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Year: 2025

Incorporating equity into the cost-effectiveness evaluation of new mobility: A comparative analysis

Ashour, L., & Shen, Q. (2025). Incorporating equity into the cost-effectiveness evaluation of new mobility: A comparative analysis. Transportation Research. Part D, Transport and Environment, 147, Article 104959. https://doi.org/10.1016/j.trd.2025.104959

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Abstract

Public transportation in suburban areas faces challenges in providing efficient mobility. Transit Incorporating Mobility on Demand (TIMOD) services have emerged as a potential solution, yet equity considerations remain underexplored. This study incorporates equity into the cost-effectiveness evaluation of TIMOD services, analyzing two suburban areas in the Seattle metropolitan region where a TIMOD service is implemented. Using distributional cost-effectiveness analysis (DCEA), we assess the comparative costs of TIMOD, fixed-route transit, and drive-alone across different income groups and built environments. The study shows that although TIMOD services offer equity benefits for lower-income travelers, they are more equitable in high-density, low-income suburbs. In contrast, their cost-effectiveness is more limited in affluent, low-density areas. These insights highlight the importance of context-specific planning for TIMOD interventions and employ tools such as DCEA for transit agencies to prioritize the deployment of such services in areas where they can maximize social welfare and reduce transportation inequities.

Incorporating mobility-on-demand into public transit in suburban areas: A comparative cost-effectiveness evaluation

Cai, M., Ashour, L. A., Shen, Q., & Chen, C. (2025). Incorporating mobility-on-demand into public transit in suburban areas: A comparative cost-effectiveness evaluation. Transportation Research. Part D, Transport and Environment, 144, Article 104775. https://doi.org/10.1016/j.trd.2025.104775

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Abstract

Transit Incorporating Mobility-on-Demand (TIMOD) represents the public–private partnerships in which transit agencies incorporate MOD services to supplement fixed-route transit. This study evaluates the cost-effectiveness of TIMOD compared to buses, driving, and ride-hailing in suburban settings. For each alternative, it estimates the marginal costs for travelers, service providers, and transportation externalities, which constitute the marginal social cost. In the study cases, TIMOD is the least cost-effective option, with marginal social cost approximately 20% higher than TNCs and over three times higher than driving. For travelers, TIMOD costs more than driving but less than buses and ride-hailing when considering time value and fare. The cost of TIMOD declines as population density increases. Suburbs with less bus services and higher income residents benefit more from TIMOD, realizing greater reductions in time costs compared to buses. Transit agencies should explore alternative ways to improve mobility for disadvantaged suburban residents by offsetting driving costs and subsidizing TNCs fares.

Keywords

Transit Incorporating Mobility-On-Demand (TIMOD); Public transit; Transportation simulation; Suburban areas; Marginal social cost of travel

A bibliometrix-based scientometric-systematic analysis and visualization of the global outlook on post-occupancy evaluation of green building

Debrah, C., Chan, A. P. C., Darko, A., Akowuah, E., Amudjie, J., Asare, K. A. B., & Ghansah, F. A. (2025). A bibliometrix-based scientometric-systematic analysis and visualization of the global outlook on post-occupancy evaluation of green building. Building Research and Information : The International Journal of Research, Development and Demonstration, 1–17. https://doi.org/10.1080/09613218.2025.2521753.

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Abstract

To achieve sustainability goals, it has become increasingly important to conduct post-occupancy evaluation (POE) to assess and understand the actual performance of green buildings (GBs). However, there has been little effort to provide researchers with a systematic and scientometric analysis of the state of the POE-in-GB field. To address this gap, this study aims to review the field and identify major trends and gaps that can be addressed in future research. This paper combined several state-of-the-art tools (i.e. Bibliometrix R-tool, Python BibexPy, VOSviewer, and Gephi) for an extensive bibliometric analysis based on 251 publications identified from Scopus and Web of Science. Utilizing a theoretical framework of office productivity, 35 empirical POE-in-GB studies were selected for further qualitative-systematic analysis. The quantitative-bibliometric analysis revealed that POE-in-GB research hotspots include energy efficiency, occupant satisfaction, thermal comfort, IEQ, LEED, and sustainability. The qualitative-systematic analysis focused on the physical environment quality and load, behavioural environment and the POE protocols of POE-in-GB. Some future research directions proposed include: exploring socio-psychological factors in POE-in-GB, developing standardized protocols for POE-in-GB, aligning GB certifications with user satisfaction, and integrating technology and big data into POE-in-GB. This study provides insights to academics and practitioners working in the POE-in-GB domain.

Keywords

Built environment; building performance evaluation; literature review; sustainable building; sustainability

Enhancing urban building energy models with Vision Transformers: A Case study in material classification from Google street view

Liu, Y., & Abbasabadi, N. (2025). Enhancing urban building energy models with Vision Transformers: A Case study in material classification from Google street view. Energy and Buildings, 333, Article 115457. https://doi.org/10.1016/j.enbuild.2025.115457.

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Abstract

The growing urbanization and increased urban energy consumption highlight the need for energy use and greenhouse gas emissions reduction strategies. Urban Building Energy Modeling (UBEM) emerged as a valuable tool for managing and optimizing energy consumption at the neighborhood and city scales to support carbon reduction goals. However, the accuracy of the UBEM is often limited by the lack of large-scale building façade material dataset. This study introduces a new approach to enhance UBEM by integrating an automatic deep learning material classification pipeline. The pipeline leverages multiple views of Google Street View Images (SVIs) to extract building façade material information, utilizing two Swin Vision Transformer (ViT) models to capture both global and local features from the SVIs. The pipeline achieved a main material classification accuracy reached 97.08%, and the sub-category accuracy reached 91.56% in a multi-class classification task. As the first study to apply a deep learning model for material classification to enhance the UBEM framework, this work was tested on the University of Washington campus, which features diverse facade materials. The model demonstrated its effectiveness by achieving an overall accuracy increase of 11.4% in year-round total operational energy simulations. The scalability of this material classification pipeline enables a more accurate and cost-effective application of UBEM at broader urban scales.

Effects of pollution on ecologically and economically important organisms of the Salish Sea

Axworthy, J. B., Bates, E. H., Grosser, M. P., & Padilla-Gamiño, J. L. (2025). Effects of pollution on ecologically and economically important organisms of the Salish Sea. Marine Pollution Bulletin, 219, Article 118322. https://doi.org/10.1016/j.marpolbul.2025.118322.

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Abstract

Marine pollution threatens ecosystems, biodiversity, and human health, impacting species fitness, disrupting food webs, and degrading essential habitats. This review examines the effects of marine pollution on key species in the Salish Sea, a vital ecosystem supporting diverse wildlife, including endangered species, and local economies reliant on fishing, aquaculture, and tourism. In total, we synthesized 116 studies including chemical pollution (78), biological pollution (15), marine debris (15), and sound pollution (8). Research on marine chemical pollution has primarily focused on pollutants in fish (41), followed by studies on birds (11), mammals (7), and bivalves (7), then invertebrates (2). Future investigations should broaden species coverage, assess various life stages, and evaluate the impact of climate change on pollutant accumulation. Biological pollution, driven mainly by intentionally introduced species like farmed shellfish and salmon, threatens native species and can spread pathogens. There is a pressing need for research on the effects of fecal-borne pathogens on marine organisms and the influence of seagrass beds, fish farms, and sewage outfalls on pathogen dynamics. Marine debris, especially derelict fishing gear, negatively impacts local organisms, while the effects of tire reefs and microplastics remain poorly understood. Research should integrate laboratory and field assessments to analyze microplastic ingestion and improve detection technologies to inform conservation efforts. Noise pollution research has focused on marine mammals like killer whales, highlighting how sound pollution disrupts communication and behavior, which can indirectly alter food webs and community dynamics. Future studies should also encompass other marine species, including fish and invertebrates. Understanding pollution impacts is crucial for developing effective mitigation strategies, protecting marine life, and ensuring sustainable ocean resource management for future generations.

Understanding the financial health of community land trusts in the United States

Wang, R. (2025). Understanding the financial health of community land trusts in the United States. Journal of Urban Affairs, 1–21. https://doi.org/10.1080/07352166.2025.2554755.

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Abstract

This article examines the financial health of community land trusts (CLTs) in the United States. CLTs are nonprofit organizations that ensure long-term community assets and equitable land use, playing a critical role in community development. Despite the importance of financial health to their mission, little is known about their financial performance over time. Using Internal Revenue Service (IRS) data and survey responses, the article found that CLTs are among the financially top-performing community-based development organizations between 2012 and 2021. Further analysis found that CLTs’ financial performances vary based on organizational characteristics such as the organization’s age, location, CLT type, and the presence of shared equity units. The study highlights the need for conceptualizing multidimensional financial indicators that account for both internal and external factors, emphasizing the importance of strategic investments to support CLT’s long-term community-focused goals.

Keywords

Community land trust; Nonprofits; Financial health; Community development; United States

Evaluating the Evolution of Alternative Dispute Resolution in Construction Projects: A Systematic Review Using Content and Bibliometric Analysis

Muiruri, K. M., & Abdel Aziz, A. (2025). Evaluating the Evolution of Alternative Dispute Resolution in Construction Projects: A Systematic Review Using Content and Bibliometric Analysis. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 17(4). https://doi.org/10.1061/JLADAH.LADR-1370.

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Abstract

The construction industry is inherently complex. It involves multiple stakeholders, complex contracts, and significant financial investments. This complexity often leads to disputes, resulting in costly delays and project disruptions. Alternative dispute resolution (ADR) methods have emerged as crucial mechanisms for managing and resolving conflicts in construction projects, offering more efficient and less adversarial solutions than traditional litigation. This paper presents a systematic literature review of existing research on the application of ADR in construction project delivery. The review spans three decades to capture the adoption and impact of the 1987 update to the AIA A201 General Conditions 14th Edition, which introduced provisions governing arbitration in construction contracts. Following the PRISMA framework for study selection, content, trend, and bibliometric analyses were utilized to identify key themes, track the evolution, synthesize findings, and highlight influential contributions. The study finds that while authors agree that ADR methods are generally effective and widely adopted in the construction industry, their implementation has significant variability. Consequently, the study recommends the adoption of hybrid dispute resolution mechanisms (HDRMs). HDRMs combine elements of multiple ADR methods to create flexible approaches that lead to faster, more cost-effective, and more adaptable resolutions in complex disputes.

Bridging the simulation-to-reality gap: A comprehensive review of microclimate integration in urban building energy modeling (UBEM)

Worthy, A., Ashayeri, M., Marshall, J., & Abbasabadi, N. (2025). Bridging the simulation-to-reality gap: A comprehensive review of microclimate integration in urban building energy modeling (UBEM). Energy and Buildings, 331, Article 115392. https://doi.org/10.1016/j.enbuild.2025.115392.

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Abstract

Buildings are significant contributors to global energy consumption, necessitating urgent action to reduce energy use and associated emissions. Urban Building Energy Modeling (UBEM) is a critical tool that provides essential insights into citywide building energy dynamics though generating quantitative energy data and enabling holistic analysis and optimization of energy systems. However, current UBEM methodologies and tools are constrained by their reliance on non-urban-specific and aggregated climate data inputs, leading to discrepancies between modeled and actual energy expenditures. This article presents a comprehensive review of the datasets, tools, methodologies, and novel case studies deployed to integrate microclimates into UBEMs, aiming to bridge the modeling gap and to address the uncertainties due to the absence of real-world microclimate data in the models. It expands beyond conventional methods by elaborating on substitutional observational-based and simulation-based datasets, addressing their spatial and temporal tradeoffs. The review highlights that while remote sensing technologies are extensively utilized for building geometric data UBEM inputs, there remains an underexplored potential in reanalysis and observational-based products for environmental data; specifically, for the inclusion of parameters that are conventionally not included in UBEM analysis such as tree canopy coverage and land surface temperature. Furthermore, adopting a hybrid methodology, which combines observational and simulation-based datasets, may be a promising approach for more accurately representing microclimate conditions in UBEMs; as this process would ensure more representative climate parameter inputs and ground-truthing, while effectively managing computational demands across extensive temporal and spatial simulations. This could be achieved through integrating local earth observation datasets with computational fluid dynamics (CFD) tools or by merging local earth observational data with simulation-based reanalysis products and coupling these weather inputs with simulation-based building energy management models. Finally, this review underscores the importance of validating UBEMs with local microclimate weather data to ensure that model results are actionable, reliable, and accurate.

Leveraging earth observational data products and machine learning to enhance urban building energy modeling (UBEM) with microclimate effects

Worthy, A., Ashayeri, M., & Abbasabadi, N. (2025). Leveraging earth observational data products and machine learning to enhance urban building energy modeling (UBEM) with microclimate effects. Sustainable Cities and Society, 130, Article 106544. https://doi.org/10.1016/j.scs.2025.106544.

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Abstract

Urban Building Energy Modeling (UBEM) is a powerful tool used for sustainable design, urban planning, and efficient energy management, as it provides essential insights into the building energy consumption patterns. However, current UBEM methodologies often lack urban-specific microclimate data, leading to discrepancies between modeled and actual energy consumption. This research develops a bottom-up statistical UBEM framework that combines and integrates earth observational climate data, climate reanalysis products, and annual energy usage data, measured by the Seattle Energy Benchmarking Dataset, to capture the impacts of microclimates on urban building energy performance. Using machine learning techniques and Seattle, Washington, USA as a proof of concept, our results demonstrate that incorporating urban-specific microclimate data substantially enhances building energy modeling prediction accuracy. Specifically, three model variable schemas are compared; the optimal model incorporating earth observational data achieved a 0.16 (from 0.55 to 0.71) increase in testing R2 over the model that did not include any climate data inputs, and a 0.056 (from 0.66 to 0.71) increase in testing R2, over the model that included TMY3 climate data inputs. These findings validate the use of earth observational datasets for urban building energy modeling to include microclimate effects. Furthermore, machine learning algorithms outperform traditional linear methods, with respective ordered rankings: CATBoost, XGBoost, Random Forest, Decision Trees, and Linear Regression. Our study underscores the importance of integrating microclimate data into UBEM frameworks and advocates for the expanded use of earth observational and remote sensing datasets for mitigation of simulation-to-reality discrepancies; to ultimately inform more accurate energy-driven design and planning strategies at the city level.