. This study integrates a reduced-complexity air quality model (RCM) with a multimodal traffic assignment model to analyze the scenario in which trucks minimize their public health costs. The study compares the least health-cost path (LHCP) with the shortest, fastest, and least-emissions paths. Two LHCP scenarios with geofences around sensitive populations differed in travel time (TT), health costs, and community impacts. LHCP had 10.9% higher TT and 29% lower costs than the next lowest scenarios. Under the LHCP, within-scenario burdens increased for some communities (e.g., PM_2.5 burden) but decreased for others (e.g., ozone burden). The other routing scenarios similarly produced differential effects across communities, highlighting tradeoffs. Geofences increased TT and health costs relative to the LHCP and produced different community impacts. This study provides new, actionable insights into methods to mitigate transportation externalities, particularly in communities.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"151 ","pages":"Article 105133"},"PeriodicalIF":7.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145682898","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}

{"title":"Strategies, opportunities, and challenges of integrating shared micromobility with public transport","authors":"Anne Brown ,&nbsp;Niels van Oort","doi":"10.1016/j.trd.2025.105165","DOIUrl":"10.1016/j.trd.2025.105165","url":null,"abstract":"<div><div>Shared micromobility may enhance accessibility as part of a comprehensive public transport network. A qualitative stakeholder perspective is needed to reflect the practical opportunities and challenges for integrating shared micromobility and public transport. This research synthesizes literature alongside 25 interviews with professionals across North America and Europe to answer: 1) what challenges and opportunities do different integration approaches offer? And 2) what should be prioritized when integrating shared micromobility and public transport? We frame integration efforts in three pillars: 1) institutional, 2) physical, and 3) digital integration. Findings show that integration efforts are effective only when foundational preconditions are met including high-quality public transport, dedicated micromobility infrastructure, supportive land uses, and issue framing. Institutional and physical integration are both necessary to facilitate multimodal travel and create cohesive regional travel networks. Digital tools can support integration but often requires substantial resources, and faces data sharing and privacy, feasibility, and scaling challenges.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"151 ","pages":"Article 105165"},"PeriodicalIF":7.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731232","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}