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 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 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 Analysis of ITMS System Impact Mechanism in Beijing Based on FD and Traffic Entropy

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ailing Huang ◽  
Wei Guan ◽  
Yimei Chang ◽  
Zhen Yang
Keyword(s):  
Positive Feedback ◽  
Intelligent Transportation Systems ◽  
Road Traffic ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Fundamental Diagram ◽  
Impact Mechanism ◽  
Traffic System ◽  
The Road ◽  
On The Road

Although more attention has been attracted to benefit evaluation of Intelligent Transportation Systems (ITS) deployment, how ITS impact the traffic system and make great effects is little considered. As a subsystem of ITS, in this paper, Intelligent Transportation Management System (ITMS) is studied with its impact mechanism on the road traffic system. Firstly, the correlative factors between ITMS and the road traffic system are presented and 3 positive feedback chains are defined. Secondly, we introduce the theory of Fundamental Diagram (FD) and traffic system entropy to demonstrate the correlative relationship between ITMS and feedback chains. The analyzed results show that ITMS, as a negative feedback factor, has damping functions on the coupling relationship of all 3 positive feedback chains. It indicates that with its deployment in Beijing, ITMS has impacted the improvement of efficiency and safety for the road traffic system. Finally, related benefits brought by ITMS are presented corresponding to the correlative factors, and effect standards are identified for evaluating ITMS comprehensive benefits.

scholarly journals Road Traffic Prediction Model Using Extreme Learning Machine: The Case Study of Tangier, Morocco

Information ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 542
Author(s):  
Mouna Jiber ◽  
Abdelilah Mbarek ◽  
Ali Yahyaouy ◽  
My Abdelouahed Sabri ◽  
Jaouad Boumhidi
Keyword(s):  
Extreme Learning Machine ◽  
Intelligent Transportation Systems ◽  
High Speed ◽  
Road Traffic ◽  
Learning Algorithm ◽  
Route Planning ◽  
Transportation Systems ◽  
Feed Forward Neural Network ◽  
Data Set ◽  
Learning Machine

An efficient and credible approach to road traffic management and prediction is a crucial aspect in the Intelligent Transportation Systems (ITS). It can strongly influence the development of road structures and projects. It is also essential for route planning and traffic regulations. In this paper, we propose a hybrid model that combines extreme learning machine (ELM) and ensemble-based techniques to predict the future hourly traffic of a road section in Tangier, a city in the north of Morocco. The model was applied to a real-world historical data set extracted from fixed sensors over a 5-years period. Our approach is based on a type of Single hidden Layer Feed-forward Neural Network (SLFN) known for being a high-speed machine learning algorithm. The model was, then, compared to other well-known algorithms in the prediction literature. Experimental results demonstrated that, according to the most commonly used criteria of error measurements (RMSE, MAE, and MAPE), our model is performing better in terms of prediction accuracy. The use of Akaike’s Information Criterion technique (AIC) has also shown that the proposed model has a higher performance.

scholarly journals LPWAN-based hybrid backhaul communication for intelligent transportation systems: architecture and performance evaluation

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Taghi Shahgholi ◽  
Amir Sheikhahmadi ◽  
Keyhan Khamforoosh ◽  
Sadoon Azizi
Keyword(s):  
Low Power ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
Road Traffic ◽  
Intelligent Transportation ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Area Network ◽  
Emergency Vehicle ◽  
Wide Area Network

AbstractIncreased number of the vehicles on the streets around the world has led to several problems including traffic congestion, emissions, and huge fuel consumption in many regions. With advances in wireless and traffic technologies, the Intelligent Transportation System (ITS) has been introduced as a viable solution for solving these problems by implementing more efficient use of the current infrastructures. In this paper, the possibility of using cellular-based Low-Power Wide-Area Network (LPWAN) communications, LTE-M and NB-IoT, for ITS applications has been investigated. LTE-M and NB-IoT are designed to provide long range, low power and low cost communication infrastructures and can be a promising option which has the potential to be employed immediately in real systems. In this paper, we have proposed an architecture to employ the LPWAN as a backhaul infrastructure for ITS and to understand the feasibility of the proposed model, two applications with low and high delay requirements have been examined: road traffic monitoring and emergency vehicle management. Then, the performance of using LTE-M and NB-IoT for providing backhaul communication infrastructure has been evaluated in a realistic simulation environment and compared for these two scenarios in terms of end-to-end latency per user. Simulation of Urban MObility has been used for realistic traffic generation and a Python-based program has been developed for evaluation of the communication system. The simulation results demonstrate the feasibility of using LPWAN for ITS backhaul infrastructure mostly in favor of the LTE-M over NB-IoT.

scholarly journals A Multi-Loop Vehicle-Counting Method under Gray Mode and RGB Mode

2021 ◽  
Vol 11 (15) ◽  
pp. 6831
Author(s):  
Yue Chen ◽  
Jian Lu
Keyword(s):  
Intelligent Transportation Systems ◽  
High Efficiency ◽  
Road Traffic ◽  
Low Cost ◽  
Rapid Development ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Counting Method ◽  
Moving Vehicle ◽  
Available Information

With the rapid development of road traffic, real-time vehicle counting is very important in the construction of intelligent transportation systems (ITSs). Compared with traditional technologies, the video-based method for vehicle counting shows great importance and huge advantages in its low cost, high efficiency, and flexibility. However, many methods find difficulty in balancing the accuracy and complexity of the algorithm. For example, compared with traditional and simple methods, deep learning methods may achieve higher precision, but they also greatly increase the complexity of the algorithm. In addition to that, most of the methods only work under one mode of color, which is a waste of available information. Considering the above, a multi-loop vehicle-counting method under gray mode and RGB mode was proposed in this paper. Under gray and RGB modes, the moving vehicle can be detected more completely; with the help of multiple loops, vehicle counting could better deal with different influencing factors, such as driving behavior, traffic environment, shooting angle, etc. The experimental results show that the proposed method is able to count vehicles with more than 98.5% accuracy while dealing with different road scenes.

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.

Sign in / Sign up

Export Citation Format

Share Document