scholarly journals Fitting Empirical Fundamental Diagrams of Road Traffic: A Comprehensive Review and Comparison of Models Using an Extensive Data Set

2021 ◽  
Author(s):  
Daniel Bramich ◽  
Monica Menendez ◽  
Lukas Ambühl
Keyword(s):  
Functional Form ◽  
Road Traffic ◽  
Information Criteria ◽  
Noise Model ◽  
Future Research ◽  
Flow Density ◽  
Fundamental Diagram ◽  
Data Set ◽  
Definitive Answer ◽  
Constant Variance

<div>Understanding the inter-relationships between traffic flow, density, and speed through the study of the fundamental diagram of road traffic is critical for traffic modelling and management. Consequently, over the last 85 years, a wealth of models have been developed for its functional form. However, there has been no clear answer as to which model is the most appropriate for observed (i.e. empirical) fundamental diagrams and under which conditions. A lack of data has been partly to blame. Motivated by shortcomings in previous reviews, we first present a comprehensive literature review on modelling the functional form of empirical fundamental diagrams. We then perform fits of 50 previously proposed models to a high quality sample of 10,150 empirical fundamental diagrams pertaining to 25 cities. Comparing the fits using information criteria, we find that the non-parametric Sun model greatly outperforms all of the other models. The Sun model maintains its winning position regardless of road type and congestion level. Our study, the first of its kind when considering the number of models tested and the amount of data used, finally provides a definitive answer to the question ``Which model for the functional form of an empirical fundamental diagram is currently the best?''. The word ``currently'' in this question is key, because previously proposed models adopt an inappropriate Gaussian noise model with constant variance. We advocate that future research should shift focus to exploring more sophisticated noise models. This will lead to an improved understanding of empirical fundamental diagrams and their underlying functional forms.</div><br>

scholarly journals Fitting Empirical Fundamental Diagrams of Road Traffic: A Comprehensive Review and Comparison of Models Using an Extensive Data Set

2021 ◽  
Author(s):  
Daniel Bramich ◽  
Monica Menendez ◽  
Lukas Ambühl
Keyword(s):  
Functional Form ◽  
Road Traffic ◽  
Information Criteria ◽  
Noise Model ◽  
Future Research ◽  
Flow Density ◽  
Fundamental Diagram ◽  
Data Set ◽  
Definitive Answer ◽  
Constant Variance

<div>Understanding the inter-relationships between traffic flow, density, and speed through the study of the fundamental diagram of road traffic is critical for traffic modelling and management. Consequently, over the last 85 years, a wealth of models have been developed for its functional form. However, there has been no clear answer as to which model is the most appropriate for observed (i.e. empirical) fundamental diagrams and under which conditions. A lack of data has been partly to blame. Motivated by shortcomings in previous reviews, we first present a comprehensive literature review on modelling the functional form of empirical fundamental diagrams. We then perform fits of 50 previously proposed models to a high quality sample of 10,150 empirical fundamental diagrams pertaining to 25 cities. Comparing the fits using information criteria, we find that the non-parametric Sun model greatly outperforms all of the other models. The Sun model maintains its winning position regardless of road type and congestion level. Our study, the first of its kind when considering the number of models tested and the amount of data used, finally provides a definitive answer to the question ``Which model for the functional form of an empirical fundamental diagram is currently the best?''. The word ``currently'' in this question is key, because previously proposed models adopt an inappropriate Gaussian noise model with constant variance. We advocate that future research should shift focus to exploring more sophisticated noise models. This will lead to an improved understanding of empirical fundamental diagrams and their underlying functional forms.</div><br>

scholarly journals Fitting Empirical Fundamental Diagrams of Road Traffic: A Comprehensive Review and Comparison of Models Using an Extensive Data Set

2022 ◽  
Author(s):  
Daniel Bramich ◽  
Monica Menendez ◽  
Lukas Ambühl
Keyword(s):  
Functional Form ◽  
Road Traffic ◽  
Transportation Systems ◽  
Information Criteria ◽  
Noise Model ◽  
Future Research ◽  
Flow Density ◽  
Fundamental Diagram ◽  
Data Set ◽  
Definitive Answer

<div>Understanding the inter-relationships between traffic flow, density, and speed through the study of the fundamental diagram of road traffic is critical for traffic modelling and management. Consequently, over the last 85 years, a wealth of models have been developed for its functional form. However, there has been no clear answer as to which model is the most appropriate for observed (i.e. empirical) fundamental diagrams and under which conditions. A lack of data has been partly to blame. Motivated by shortcomings in previous reviews, we first present a comprehensive literature review on modelling the functional form of empirical fundamental diagrams. We then perform fits of 50 previously proposed models to a high quality sample of 10,150 empirical fundamental diagrams pertaining to 25 cities. Comparing the fits using information criteria, we find that the non-parametric Sun model greatly outperforms all of the other models. The Sun model maintains its winning position regardless of road type and congestion level. Our study, the first of its kind when considering the number of models tested and the amount of data used, finally provides a definitive answer to the question ``Which model for the functional form of an empirical fundamental diagram is currently the best?''. The word ``currently'' in this question is key, because previously proposed models adopt an inappropriate Gaussian noise model with constant variance. We advocate that future research should shift focus to exploring more sophisticated noise models. This will lead to an improved understanding of empirical fundamental diagrams and their underlying functional forms.</div><div><br></div><div>Accepted by IEEE Transactions On Intelligent Transportation Systems on 14th Dec 2021<br></div><br>

scholarly journals Application of Macroscopic Fundamental Diagram under Flooding Situation to Traffic Management Measures

2021 ◽  
Vol 13 (20) ◽  
pp. 11227
Author(s):  
Piyapong Suwanno ◽  
Rattanaporn Kasemsri ◽  
Kaifeng Duan ◽  
Atsushi Fukuda
Keyword(s):  
Traffic Flow ◽  
Traffic Management ◽  
Road Network ◽  
Road Traffic ◽  
Control Measures ◽  
Flow Density ◽  
Fundamental Diagram ◽  
Urban Flood ◽  
Traffic Conditions ◽  
Macroscopic Fundamental Diagram

Bangkok, Thailand is prone to flooding after heavy rain. Many road sections become impassable, causing severe traffic congestion and greatly impacting activities. Optimal vehicle management requires the knowledge of flooding impact on road traffic conditions in specific areas. A method is proposed to quantify urban flood situations by expressing traffic conditions in specific ranges using the concept of macroscopic fundamental diagram (MFD). MFD-based judgement allows for a road manager to understand the current traffic situation and take appropriate traffic control measures. MFD analysis identified traffic flow–density and density–velocity relationships by using the shape of the estimated MFD travel time-series plots. Then, results were applied to develop a traffic model with vehicle-flow parameters as a measuring method for road-network performance. The developed model improved road-network traffic-flow performance under different flood conditions. A method is also presented for traffic management evaluation on the assumption that flooding occurs.

scholarly journals The COVID Connection: Pandemic Anxiety, COVID-19 Comprehension, and Digital Confidence

2021 ◽  
pp. 000276422110031
Author(s):  
Laura Robinson ◽  
Jeremy Schulz ◽  
Øyvind N. Wiborg ◽  
Elisha Johnston
Keyword(s):  
Logistic Models ◽  
The United States ◽  
Sociodemographic Factors ◽  
Future Research ◽  
Lower Probability ◽  
Data Set ◽  
Digital Inequality ◽  
Partner Status ◽  
General Anxiety ◽  
Nationally Representative

This article presents logistic models examining how pandemic anxiety and COVID-19 comprehension vary with digital confidence among adults in the United States during the first wave of the pandemic. As we demonstrate statistically with a nationally representative data set, the digitally confident have lower probability of experiencing physical manifestations of pandemic anxiety and higher probability of adequately comprehending critical information on COVID-19. The effects of digital confidence on both pandemic anxiety and COVID-19 comprehension persist, even after a broad range of potentially confounding factors are taken into account, including sociodemographic factors such as age, gender, race/ethnicity, metropolitan status, and partner status. They also remain discernable after the introduction of general anxiety, as well as income and education. These results offer evidence that the digitally disadvantaged experience greater vulnerability to the secondary effects of the pandemic in the form of increased somatized stress and decreased COVID-19 comprehension. Going forward, future research and policy must make an effort to address digital confidence and digital inequality writ large as crucial factors mediating individuals’ responses to the pandemic and future crises.

Acoustic Modeling of Meteorological Effects on Roadway Noise

2021 ◽  
pp. 036119812199458
Author(s):  
Roger L. Wayson ◽  
Kenneth Kaliski
Keyword(s):  
Road Traffic ◽  
Traffic Noise ◽  
Model Development ◽  
The United States ◽  
Noise Model ◽  
Road Traffic Noise ◽  
Atmospheric Conditions ◽  
Noise Propagation ◽  
Transportation Research ◽  
Meteorological Effects

Modeling road traffic noise levels without including the effects of meteorology may lead to substantial errors. In the United States, the required model is the Traffic Noise Model which does not include meteorology effects caused by refraction. In response, the Transportation Research Board sponsored NCHRP 25-52, Meteorological Effects on Roadway Noise, to collect highway noise data under different meteorological conditions, document the meteorological effects on roadway noise propagation under different atmospheric conditions, develop best practices, and provide guidance on how to: (a) quantify meteorological effects on roadway noise propagation; and (b) explain those effects to the public. The completed project at 16 barrier and no-barrier measurement positions adjacent to Interstate 17 (I-17) in Phoenix, Arizona provided the database which has enabled substantial developments in modeling. This report provides more recent information on the model development that can be directly applied by the noise analyst to include meteorological effects from simple look-up tables to more precise use of statistical equations.

scholarly journals A data acquisition setup for data driven acoustic design

2021 ◽  
pp. 1351010X2098690
Author(s):  
Romana Rust ◽  
Achilleas Xydis ◽  
Kurt Heutschi ◽  
Nathanael Perraudin ◽  
Gonzalo Casas ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Interdisciplinary Approach ◽  
Computational Design ◽  
Surface Structures ◽  
Future Research ◽  
Data Set ◽  
Process Measures ◽  
First Results ◽  
Spatio Temporal ◽  
Automated Data Acquisition

In this paper, we present a novel interdisciplinary approach to study the relationship between diffusive surface structures and their acoustic performance. Using computational design, surface structures are iteratively generated and 3D printed at 1:10 model scale. They originate from different fabrication typologies and are designed to have acoustic diffusion and absorption effects. An automated robotic process measures the impulse responses of these surfaces by positioning a microphone and a speaker at multiple locations. The collected data serves two purposes: first, as an exploratory catalogue of different spatio-temporal-acoustic scenarios and second, as data set for predicting the acoustic response of digitally designed surface geometries using machine learning. In this paper, we present the automated data acquisition setup, the data processing and the computational generation of diffusive surface structures. We describe first results of comparative studies of measured surface panels and conclude with steps of future research.

scholarly journals What Countries Select More Experienced Leaders? The PolEx Measure of Political Experience

2021 ◽  
pp. 1-10
Author(s):  
Alexander Baturo ◽  
Johan A. Elkink
Keyword(s):  
Career Paths ◽  
Latent Variable ◽  
Empirical Studies ◽  
Future Research ◽  
Variable Model ◽  
Data Set ◽  
Political Experience ◽  
National Leaders ◽  
Regime Types ◽  
The Difference

Abstract How can one assess which countries select more experienced leaders for the highest office? There is wide variation in prior career paths of national leaders within, and even more so between, regime types. It is therefore challenging to obtain a truly comparative measure of political experience; empirical studies have to rely on proxies instead. This article proposes PolEx, a measure of political experience that abstracts away from the details of career paths and generalizes based on the duration, quality and breadth of an individual's experience in politics. The analysis draws on a novel data set of around 2,000 leaders from 1950 to 2017 and uses a Bayesian latent variable model to estimate PolEx. The article illustrates how the new measure can be used comparatively to assess whether democracies select more experienced leaders. The authors find that while on average they do, the difference with non-democracies has declined dramatically since the early 2000s. Future research may leverage PolEx to investigate the role of prior political experience in, for example, policy making and crisis management.

In the Face of Critique

2011 ◽  
Vol 34 (2) ◽  
pp. 241-282 ◽  
Author(s):  
Elyse Amend ◽  
David M. Secko
Keyword(s):  
The State ◽  
Future Research ◽  
Jigsaw Puzzle ◽  
Science Journalism ◽  
Data Set ◽  
Literature Searches ◽  
The Face ◽  
Qualitative Knowledge ◽  
Qualitative Literature

The qualitative literature related to health and science journalism often states that little is known about the perspectives of journalists. This is, in part, because of individual studies being like scattered pieces of a jigsaw puzzle. In this article, the authors report the results of a qualitative metasynthesis aimed at reassembling the qualitative literature involving health and science journalists. Comprehensive literature searches gave a data set of 21 studies whose synthesis produced 14 metathemes and four taxonomic groupings. This synthesis is used to show the state of qualitative knowledge and the potential for future research.

scholarly journals Product formalisms for measures on spaces with binary tree structures: representation, visualization, and multiscale noise

2020 ◽  
Vol 8 ◽  
Author(s):  
Devasis Bassu ◽  
Peter W. Jones ◽  
Linda Ness ◽  
David Shallcross
Keyword(s):  
Data Science ◽  
Theoretical Foundation ◽  
Continuous Functions ◽  
Noise Model ◽  
Data Types ◽  
Data Set ◽  
Tree Structures ◽  
Simple Concept ◽  
Wide Range ◽  
Computer Scientists

Abstract In this paper, we present a theoretical foundation for a representation of a data set as a measure in a very large hierarchically parametrized family of positive measures, whose parameters can be computed explicitly (rather than estimated by optimization), and illustrate its applicability to a wide range of data types. The preprocessing step then consists of representing data sets as simple measures. The theoretical foundation consists of a dyadic product formula representation lemma, and a visualization theorem. We also define an additive multiscale noise model that can be used to sample from dyadic measures and a more general multiplicative multiscale noise model that can be used to perturb continuous functions, Borel measures, and dyadic measures. The first two results are based on theorems in [15, 3, 1]. The representation uses the very simple concept of a dyadic tree and hence is widely applicable, easily understood, and easily computed. Since the data sample is represented as a measure, subsequent analysis can exploit statistical and measure theoretic concepts and theories. Because the representation uses the very simple concept of a dyadic tree defined on the universe of a data set, and the parameters are simply and explicitly computable and easily interpretable and visualizable, we hope that this approach will be broadly useful to mathematicians, statisticians, and computer scientists who are intrigued by or involved in data science, including its mathematical foundations.

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