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

Growing disparities in transportation noise exposure across major US cities over time 随着时间的推移，美国各大城市交通噪声暴露的差距越来越大

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2024-09-25 DOI: 10.1016/j.trd.2024.104430

Minmeng Tang , Xinwei Li

引用次数: 0

Across the city boundaries: Exploring the impact of neighborhood environment on intercity commuters’ life satisfaction 跨越城市边界：探索邻里环境对城际通勤者生活满意度的影响

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2024-09-25 DOI: 10.1016/j.trd.2024.104433

Ying Zhao, Dantian Xu, Zidan Mao

{"title":"Across the city boundaries: Exploring the impact of neighborhood environment on intercity commuters’ life satisfaction","authors":"Ying Zhao, Dantian Xu, Zidan Mao","doi":"10.1016/j.trd.2024.104433","DOIUrl":"10.1016/j.trd.2024.104433","url":null,"abstract":"<div><div>The relationship between the neighborhood environment and life satisfaction has long been studied. However, few studies have examined the association between intercity commuters who reside and work in different urban areas. This paper identifies how physical and social environmental elements affect life satisfaction through residential, travel, and leisure satisfaction using data collected among intercity commuters within the Great Bay Area in China. The results suggest that high density and mixture level in the neighborhood are positively related to their daily experience with residence and travel, but the higher density in workplace negatively influents residential satisfaction. Social environment is of great significance to intercity commuters. Life domain satisfaction (including travel, leisure, and residential satisfaction) mediates the relationship between the neighborhood environment and life satisfaction. Travel and leisure satisfaction affect residential satisfaction, which in turn affect life satisfaction. Although travelling across city boundaries is part of intercity commuters’ daily practice, neighborhood environment still plays an important role in shaping their life domain, and further contribute to overall satisfaction.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319880","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

Fleet availability analysis and prediction for shared e-scooters: An energy perspective 共享电动滑板车的车队可用性分析和预测：能源视角

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2024-09-24 DOI: 10.1016/j.trd.2024.104425

Jiahui Zhao , Jiaming Wu , Sunney Fotedar , Zhibin Li , Pan Liu

{"title":"Fleet availability analysis and prediction for shared e-scooters: An energy perspective","authors":"Jiahui Zhao , Jiaming Wu , Sunney Fotedar , Zhibin Li , Pan Liu","doi":"10.1016/j.trd.2024.104425","DOIUrl":"10.1016/j.trd.2024.104425","url":null,"abstract":"<div><div>E-scooters have become a prevalent mode of transportation in many cities. The availability of e-scooters is a crucial indicator of service quality but has not been sufficiently investigated. We propose a two-stage method for fleet availability analysis and prediction, considering stochastic demand and a new energy perspective. First, we developed a SpatioTemporalAttentionNet (STAN) model to predict trip OD. Second, we propose a Monte Carlo-based algorithm to match demand with existing e-scooters across spatiotemporal and energy dimensions. We conduct case studies using real-world data from Gothenburg, Sweden. The results indicate an average unavailability rate of 6.71%, nearly doubling that of the benchmark group, which uses a 20% SoC threshold for determining availability. This rate is significant considering the large fleet size and highlights the need to incorporate battery levels into fleet management. We further investigate the multifaceted impacts of land use and walking distance on availability dynamics.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1361920924003821/pdfft?md5=c55a3a816e01c619b607a1dd0e6d52db&pid=1-s2.0-S1361920924003821-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315870","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

Examining Determinants of Transport-Related Carbon Dioxide Emissions by Novel Super Learner Algorithm 利用新型超级学习算法研究与运输相关的二氧化碳排放决定因素

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2024-09-21 DOI: 10.1016/j.trd.2024.104429

Mustafa Tevfik Kartal , Ugur Korkut Pata , Özer Depren

{"title":"Examining Determinants of Transport-Related Carbon Dioxide Emissions by Novel Super Learner Algorithm","authors":"Mustafa Tevfik Kartal , Ugur Korkut Pata , Özer Depren","doi":"10.1016/j.trd.2024.104429","DOIUrl":"10.1016/j.trd.2024.104429","url":null,"abstract":"<div><p>Combating carbon dioxide (CO<sub>2</sub>) emissions across sectors becomes inevitable due to negative impacts. The transport sector takes place among the most important sectors. Accordingly, the study examines transport-related CO<sub>2</sub> (TCO<sub>2</sub>) emissions in the top four emitting countries (namely, the United States, Canada, Saudi Arabia, & Australia) by considering six explanatory variables, using data from 1990/Q1 to 2020/Q4, and performing an artificial intelligence approach. The outcomes show fresh insights that (i) super learner (SL) algorithm overwhelms other machine-learning algorithms in terms of model performance; (ii) energy intensity has an increasing impact on TCO<sub>2</sub> emissions, whereas others (e.g., financial development, income, globalization, oil use, & urbanization) have a mixed impact across countries; (iii) the influential variables have some critical thresholds, where the power of impacts differentiate across these limits. Hence, the SL algorithm presents robust outcomes for TCO<sub>2</sub> emissions. Accordingly, a set of policy endeavors for the countries examined are also discussed.</p></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274277","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

Understanding how extreme heat impacts human activity-mobility and time use patterns 了解极端高温如何影响人类活动--流动性和时间使用模式

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2024-09-21 DOI: 10.1016/j.trd.2024.104431

Irfan Batur , Victor O. Alhassan , Mikhail V. Chester , Steven E. Polzin , Cynthia Chen , Chandra R. Bhat , Ram M. Pendyala

{"title":"Understanding how extreme heat impacts human activity-mobility and time use patterns","authors":"Irfan Batur , Victor O. Alhassan , Mikhail V. Chester , Steven E. Polzin , Cynthia Chen , Chandra R. Bhat , Ram M. Pendyala","doi":"10.1016/j.trd.2024.104431","DOIUrl":"10.1016/j.trd.2024.104431","url":null,"abstract":"<div><p>There is growing interest in understanding the interaction between weather and transportation and the ability of communities and the nation’s infrastructure to withstand extreme conditions and events. This study aims to provide detailed insights on how people adjust and change their activity-travel and time use behaviors in the face of extreme heat conditions.By leveraging time use records integrated with weather data, the study compares activity-mobility patterns between extreme heat days and non-extreme days. A series of models are estimated to understand the impact of extreme heat even after controlling for other variables. The findings reveal that heat significantly impacts time use and activity-mobility patterns, with some groups exhibiting potentially greater vulnerability arising from the inability to adapt sufficiently to extreme heat. Designing dense, shaded urban environments, declaring heat days to facilitate indoor stays, and providing transportation vouchers for vulnerable populations can help mitigate the ill-effects of extreme heat.</p></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274275","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

Taking the wheel: Systematic review of reviews of policies driving BEV adoption 掌握方向盘：对推动采用新能源汽车的政策进行系统回顾

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2024-09-21 DOI: 10.1016/j.trd.2024.104424

V. Anilan , Akshay Vij

{"title":"Taking the wheel: Systematic review of reviews of policies driving BEV adoption","authors":"V. Anilan , Akshay Vij","doi":"10.1016/j.trd.2024.104424","DOIUrl":"10.1016/j.trd.2024.104424","url":null,"abstract":"<div><p>This study is motivated by the need to understand what the most appropriate policies are to promote battery electric vehicles (BEVs) in various countries around the world. A systematic review of reviews on policies promoting BEVs was conducted to synthesise knowledge on the relative effectiveness of various policies. This study addresses three limitations in existing research on policies to promote electric vehicles (EVs). Firstly, it disentangles findings for BEVs from that of other EVs. Secondly, it examines the relative effectiveness of these policies to find optimal policy mixes. Finally, it compares policy effectiveness across nations at various stages of economic development and EV adoption. Purchase subsidies and tax incentives were found to be highly effective policies to kickstart EV adoption but may not be as effective at later stages of the EV adoption and may not be an affordable policy for all countries. Some countries that led the way with such subsidies and tax incentives have now begun curtailing or ceasing them, and contrary to earlier reviews, without serious adverse effect on adoption rates. Policies supporting public charging infrastructure are crucial enablers that complement EV purchase subsidies but are important even without purchase subsidies in place. High-occupancy vehicle (HOV) lane access and toll waivers are the next most effective demand side policies, but only in cities where such incentives make sense and cents for consumers. Supply side policies, such as zero-emission vehicle (ZEV) mandates and vehicle emissions standards, must match or precede demand side policies to avoid bottlenecks and effectively drive uptake of BEVs in the earliest stage of adoption.</p></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274276","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

Impact of vehicle-to-everything connectivity on fuel economy 车联网对燃油经济性的影响

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2024-09-20 DOI: 10.1016/j.trd.2024.104423

Eric Fong, Blake Lane, Scott Samuelsen

引用次数: 0

Risk substance identification of asphalt VOCs integrating machine learning and network pharmacology 结合机器学习和网络药理学识别沥青挥发性有机化合物的风险物质

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2024-09-20 DOI: 10.1016/j.trd.2024.104434

Lei Ge , Jue Li , Ziyang Lin , Xinqiang Zhang , Yongsheng Yao , Gang Cheng , Yifa Jiang

{"title":"Risk substance identification of asphalt VOCs integrating machine learning and network pharmacology","authors":"Lei Ge , Jue Li , Ziyang Lin , Xinqiang Zhang , Yongsheng Yao , Gang Cheng , Yifa Jiang","doi":"10.1016/j.trd.2024.104434","DOIUrl":"10.1016/j.trd.2024.104434","url":null,"abstract":"<div><p>Asphalt releases volatile organic compounds (VOCs) during paving processes, posing risks to workers and the environment. The complex composition of asphalt and the evolving of VOCs present challenges in accurately assessing their potential environmental and health impacts using traditional experimental approaches. This study aimed to develop a robust computational framework integrating machine learning and network pharmacology to predict the risks from the asphalt VOCs. The results show that the MACCS+XGBoost model achieved the highest predictive performance, with an accuracy of 0.85, balanced accuracy of 0.84, sensitivity of 0.83, specificity of 0.84, and F1-score of 0.84 in the external validation. The network pharmacology analysis revealed that the identified VOCs with reproductive toxicity potential may disrupt key processes such as spermatogenesis, ovarian function, and hormonal regulation, providing mechanistic insights into their potential impacts. This advancement supports a proactive approach to environmental protection and fosters the transition towards a more sustainable, low-carbon transportation.</p></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274278","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

Evaluation of urban transportation carbon footprint − Artificial intelligence based solution 城市交通碳足迹评估--基于人工智能的解决方案

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2024-09-19 DOI: 10.1016/j.trd.2024.104406

Huan Wang , Xinyu Wang , Yuanxing Yin , Xiaojun Deng , Muhammad Umair

{"title":"Evaluation of urban transportation carbon footprint − Artificial intelligence based solution","authors":"Huan Wang , Xinyu Wang , Yuanxing Yin , Xiaojun Deng , Muhammad Umair","doi":"10.1016/j.trd.2024.104406","DOIUrl":"10.1016/j.trd.2024.104406","url":null,"abstract":"<div><p>This research uses three machine learning algorithms to predict transport-related CO₂ emissions, considering transport-related factors and socioeconomic aspects. We analyze the top 30 countries that produce the highest transport-related global CO₂ emissions, split evenly between Tier 1 and 2. Tier 1 comprises the five leading nations that produce 61% of the world’s CO₂ emissions, while Tier 2 comprises the subsequent twenty-five nations that produce 35% of the global CO₂ emissions. We assess the efficacy of our model by using four statistical measures (R2, MAE, rRMSE, and MAPE) in a four-fold cross-validation procedure. The Gradient-Boosted Regression (GBR) machine learning model, which incorporates a combination of economic and transportation factors, outperforms the other two machine learning approaches (Support Vector Machine and Ordinary Less Square). Our findings indicate that among Tier 1 and Tier 2 countries, socioeconomic factors like population and GDP are more influential on the models than transportation-related factors.</p></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274289","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

Real-time logistics transport emission monitoring-Integrating artificial intelligence and internet of things 实时物流运输排放监控--人工智能与物联网的结合

IF 7.3 1区工程技术

Transportation Research Part D-transport and Environment Pub Date : 2024-09-19 DOI: 10.1016/j.trd.2024.104426

Yuanxing Yin , Huan Wang , Xiaojun Deng

{"title":"Real-time logistics transport emission monitoring-Integrating artificial intelligence and internet of things","authors":"Yuanxing Yin , Huan Wang , Xiaojun Deng","doi":"10.1016/j.trd.2024.104426","DOIUrl":"10.1016/j.trd.2024.104426","url":null,"abstract":"<div><p>The lack of a globally recognized measurement technique combined with a limited ability to comprehend the actual level of GHG emissions in intricate logistics operations causes significant obstacles for firms in assessing the magnitude of their environmental footprint. Nevertheless, linking, upkeeping, and managing gas detectors on mobile vehicles under varying road and weather circumstances present an expensive solution for predicting GHG emissions. This article presents the development and evaluation of a reliable and accurate real-time technique for capturing GHG emissions using the Internet of Things (IoT) and Artificial Intelligence (AI). The findings indicate that the integration of gradient-boosting models (LightGBM, xGBoost, and gradient-boosting decision trees) via ensemble learning enhances the precision of CO2 emission predictions. The weighted ensemble method attains an RMSE of 1.8625, surpassing the performance of individual models. Visualizations validated a robust correlation between anticipated and actual CO2 concentrations, illustrating the model’s precision and negligible prediction errors.</p></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":null,"pages":null},"PeriodicalIF":7.3,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274274","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