20 research outputs found
Data fitting script for speed/flow density relationships
No full text<p>These script are published along with the conference proceeding "The evaluation of data fitting approaches for speed/flow density relationships" by Arthur Rohaert, Jonathan Walqvist, Hana Najmanová, Nikolai Bode and Enrico Ronchi in Collective Dynamics.</p>
<p>The script can be used to implement the methodologies explained in the proceeding.</p>
<p><strong>PLEASE CITE THE PROCEEDING WHEN USING THIS SCRIPT FOR YOUR PUBLICATIONS</strong></p>
Dataset of traffic dynamics during the 2020 Glass Wildfire Evacuation
No full textThis dataset contains the
This dataset has been sourced from the Performance Measurement System of the California Department of Transportation. The data has been processed, analysed, presented and summarized in the paper: Rohaert et al., ‘The analysis of traffic data of wildfire evacuation: the case study of the 2020 Glass Fire’, [Submitted for peer-review to an international journal.], 2023.
Acknowledgements
This work has been funded under award 60NANB21D118 from the National Institute of Standards and Technology (NIST), U.S. Department of Commerce
Dataset of traffic dynamics during the 2019 Kincade Wildfire Evacuation
No full textThis dataset has been sourced from the Performance Measurement System of the California Department of Transportation. The data has been processed, analysed, presented and summarized in the paper: Rohaert et al., ‘Traffic dynamics during the 2019 Kincade wildfire evacuation’, [Submitted for peer-review to an international journal.], 2022.
CRediT author statement
Arthur Rohaert: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing - original draft, Writing - review & editing. Erica D. Kuligowski: Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. Adam Ardinge: Conceptualization, Formal analysis, Investigation, Methodology, Resources, Validation, Writing - review & editing. Jonathan Wahlqvist: Conceptualization, Formal analysis, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. Steven M.V. Gwynne: Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. Amanda Kimball: Conceptualization, Funding acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Writing - review & editing. Noureddine Bénichou: Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. Enrico Ronchi: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - original draft, Writing - review & editing
Acknowledgements
This work has been funded under award 60NANB20D191 from the National Institute of Standards and Technology (NIST), U.S. Department of Commerce. The authors would like to thank the WUI-NITY team (Guillermo Rein, Nikolaos Kalogeropoulos, Harry Mitchell, Max Kinateder, Maxime Berthiaume). The authors also acknowledge the technical panel of the project for their support and guidance: Carole Adam, Amy Christianson, Tom Cova, Lauren Folk, Abishek Gaur, Paolo Intini, Justice Jones, Bryan Klein, Chris Lautenberger, Ruggiero Lovreglio, Jerry McAdams, Ruddy Mell, Elise Miller-Hooks, Cathy Stephens, Steve Taylor, Sandra Vaiciulyte, Xilei Zhao, Rita Fahy, Lucian Deaton, and Michele Steinberg
Driving behaviour during wildfire evacuation
No full textIntroduction: Wildfires pose escalating risks to communities at the wildland--urban interface, often necessitating extensive evacuations. This thesis explores how driving behaviour during wildfire evacuations differs from routine conditions and how these differences can be modelled to improve traffic simulations, thereby supporting more informed planning and response decisions by authorities.Objectives: Three main research objectives are addressed: (1) characterising how macroscopic traffic dynamics differ between wildfire evacuations and routine traffic conditions, (2) assessing the impact of wildfire smoke on car-following behaviour, and (3) developing a framework for traffic simulations that can support emergency planning for and response during wildfire evacuations.Methods and outcomes: (1) A dedicated data analysis method was developed to compare traffic dynamics during routine and evacuation scenarios, using traffic detector data from recent wildfire events in California. The analysis revealed that drivers move more slowly and leave larger gaps between vehicles during evacuations. If unaccounted for in simulations, these behavioural shifts can lead to overly optimistic evacuation time estimates.(2) A custom driving simulator and virtual reality environment were designed to assess how reduced visibility from wildfire smoke affects driver behaviour. Results indicate that, under reduced visibility, drivers reduce their speed when travelling alone, but do not consistently adjust their following distance in congested traffic. These findings inform a visibility-sensitive car-following model.(3) Finally, a simulation framework was proposed, combining data from real wildfire events, virtual reality experiments and evacuation drills. This framework supports the evaluation of evacuation strategies and planning decisions under varying conditions. A case study applied to a community of more than a thousand households in Colorado demonstrated the framework’s utility in assessing traffic management interventions.Conclusion: By capturing evacuation-specific driving behaviours and their impact on traffic, this thesis provides practical approaches to enhance the realism of evacuation models, which can, in turn, support more reliable planning and safer wildfire evacuations
Project work on wellbeing in multidisciplinary student teams: A triple testimonial on eps at artesis
No full textThe European Project Semester (EPS) programme offers an educational framework to support students to practice problem-and project-based cross-disciplinary product innovation and research, in small multidisciplinary and international teams. To explore the potential and the restrictions of this international educational concept, in 2012 the Artesis University College became the first Belgian EPS provider to offer a multidisciplinary EPS programme in close collaboration with the following study programs: product development, engineering, business studies, social work and teacher training. This paper reflects on the learning process for the teaching staff and the 16 students who have participated to the first edition. First, insights related to the preparation, initiation and overall implementation of this new multidisciplinary teaching approach and interdepartmental semester program are discussed. Second, we focus especially on one specific EPS project which addressed the development of smart textile applications in health care. Reflections and lessons learned are shared from the complementary perspective of the EPS study programme coordinator, the ’smart textile applications’ team supervisor and an engineering student who participated in this project.Design EngineeringIndustrial Design Engineerin
Dataset of traffic dynamics during the 2019 Kincade Wildfire Evacuation
No full textThis dataset has been sourced from the Performance Measurement System of the California Department of Transportation. The data has been processed, analysed, presented and summarized in the paper: Rohaert et al., ‘Traffic dynamics during the 2019 Kincade wildfire evacuation’, [Submitted for peer-review to an international journal.], 2022.
CRediT author statement
Arthur Rohaert: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing - original draft, Writing - review & editing. Erica D. Kuligowski: Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. Adam Ardinge: Conceptualization, Formal analysis, Investigation, Methodology, Resources, Validation, Writing - review & editing. Jonathan Wahlqvist: Conceptualization, Formal analysis, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. Steven M.V. Gwynne: Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. Amanda Kimball: Conceptualization, Funding acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Writing - review & editing. Noureddine Bénichou: Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. Enrico Ronchi: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - original draft, Writing - review & editing
Acknowledgements
This work has been funded under award 60NANB20D191 from the National Institute of Standards and Technology (NIST), U.S. Department of Commerce. The authors would like to thank the WUI-NITY team (Guillermo Rein, Nikolaos Kalogeropoulos, Harry Mitchell, Max Kinateder, Maxime Berthiaume). The authors also acknowledge the technical panel of the project for their support and guidance: Carole Adam, Amy Christianson, Tom Cova, Lauren Folk, Abishek Gaur, Paolo Intini, Justice Jones, Bryan Klein, Chris Lautenberger, Ruggiero Lovreglio, Jerry McAdams, Ruddy Mell, Elise Miller-Hooks, Cathy Stephens, Steve Taylor, Sandra Vaiciulyte, Xilei Zhao, Rita Fahy, Lucian Deaton, and Michele Steinberg
The analysis of traffic data of wildfire evacuation: the case study of the 2020 Glass Fire
No full textEvacuation is a crucial policy to mitigate wildfire impacts. Understanding traffic dynamics during a wildfire evacuation can help authorities to improve in improving emergency management plans, thus improving life safety. In this study, we developed a methodology to extract historical traffic data from vehicle detector stations and automate the analysis of traffic dynamics for actual wildfire evacuations. This has been implemented in an open-access tool called Traffic Dynamic Analyser (TDA) which generates speed-density and flow-density relationships from data using both commonly used macroscopic traffic models as well as machine learning techniques (e.g., support vector regression). The use of the methodology is demonstrated with a case study of the 2020 Glass Fire in California, USA. The results from TDA showed a slight reduction in speeds and flows on US Highway 101 during the evacuation scenario, compared with the routine scenario. Moreover, background traffic has been shown to play a key role in the 2020 Glass Fire compared with previous wildfire evacuation scenarios (e.g., the 2019 Kincade fire). The case study showed that the methodology implemented in the TDA can be used to understand traffic evacuation dynamics in wildfire scenarios and to validate evacuation models
Tourist Population Vulnerability Assessment in Cross-Border Wildfire-Prone Areas
No full textFrequent wildfires increasingly impact tourist populations, yet there is a shortage of evidence-based, human-centered tools for wildfire risk reduction tailored to these areas. Most current tools focus primarily on assessing and reducing physical vulnerabilities, overlooking human aspects. While some community wildfire management guidelines exist, actionable strategies for disaster managers to address tourist-specific vulnerabilities are absent. This study aligned with existing vulnerability assessment methodologies, utilizing qualitative interviews, site visits, and literature review to identify key characteristics of tourist vulnerabilities and develop effective mitigation strategies. As a result, we developed TOURSAFE—a freely accessible tool for disaster risk managers in tourist areas. Based on human behavior in fire scenarios, expected evacuation decisions, and key actors’ expertise, TOURSAFE assists in identifying tourism-related wildfire vulnerabilities and offers relevant, adaptable mitigation strategies. This tool is easy to use, accessible, and provides actionable advice for short-, medium-, and long-term planning
Progress Report 1: Resilience and Adaptation to Climatic Extreme Wildfires (RACE Wildfires)
Get PDFThis is the first progress report of the international project funded by the National Research Council of Canada called Resilience and Adaptation to Climatic Extreme Wildfires (RACE Wildfires). In this first phase, the research performed included two main tasks: 1) the development of a sub-model for the representation of the impact of reduced visibility conditions on driving speed and 2) the development of a conceptual model for the study of the impact of the pandemic on shelter availability and destination choice. An experimental dataset collected in a virtual reality environment has been used to develop a sub-model for macroscopic traffic models considering the impact of reduced visibility conditions on driving speed. An application of a calibrated traffic model considering the impact of smoke has been performed using the WUI-NITY platform, an open multi-physics platform which includes wildfire spread, pedestrian response and traffic modelling. Verification testing has been performed as well. A conceptual framework for the development of a destination choice model to be applied in wildfire scenarios has also been developed
Progress Report 2 [Elektronisk resurs] : Resilience and Adaptation to Climatic Extreme Wildfires (RACE Wildfires)
No full textThis is the second progress report of the international project funded by the National Research Council of Canada called Resilience and Adaptation to Climatic Extreme Wildfires (RACE Wildfires). In this second phase, the research performed included two main tasks: 1) developments concerning the modelling of smoke and 2) development of analysis methods concerning validation datasets for wildfire evacuation. Visibility in smoke is a key aspect in terms of safe evacuation in wildfire scenarios. As valid results of evacuation modelling tools would rely on an accurate representation of the impact of smoke on people, physical accuracy is required. Therefore, the rendering of smoke needs to be physically based while still being computationally inexpensive so that it can be run in a multi-physics tool in real-time. This report presents an approach for rendering smoke with a single in-scattering term which allows for smoke and light interaction over multiple wavelengths. In addition, analysis methods concerning validation datasets for wildfire evacuation models are presented and discussed. This includes both traditional regression methods as well as approaches based on machine learning
