This study explores how perceived streetscape quality and bike lane types—striped and protected—are associated with urban cycling behavior. Using computer vision technology to analyze street view images from Berlin, Germany, we assessed visual safety scores and their association with cycling trips. Our findings reveal that both perceived safety and bike lanes significantly enhance cycling activity; however, the interplay between the two varies by the type of bike lanes. Striped bike lanes are more effective than protected bike lanes on streets perceived as safe, while protected bike lanes provide greater benefits in visually unsafe areas compared to striped bike lanes. These results imply that by enhancing the visual appeal and safety of streetscapes alongside bike lane installations, cities can promote active transportation, fostering more sustainable, healthy, and vibrant urban environments.
{"title":"Perceived streetscape quality and bike lane effectiveness: a computer vision approach","authors":"Uijeong Hwang , Patricia L. Mokhtarian , Bon Woo Koo , Subhrajit Guhathakurta","doi":"10.1016/j.trd.2026.105248","DOIUrl":"10.1016/j.trd.2026.105248","url":null,"abstract":"<div><div>This study explores how perceived streetscape quality and bike lane types—striped and protected—are associated with urban cycling behavior. Using computer vision technology to analyze street view images from Berlin, Germany, we assessed visual safety scores and their association with cycling trips. Our findings reveal that both perceived safety and bike lanes significantly enhance cycling activity; however, the interplay between the two varies by the type of bike lanes. Striped bike lanes are more effective than protected bike lanes on streets perceived as safe, while protected bike lanes provide greater benefits in visually unsafe areas compared to striped bike lanes. These results imply that by enhancing the visual appeal and safety of streetscapes alongside bike lane installations, cities can promote active transportation, fostering more sustainable, healthy, and vibrant urban environments.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"154 ","pages":"Article 105248"},"PeriodicalIF":7.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146070872","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}
Pub Date : 2026-04-01Epub Date: 2026-01-09DOI: 10.1016/j.trd.2025.105195
Takuma Oda , Yuji Yoshimura
As climate extremes intensify, outdoor exposure in walkable cities becomes costly, increasing the reliance on door-to-door transportation. Using a multi-year dataset of app-based taxi trips from five Japanese metropolitan areas (2022–2024), we quantify behavioral adaptation to heat and rain. Above 27°C, ride-hailing demand increases near-linearly to 38% at 37°C, a pattern observed across all cities. This demand is strongest among infrequent users and for short-distance trips, particularly in high-density areas with poor transit access for first/last-mile connections. Hourly rainfall elicits a nearly identical response pattern, suggesting a general mobility adaptation mechanism. By translating the demand elasticity of short trips by infrequent users into a welfare metric, we map hidden “heat-mobility stress” hotspots around major rail hubs. Our findings show taxis are crucial buffers for urban mobility in extreme weather, with demand varying by user frequency, trip distance, and transit accessibility.
{"title":"Heat-driven taxi demand reveals hidden mobility stress in walkable cities","authors":"Takuma Oda , Yuji Yoshimura","doi":"10.1016/j.trd.2025.105195","DOIUrl":"10.1016/j.trd.2025.105195","url":null,"abstract":"<div><div>As climate extremes intensify, outdoor exposure in walkable cities becomes costly, increasing the reliance on door-to-door transportation. Using a multi-year dataset of app-based taxi trips from five Japanese metropolitan areas (2022–2024), we quantify behavioral adaptation to heat and rain. Above 27°C, ride-hailing demand increases near-linearly to 38% at 37°C, a pattern observed across all cities. This demand is strongest among infrequent users and for short-distance trips, particularly in high-density areas with poor transit access for first/last-mile connections. Hourly rainfall elicits a nearly identical response pattern, suggesting a general mobility adaptation mechanism. By translating the demand elasticity of short trips by infrequent users into a welfare metric, we map hidden “heat-mobility stress” hotspots around major rail hubs. Our findings show taxis are crucial buffers for urban mobility in extreme weather, with demand varying by user frequency, trip distance, and transit accessibility.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"153 ","pages":"Article 105195"},"PeriodicalIF":7.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928581","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}
Pub Date : 2026-04-01Epub Date: 2026-01-06DOI: 10.1016/j.trd.2026.105208
Jin Li , Wentao He , He Zhang , Hao Shi , Huailei Cheng , Lijun Sun
This study quantifies competing climate-change effects, temperature rise versus reduced freezing, on pavement networks using over 35 years of records from more than 1,100 sections. We combine explainable machine learning (ML) with Monte Carlo simulation to propagate global climate model (GCM) projections to future infrastructure impacts, considering the two-layer uncertainty from climate ensemble and ML residuals. Results reveal substantial inter-GCM model differences and occasional opposing trends, underscoring climate projection uncertainty. Trained ML models accurately predict long-term pavement performance; the freezing index and air temperature are the two dominant drivers. Reduced future freezing tends to extend service life, partially offsetting warming’s negative effects. Thus, climate change does not always accelerate pavement deterioration: in some regions (notably wet, freeze-prone zones) and for some time horizons or scenarios, net effects can be neutral or beneficial. In wet, freeze zones, pavement service life is being extended in nearly 65 % simulations under SSP585 by 2050–2060, whereas dry, freeze counterparts only show a figure of around 35 %. These findings indicate that pavement resilience assessments should consider both warming and changing freeze–thaw regimes rather than temperature alone under climate uncertainty and inform local adaptation decisions practically.
{"title":"Quantifying the counteracting impacts of climate change on large-scale pavement infrastructure serviceability","authors":"Jin Li , Wentao He , He Zhang , Hao Shi , Huailei Cheng , Lijun Sun","doi":"10.1016/j.trd.2026.105208","DOIUrl":"10.1016/j.trd.2026.105208","url":null,"abstract":"<div><div>This study quantifies competing climate-change effects, temperature rise versus reduced freezing, on pavement networks using over 35 years of records from more than 1,100 sections. We combine explainable machine learning (ML) with Monte Carlo simulation to propagate global climate model (GCM) projections to future infrastructure impacts, considering the two-layer uncertainty from climate ensemble and ML residuals. Results reveal substantial inter-GCM model differences and occasional opposing trends, underscoring climate projection uncertainty. Trained ML models accurately predict long-term pavement performance; the freezing index and air temperature are the two dominant drivers. Reduced future freezing tends to extend service life, partially offsetting warming’s negative effects. Thus, climate change does not always accelerate pavement deterioration: in some regions (notably wet, freeze-prone zones) and for some time horizons or scenarios, net effects can be neutral or beneficial. In wet, freeze zones, pavement service life is being extended in nearly 65 % simulations under SSP585 by 2050–2060, whereas dry, freeze counterparts only show a figure of around 35 %. These findings indicate that pavement resilience assessments should consider both warming and changing freeze–thaw regimes rather than temperature alone under climate uncertainty and inform local adaptation decisions practically.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"153 ","pages":"Article 105208"},"PeriodicalIF":7.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928583","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}
Road network expansion is a key factor driving fragmentation of wildlife habitats and threatening the biodiversity of national parks. Ecological corridors identification and wildlife crossing site selection based on MaxEnt and MCR model are an innovative pathway to coordinate the conflict. Taking Hainan Tropical Rainforest National Park and its circle road as a case, 145 ecological habitat sources of terrestrial, arboreal and amphibious reptile animals were identified, 298 ecological corridors were simulated using Linkage Mapper, and 585 conflict points between road and ecological corridor are identified with verification. Based on distance thresholds and species-specific behaviors, 274 wildlife crossing sites of terrestrial, arboreal and amphibian crossing were laid out along road with 1.7 km spacing, and 4 kind of wildlife crossing were designed according to animal habits. This study provides a theoretical framework for the combination of transportation network with ecological protection with wildlife crossings to improve the biodiversity of nature reserves.
{"title":"Road and eco-corridor conflict mitigation through multi-species wildlife crossings around national park","authors":"Yuting Peng , Hongwei Zhang , Gaoru Zhu , Xing Yang , Xueyan Zhao , Huaping Liang","doi":"10.1016/j.trd.2026.105235","DOIUrl":"10.1016/j.trd.2026.105235","url":null,"abstract":"<div><div>Road network expansion is a key factor driving fragmentation of wildlife habitats and threatening the biodiversity of national parks. Ecological corridors identification and wildlife crossing site selection based on MaxEnt and MCR model are an innovative pathway to coordinate the conflict. Taking Hainan Tropical Rainforest National Park and its circle road as a case, 145 ecological habitat sources of terrestrial, arboreal and amphibious reptile animals were identified, 298 ecological corridors were simulated using Linkage Mapper, and 585 conflict points between road and ecological corridor are identified with verification. Based on distance thresholds and species-specific behaviors, 274 wildlife crossing sites of terrestrial, arboreal and amphibian crossing were laid out along road with 1.7 km spacing, and 4 kind of wildlife crossing were designed according to animal habits. This study provides a theoretical framework for the combination of transportation network with ecological protection with wildlife crossings to improve the biodiversity of nature reserves.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"153 ","pages":"Article 105235"},"PeriodicalIF":7.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038814","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}
Pub Date : 2026-04-01Epub Date: 2026-01-24DOI: 10.1016/j.trd.2026.105236
Quanxu Zhou, Mengying Huang, Haoyu Tang, Jiapeng Cheng, Ji Wu
As urban transportation electrification advances, range anxiety has become a key concern for electric vehicles (EVs). Battery swapping stations (BSS) offer a fast and efficient solution, yet traditional scheduling methods struggle to balance profitability with operational risk in dynamic environments. To address this, we propose a data-driven optimization method. First, historical data is used to derive optimal operational decisions, and an EV battery swapping demand forecasting model is built. Based on the forecasted demand and historical strategies, a long short-term memory network predicts the BSS’s overall charging and discharging power. A double deep Q-network is then employed to allocate this power to individual batteries, ensuring timely swaps. Validation using real operational data from Chengdu, China, shows the proposed method effectively meets battery swapping demand, enhances scheduling efficiency and station profitability, and reduces peak loads and power fluctuations, demonstrating the potential for practical application in managing smart EV infrastructure.
{"title":"Profit-aware battery swapping station energy scheduling via hybrid hierarchical deep reinforcement learning","authors":"Quanxu Zhou, Mengying Huang, Haoyu Tang, Jiapeng Cheng, Ji Wu","doi":"10.1016/j.trd.2026.105236","DOIUrl":"10.1016/j.trd.2026.105236","url":null,"abstract":"<div><div>As urban transportation electrification advances, range anxiety has become a key concern for electric vehicles (EVs). Battery swapping stations (BSS) offer a fast and efficient solution, yet traditional scheduling methods struggle to balance profitability with operational risk in dynamic environments. To address this, we propose a data-driven optimization method. First, historical data is used to derive optimal operational decisions, and an EV battery swapping demand forecasting model is built. Based on the forecasted demand and historical strategies, a long short-term memory network predicts the BSS’s overall charging and discharging power. A double deep Q-network is then employed to allocate this power to individual batteries, ensuring timely swaps. Validation using real operational data from Chengdu, China, shows the proposed method effectively meets battery swapping demand, enhances scheduling efficiency and station profitability, and reduces peak loads and power fluctuations, demonstrating the potential for practical application in managing smart EV infrastructure.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"153 ","pages":"Article 105236"},"PeriodicalIF":7.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038813","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}
Pub Date : 2026-04-01Epub Date: 2026-01-17DOI: 10.1016/j.trd.2025.105170
Connor R. Forsythe , Kenneth T. Gillingham , Jeremy J. Michalek , Kate S. Whitefoot
Pickup trucks comprise the last U.S. light-duty vehicle segment to produce modern electric vehicle offerings, and they face unique engineering, economic and consumer acceptance challenges. To estimate adoption potential under engineering and economic projections, we conduct a discrete choice experiment with 534 U.S. pickup-truck buyers. We find a large majority (74%) of pickup truck buyers belong to latent classes that prefer or are indifferent to electric trucks when they offer comparable price, operating cost, range, towing and payload capacity with fast-charge capabilities. However, 26% are strongly opposed under any plausible vehicle technology trajectory. Price and range are the vehicle attributes that pose the largest barriers to increased adoption for most pickup truck buyers today. If electric pickup trucks achieve National Academies’ 2030 cost and range projections and are as widely available as conventional pickup trucks, our simulations suggest the majority of new U.S. pickup truck choices could be electric.
{"title":"Will pickup-truck buyers go electric?","authors":"Connor R. Forsythe , Kenneth T. Gillingham , Jeremy J. Michalek , Kate S. Whitefoot","doi":"10.1016/j.trd.2025.105170","DOIUrl":"10.1016/j.trd.2025.105170","url":null,"abstract":"<div><div>Pickup trucks comprise the last U.S. light-duty vehicle segment to produce modern electric vehicle offerings, and they face unique engineering, economic and consumer acceptance challenges. To estimate adoption potential under engineering and economic projections, we conduct a discrete choice experiment with 534 U.S. pickup-truck buyers. We find a large majority (74%) of pickup truck buyers belong to latent classes that prefer or are indifferent to electric trucks when they offer comparable price, operating cost, range, towing and payload capacity with fast-charge capabilities. However, 26% are strongly opposed under any plausible vehicle technology trajectory. Price and range are the vehicle attributes that pose the largest barriers to increased adoption for most pickup truck buyers today. If electric pickup trucks achieve National Academies’ 2030 cost and range projections and are as widely available as conventional pickup trucks, our simulations suggest the majority of new U.S. pickup truck choices could be electric.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"153 ","pages":"Article 105170"},"PeriodicalIF":7.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979349","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}
Pub Date : 2026-04-01Epub Date: 2026-01-28DOI: 10.1016/j.trd.2026.105246
Liyang Huang , Liyuan Zhao , Zhi-Chun Li
Urban logistics have shifted from a “goods-to-store” to a “goods-to-residents” paradigm, resulting in the proliferation of small logistics facilities such as micro-hubs and pick-up points in urban areas. As these facilities are increasingly embedded within urban functional spaces, understanding the driving factors of their spatial layout has become ever more crucial. This study examines the spatial layout characteristics and influencing factors of small logistics facilities in the Wuhan Metropolitan Development Area between 2010 and 2018. A Gradient Boosting Decision Tree (GBDT) model is employed to analyze the nonlinear relationships and threshold effects of urban spatial variables on facility layout. The results indicate that land price exerts the strongest influence on spatial distribution, followed by transportation accessibility and demand-related variables in 2010 and 2018, respectively. Based on these findings, a decision-support tool is developed to optimize facility placement, offering policy-relevant insights for sustainable urban logistics planning from a supply–demand perspective.
{"title":"Effects of functional spaces on small logistics facilities: machine learning-based decision-support tool","authors":"Liyang Huang , Liyuan Zhao , Zhi-Chun Li","doi":"10.1016/j.trd.2026.105246","DOIUrl":"10.1016/j.trd.2026.105246","url":null,"abstract":"<div><div>Urban logistics have shifted from a “goods-to-store” to a “goods-to-residents” paradigm, resulting in the proliferation of small logistics facilities such as micro-hubs and pick-up points in urban areas. As these facilities are increasingly embedded within urban functional spaces, understanding the driving factors of their spatial layout has become ever more crucial. This study examines the spatial layout characteristics and influencing factors of small logistics facilities in the Wuhan Metropolitan Development Area between 2010 and 2018. A Gradient Boosting Decision Tree (GBDT) model is employed to analyze the nonlinear relationships and threshold effects of urban spatial variables on facility layout. The results indicate that land price exerts the strongest influence on spatial distribution, followed by transportation accessibility and demand-related variables in 2010 and 2018, respectively. Based on these findings, a decision-support tool is developed to optimize facility placement, offering policy-relevant insights for sustainable urban logistics planning from a supply–demand perspective.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"153 ","pages":"Article 105246"},"PeriodicalIF":7.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072514","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}
Pub Date : 2026-04-01Epub Date: 2026-01-28DOI: 10.1016/j.trd.2025.105168
Jiayou Lei , Mingwei He , Min He , Zhuangbin Shi , Yueren He , Yang Liu , Huimin Qian
Understanding intermodal transit trip patterns is essential for advancing bus-metro integration. However, most existing studies focus on aggregate transit ridership, while empirical evidence on the characteristics of intermodal transit trips and the effects of the built environment (BE) on them remains limited. To address this gap, this study proposes a probabilistic topic modeling-artificial neural network (ANN) framework to identify representative intermodal transit trip patterns and examine their interactions with BE, using one week of large-scale smart card data from Beijing, China. A multidimensional latent Dirichlet allocation (LDA) model extracts grid-level patterns, revealing five spatiotemporal profiles with probabilistic interpretations. A multi-input, multi-output ANN, interpreted via SHapley Additive exPlanations (SHAP), uncovers complex nonlinear relationships and threshold effects between BE and trip pattern distributions. Results demonstrate that BE influences trip pattern distributions in ways distinct from its effects on overall ridership, providing new insights for coordinated bus-metro development and informing targeted integration strategies.
{"title":"Quantifying built environment effects on intermodal transit trip patterns at grid level","authors":"Jiayou Lei , Mingwei He , Min He , Zhuangbin Shi , Yueren He , Yang Liu , Huimin Qian","doi":"10.1016/j.trd.2025.105168","DOIUrl":"10.1016/j.trd.2025.105168","url":null,"abstract":"<div><div>Understanding intermodal transit trip patterns is essential for advancing bus-metro integration. However, most existing studies focus on aggregate transit ridership, while empirical evidence on the characteristics of intermodal transit trips and the effects of the built environment (BE) on them remains limited. To address this gap, this study proposes a probabilistic topic modeling-artificial neural network (ANN) framework to identify representative intermodal transit trip patterns and examine their interactions with BE, using one week of large-scale smart card data from Beijing, China. A multidimensional latent Dirichlet allocation (LDA) model extracts grid-level patterns, revealing five spatiotemporal profiles with probabilistic interpretations. A multi-input, multi-output ANN, interpreted via SHapley Additive exPlanations (SHAP), uncovers complex nonlinear relationships and threshold effects between BE and trip pattern distributions. Results demonstrate that BE influences trip pattern distributions in ways distinct from its effects on overall ridership, providing new insights for coordinated bus-metro development and informing targeted integration strategies.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"153 ","pages":"Article 105168"},"PeriodicalIF":7.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071722","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}
Pub Date : 2026-04-01Epub Date: 2026-01-12DOI: 10.1016/j.trd.2026.105222
Yang Wang, Jin Li
High-speed railway (HSR) is a sustainable transportation infrastructure, yet its comprehensive impacts on air quality remain unclear. This study examines the effects of HSR on six key air pollutants (i.e., PM2.5, PM10, NO2, O3, SO2, and CO) across 283 prefecture-level cities in China from 2008 to 2018. The results indicate that: (1) HSR has heterogeneous impacts on different pollutants. The marginal benefits for most pollutants strengthen over time, although the magnitude and direction of impacts vary considerably by pollutant type; (2) Further analysis identifies distinct pathways through which HSR affects air quality: particulate matter reduction occurs primarily through industrial transfer and upgrading, whereas NO2 mitigation results from the substitution of road and air transport with rail services; (3) The relocation of pollution-intensive enterprises driven by HSR development may offset potential environmental gains in less developed regions. These findings offer crucial insights into the multifaceted environmental impacts of HSR.
{"title":"Unveiling the spatial-temporal heterogeneity of high-speed railway’s impacts on multiple air pollutants","authors":"Yang Wang, Jin Li","doi":"10.1016/j.trd.2026.105222","DOIUrl":"10.1016/j.trd.2026.105222","url":null,"abstract":"<div><div>High-speed railway (HSR) is a sustainable transportation infrastructure, yet its comprehensive impacts on air quality remain unclear. This study examines the effects of HSR on six key air pollutants (i.e., PM<sub>2.5</sub>, PM<sub>10</sub>, NO<sub>2</sub>, O<sub>3</sub>, SO<sub>2,</sub> and CO) across 283 prefecture-level cities in China from 2008 to 2018. The results indicate that: (1) HSR has heterogeneous impacts on different pollutants. The marginal benefits for most pollutants strengthen over time, although the magnitude and direction of impacts vary considerably by pollutant type; (2) Further analysis identifies distinct pathways through which HSR affects air quality: particulate matter reduction occurs primarily through industrial transfer and upgrading, whereas NO<sub>2</sub> mitigation results from the substitution of road and air transport with rail services; (3) The relocation of pollution-intensive enterprises driven by HSR development may offset potential environmental gains in less developed regions. These findings offer crucial insights into the multifaceted environmental impacts of HSR.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"153 ","pages":"Article 105222"},"PeriodicalIF":7.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962594","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}
Pub Date : 2026-04-01Epub Date: 2025-12-30DOI: 10.1016/j.trd.2025.105179
Younghwi Kim , Jaehoon Lee , Hyerim Bae , Sunghyun Sim
{"title":"Corrigendum to “How will arctic shipping emissions evolve? A spatiotemporal topology-aware transformer approach”. [Transp. Res. Part D Transp. Environ. 151 (2025) 105134]","authors":"Younghwi Kim , Jaehoon Lee , Hyerim Bae , Sunghyun Sim","doi":"10.1016/j.trd.2025.105179","DOIUrl":"10.1016/j.trd.2025.105179","url":null,"abstract":"","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"153 ","pages":"Article 105179"},"PeriodicalIF":7.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893783","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}
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