Transportation Research Part D-transport and Environment最新文献_第10页

Can transportation network companies improve the sustainability of urban transportation? 交通网络公司能提高城市交通的可持续性吗？

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-04-18 DOI: 10.1016/j.trd.2025.104765

Shouheng Sun , Yiran Wang , Myriam Ertz

引用次数: 0

Market-based measures for decarbonizing the shipping industry: Balancing profitability and emissions reduction 航运业脱碳的市场化措施：平衡盈利与减排

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-04-17 DOI: 10.1016/j.trd.2025.104725

Ying Kou , Yan Zhan , Xiaoyi Shen , Huiming Wang , Mengchi Li

引用次数: 0

Non-linear built environment effects on travel behavior resilience under extreme weather events 极端天气条件下非线性建筑环境对出行行为弹性的影响

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-04-16 DOI: 10.1016/j.trd.2025.104753

Jixiang Liu , Jianqiang Cui , Longzhu Xiao , Dong Lin , Linchuan Yang

{"title":"Non-linear built environment effects on travel behavior resilience under extreme weather events","authors":"Jixiang Liu , Jianqiang Cui , Longzhu Xiao , Dong Lin , Linchuan Yang","doi":"10.1016/j.trd.2025.104753","DOIUrl":"10.1016/j.trd.2025.104753","url":null,"abstract":"<div><div>Resilience has been a widely used approach to assessing the response and recovery of transportation systems to various disruptions and stresses, such as extreme weather events. However, limited studies have investigated travel behavior resilience from the demand perspective, and even fewer have delved into its relationships with the built environment. Therefore, using a taxi trip dataset in Xiamen, China, and employing an advanced machine learning method (LightGBM), this study examines the non-linear effects of the built environment on travel behavior resilience during a rainstorm. The findings are as follows: (1) Location and development intensity are the most important variables for travel behavior resilience; (2) Salient non-linear relationships exist between built-environment variables and travel behavior resilience; and (3) Apparent interaction effects exist among some variables, especially between location and others. This study provides valuable insights for policymakers, offering guidance for targeted interventions to facilitate climate adaptation and enhancing resilience.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"143 ","pages":"Article 104753"},"PeriodicalIF":7.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834826","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

EV charging habits and their impact on public charging infrastructure usage 电动汽车充电习惯及其对公共充电基础设施使用的影响

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-04-15 DOI: 10.1016/j.trd.2025.104730

Simon Weekx , Ona Van den bergh , Gil Tal , Lieselot Vanhaverbeke

引用次数: 0

Revealing considerable emissions reduction potential in flight operations: A real-time emission perspective 揭示飞行操作中可观的减排潜力：实时排放视角

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-04-15 DOI: 10.1016/j.trd.2025.104745

Simeng Ma , Shizhuo Lin , Bo Han , Chen Zhang , Zhiqiang Wei , Lingxiang Xue , Jingbo Zhao , Kun Wang , Jian Yu , Philip K. Hopke

{"title":"Revealing considerable emissions reduction potential in flight operations: A real-time emission perspective","authors":"Simeng Ma , Shizhuo Lin , Bo Han , Chen Zhang , Zhiqiang Wei , Lingxiang Xue , Jingbo Zhao , Kun Wang , Jian Yu , Philip K. Hopke","doi":"10.1016/j.trd.2025.104745","DOIUrl":"10.1016/j.trd.2025.104745","url":null,"abstract":"<div><div>Accurately estimating aviation emissions is crucial for developing strategies to mitigate environmental impacts. However, aircraft’s unique four-dimensional operation characteristics and limitations in aircraft-to-ground communication technology pose challenges for precise and dynamic emissions estimation. This study proposed a hybrid machine learning approach for real-time aviation emissions estimation based on four-dimensional flight trajectories. Using Qingdao Airport as a case study, we generated a high-resolution emission inventory and explored the emission reduction potential in flight operations. Results demonstrated that: (1) our approach achieved higher prediction accuracy for real-time fuel flow rates and emissions than conventional models, owing to a comprehensively considering the nonlinear relationships between aircraft performance and trajectories under complex operating conditions; (2) emission estimation results from our approach were 22 %−78 % higher than the ICAO reference values; (3) an emission reduction potential of 5 %−65 % was identified in flight operations. Finally, four policy recommendations were developed to reduce emissions from airport flight activities.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"143 ","pages":"Article 104745"},"PeriodicalIF":7.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829342","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

Decarbonizing the last mile. Innovations from an online retailers’ perspective 使最后一英里脱碳。从在线零售商的角度进行创新

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-04-15 DOI: 10.1016/j.trd.2025.104752

Iria González-Romero , Ángel Ortiz-Bas , J.Carlos Prado-Prado

引用次数: 0

Zooming in on virtual commutes: Telecommuting impacts on mobility and sustainability 放大虚拟通勤：远程办公对流动性和可持续性的影响

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-04-14 DOI: 10.1016/j.trd.2025.104731

Nazanin Rezaei , Juan David Caicedo , Thomas P. Wenzel , Annika Todd-Blick , K. Sydny Fujita , Cristian Poliziani , Zachary A. Needell , Carlos Guirado , C. Anna Spurlock

{"title":"Zooming in on virtual commutes: Telecommuting impacts on mobility and sustainability","authors":"Nazanin Rezaei , Juan David Caicedo , Thomas P. Wenzel , Annika Todd-Blick , K. Sydny Fujita , Cristian Poliziani , Zachary A. Needell , Carlos Guirado , C. Anna Spurlock","doi":"10.1016/j.trd.2025.104731","DOIUrl":"10.1016/j.trd.2025.104731","url":null,"abstract":"<div><div>Motivated by a societal shift towards remote work and rapid advancement in information and communication technologies, this study examines the impact of telecommuting on urban mobility across the nine counties of the San Francisco Bay Area, California. Utilizing a behaviorally realistic integrated agent-based transportation model and recent data on telecommuting patterns, we simulate the impact of multiple telecommuting scenarios on transportation system outcomes. This provides a comprehensive picture of the impact of remote work on travel costs and accessibility of all travelers, factoring in changes in mode use and congestion. We analyze how telecommuting influences broader societal factors such as transportation energy consumption. Our findings indicate that increased telecommuting reduces overall person miles traveled and transportation energy consumption. Furthermore, telecommuting results in externality benefits by improving accessibility and reducing commute times for non-telecommuters.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"143 ","pages":"Article 104731"},"PeriodicalIF":7.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825864","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

Driving factors and interactions of urban transportation carbon emissions: A case study of China 城市交通碳排放驱动因素及相互作用——以中国为例

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-04-14 DOI: 10.1016/j.trd.2025.104740

Jiangjun Wan, Xiong Wu, Yuye Li, Zhonglin Li, Kuntao Deng, Jiaqi Zeng, Xinrui Fan, Ying Cao

{"title":"Driving factors and interactions of urban transportation carbon emissions: A case study of China","authors":"Jiangjun Wan, Xiong Wu, Yuye Li, Zhonglin Li, Kuntao Deng, Jiaqi Zeng, Xinrui Fan, Ying Cao","doi":"10.1016/j.trd.2025.104740","DOIUrl":"10.1016/j.trd.2025.104740","url":null,"abstract":"<div><div>Regional disparities among different types of cities pose significant challenges to reducing carbon emissions from urban transportation. This research classifies cities into six categories and examines the factors driving urban transportation carbon emissions, along with their interactions, using the Geographical Detector Model (GDM) and the Logarithmic Mean Divisia Index (LMDI). Building on this foundation, a novel decoupling effort model is developed to assess the contributions of each driving factor to the decoupling process. The findings highlight that economic growth is a major driver of emissions in large cities like Shanghai and Chengdu, while industrial structure plays a key role in large coastal cities such as Yantai. In contrast, urban public transportation participation and carrying capacity are pivotal factors in large inland cities like Shijiazhuang and medium-sized cities like Sanya and Jinhua. Notably, the study reveals a post-2016 challenge in balancing public transportation engagement and carrying capacity, emphasizing the need for tailored low-carbon policies in urban transport planning. These insights provide valuable guidance for cities worldwide facing similar transportation carbon emission challenges, offering a framework for more context-specific and effective carbon reduction strategies.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"143 ","pages":"Article 104740"},"PeriodicalIF":7.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829341","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

Cold waves and electric vehicle adoption: Evidence from Chinese cities 寒潮与电动汽车普及：来自中国城市的证据

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-04-14 DOI: 10.1016/j.trd.2025.104748

Jiayang Gao , Ning Zhang , Tao Zhang

引用次数: 0

Decoding urban transportation: Trade-offs in mode choices using big data 解码城市交通：利用大数据权衡模式选择

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2025-04-13 DOI: 10.1016/j.trd.2025.104756

Linmu Zou , Zijia Wang , Rui Guo , Lu Zhao

{"title":"Decoding urban transportation: Trade-offs in mode choices using big data","authors":"Linmu Zou , Zijia Wang , Rui Guo , Lu Zhao","doi":"10.1016/j.trd.2025.104756","DOIUrl":"10.1016/j.trd.2025.104756","url":null,"abstract":"<div><div>Understanding the determinants of urban transportation mode choice is crucial for developing efficient, sustainable, and green transit systems in metropolitan areas. This study proposes a comprehensive framework that integrates big data and machine learning techniques, leveraging a large dataset comprising over ten million trips to investigate the factors influencing transportation mode choice under clear competition during peak hours. We examine how travel attributes, land use, network centrality, and demographics shape the choices of subway, bus, taxi, and bike-share users. Employing oversampling and interpretable techniques, our analysis reveals that travel attributes significantly influence transportation mode choices, especially for public transportation. Additionally, the marginal effects of the features on mode choice are efficiently captured. Interaction effects between travel time and cost further highlight the complex trade-offs travelers make under different choice probabilities. The findings underscore the importance of integrating diverse data sources for a holistic understanding of urban transportation dynamics.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"143 ","pages":"Article 104756"},"PeriodicalIF":7.3,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825994","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