Article
Chemistry, Analytical
Jongtae Lim, Songhee Park, Dojin Choi, Kyoungsoo Bok, Jaesoo Yoo
Summary: This paper proposes a machine-learning-based road speed prediction scheme that utilizes road environment data analysis. It accurately predicts both average road speed and rapidly changing road speeds by analyzing speed data from the target road and neighboring roads. It considers historical average speed data and events as weights for prediction and uses the LSTM algorithm for sequential data learning.
Article
Economics
Fuliang Wu, Tolga Bektas, Ming Dong, Hongbo Ye, Dali Zhang
Summary: This paper introduces two stochastic optimal speed control models to minimize fuel consumption of a vehicle traveling over a given stretch of road. These models optimize the speed profile of the vehicle in the presence of uncertainty in traffic speeds.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2021)
Article
Green & Sustainable Science & Technology
Matteo Bohm, Mirco Nanni, Luca Pappalardo
Summary: This study used GPS traces and a microscopic model to analyze the emissions from thousands of private vehicles in three European cities, identifying gross polluters and grossly polluted roads. The research showed that emissions reduction policies targeting gross polluters are more effective than general vehicle restrictions.
NATURE SUSTAINABILITY
(2022)
Article
Green & Sustainable Science & Technology
Alessandra Boggio-Marzet, Andres Monzon, Ana M. Rodriguez-Alloza, Yang Wang
Summary: The widespread use of vehicles has led to increased fuel consumption and air pollutant emissions, negatively impacting human health and climate change. Various factors such as traffic flow and road type influence vehicle fuel consumption. This study suggests that avoiding congestion on high-capacity roads and selecting green itineraries can improve energy efficiency.
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION
(2022)
Article
Engineering, Multidisciplinary
Khalid Mohammed Almatar
Summary: This study aims to identify the most congested areas in the road network and understand their relationship with driver demand. The Floating Car Data method is used to analyze traffic congestion and determine if observed congestion clusters represent meaningful patterns. The findings demonstrate the effectiveness of this approach in identifying traffic congestion patterns in urban road networks.
AIN SHAMS ENGINEERING JOURNAL
(2023)
Article
Green & Sustainable Science & Technology
Jinrui Zang, Pengpeng Jiao, Sining Liu, Xi Zhang, Guohua Song, Lei Yu
Summary: This paper aims to identify traffic congestion patterns of the urban road network based on the traffic performance index (TPI) in Beijing. The self-organizing maps (SOM) method is used to cluster congestion patterns based on time-varying TPI. Different patterns for weekdays, weekends, and holidays are recognized. The proposed method improves the accuracy of clustering and provides valuable insights for traffic management.
Article
Economics
Jinwon Kim
Summary: This paper uses panel data from urban freeways in California to estimate the effects of roadwork on road speed and traffic volume, and supports the induced-demand hypothesis. The study finds that roadwork can increase road speed shortly after completion, but the effect is not long-lasting, while traffic volume increases after around one year. Additionally, the paper evaluates the time cost savings of roadwork and public spending on freeways.
REGIONAL SCIENCE AND URBAN ECONOMICS
(2022)
Article
Computer Science, Hardware & Architecture
Maram Bani Younes, Azzedine Boukerche
Summary: Researchers are concerned about the fuel consumption and gas emissions of vehicles, and various technologies and protocols have been developed to improve the fuel efficiency of transportation vehicles. This study aims to introduce a green protocol to assist drivers and self-driving vehicles in driving efficiently on highways to reduce fuel consumption and emissions.
IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING
(2022)
Article
Green & Sustainable Science & Technology
Navin Ranjan, Sovit Bhandari, Pervez Khan, Youn-Sik Hong, Hoon Kim
Summary: This paper presents an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, as well as a deep neural network architecture formed from Convolutional Autoencoder, both of which show high efficiency in predicting urban traffic conditions based on a case study conducted in Seoul city.
Article
Computer Science, Information Systems
Maram Bani Younes, Azzedine Boukerche, Floriano De Rango
Summary: Traveling vehicles contribute to global warming through gas emissions. The increasing number of vehicles has worsened pollution, prompting environmental organizations to develop green vehicles. Countries have also implemented green driving rules and technologies. This study introduces an efficient traffic light scheduling algorithm (SmartLight) to reduce fuel consumption and emissions at road intersections.
Article
Energy & Fuels
Madina Doumbia, Adjon A. Kouassi, Siele Silue, Veronique Yoboue, Cathy Liousse, Arona Diedhiou, N'Datchoh E. Toure, Sekou Keita, Eric-Michel Assamoi, Adama Bamba, Maurin Zouzoua, Alima Dajuma, Kouakou Kouadio
Summary: The study reveals high emissions of black carbon (BC), carbon monoxide (CO), and nitrogen oxides (NOx) from vehicles at major road intersections and highways. The highest emissions come from carbon monoxide and nitrogen oxides, with CO emissions from personal cars being influenced by the age of the vehicle fleet.
Article
Environmental Sciences
Xiao Zhou, Han Wang, Zhou Huang, Yi Bao, Guoqing Zhou, Yu Liu
Summary: This study analyzes the CO2 emissions of road traffic in Shenzhen based on traffic trajectory data, and investigates the impact of built environment factors on road traffic emissions. The results provide a detailed map of CO2 emissions and identify several factors that significantly influence road traffic emissions, such as population density, number of workplaces, number of dwellings, density of main roads, access to metro stations, and access to bus stops.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Environmental Sciences
Xin Li, Dasa Gu, Tilman Leo Hohenberger, Yik Him Fung, Jimmy C. H. Fung, Alexis K. H. Lau, Zhenxing Liang
Summary: This study develops a dynamic on-road emission inventory for Hong Kong, using traffic congestion index, traffic density model, and local emission factors. The results show higher emissions on weekdays and public transportation as the major emission sources.
ATMOSPHERIC ENVIRONMENT
(2023)
Article
Environmental Studies
Shirui Zhou, Junfang Tian, Ying-En Ge, Shaowei Yu, Rui Jiang
Summary: This paper investigates the emissions and fuel consumption features in a car-following platoon using two experimental datasets. Four classical models are employed for emissions and fuel consumption prediction, and a universal concave growth pattern is observed. The study also tests a general framework for coupling emissions and car-following models, finding that all models perform well at the vehicle-pair level. However, at the platoon level, the predicted fuel consumption remains constant, which is different from the experimental observation. The research highlights the significance of considering oscillation growth and evolution in fuel consumption estimation for platoon-level analysis.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2023)
Article
Thermodynamics
Davor B. Vujanovic, Sladjana R. Jankovic, Marko A. Stokic, Stefan S. Zdravkovic
Summary: This paper investigates the impact of different terrain and traffic conditions on the driver's driving performances, specifically on car energy efficiency and CO2 emissions. A methodology is proposed to determine the extent to which unfavorable conditions contribute to worse driving performances, as well as to identify when aggressive driving style leads to increased fuel consumption and CO2 emission. The results demonstrate that this methodology is a useful tool for managers to identify potential opportunities for improving fleet energy efficiency and reducing CO2 emissions.
Article
Environmental Sciences
Alan Lex Brown, Kin Che Lam, Irene van Kamp
ENVIRONMENTAL HEALTH
(2015)
Article
Acoustics
A. L. Brown
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
(2015)
Article
Acoustics
Bert De Coensel, A. L. Brown, Deanna Tomerini
Article
Construction & Building Technology
Jian Kang, Francesco Aletta, Truls T. Gjestland, Lex A. Brown, Dick Botteldooren, Brigitte Schulte-Fortkamp, Peter Lercher, Irene van Kamp, Klaus Genuit, Andre Fiebig, J. Luis Bento Coelho, Luigi Maffei, Lisa Lavia
BUILDING AND ENVIRONMENT
(2016)
Review
Environmental Sciences
Alan Lex Brown, Irene van Kamp
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2017)
Article
Acoustics
A. L. Brown, Bert De Coensel
Article
Acoustics
Bert De Coensel, Mats E. Nilsson, Birgitta Berglund, A. L. Brown
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
(2013)
Editorial Material
Audiology & Speech-Language Pathology
Mathias Basner, Lex Brown
Article
Environmental Sciences
Irene van Kamp, Kerstin Persson Waye, Katja Kanninen, John Gulliver, Alessandro Bozzon, Achilleas Psyllidis, Hendriek Boshuizen, Jenny Selander, Peter van den Hazel, Marco Brambilla, Maria Foraster, Jordi Julvez, Maria Klatte, Sonja Jeram, Peter Lercher, Dick Botteldooren, Gordana Ristovska, Jaakko Kaprio, Dirk Schreckenberg, Maarten Hornikx, Janina Fels, Miriam Weber, Ella Braat-Eggen, Julia Hartmann, Charlotte Clark, Tanja Vrijkotte, Lex Brown, Gabriele Bolte
Summary: The Equal-Life project aims to study the impact of combined exposures on children's mental health and cognitive development, by integrating different aspects of the child's environment and looking at supportive environments for child development. The project utilizes a variety of data sources, including GIS-based environmental indicators and omics approaches, to form a comprehensive early-life exposome. Through statistical analysis and machine learning models, Equal-Life seeks to provide insights into the effects of physical and social exposures on children's development over time.
ENVIRONMENTAL EPIDEMIOLOGY
(2022)
Proceedings Paper
Environmental Sciences
A. Lex Brown
ENVIRONMENTAL FORENSICS 2015
(2015)
Review
Acoustics
A. Lex Brown
INTERNATIONAL JOURNAL OF ACOUSTICS AND VIBRATION
(2012)
Article
Environmental Studies
Olivia Bina, Wu Jing, Lex Brown, Maria Rosario Partidario
ENVIRONMENTAL IMPACT ASSESSMENT REVIEW
(2011)
Article
Transportation
A. L. (Lex) Brown, Deanna Tomerini
ROAD & TRANSPORT RESEARCH
(2011)
Article
Environmental Studies
Jia He, Cun-Kuan Bao, Ting-Fei Shu, Xiao-Xue Yun, Dahe Jiang, Lex Brown
ENVIRONMENTAL IMPACT ASSESSMENT REVIEW
(2011)
Article
Environmental Studies
A. Bond, F. Retief, B. Cave, M. Fundingsland, P. N. Duinker, R. Verheem, A. L. Brown
ENVIRONMENTAL IMPACT ASSESSMENT REVIEW
(2018)
Article
Computer Science, Interdisciplinary Applications
Jeffrey Wade, Christa Kelleher, Barret L. Kurylyk
Summary: This study developed a physically-based water temperature model coupled with the National Water Model (NWM) to assess the potential for water temperature prediction to be incorporated into the NWM at the continental scale. By evaluating different model configurations of increasing complexity, the study successfully simulated hourly water temperatures in the forested headwaters of H.J. Andrews Experimental Forest in Oregon, USA, providing a basis for integrating water temperature simulation with predictions from the NWM.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Shaun SH. Kim, Lucy A. Marshall, Justin D. Hughes, Lynn Seo, Julien Lerat, Ashish Sharma, Jai Vaze
Summary: A major challenge in hydrologic modelling is producing reliable uncertainty estimates outside of calibration periods. This research addresses the challenge by improving model structures and error models to more reliably estimate uncertainty. The combination of the RBS model and SPUE produces statistically reliable predictions and shows better matching performance in tests.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Juan Pedro Carbonell-Rivera, Javier Estornell, Luis Angel Ruiz, Pablo Crespo-Peremarch, Jaime Almonacid-Caballer
Summary: This study presents Class3Dp, a software for classifying vegetation species in colored point clouds. The software utilizes geometric, spectral, and neighborhood features along with machine learning methods to classify the point cloud, allowing for the recognition of species composition in an ecosystem.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Zhi Li, Daniel Caviedes-Voullieme, Ilhan Oezgen-Xian, Simin Jiang, Na Zheng
Summary: The optimal strategy for solving the Richards equation numerically depends on the specific problem, particularly when using GPUs. This study investigates the parallel performance of four numerical schemes on both CPUs and GPUs. The results show that the scaling of Richards solvers on GPUs is influenced by various factors. Compared to CPUs, parallel simulations on GPUs exhibit significant variation in scaling across different code sections, with poorly-scaled components potentially impacting overall performance. Nonetheless, using GPUs can greatly enhance computational speed, especially for large-scale problems.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ludovic Cassan, Leo Pujol, Paul Lonca, Romain Guibert, Helene Roux, Olivier Mercier, Dominique Courret, Sylvain Richard, Pierre Horgue
Summary: Methods and algorithms for measuring stream surface velocities have been continuously developed over the past five years to adapt to specific flow typologies. The free software ANDROMEDE allows easy use and comparison of these methods with image processing capabilities designed for measurements in natural environments and with unmanned aerial vehicles. The validation of the integrated algorithms is presented on three case studies that represent the targeted applications: the study of currents for eco-hydraulics, the measurement of low water flows and the diagnosis of hydraulic structures. The field measurements are in very good agreement with the optical measurements and demonstrate the usefulness of the tool for rapid flow diagnosis for all the intended applications.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Mariia Kozlova, Robert J. Moss, Julian Scott Yeomans, Jef Caers
Summary: This paper introduces a framework for quantitative sensitivity analysis using the SimDec visualization method, and tests its effectiveness on decision-making problems. The framework captures critical information in the presence of heterogeneous effects, and enhances its practicality by introducing a formal definition and classification of heterogeneous effects.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Chad R. Palmer, Denis Valle, Edward V. Camp, Wendy-Lin Bartels, Martha C. Monroe
Summary: Simulation games have been used in natural resource management for education and communication purposes, but not for data collection. This research introduces a new design process which involves stakeholders and emphasizes usability, relevance, and credibility testing criteria. The result is a finalized simulation game for future research.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Tao Wang, Chenming Zhang, Ye Ma, Harald Hofmann, Congrui Li, Zicheng Zhao
Summary: This study used numerical modeling to investigate the formation process of iron curtains under different freshwater and seawater conditions. It was found that Fe(OH)3 accumulates on the freshwater side, while the precipitation is inhibited on the seaward side due to high H+ concentrations. These findings enhance our understanding of iron transformation and distribution in subterranean estuaries.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Grant Hutchings, James Gattiker, Braden Scherting, Rodman R. Linn
Summary: Computational models for understanding and predicting fire in wildland and managed lands are becoming increasingly impactful. This paper addresses the characterization and population of mid-story fuels, which are not easily observable through traditional survey or remote sensing. The authors present a methodology to populate the mid-story using a generative model for fuel placement, which can be calibrated based on limited observation datasets or expert guidance. The connection of terrestrial LiDAR as the observations used to calibrate the generative model is emphasized. Code for the methods in this paper is provided.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Saswata Nandi, Pratiman Patel, Sabyasachi Swain
Summary: IMDLIB is an open-source Python library that simplifies the retrieval and processing of gridded meteorological data from IMD, enhancing data accessibility and facilitating hydro-climatic research and analysis.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)
Article
Computer Science, Interdisciplinary Applications
Pengfei Wu, Jintao Liu, Meiyan Feng, Hu Liu
Summary: In this paper, a new flow distance algorithm called D infinity-TLI is proposed, which accurately estimates flow distance and width function using a two-segment-distance strategy and triangulation with linear interpolation method. The evaluation results show that D infinity-TLI outperforms existing algorithms and has a low mean absolute relative error.
ENVIRONMENTAL MODELLING & SOFTWARE
(2024)