Transportation Research Part D-transport and Environment最新文献

英文中文

Real-time air quality prediction using traffic videos and machine learning

IF 7.3 1区工程技术 Q1 ENVIRONMENTAL STUDIES

Transportation Research Part D-transport and Environment

Pub Date : 2025-03-06 DOI: 10.1016/j.trd.2025.104688

Laura Deveer , Laura Minet

Machine learning techniques are yielding better results than traditional statistical techniques to estimate traffic-related air pollutant (TRAP) concentrations. However, required data inputs, particularly complex traffic data, are costly and rarely collected in real-time. This study leverages real-time object detection techniques to accurately predict TRAP concentrations by extracting traffic variables solely from videos. Fine particulate matter (PM_2.5), nitrogen dioxide (NO₂) and ozone (O₃) concentrations are recorded by low-cost sensors, with traffic data extracted using object detection and tracking algorithms. Extreme Gradient Boosting, random forest, and multilinear regression models are employed to predict concentrations across different predictor combinations. Our optimal models accurately predict PM_2.5, NO_2, and O₃ concentrations with R² values of 0.94, 0.95, and 0.92, respectively. This study demonstrates a cost-effective approach with high accuracies in predicting real-time TRAP using a low-cost and low-maintenance tool: a video camera. Cities could similarly track TRAP using traffic camera infrastructure without additional sensor deployment.

{"title":"Real-time air quality prediction using traffic videos and machine learning","authors":"Laura Deveer , Laura Minet","doi":"10.1016/j.trd.2025.104688","DOIUrl":"10.1016/j.trd.2025.104688","url":null,"abstract":"<div><div>Machine learning techniques are yielding better results than traditional statistical techniques to estimate traffic-related air pollutant (TRAP) concentrations. However, required data inputs, particularly complex traffic data, are costly and rarely collected in real-time. This study leverages real-time object detection techniques to accurately predict TRAP concentrations by extracting traffic variables solely from videos. Fine particulate matter (PM<sub>2.5</sub>), nitrogen dioxide (NO<sub>2</sub>) and ozone (O<sub>3</sub>) concentrations are recorded by low-cost sensors, with traffic data extracted using object detection and tracking algorithms. Extreme Gradient Boosting, random forest, and multilinear regression models are employed to predict concentrations across different predictor combinations. Our optimal models accurately predict PM<sub>2.5</sub>, NO<sub>2,</sub> and O<sub>3</sub> concentrations with R<sup>2</sup> values of 0.94, 0.95, and 0.92, respectively. This study demonstrates a cost-effective approach with high accuracies in predicting real-time TRAP using a low-cost and low-maintenance tool: a video camera. Cities could similarly track TRAP using traffic camera infrastructure without additional sensor deployment.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"142 ","pages":"Article 104688"},"PeriodicalIF":7.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552148","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

Analyzing disparities in app-hailed travel during extreme heat in New York City

IF 7.3 1区工程技术 Q1 ENVIRONMENTAL STUDIES

Transportation Research Part D-transport and Environment

Pub Date : 2025-03-04 DOI: 10.1016/j.trd.2025.104650

Mahtot Gebresselassie, Jeremy Michalek, Destenie Nock, Corey Harper

To understand extreme weather effects on travel behavior, we examine changes in New York City app-hailed travel during extreme-heat days and estimate whether such changes vary across low- and high-income neighborhoods. We use trip records for July 2019, when several heat-related messages were issued by the National Weather Services, and find that daily ridership was six to nine percent higher on days when heat messages were issued than on matching weekdays in the same month. Our fixed effects regression models estimate that for median income neighborhoods afternoon peak-hour ridership was 1.7 to 3.2 rides higher per 1,000 people during the heat events than on matching weekdays in the same month. This effect increased by an additional 0.5 to 0.6 rides per 1,000 people for every $10,000 increase in average neighborhood per capita income, suggesting that higher income travelers increased trip frequency at a higher rate than lower-income travelers.

引用次数: 0

A Copula-ECAC model for estimating aviation noise around airports

IF 7.3 1区工程技术 Q1 ENVIRONMENTAL STUDIES

Transportation Research Part D-transport and Environment

Pub Date : 2025-03-03 DOI: 10.1016/j.trd.2025.104666

Wentao Guo , Weili Zeng , Yadong Zhou

The European Civil Aviation Conference (ECAC) noise estimation model is widely used for its simplicity and efficiency. However, its reliance on fixed thrust coefficients for thrust calculation limits the accuracy of noise estimation. This study presents an innovative Copula-ECAC model for estimating aviation noise around airports to address this limitation. The core of the proposed model lies in leveraging the Copula theory to construct joint distribution models of thrust coefficients for various aircraft types. By utilizing the robust capability of Copula functions to characterize dependency relationships, the model effectively captures and simulates the complex dynamic interactions between different thrust coefficients. Applied to Nanjing Lukou International Airport in China, the Copula-ECAC model demonstrates superior performance compared to mainstream ECAC models in terms of prediction accuracy and generalization ability. With a coefficient of determination consistently exceeding 0.90, the model’s reliability and practical value are fully validated.

引用次数: 0

The heterogeneity of travel mode choice behavior under unplanned metro service disruptions

IF 7.3 1区工程技术 Q1 ENVIRONMENTAL STUDIES

Transportation Research Part D-transport and Environment

Pub Date : 2025-02-28 DOI: 10.1016/j.trd.2025.104683

Qing-Chang Lu , Xin-Yu Zuo , Chao Chen , Zhi Dong , Peng-Cheng Xu , Jing Li

With the increasing frequency of unplanned service disruptions in metro system, understanding the travel mode choice behavior is essential for the improvement of passenger evacuation strategies and enhancement of metro system security. Individuals’ choice behavior may exhibit heterogeneity, particularly when faced with diverse activities and multimodal transport modes. Based on the survey data of Xi’an, China, an error component mixed logit model was developed to explore the decision-making mechanism of mode choices and heterogeneous preferences for multimodal transport modes. The results indicate that activity-dependent attributes significantly influence mode choices. Longer travel distance increases the likelihood of individuals continuing metro travel. Activity urgency level has a significant negative effect on waiting for service resumption, but positively influences the choice of taxis. Furthermore, significant heterogeneity is observed in waiting tolerance and preferences for intermodal transport modes. These findings provide insights for the emergency management of metro system under unplanned service disruptions.

{"title":"The heterogeneity of travel mode choice behavior under unplanned metro service disruptions","authors":"Qing-Chang Lu , Xin-Yu Zuo , Chao Chen , Zhi Dong , Peng-Cheng Xu , Jing Li","doi":"10.1016/j.trd.2025.104683","DOIUrl":"10.1016/j.trd.2025.104683","url":null,"abstract":"<div><div>With the increasing frequency of unplanned service disruptions in metro system, understanding the travel mode choice behavior is essential for the improvement of passenger evacuation strategies and enhancement of metro system security. Individuals’ choice behavior may exhibit heterogeneity, particularly when faced with diverse activities and multimodal transport modes. Based on the survey data of Xi’an, China, an error component mixed logit model was developed to explore the decision-making mechanism of mode choices and heterogeneous preferences for multimodal transport modes. The results indicate that activity-dependent attributes significantly influence mode choices. Longer travel distance increases the likelihood of individuals continuing metro travel. Activity urgency level has a significant negative effect on waiting for service resumption, but positively influences the choice of taxis. Furthermore, significant heterogeneity is observed in waiting tolerance and preferences for intermodal transport modes. These findings provide insights for the emergency management of metro system under unplanned service disruptions.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"142 ","pages":"Article 104683"},"PeriodicalIF":7.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521187","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

Promoting community resident support for private charging pile sharing: A micro survey

IF 7.3 1区工程技术 Q1 ENVIRONMENTAL STUDIES

Transportation Research Part D-transport and Environment

Pub Date : 2025-02-28 DOI: 10.1016/j.trd.2025.104675

Lei Shi , Meirong Guo , Xiaohui Lyu , Shanliang Li

The Chinese government promotes private charging pile sharing (PCPS) as a model of the sharing economy in the transport sector, but there are barriers to its widespread adoption. Through a survey with 2,578 responses and by employing an ordered probit model, we analyzed the factors influencing PCPS adoption. Our research shows that PCPS and urban public charging piles serve different charging needs: daily convenient slow charging and emergency rapid charging, respectively, precluding direct competition. Residents’ main concerns about adopting PCPS include unfamiliar vehicles entering the community, safety risks, and unclear liability in accidents. Understanding of policy boosts support for PCPS, and residents residing in relatively older neighborhoods with self-owned houses exhibit more pronounced support. Our findings suggest that policy awareness promotion, enhanced management of unfamiliar vehicles by property companies, and targeted promotion to residents with self-owned houses but no parking spaces can effectively foster the development of the PCPS model.

{"title":"Promoting community resident support for private charging pile sharing: A micro survey","authors":"Lei Shi , Meirong Guo , Xiaohui Lyu , Shanliang Li","doi":"10.1016/j.trd.2025.104675","DOIUrl":"10.1016/j.trd.2025.104675","url":null,"abstract":"<div><div>The Chinese government promotes private charging pile sharing (PCPS) as a model of the sharing economy in the transport sector, but there are barriers to its widespread adoption. Through a survey with 2,578 responses and by employing an ordered probit model, we analyzed the factors influencing PCPS adoption. Our research shows that PCPS and urban public charging piles serve different charging needs: daily convenient slow charging and emergency rapid charging, respectively, precluding direct competition. Residents’ main concerns about adopting PCPS include unfamiliar vehicles entering the community, safety risks, and unclear liability in accidents. Understanding of policy boosts support for PCPS, and residents residing in relatively older neighborhoods with self-owned houses exhibit more pronounced support. Our findings suggest that policy awareness promotion, enhanced management of unfamiliar vehicles by property companies, and targeted promotion to residents with self-owned houses but no parking spaces can effectively foster the development of the PCPS model.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"142 ","pages":"Article 104675"},"PeriodicalIF":7.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521186","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

Quantifying carbon emissions in cold chain transport: A real-world data-driven approach

IF 7.3 1区工程技术 Q1 ENVIRONMENTAL STUDIES

Transportation Research Part D-transport and Environment

Pub Date : 2025-02-28 DOI: 10.1016/j.trd.2025.104679

Hung-Jui Lin , Pei-Ci Chen , Hsuan-Po Lin , I-Yun Lisa Hsieh

Sustainable urban development necessitates advanced low-carbon transportation strategies, particularly within the cold chain logistics sector, where transporting perishable goods significantly contributes to environmental emissions. This study addresses the gap in empirical research by deploying real-world data from 128 long-haul trips, provided by a logistics company, to develop a comprehensive bottom-up operational-level carbon emission model. Our model quantifies emissions through various operational phases—loading, unloading, and transport—capturing contributions from vehicle operation (driving and idling), refrigeration processes (including transmission, infiltration, and pre-cooling), and refrigerant leakage. It further assesses the impact of ambient temperature on emissions and examines the effectiveness of decarbonization strategies such as employing shore power for pre-cooling and adopting low-carbon refrigerants. Validated against actual fuel consumption with an impressive accuracy of −1.84%, our findings significantly advance green logistics practices, offering practical insights for a transition towards net-zero emissions and improving the sustainability of cold chain transportation systems.

{"title":"Quantifying carbon emissions in cold chain transport: A real-world data-driven approach","authors":"Hung-Jui Lin , Pei-Ci Chen , Hsuan-Po Lin , I-Yun Lisa Hsieh","doi":"10.1016/j.trd.2025.104679","DOIUrl":"10.1016/j.trd.2025.104679","url":null,"abstract":"<div><div>Sustainable urban development necessitates advanced low-carbon transportation strategies, particularly within the cold chain logistics sector, where transporting perishable goods significantly contributes to environmental emissions. This study addresses the gap in empirical research by deploying real-world data from 128 long-haul trips, provided by a logistics company, to develop a comprehensive bottom-up operational-level carbon emission model. Our model quantifies emissions through various operational phases—loading, unloading, and transport—capturing contributions from vehicle operation (driving and idling), refrigeration processes (including transmission, infiltration, and pre-cooling), and refrigerant leakage. It further assesses the impact of ambient temperature on emissions and examines the effectiveness of decarbonization strategies such as employing shore power for pre-cooling and adopting low-carbon refrigerants. Validated against actual fuel consumption with an impressive accuracy of −1.84%, our findings significantly advance green logistics practices, offering practical insights for a transition towards net-zero emissions and improving the sustainability of cold chain transportation systems.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"142 ","pages":"Article 104679"},"PeriodicalIF":7.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521185","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

Integrated optimization of charging infrastructure, electric bus scheduling and energy systems

IF 7.3 1区工程技术 Q1 ENVIRONMENTAL STUDIES

Transportation Research Part D-transport and Environment

Pub Date : 2025-02-28 DOI: 10.1016/j.trd.2025.104664

Arsalan Najafi , Kun Gao , Omkar Parishwad , Georgios Tsaousoglou , Sheng Jin , Wen Yi

The adoption of Battery Electric Buses (BEBs) in electric public transit systems presents a significant opportunity for advancing sustainable transportation. This study introduces a holistic framework for joint optimization of charging infrastructure, charging scheduling, and integration of renewable energy resources (RES), considering impacts on Power distribution network (PDN). To address the complex optimization, a decomposition approach is employed to linearize the problem and divide it into master and subproblems for efficient resolution. A case study in Skövde, Sweden demonstrates that the proposed methodology optimizes charging infrastructure deployment and scheduling to reduce the overall system costs. Meanwhile, high charging demand from BEBs in some periods to fulfil operation scheduling may result in violation of technical constraints of the PDN (more than 4%), without RES. The incorporation and optimization of RES with battery energy storage can cater to spatiotemporal charging demand of BEB while enhancing stability and safety of PDN.

{"title":"Integrated optimization of charging infrastructure, electric bus scheduling and energy systems","authors":"Arsalan Najafi , Kun Gao , Omkar Parishwad , Georgios Tsaousoglou , Sheng Jin , Wen Yi","doi":"10.1016/j.trd.2025.104664","DOIUrl":"10.1016/j.trd.2025.104664","url":null,"abstract":"<div><div>The adoption of Battery Electric Buses (BEBs) in electric public transit systems presents a significant opportunity for advancing sustainable transportation. This study introduces a holistic framework for joint optimization of charging infrastructure, charging scheduling, and integration of renewable energy resources (RES), considering impacts on Power distribution network (PDN). To address the complex optimization, a decomposition approach is employed to linearize the problem and divide it into master and subproblems for efficient resolution. A case study in Skövde, Sweden demonstrates that the proposed methodology optimizes charging infrastructure deployment and scheduling to reduce the overall system costs. Meanwhile, high charging demand from BEBs in some periods to fulfil operation scheduling may result in violation of technical constraints of the PDN (more than 4%), without RES. The incorporation and optimization of RES with battery energy storage can cater to spatiotemporal charging demand of BEB while enhancing stability and safety of PDN.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"141 ","pages":"Article 104664"},"PeriodicalIF":7.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510963","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

Assessment of full-process VOCs emissions of on-road vehicles considering individual parking behaviors

IF 7.3 1区工程技术 Q1 ENVIRONMENTAL STUDIES

Transportation Research Part D-transport and Environment

Pub Date : 2025-02-28 DOI: 10.1016/j.trd.2025.104678

Xinru Yang , Dawen Yao , Rui Xu , Yuzhuang Pian , Shikun Liu , Yonghong Liu

Vehicle-emitted volatile organic compounds (VOCs) significantly contribute to ozone (O₃) formation, affecting health and environment. Prior studies focused on exhaust emissions, often overlooking evaporative emissions due to a lack of detailed parking behaviors, underestimating total VOCs. Using automated vehicle identification (AVI) data from Xuancheng, this study developed a full-process VOCs emission model covering both exhaust and evaporative emissions. Findings show that 80 % of vehicles are parked for over 90 % of the day, with parking behaviors causing significant emissions variations among vehicles. Total VOCs emissions were 44.46 tons from April 15 to May 15, 2022. Specifically, exhaust, diurnal, running losses, and hot soak emissions were 59.2 %, 24.2 %, 14.9 %, and 1.7 %, respectively, with parking emissions at 25.9 %. During Labor Day, reduced traffic and increased parking raised daily VOCs emissions, with parking emissions rising to 33.4 %. This underscores the need to consider evaporative emissions and parking behaviors in emission management and pollution control.

{"title":"Assessment of full-process VOCs emissions of on-road vehicles considering individual parking behaviors","authors":"Xinru Yang , Dawen Yao , Rui Xu , Yuzhuang Pian , Shikun Liu , Yonghong Liu","doi":"10.1016/j.trd.2025.104678","DOIUrl":"10.1016/j.trd.2025.104678","url":null,"abstract":"<div><div>Vehicle-emitted volatile organic compounds (VOCs) significantly contribute to ozone (O<sub>3</sub>) formation, affecting health and environment. Prior studies focused on exhaust emissions, often overlooking evaporative emissions due to a lack of detailed parking behaviors, underestimating total VOCs. Using automated vehicle identification (AVI) data from Xuancheng, this study developed a full-process VOCs emission model covering both exhaust and evaporative emissions. Findings show that 80 % of vehicles are parked for over 90 % of the day, with parking behaviors causing significant emissions variations among vehicles. Total VOCs emissions were 44.46 tons from April 15 to May 15, 2022. Specifically, exhaust, diurnal, running losses, and hot soak emissions were 59.2 %, 24.2 %, 14.9 %, and 1.7 %, respectively, with parking emissions at 25.9 %. During Labor Day, reduced traffic and increased parking raised daily VOCs emissions, with parking emissions rising to 33.4 %. This underscores the need to consider evaporative emissions and parking behaviors in emission management and pollution control.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"142 ","pages":"Article 104678"},"PeriodicalIF":7.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511049","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-driving for connected automated hybrid electric vehicles in learning-enabled layered transportation systems

IF 7.3 1区工程技术 Q1 ENVIRONMENTAL STUDIES

Transportation Research Part D-transport and Environment

Pub Date : 2025-02-28 DOI: 10.1016/j.trd.2025.104677

Su Yan , Jiayi Fang , Chao Yang , Ruihu Chen , Hui Liu

Eco-driving strategies have the potential to enhance energy savings, safety, and transportation efficiency by optimizing vehicle interactions with dynamic traffic environments. This study addresses the challenge of balancing computational efficiency and optimization effectiveness amid the high-dimensional state and control variables driven by extensive traffic information. The novelty different from existing methods lies in developing an eco-driving strategy within a traffic information cyber–physical system. The cyber-layer maps simulated road segments for training vehicles equipped with the Proximal Policy Optimization (PPO) algorithm, enabling effective planning of economical speeds. During vehicle operation, the cyber-layer maps the real-time physical environment, providing a predictive state sequence for the vehicle’s adaptive equivalent fuel consumption minimization strategy. Then, optimizing the efficiency factor in a rolling manner further improves fuel economy. A comparative analysis with existing methods across different scenarios shows that the proposed strategy significantly improves fuel economy while ensuring real-time speed planning and reliable speed-tracking performance.

{"title":"Eco-driving for connected automated hybrid electric vehicles in learning-enabled layered transportation systems","authors":"Su Yan , Jiayi Fang , Chao Yang , Ruihu Chen , Hui Liu","doi":"10.1016/j.trd.2025.104677","DOIUrl":"10.1016/j.trd.2025.104677","url":null,"abstract":"<div><div>Eco-driving strategies have the potential to enhance energy savings, safety, and transportation efficiency by optimizing vehicle interactions with dynamic traffic environments. This study addresses the challenge of balancing computational efficiency and optimization effectiveness amid the high-dimensional state and control variables driven by extensive traffic information. The novelty different from existing methods lies in developing an eco-driving strategy within a traffic information cyber–physical system. The cyber-layer maps simulated road segments for training vehicles equipped with the Proximal Policy Optimization (PPO) algorithm, enabling effective planning of economical speeds. During vehicle operation, the cyber-layer maps the real-time physical environment, providing a predictive state sequence for the vehicle’s adaptive equivalent fuel consumption minimization strategy. Then, optimizing the efficiency factor in a rolling manner further improves fuel economy. A comparative analysis with existing methods across different scenarios shows that the proposed strategy significantly improves fuel economy while ensuring real-time speed planning and reliable speed-tracking performance.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"142 ","pages":"Article 104677"},"PeriodicalIF":7.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521188","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

Shaping greener mobility: Impact of urban greening structure on time-dependent bike-sharing usage

IF 7.3 1区工程技术 Q1 ENVIRONMENTAL STUDIES

Transportation Research Part D-transport and Environment

Pub Date : 2025-02-27 DOI: 10.1016/j.trd.2025.104657

Tianlin Zhang , Fengliang Tang , Yike Hu , Longhao Zhang , Yuanyuan Guo

Free-floating bike-sharing systems (FFBS), known for their convenience and low-carbon benefits, have become crucial for advancing green urban mobility and sustainable development. However, the specific impacts of urban greening structures on FFBS usage remain underexplored. This study focuses on Shenzhen’s core area, systematically evaluating the effects of greening structures on FFBS usage at different times by integrating street view images, remote sensing data, and interpretable machine learning models. Results indicate that daytime cycling is mainly influenced by trip purposes and urban morphology, while nighttime cycling depends more heavily on urban greening structures. Among greening factors, tree view index exhibits the most substantial nonlinear impact on cycling across all time periods. Factors like the bush view index, vegetation configuration ratios, and green space shape characteristics are more influential at nighttime. This study highlights the crucial role of location-specific greening optimization strategies in promoting sustainable green transportation development.

{"title":"Shaping greener mobility: Impact of urban greening structure on time-dependent bike-sharing usage","authors":"Tianlin Zhang , Fengliang Tang , Yike Hu , Longhao Zhang , Yuanyuan Guo","doi":"10.1016/j.trd.2025.104657","DOIUrl":"10.1016/j.trd.2025.104657","url":null,"abstract":"<div><div>Free-floating bike-sharing systems (FFBS), known for their convenience and low-carbon benefits, have become crucial for advancing green urban mobility and sustainable development. However, the specific impacts of urban greening structures on FFBS usage remain underexplored. This study focuses on Shenzhen’s core area, systematically evaluating the effects of greening structures on FFBS usage at different times by integrating street view images, remote sensing data, and interpretable machine learning models. Results indicate that daytime cycling is mainly influenced by trip purposes and urban morphology, while nighttime cycling depends more heavily on urban greening structures. Among greening factors, tree view index exhibits the most substantial nonlinear impact on cycling across all time periods. Factors like the bush view index, vegetation configuration ratios, and green space shape characteristics are more influential at nighttime. This study highlights the crucial role of location-specific greening optimization strategies in promoting sustainable green transportation development.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"141 ","pages":"Article 104657"},"PeriodicalIF":7.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509984","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

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