The varying phenomena that characterize a pedestrian flow make it one of the most challenging traffic flow processes to manage and control. In the past three decades, we have started to unravel the science behind the crowd.
This has led to some important insights that are not only needed to reproduce, predict, and manage pedestrian flow, but will also provide potential avenues to managing other phenomena. In this talk, we will provide a historic perspective on pedestrian flow theory and crowd management. We show some of the key phenomena that have been observed (in controlled experiments, in the field), and how these phenomena can be explained, used or prevented.
We will also highlight some of the recent contributions in the field, including the role of AI, novel monitoring technology, and digital twins. We round up the talk showing how the finding can be generalized. We show how the game-theoretical modeling proposed for pedestrian flow models can form a basis for controlling connected autonomous vehicles. Using various examples, we show how self-organization, omnipresent in pedestrian flow, can inspire decentralized control approaches of other flow processes (e.g., autonomous vessels, drones). We show how approaches to reduce flow breakdown for pedestrian flows can be generalized for other flow processes.
Prediciting restaurant and popularity based on Yelp Dataset project reportALIN BABU
This document discusses a machine learning project that aims to predict restaurant ratings and popularity changes on Yelp using logistic regression, naive bayes, and multinomial naive bayes models. The project uses a Yelp dataset containing 20000 restaurant reviews to evaluate these algorithms. Logistic regression performed slightly better than the other methods at predicting ratings, though all methods' predictions still require improvement. More data and tailored methods may enhance predictions.
The document provides an introduction to linear algebra concepts for machine learning. It defines vectors as ordered tuples of numbers that express magnitude and direction. Vector spaces are sets that contain all linear combinations of vectors. Linear independence and basis of vector spaces are discussed. Norms measure the magnitude of a vector, with examples given of the 1-norm and 2-norm. Inner products measure the correlation between vectors. Matrices can represent linear operators between vector spaces. Key linear algebra concepts such as trace, determinant, and matrix decompositions are outlined for machine learning applications.
The document discusses fuzzy measures and belief theory. It begins by defining fuzzy sets and fuzzy measures, which assign a degree of membership between 0 and 1 to subsets of a universal set. Belief and plausibility measures are then introduced as generalizations of probability measures that satisfy additional axioms. Combining evidence from multiple sources is discussed, along with deriving a basic assignment from a belief measure and combining basic assignments using Dempster's rule of combination. An example combines the assessments of two experts examining a painting to derive joint belief. Marginal basic assignments are also briefly mentioned.
Chaotic system and its Application in CryptographyMuhammad Hamid
A seminar on Chaotic System and Its application in cryptography specially in image encryption. Slide covers
Introduction
Bifurcation Diagram
Lyapnove Exponent
The document discusses Bessel functions, which are solutions to a second order differential equation that arises in diverse situations. It also discusses the Hankel transform, which expresses functions as a weighted sum of Bessel functions of the first kind. The Hankel transform is useful for problems with cylindrical or spherical symmetry, as it appears when taking the multidimensional Fourier transform in hyperspherical coordinates. It has advantages like being applicable to both homogeneous and inhomogeneous problems and simplifying calculations.
Properties of Caputo Operator and Its Applications to Linear Fractional Diffe...IJERA Editor
The purpose of this paper is to demonstrate the power of two mostly used definitions for fractional differentiation, namely, the Riemann-Liouville and Caputo fractional operator to solve some linear fractional-order differential equations. The emphasis is given to the most popular Caputo fractional operator which is more suitable for the study of differential equations of fractional order..Illustrative examples are included to demonstrate the procedure of solution of couple of fractional differential equations having Caputo operator using Laplace transformation. Itshows that the Laplace transforms is a powerful and efficient technique for obtaining analytic solution of linear fractional differential equations
Introduction of Quantum Annealing and D-Wave MachinesArithmer Inc.
This slide was used for Arithmer seminar in April 2021, by Dr. Yuki Bando.
It is for introduction of quantum computer, D-wave series, and its application to optimization problems in industry.
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
A brief introduction to mutual information and its applicationHyun-hwan Jeong
Mutual information is introduced as a measure for dependence between random variables that is based on entropy. It is defined as the difference between the joint entropy of two variables and the sum of their individual entropies. Mutual information has various applications including association measures between genomic features and outcomes, using mutual information for distance measures in clustering to detect epistatic interactions, and constructing outcome-guided mutual information networks for prediction. Challenges with mutual information include handling noise in continuous data and assessing statistical significance while accounting for multiple testing.
Prediciting restaurant and popularity based on Yelp Dataset project reportALIN BABU
This document discusses a machine learning project that aims to predict restaurant ratings and popularity changes on Yelp using logistic regression, naive bayes, and multinomial naive bayes models. The project uses a Yelp dataset containing 20000 restaurant reviews to evaluate these algorithms. Logistic regression performed slightly better than the other methods at predicting ratings, though all methods' predictions still require improvement. More data and tailored methods may enhance predictions.
The document provides an introduction to linear algebra concepts for machine learning. It defines vectors as ordered tuples of numbers that express magnitude and direction. Vector spaces are sets that contain all linear combinations of vectors. Linear independence and basis of vector spaces are discussed. Norms measure the magnitude of a vector, with examples given of the 1-norm and 2-norm. Inner products measure the correlation between vectors. Matrices can represent linear operators between vector spaces. Key linear algebra concepts such as trace, determinant, and matrix decompositions are outlined for machine learning applications.
The document discusses fuzzy measures and belief theory. It begins by defining fuzzy sets and fuzzy measures, which assign a degree of membership between 0 and 1 to subsets of a universal set. Belief and plausibility measures are then introduced as generalizations of probability measures that satisfy additional axioms. Combining evidence from multiple sources is discussed, along with deriving a basic assignment from a belief measure and combining basic assignments using Dempster's rule of combination. An example combines the assessments of two experts examining a painting to derive joint belief. Marginal basic assignments are also briefly mentioned.
Chaotic system and its Application in CryptographyMuhammad Hamid
A seminar on Chaotic System and Its application in cryptography specially in image encryption. Slide covers
Introduction
Bifurcation Diagram
Lyapnove Exponent
The document discusses Bessel functions, which are solutions to a second order differential equation that arises in diverse situations. It also discusses the Hankel transform, which expresses functions as a weighted sum of Bessel functions of the first kind. The Hankel transform is useful for problems with cylindrical or spherical symmetry, as it appears when taking the multidimensional Fourier transform in hyperspherical coordinates. It has advantages like being applicable to both homogeneous and inhomogeneous problems and simplifying calculations.
Properties of Caputo Operator and Its Applications to Linear Fractional Diffe...IJERA Editor
The purpose of this paper is to demonstrate the power of two mostly used definitions for fractional differentiation, namely, the Riemann-Liouville and Caputo fractional operator to solve some linear fractional-order differential equations. The emphasis is given to the most popular Caputo fractional operator which is more suitable for the study of differential equations of fractional order..Illustrative examples are included to demonstrate the procedure of solution of couple of fractional differential equations having Caputo operator using Laplace transformation. Itshows that the Laplace transforms is a powerful and efficient technique for obtaining analytic solution of linear fractional differential equations
Introduction of Quantum Annealing and D-Wave MachinesArithmer Inc.
This slide was used for Arithmer seminar in April 2021, by Dr. Yuki Bando.
It is for introduction of quantum computer, D-wave series, and its application to optimization problems in industry.
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
A brief introduction to mutual information and its applicationHyun-hwan Jeong
Mutual information is introduced as a measure for dependence between random variables that is based on entropy. It is defined as the difference between the joint entropy of two variables and the sum of their individual entropies. Mutual information has various applications including association measures between genomic features and outcomes, using mutual information for distance measures in clustering to detect epistatic interactions, and constructing outcome-guided mutual information networks for prediction. Challenges with mutual information include handling noise in continuous data and assessing statistical significance while accounting for multiple testing.
This document discusses unconditionally stable finite-difference time-domain (FDTD) methods for solving Maxwell's equations numerically. It outlines FDTD algorithms such as Yee's method from 1966 which discretize the equations on a staggered grid. It also discusses the von Neumann stability analysis and compares implicit Crank-Nicolson and alternating-direction implicit methods to conventional explicit FDTD methods. The document notes the advantages of unconditionally stable methods but also mentions potential disadvantages.
Application of calculus in our daily liferehan9911
Calculus is used in many fields including physics, engineering, economics, statistics, and medicine. It is used to create mathematical models and arrive at optimal solutions. For example, in physics, many concepts are based on calculus. Weather prediction also uses calculus-based computer modeling to more accurately predict weather based on variables like temperature, wind speed, and moisture levels. The spread of infectious diseases can also be modeled using calculus to determine how far and fast a disease may spread based on susceptible, infected, and recovered populations. Calculus is widely applied in engineering fields from civil to mechanical engineering to analyze structures, fluids, thermal systems, and more.
Este documento presenta varios ejemplos de problemas de razones relacionadas. Explica la estrategia para resolver este tipo de problemas, que involucra establecer una ecuación entre las variables, derivarla para obtener la ecuación de razones relacionadas, y sustituir los valores conocidos para encontrar la razón desconocida. Luego, resuelve cuatro ejemplos ilustrativos aplicando esta metodología.
The document provides an overview of chaos theory, including its key characteristics and history. It discusses Edward Lorenz's discovery of the butterfly effect using a simplified weather model. Lorenz found that small changes to initial conditions could drastically alter long-term outcomes, making predictions impossible. This led to the concept of sensitive dependence on initial conditions in chaotic systems. The document also describes Lorenz's water wheel experiment and the Lorenz attractor diagram that helped establish chaos theory.
This document summarizes key concepts from a lecture on stability and bifurcation theory of dynamical systems. It defines fundamental concepts like orbits, stability of equilibria and periodic orbits, and introduces bifurcation theory. Bifurcations occur when stability is lost as parameters change, resulting in qualitative changes to dynamics. The document discusses linear stability analysis and how bifurcations can involve changes in the number or stability of equilibria or periodic orbits. It distinguishes between static, dynamic, and mixed bifurcations that can occur at non-hyperbolic equilibrium points of parameter-dependent systems.
Probabilistic Programming allows very flexible creation of custom probabilistic models and is mainly concerned with insight and learning from your data. The approach is inherently Bayesian so we can specify priors to inform and constrain our models and get uncertainty estimation in form of a posterior distribution. Using MCMC sampling algorithms we can draw samples from this posterior to very flexibly estimate these models. PyMC3 and Stan are the current state-of-the-art tools to construct and estimate these models.
One major drawback of sampling, however, is that it's often very slow, especially for high-dimensional models. That's why more recently, variational inference algorithms have been developed that are almost as flexible as MCMC but much faster. Instead of drawing samples from the posterior, these algorithms instead fit a distribution (e.g. normal) to the posterior turning a sampling problem into and optimization problem. ADVI -- Automatic Differentation Variational Inference -- is implemented in PyMC3 and Stan, as well as a new package called Edward which is mainly concerned with Variational Inference.
This presentation discusses the application of numerical methods in real-life scenarios. It provides examples such as estimating ocean currents, modeling combustion flow in coal power plants, and simulating airflow over airplane bodies. The presentation also examines modeling electromagnetics, shuttle/tank separation, and other applications involving differential equations, programming, control systems, and data fitting. In total, 16 real-world uses of numerical methods are outlined.
Advanced MATLAB Tutorial for Engineers & ScientistsRay Phan
This is a more advanced tutorial in the MATLAB programming environment for upper level undergraduate engineers and scientists at Ryerson University. The first half of the tutorial covers a quick review of MATLAB, which includes how to create vectors, matrices, how to plot graphs, and other useful syntax. The next part covers how to create cell arrays, logical operators, using the find command, creating Transfer Functions, finding the impulse and step response, finding roots of equations, and a few other useful tips. The last part covers more advanced concepts such as analytically calculating derivatives and integrals, polynomial regression, calculating the area under a curve, numerical solutions to differential equations, and sorting arrays.
This document provides an overview of the practical applications of mathematics in daily life and various fields. It discusses how basic math is used for everyday tasks like shopping and bills. More advanced topics covered include relations and functions and their uses in physics, biology, and economics. Other mathematical concepts like matrices, determinants, derivatives, and probability are explained with examples of how they are applied in fields such as cryptography, engineering, medicine, chemistry, and weather forecasting. The document encourages feedback on understanding the practical uses of mathematics.
This document discusses differential equations and their application. It begins by defining what a differential equation is and provides examples of first order differential equations. It then discusses Newton's Law of Cooling, providing the derivation and formulation of the law. Several applications of Newton's Law of Cooling are presented, including using it to estimate time of death from temperature readings and determining cooling system specifications for computer processors. Other topics covered include the Mean Value Theorem, precalculus concepts, and examples of how calculus is applied in various fields such as credit cards, biology, engineering, architecture, and more.
Linear algebra application in linear programming Lahiru Dilshan
Linear programming is used to maximize or minimize quantities subject to constraints. It can be applied to problems with any number of variables and constraints, as long as the relationships are linear. Key aspects include defining an objective function to optimize, determining the feasible region where all constraints are satisfied, and finding extreme points where the objective function may be maximized or minimized. An example problem involves determining how to allocate candy mixtures to maximize revenue given constraints on available ingredients. The optimal solution is found at an extreme point within the bounded feasible region.
The document discusses backward difference, which is a method used in interpolation and numerical integration of functions. It involves taking the difference between function values at equally spaced points, working backward from the last point. The first backward difference is between the last two points, the second is the difference between the first differences, and so on. An example calculates the backward differences for a set of data points and uses the formula to interpolate the function value at x=42.
The document provides a word-for-word meaning and translation of the Sri Vishnu Sahasra Naama Stotram in English. It was started by Sudhakar V.Rao on May 22, 2005 during the Paarthiva Naama Samvatsara year on the Vaishakha Shuddha Chaturdasi day under the Swaati Nakshatram. The document includes introductions and translations of the first 18 verses.
numericai matmatic matlab uygulamalar ali abdullahAli Abdullah
The document discusses various interpolation methods including Newton's forward and backward interpolation methods. Newton's forward interpolation method uses forward difference operators to calculate interpolated values near the beginning of a data set. Newton's backward interpolation method uses backward difference operators to calculate interpolated values near the end of a data set. The document provides examples of applying Newton's forward and backward interpolation methods to calculate interpolated values using given data tables. It also discusses writing a MATLAB program to calculate interpolated values using a third degree polynomial interpolation.
The document discusses the history and development of the ALGOL programming language. It describes how the need for a universal, machine-independent programming language led to the creation of ALGOL in the late 1950s by an international committee. Key aspects of ALGOL included it being based on mathematical notation, its ability to describe computing processes for publications, and its potential for mechanical translation into machine code. ALGOL established many standards that influenced programming languages for decades but also had limitations that contributed to its eventual decline.
Can we use methods from cooperative traffic and crowd modelling and management to manage drone traffic flows? I think we can! In this ppt, I explain how we can instill distributed traffic management in 3D...
This document discusses unconditionally stable finite-difference time-domain (FDTD) methods for solving Maxwell's equations numerically. It outlines FDTD algorithms such as Yee's method from 1966 which discretize the equations on a staggered grid. It also discusses the von Neumann stability analysis and compares implicit Crank-Nicolson and alternating-direction implicit methods to conventional explicit FDTD methods. The document notes the advantages of unconditionally stable methods but also mentions potential disadvantages.
Application of calculus in our daily liferehan9911
Calculus is used in many fields including physics, engineering, economics, statistics, and medicine. It is used to create mathematical models and arrive at optimal solutions. For example, in physics, many concepts are based on calculus. Weather prediction also uses calculus-based computer modeling to more accurately predict weather based on variables like temperature, wind speed, and moisture levels. The spread of infectious diseases can also be modeled using calculus to determine how far and fast a disease may spread based on susceptible, infected, and recovered populations. Calculus is widely applied in engineering fields from civil to mechanical engineering to analyze structures, fluids, thermal systems, and more.
Este documento presenta varios ejemplos de problemas de razones relacionadas. Explica la estrategia para resolver este tipo de problemas, que involucra establecer una ecuación entre las variables, derivarla para obtener la ecuación de razones relacionadas, y sustituir los valores conocidos para encontrar la razón desconocida. Luego, resuelve cuatro ejemplos ilustrativos aplicando esta metodología.
The document provides an overview of chaos theory, including its key characteristics and history. It discusses Edward Lorenz's discovery of the butterfly effect using a simplified weather model. Lorenz found that small changes to initial conditions could drastically alter long-term outcomes, making predictions impossible. This led to the concept of sensitive dependence on initial conditions in chaotic systems. The document also describes Lorenz's water wheel experiment and the Lorenz attractor diagram that helped establish chaos theory.
This document summarizes key concepts from a lecture on stability and bifurcation theory of dynamical systems. It defines fundamental concepts like orbits, stability of equilibria and periodic orbits, and introduces bifurcation theory. Bifurcations occur when stability is lost as parameters change, resulting in qualitative changes to dynamics. The document discusses linear stability analysis and how bifurcations can involve changes in the number or stability of equilibria or periodic orbits. It distinguishes between static, dynamic, and mixed bifurcations that can occur at non-hyperbolic equilibrium points of parameter-dependent systems.
Probabilistic Programming allows very flexible creation of custom probabilistic models and is mainly concerned with insight and learning from your data. The approach is inherently Bayesian so we can specify priors to inform and constrain our models and get uncertainty estimation in form of a posterior distribution. Using MCMC sampling algorithms we can draw samples from this posterior to very flexibly estimate these models. PyMC3 and Stan are the current state-of-the-art tools to construct and estimate these models.
One major drawback of sampling, however, is that it's often very slow, especially for high-dimensional models. That's why more recently, variational inference algorithms have been developed that are almost as flexible as MCMC but much faster. Instead of drawing samples from the posterior, these algorithms instead fit a distribution (e.g. normal) to the posterior turning a sampling problem into and optimization problem. ADVI -- Automatic Differentation Variational Inference -- is implemented in PyMC3 and Stan, as well as a new package called Edward which is mainly concerned with Variational Inference.
This presentation discusses the application of numerical methods in real-life scenarios. It provides examples such as estimating ocean currents, modeling combustion flow in coal power plants, and simulating airflow over airplane bodies. The presentation also examines modeling electromagnetics, shuttle/tank separation, and other applications involving differential equations, programming, control systems, and data fitting. In total, 16 real-world uses of numerical methods are outlined.
Advanced MATLAB Tutorial for Engineers & ScientistsRay Phan
This is a more advanced tutorial in the MATLAB programming environment for upper level undergraduate engineers and scientists at Ryerson University. The first half of the tutorial covers a quick review of MATLAB, which includes how to create vectors, matrices, how to plot graphs, and other useful syntax. The next part covers how to create cell arrays, logical operators, using the find command, creating Transfer Functions, finding the impulse and step response, finding roots of equations, and a few other useful tips. The last part covers more advanced concepts such as analytically calculating derivatives and integrals, polynomial regression, calculating the area under a curve, numerical solutions to differential equations, and sorting arrays.
This document provides an overview of the practical applications of mathematics in daily life and various fields. It discusses how basic math is used for everyday tasks like shopping and bills. More advanced topics covered include relations and functions and their uses in physics, biology, and economics. Other mathematical concepts like matrices, determinants, derivatives, and probability are explained with examples of how they are applied in fields such as cryptography, engineering, medicine, chemistry, and weather forecasting. The document encourages feedback on understanding the practical uses of mathematics.
This document discusses differential equations and their application. It begins by defining what a differential equation is and provides examples of first order differential equations. It then discusses Newton's Law of Cooling, providing the derivation and formulation of the law. Several applications of Newton's Law of Cooling are presented, including using it to estimate time of death from temperature readings and determining cooling system specifications for computer processors. Other topics covered include the Mean Value Theorem, precalculus concepts, and examples of how calculus is applied in various fields such as credit cards, biology, engineering, architecture, and more.
Linear algebra application in linear programming Lahiru Dilshan
Linear programming is used to maximize or minimize quantities subject to constraints. It can be applied to problems with any number of variables and constraints, as long as the relationships are linear. Key aspects include defining an objective function to optimize, determining the feasible region where all constraints are satisfied, and finding extreme points where the objective function may be maximized or minimized. An example problem involves determining how to allocate candy mixtures to maximize revenue given constraints on available ingredients. The optimal solution is found at an extreme point within the bounded feasible region.
The document discusses backward difference, which is a method used in interpolation and numerical integration of functions. It involves taking the difference between function values at equally spaced points, working backward from the last point. The first backward difference is between the last two points, the second is the difference between the first differences, and so on. An example calculates the backward differences for a set of data points and uses the formula to interpolate the function value at x=42.
The document provides a word-for-word meaning and translation of the Sri Vishnu Sahasra Naama Stotram in English. It was started by Sudhakar V.Rao on May 22, 2005 during the Paarthiva Naama Samvatsara year on the Vaishakha Shuddha Chaturdasi day under the Swaati Nakshatram. The document includes introductions and translations of the first 18 verses.
numericai matmatic matlab uygulamalar ali abdullahAli Abdullah
The document discusses various interpolation methods including Newton's forward and backward interpolation methods. Newton's forward interpolation method uses forward difference operators to calculate interpolated values near the beginning of a data set. Newton's backward interpolation method uses backward difference operators to calculate interpolated values near the end of a data set. The document provides examples of applying Newton's forward and backward interpolation methods to calculate interpolated values using given data tables. It also discusses writing a MATLAB program to calculate interpolated values using a third degree polynomial interpolation.
The document discusses the history and development of the ALGOL programming language. It describes how the need for a universal, machine-independent programming language led to the creation of ALGOL in the late 1950s by an international committee. Key aspects of ALGOL included it being based on mathematical notation, its ability to describe computing processes for publications, and its potential for mechanical translation into machine code. ALGOL established many standards that influenced programming languages for decades but also had limitations that contributed to its eventual decline.
Can we use methods from cooperative traffic and crowd modelling and management to manage drone traffic flows? I think we can! In this ppt, I explain how we can instill distributed traffic management in 3D...
Talk given at the kick-off of the ERC MAGnUM PhD week on the ALLEGRO program. The talk gives both an overview of ALLEGRO and then focusses more on active mode traffic operations.
In this short presentation, we will provide some recent developments in the field of crowd monitoring, modelling and management. We will illustrate these by showing various projects that we are involved in, including the SmartStation project, and the different events organised in and around the city of Amsterdam (including the Europride, SAIL, etc.).
In the talk, we will discuss the different components of the system and the methods and technology involved in these. We focus on advanced data collection techniques, the use of social media data, data fusion and the advanced macroscopic modelling required for this. Also, we will show examples of interventions that have been tested, showing how these systems are used in practise.
Presentation given during the 2016 conference Analysis and Control on Networks: trends and perspectives in Padua, Italy. Presentation provides an engineerings perspective on the various issues with see with the modelling and management of crowds, and some of the new modelling approaches.
Differential game theory for Traffic Flow ModellingSerge Hoogendoorn
Lecture given at the INdAM symposium in Rome, 2017. The lecture shows how you can use differential games to model traffic flows, focussing on pedestrian simulation.
In this keynote, I discuss 25 years of active mode research performed at Transport & Planning. We discuss the role of data, and the use of game-theory to model active mode traffic. We also show how complex models can be simplified, looking at multi-scale approaches.
This talk presents a novel microscopic modelling framework for bicycle flow operations. The model does justice to the kinematics of cyclists. Contrary to pedestrians, cyclist are more restricted in their movement. The model approximates these restrictions by considering speed and movement direction and changes therein. Secondly, the model includes different strategies (cooperative, zero-acceleration, demon opponent) in its underlying game-theoretical framework. This allows us to model different attitudes towards risk.
The (qualitative) insights gained by application of the model pertain to one-on-one interactions between cyclists and the impact of the strategy assumptions and parameter choices on those interactions as well as on the collective phenomena that occur in the cyclist flow and their sensitivity to parameters (reflecting the extent of the prediction horizon, the level of anisotropy, and the relative importance of keeping the desired path). With respect to the collective phenomena, we look at efficiency and self-organised patterns.
We conclude that the model acts in a plausible manner. While we do not aim to show absolute validity, we see that the qualitative behaviour of one-on-one interactions is plausible. We also observe plausible collective patterns, including self-organisation. The latter is not trivial given the fundamental differences in bicycle and pedestrian flow.
Information Spread in the Context of Evacuation OptimizationDr. Mirko Kämpf
The document describes a simulation of evacuation from a building using an agent-based model. Agents represent individuals, groups, or people with communication devices. The simulation analyzes how information spreads during evacuation and compares results between open and restricted geometries. Statistical analysis methods are applied to detect phases or transitions in the system. The impacts of different communication technologies and evacuation strategies are also studied. The goal is to define requirements for communication networks and sensors to optimize the evacuation process based on the simulation results.
Linear Regression Model Fitting and Implication to Self Similar Behavior Traf...IOSRjournaljce
We present a simple linear regression model fit in the direction of self-similarity behavior of internet user’s arrival data pattern. It has been reported that Internet traffic exhibits self-similarity. Motivated by this fact, real time internet users arrival patterns considered as traffic and the results carried out and proven that it has the self-similar nature by various Hurst index methods. The present study provides a mathematical model equation in terms linear regression as a tool to predict the arrival pattern of Internet users data at web centers. Numerical results, analysis discussed and presented here plays a significant role in improvement of the services and forecasting analysis of arrival protocols at web centers in the view of quality of service (QOS).
Presentation of GreenYourMove's hybrid approach in 3rd International Conferen...GreenYourMove
Presentation of the Journey planning problem and GreenYourMove's hybrid approach.
Dr. Georgios Saharidis, Fragogios Antonis, Rizopoulos Dimitris, Chrysostomos Chatzigeorgiou
The document summarizes a presentation on a proposed hybrid approach to solve the multi-modal journey planning problem. The approach combines mathematical programming and heuristic methods like Dijkstra's algorithm. It develops a mixed integer linear program model to minimize travel time and environmental cost. Future work aims to improve the algorithm by reducing the model's dimensionality and constraints to enhance computational speed for online applications.
When and where are bus express services justified?BRTCoE
Express bus services are more beneficial in corridors with:
- Increasing system demand
- Increasing trip lengths
- Increasing trip concentration
- Increasing dwell times
- Decreasing vehicle capacity
- Increasing critical arc loads
- Increasing value of travel time
- Decreasing value of waiting time
The researchers used a regression model to analyze 972 scenarios of express service provisions on bus corridors. The model showed that express services provide greater benefits with higher potential travel time savings, trip concentration, and overcrowding levels.
The document presents a mathematical model for macroscopic traffic flow. It introduces three key variables: traffic flow (q), density (ρ), and speed (v). It uses the conservation principle to relate these variables, stating that the change in the number of cars within a road segment over time is equal to the net flow of cars into and out of that segment. This leads to an equation showing that traffic flow is equal to the product of traffic density and traffic speed. The document lays the groundwork for formulating traffic problems in terms of partial differential equations that can be solved.
International Journal of Engineering Inventions (IJEI) provides a multidisciplinary passage for researchers, managers, professionals, practitioners and students around the globe to publish high quality, peer-reviewed articles on all theoretical and empirical aspects of Engineering and Science.
Modeling business management systems transportationSherin El-Rashied
Introduction
How IT &Business Process Fit Together
What is modeling?
What is Simulation?
Modeling & Simulation in Business Process Management
The Seven-Step Model-Building Process
Transportation
An overview on transportation modeling
Transport model scope & structure
Car Traffic Jam Problem
Aim of Transportation Model
Types of Traffic Models
Microscopic Traffic model & Simulation
Cellular Automaton model
Conclusion
Solving Transportation Problem by Software Application
Class Example
Active modes and urban mobility: outcomes from the ALLEGRO projectSerge Hoogendoorn
In this presentation, we present some examples of the main outcomes of the ALLEGRO project so far. The talks starts with showing how active mode traffic can play a major role given that cities are getting denser.
This document discusses multi-modal journey planning and describes a proposed solution approach. It summarizes the multi-modal journey planning problem, characteristics, previous work, and proposes a hybrid approach using a mathematical programming model combined with heuristic methods like Dijkstra's algorithm. The approach involves using the programming model to solve the multi-modal journey planning problem after applying Dijkstra's algorithm and graph techniques to pre-process the data.
Similaire à IEEE-ITSC 2023 Keynote - What Crowds can Teach Us (20)
Opening intelligent bicycle road - 16th of June, 2022. In this talk (in Dutch), we have introduced the investments in monitoring at the TU Delft campus.
This presentation provides an overview of our work on pedestrian flows and management. I discuss basic pedestrian flow dynamics, technology to support safe flow operations during the pandemic, and novel deployment of these technologies after the pandemic.
Short talk impact Covid-19 on supply and demand during the RA webinarSerge Hoogendoorn
We sketch a conceptual framework showing (lasting) impact on demand and supply. We illustrate complications at the supply side due to changing behaviour. We show how to include interventions and how to assess them.
Short presentation about the role of AMS in solving Amsterdam mobility issues and setting the mobility agenda. Linking science and practise using Amsterdam as a Living Lab.
Presentatie gegeven tijdens de Masterclass Stresstesten RWS. Wat is veerkracht? Welke verstoringen kunnen optreden? Hoe ontwikkelt dit zich in de toekomst? Wat kunnen we doen om de veerkracht te vergroten? Deze en andere vragen komen aan bod in deze presentatie...
Talk given about current PhD projects that are relevant for shaping urban mobility. In particular, focus has been on behavioural insights relating to sustainable transport modes (such as walking, cycling, and MaaS).
This document discusses transport resilience, which refers to the impact of and recovery from disruptions to transport systems. It examines challenges in understanding and improving resilience due to increasing complexity, uncertainty, and disruption probabilities in transport systems. The goal is to develop methods to resiliently design, plan and operate urban transport systems by applying principles like containment, adaptiveness and recourse. Experiments observe how behavior, coping strategies and system impacts vary greatly during disruptions. Tools are being developed for predictive modeling and real-time decision support to optimize multi-modal transport operations during disruptions. Trade-offs between efficiency and resilience must also be considered.
The presentation deals with the Importance of resilience in transportation systems: factors that influence its relevance, the trade-off between robustness and efficiency, and the relation of resilience and evacuation management.
In many countries, cities are expanding in terms of size, number residents and visitors, etc. The resulting increase in concentration of people, with their mobility needs, causes major traffic and transportation problems in and around our cities. Next to the economic impacts due to delay and unreliability of travel time, concerns regarding safety and security, emissions and sustainability become more and more urgent.
ITS (Intelligent Transportation Systems) hold the potential to reduce these issues. In the past decade, we have been more and more successful in making better use of the available infrastructure by using traditional ITS measures. As we will show in this talk, key to this success has been in achieving a profound understanding of what are the key phenomena that characterise network traffic flows, and designing solutions that capitalise on this.
The playing field is however rapidly changing. For one, we see a transition from road-side to in-car technology in terms of sensing and actuation. This provides great opportunities, but making best use of these is not trivial and requires a paradigm shift in the way we think about managing traffic flows where collaboration between the old stakeholders (e.g. road authorities) and the new stakeholders (e.g. companies like Google, and TomTom) becomes increasingly important. This will be illustrated in this talk by some examples showing how we can put the transition to in-car traffic management to use, both in terms of making optimal use of the new data sources and the use of the car as an actuator.
With respect to the latter, we will see that even for low penetration levels, which will occur in the transition phase towards a more highly automated traffic stream, considerable impacts can be achieved if we adequately consider the non-automated vehicles. Furthermore, it requires vehicles to be able to communicate and cooperate with each other.
These two elements are two of the five steps that was identified in the transition towards a fully automated system.
The final part of the talk will deal with the other steps that are deemed important to understand which of the scenarios in a urban self-driving future will unfold. These pertain to the interaction between man and machine, the need and willingness to invest in separate infrastructure in city, and whether automated car can co-exist with other (active) travel modes. With respect to the latter, we will also consider what ITS can mean for the other modes of travel.
Korte presentatie met de verschillende onderzoeksthema's die relevant zijn binnen het onderzoeksdomein Veilig Ontruimen. De presentatie heeft tot doel ideeën te genereren voor een onderzoeksagenda.
Keynote gegeven tijdens het NDW symposium over mogelijkheden van nieuwe databronnen. We kijken met name naar toepassingen binnen het netwerkbroed dynamisch verkeersmanagement.
In deze lezing worden recent afgeronde TRAIL proefschriften besproken, met focus op de relevantie voor de praktijk. We bespreken recente ontwikkeling in verkeersmanagement en coöperatieve systemen, crowd- en evacuatiemanagement en transport security. We bespreken ook kort de verschuiving van de focus binnen de leerstoel Traffic Operations and Management.
1) The document discusses innovations in traffic management, using suppression of wide moving jams as the main example.
2) It emphasizes the importance of integrating different traffic management measures and field trials to drive innovations.
3) Monitoring innovations like vehicle-to-vehicle technology are needed to improve integrated network management, especially as vehicles become actuators that can be controlled.
Presentation about active mode transport given at the AITPM workshop on active mode mobility. Provides overview of our pedestrian research and the first results of the ALLEGRO project.
Vision on Smart Urban Mobility given during the AITPM conference in Sydney. Talk was about key elements needed to provide the urban transportation system for the future. See http://www.aitpm.com.au/Conference/Program/conference-home for the conference details.
Presentation given during the first transportation workshop at Melbourne Uni. Focus on crowd monitoring and management. With examples from various projects (SAIL, Mekka, etc.)
IPAM Hoogendoorn 2015 - workshop on Decision Support SystemsSerge Hoogendoorn
Presentation during IPAM workshop in Los Angeles where I shared the results of the Practical Pilot Amsterdam (a pilot of Integrated Network Management in Amsterdam), the lessons learnt and the plans for the next phase.
- Increasing vehicle automation will fundamentally change network traffic flow characteristics beyond just changes in roadway capacity, affecting stability, queues, and heterogeneity.
- These changes impact traffic flow theory and tools used for modeling, simulation, and assessment of cooperative systems and automation.
- Two case studies illustrate impacts on traffic management and how traffic flow properties like shockwave speeds will change with different market penetration rates of automated vehicles.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
ESR spectroscopy in liquid food and beverages.pptx
IEEE-ITSC 2023 Keynote - What Crowds can Teach Us
1. Harnassing Crowd
Intelligence…
Prof. Dr. Serge Hoogendoorn
Transport & Planning Department
Transport & Mobility Institute
Delft University of Technology
What crowds
can teach us…
2. Stress testing crowds in CrowdLimits
In June 2018, we tried to establish the limits to self-organization in
pedestrian flows…
5. Examples of self-organisation
Formation of diagonal stripes in
crossing flows…
Viscous fingering when standing and
moving pedestrians interact
Efficient multi-direction flow
interactions
7. 25 years of fascination for pedestrian and bicycle flows:
Active modes are wonderfully complex and showcase unexpected dynamics
8. But there are other reasons to
focus on active modes…
9. Sustainable urban mobility is impossible without active modes
due to their limited spatial and ecological impact, health impact,
relevance as first / last mile / transfer mode
10. There are major scientific, technological and engineering
challenges to solve, including data collection
11. We can learn a lot from the active modes and the modeling
and control thereof…
17. Our take on pedestrian modeling…
Our behavioral model for pedestrian flow dynamics
• Main assumption “pedestrian economicus” based on
principle of least effort:
From all possible actions (accelerate, decelerate,
changing direction, do nothing) a pedestrian chooses
the action yielding smallest predicted effort (disutility)
• The predicted effort is the (weighed) sum of different
effort components (e.g., walking too close / colliding,
walking too slowly or too fast, straying from intended
path, etc.) - like attributes in utility models
18. How does the predicted effort work?
Path A
Path B
Path C
Destination
Shortest path
Effort component examples:
• Straying from shortest path
• Being too close to other pedestrians
• Accelerating / stopping
• Not adhering to traffic rules…
Possible paths result from candidate
control actions; note: there are an infinite
number of these paths possible
19. Anticipation strategies…
Furthering the behavioral foundation
• A key element in our modeling approach is that we assume that
the ego pedestrian anticipates on the behavior of other pedestrians…
• Research in the Seventies and Eighties have shown that:
• Pedestrians unconsciously communicate via very subtle movements
exchanging their intentions when interacting
• Communication sometimes fails, in particular when pedestrians from
different cultures interact (“reciprocal dance”)
• Our differential game model allows for three different strategies reflecting
different levels of (non-) cooperation
20. Solving the differential game…
Numerical solution scheme
• We determine the optimal acceleration
assuming predicted effort minimization:
⃗
𝑎[","$%)
∗
= arg min 𝐽(𝑢[","$%))
subject to pedestrians’ motion dynamics
• Minimum Principle of Pontryagin
results in necessary conditions, forms
basis for Iterative Real-time Trajectory
Optimization Algorithm (IRTA)
• IRTA computes equilibrium where ego-
pedestrian cannot improve her situation
given assumed reaction of others
4.2 Iterative numerical solution
In this section, we briefly discuss the iterative numerical solution approach.
The algorithm is shown for one prediction period only; the receding horizon
generalization is straightforward and left to the reader. Moreover, for the sake
of simplicity, we have omitted obstacles, and terminal costs.
1. Initialization of control variables (prediction horizon T, time step h);
2. Initialization of parameters (weights, desired speed; relaxation parameter
a, cut-o↵ error eps
3. For each pedestrian, initialization of initial position ~
r(0) and velocities
~
v(0) and target position ~
r1
4. Initialize co-states for the positions ~
⇤r(t) = ~
0 and velocities ~
⇤v(t) = ~
0 for
all t = 0 : t : T
5. While error > eps do
(a) Set ~r(t) = ~
⇤r(t) and ~v(t) = ~
⇤v(t)
(b) For t = 0 : t : T t
i. For i = 1 : n
A. ~
u(t|i) = ~v(t|i)
B. ~
v(t + t|i) = ~
v(t|i) + t · ~
u(t|i)
C. ~
x(t + t|i) = ~
x(t|i) + t · ~
v(t|i)
(c) For t = T : t : t
i. For i = 1 : n
A. Compute desired velocity ~
v0
i (t)
B. ~r(t t|i) = ~r(t|i) + t · d0
P
j6=i e dij /d0
~
nij
C. ~v(t t|i) = ~v(t|i) + t ·
⇣
↵(~
v0
i ~
v(t|i)) + ~r(t|i)
⌘
(d) Relaxation ~
⇤r(t) = (1 a) · ~
⇤r(t) + a · r(t) and ~
⇤v(t) = (1 a) ·
~
⇤v(t) + a · v(t)
(e) error = ||~
⇤ ~||
It is beyond the scope of the paper to analyze the performance of the numer-
ical solution in detail. For illustration purposes, Fig. 1 shows the convergence
properties of the scheme for a one-on-one drone interaction scenario, with a
21. Validation outcomes
• Calibration using ML approach + trajectory data
• Reproduces all coll. self-organized phenomena
• Model yields realistic flow – density relation for a
location (FD) and for an entire network (p-MFD)
25. ▪ Use of simple control strategy
Modelling cyclist & pedestrians
Control of connected & autonomous vessels
Lane-free control schemes for CAVs
Generic machinery:
Differential game theory
and dedicated numerical
solution algorithm IRTA
are broadly applicable
Cooperative decentralized schemes for drones
26. Decentralized multi-
drone conflict
resolution
• Prospect of drones in (urban) transport and logistics
depend on our ability to solve complex drone
interaction problems in high density airspace
• Multi-drone conflict resolution is a key challenge!
• Our proposition: use game-theoretical approach
used for pedestrian modeling to formulate and solve
multi-drone conflict resolution, assuming that many
of the self-organization properties carry over to 3D…
28. Multi-drone conflict resolution
Path A
Path B
Path C
Destination
Shortest path
Cost component examples:
• Straying from shortest path
• Being too close to the other drones
• Acceleration / braking
• Not adhering to airspace regulation…
Ego-drone can use different strategies that represent
different levels of risk taken by the drone given
sensor and communication accuracy and reliability
Adapted version of IRTA
used as solver
33. Decentralized multi-drone conflict resolution
Self-organized drone roundabouts
N=20
• Different factors influence self-
organized patterns, including
demand level
• Important factor: desired speed
variability
• Large variation breaks formation
of roundabouts
• Other self-organized patterns are
also influenced by heterogeneity
34. Limits to
self-organization
• Impact of heterogeneity well
known for pedestrian flows:
“freezing by heating” describes
the fact heterogeneity messes
up self organization
• As a result, heterogeneous flows
break down at a lower demand
than homogenous flows
• Shows possible impact of (local)
homogenization to increase
capacity of a bottleneck
1.2 1.4 1.6 1.8 2.0
1.0
0
1
Demand (P/s)
Breakdown
prob.
medium low
high
35. Limits to
self-organization
Higher pressure leads to reduced capacity and longer evacuation times
• Faster-is-slower effect
describes the reduction of
bottleneck capacity due to
increase haste due to arc
formation
• Insight leads to different types
of local interventions to improve
situation (e.g., placing obstacle
in front of door to reduce
pressure, or the ‘polonaise’)
36. Queues at local bottlenecks spill back, possibly causing grid-lock
effects, in turn leading to turbulence and asphyxiation…
When self-organization fails:
Local problems may eventually lead
to deterioration at network level
37. Using insights for design and management
Improved design to
limit crossing flows
prev. spill-back
Inflow reduction by using
gating
Spreading of Pilgrims
using different flows
Remove bottlenecks
in design Testing interventions by simulation
Using our understanding for Management &
Design: Example Grand Mosque
38. Towards effective crowd management
Classify intervention strategies at 3 levels…
INDIVIDUAL
Efficient decentralised strategies
Influencing individual behaviour
BOTTLENECK
Increase bottleneck capacity
Reduce break-down probability
by homogenisation
NETWORK
Reduce inflow into network
Increase network outflow
Spread traffic over network and
separate flows
Increasing traffic demand
39. Similar approach could work for drones!
Three level approach to managing drone traffic operations
INDIVIDUAL
Efficient decentralised strategies
LOCAL
Priority regulations
Speed homogenisation
Control of interacting flows
NETWORK
Schedule inflow into network
Reroute drone flows
Increasing traffic demand
40. But if we understand the
processes so well…
Why does it still go wrong?
41. Lack of accurate
and reliable real-
time datasources
Lack of effective
decision support
tools for real-time
decision making
and planning
42. Data collection
Sensing technology
• Adequate data collection
technologies have become
available only recently
• Still, single datasources
seldom provide complete
picture (spatial coverage,
granularity, bias)
• Acurate / complete
information requires
methods to process, fuse,
and enrich multi-source data
3D camera, BT scanner, and
climate sensor
Mood & stress detection (DCM,
GreshamSmith)
Use of
location-based
services
(Resono)
Social-data
crawler
43. Use of AI for prediction and risk assessment
Digital Twin for Real-Time Decision Support and event planning
• Advanced multi-source
data collection and
effective decision
support come together
in CSM
• XAI technology for data
fusion, short-term and
long-term prediction of
crowdedness
• Future work focusses
on risk assessment
(EMERALDS)
45. Asphyxiation due to overcrowding Riots during the pandemic
Stabbing incidents after a hot and
crowded day at the beach
Risk of being pushed of platform
(courtesy of J van den Heuvel, NS Stations)
46. Our current work focuses on using
advanced monitoring, data fusion,
XAI, and decision support tools for
advanced predictive risk
assessment
47.
48. Making impact!
Keeping education open
during the pandemic…
• Sensing locations and distances with
wearables and beacons
• Dashboard shows areas of concern:
where do the critical interactions occur?
• Design interventions (floorplans,
circulation strategies, occupancy limits)
• Establish critical interactions between
“bubbles” (groups/classes), so that only
students at risk had to be isolated in
case of infection
49. Main take aways
From Crowd Intelligence to Artifical Crowd Intelligence
• Show how efficient self-organized phenomena in active mode traffic can be
modeled using decentralized schemes, generalizing well-known models
• Show how approaches can be generalized to other problems, including multi-
drone conflict resolution
• Show (limits to) self-organization and how interventions can help improve
• Discuss future steps in decision support using Artificial Crowd Intelligence
Overall, I aimed to show you the importance of sharing knowledge across
domains and not reinventing the wheel!