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.
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.
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.
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.
Presentation given during the first transportation workshop at Melbourne Uni. Focus on crowd monitoring and management. With examples from various projects (SAIL, Mekka, etc.)
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.
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.
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.
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.
Presentation given during the first transportation workshop at Melbourne Uni. Focus on crowd monitoring and management. With examples from various projects (SAIL, Mekka, etc.)
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.
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.
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.
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.
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.
MEASURING SIMILARITY BETWEEN MOBILITY MODELS AND REAL WORLD MOTION TRAJECTORIEScscpconf
Various mobility models have been proposed to represent the motion behaviour of mobile nodes in the real world. Selection of the most similar mobility model to a given real world environment is a challenging issue which has a significant impact on the quality of performance evaluation
of different network protocols. In this paper we propose a methodology for measurement of similarity between mobility models used in mobile networks simulation and real world mobility scenarios with different transportation modes. We explain our mobility metrics we have used for analysis of motion behavior of mobile nodes and a pre-processing method which makes our trajectories suitable for extraction and calculation of these metrics considering shape of the road networks and GPS noise. Then we use a feature selection method to find the most
discriminative features which are able to distinguish between trajectories with different transportation modes using a supervised learning and feature ranking method. Subsequently,
using our selected feature space we perform Fuzzy C-means Clustering to find the degree of similarity between each of our mobility models and real world trajectories with different
transportation modes. Our methodology can be used to select the most similar mobility model suitable for simulation of mobile network protocols (such as DTN and MANETs protocols) in a
particular real world area.
Measuring similarity between mobility models and real world motion trajectoriescsandit
Various mobility models have been proposed to represent the motion behaviour of mobile nodes
in the real world. Selection of the most similar mobility model to a given real world environment
is a challenging issue which has a significant impact on the quality of performance evaluation
of different network protocols. In this paper we propose a methodology for measurement of
similarity between mobility models used in mobile networks simulation and real world mobility
scenarios with different transportation modes. We explain our mobility metrics we have used for
analysis of motion behavior of mobile nodes and a pre-processing method which makes our
trajectories suitable for extraction and calculation of these metrics considering shape of the
road networks and GPS noise. Then we use a feature selection method to find the most
discriminative features which are able to distinguish between trajectories with different
transportation modes using a supervised learning and feature ranking method. Subsequently,
using our selected feature space we perform Fuzzy C-means Clustering to find the degree of
similarity between each of our mobility models and real world trajectories with different
transportation modes. Our methodology can be used to select the most similar mobility model
suitable for simulation of mobile network protocols (such as DTN and MANETs protocols) in a
particular real world area.
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.
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.
Develop a mobility model for MANETs networks based on fuzzy Logiciosrjce
The study and research in the field of networks MANETs depends alleged understand the protocols
well of the simulation process before they are applied in the real world, so that we create an environment
similar to these networks. The problem of a set of nodes connected with each other wirelessly, this requires the
development of a comprehensive model and full and real emulator for the movement of the contract on behalf of
stochastic models. Many models came to address the problems of random models that restricted the movement
of decade barriers as well as the signals exchanged between them, but these models were not receiving a lot of
light on the movement of the contract, such as direction, speed and path that is going by the node. The main
goal is to get a comprehensive model and simulator for all parts of the environment of the barriers and
obstacles to the movement of the nodes and the mobile signal between them as well as to focus on the movement
transactions for the node of the direction, speed, and best way. . This research aims to provide a realistic
mobility model for MANET networks. It also addresses the problem of imprecision in social relationships and
the location where we apply Fuzzy logic.
This document proposes developing a mobility model for mobile ad hoc networks (MANETs) based on fuzzy logic. It discusses existing mobility models and their limitations in capturing realistic node movement. The proposed model aims to provide a more realistic mobility model for MANETs by incorporating fuzzy logic to address imprecision in social relationships and node locations. It defines mathematical formulas to model social relationships between nodes and calculate the probability of nodes visiting locations based on these relationships and associated weights that vary over time. The model aims to take a more comprehensive approach to mobility modeling in MANETs by considering social, geographical, and temporal factors.
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
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 provides an overview of traffic flow modeling and simulation methods for intelligent transportation systems. It discusses both macroscopic and microscopic modeling approaches. Macroscopic models view traffic as a continuous flow and use partial differential equations involving density, speed, and flow rate over time and space. Microscopic models treat each vehicle individually using ordinary differential equations to model driver behavior and car-following dynamics. The document also reviews several traffic simulation software tools and concludes that modeling and simulation can help design and evaluate new transportation control strategies before implementation.
This document presents a new individual modeling process for more accurately estimating populations' capacity for making journeys by walking and cycling. The method uses spatial microsimulation to account for individual attributes like age, fitness level, and bicycle availability. It improves upon current methods like simple buffer zones that do not consider individual differences. The new approach provides a more detailed understanding of travel capabilities and potential for increasing active transportation.
The Future of Mixed-Autonomy Traffic (AIS302) - AWS re:Invent 2018Amazon Web Services
How will self-driving cars change urban mobility patterns? This talk examines scientific contributions in the field of reinforcement learning, presented in the context of enabling mixed-autonomy mobility—the gradual and complex integration of autonomous vehicles into existing traffic systems. We explore the potential impact of a small fraction of autonomous vehicles on low-level traffic flow dynamics, using novel techniques in model-free deep reinforcement learning. We share examples in the context of a new open-source computational platform and state-of-the-art microsimulation tools with deep-reinforcement libraries.
A Computational Study Of Traffic Assignment AlgorithmsNicole Adams
The document summarizes a study comparing algorithms for solving traffic assignment problems. It classified algorithms as link-based (using link flows), path-based (using path flows), or origin-based (using link flows from origins). It reviewed literature on algorithms like Frank-Wolfe (link-based), path equilibration (path-based), and origin-based algorithm. It chose to implement representative algorithms from each class: Frank-Wolfe, conjugate Frank-Wolfe, bi-conjugate Frank-Wolfe (link-based), path equilibration, gradient projection, projected gradient, improved social pressure (path-based), and Algorithm B (origin-based) to compare their performance on benchmark problems.
A Computational Study Of Traffic Assignment AlgorithmsAlicia Buske
This document summarizes a research study that compares different algorithms for solving traffic assignment problems. The study performs a literature review of prominent traffic assignment algorithms, classifying them based on how the solution is represented (link-based, path-based, origin-based). It then implements representative algorithms from each class and conducts computational tests on benchmark networks of varying sizes. The results are analyzed to compare algorithm performance and identify the impact of different algorithm components on running time.
THRESHOLD RANGE FOR TRAFFIC FLOW PARAMETERS USING FUZZY LOGIC.pdfYMYerima
The document discusses fuzzy logic and its application in determining thresholds for traffic flow parameters. It provides context on fuzzy logic and fuzzy input. It then discusses literature on using fuzzy logic to set thresholds for factors like traffic density, speed, and congestion. The literature emphasizes dynamic adjustment of thresholds based on real-time traffic conditions. The document aims to determine an optimal range of thresholds for traffic flow parameters using fuzzy logic models to handle uncertainty in a way that mimics human reasoning. It reviews recent related studies and discusses challenges and potential future directions for research.
Predictive Analysis of Bike Sharing System Using Machine Learning Algorithmssushantparte
Provided business solutions based on the ethical aspects of data collection and shortcomings of business by visualizing data and forecasting the demand using Ensemble Learning Technique (Random Forest) with an RMSE of 89.09%.
- The document discusses a project in Pisa, Italy to redesign the city's bicycle lanes using a participatory approach. An online survey and GIS tools were used to collect and analyze data on citizen preferences, mobility patterns, and potential bicycle lane routes.
- Data mining techniques like decision trees were applied to the spatial, temporal, socioeconomic and survey data to extract rules about transport choices. Most bicycle use was associated with lunch/afternoon activities, shopping trips under 45 minutes, and bringing things for citizens with low incomes.
- The results provide guidance for the municipality on how to best connect existing bicycle lanes to tourist areas and accommodate citizen preferences in the redesign.
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.
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.
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.
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.
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.
MEASURING SIMILARITY BETWEEN MOBILITY MODELS AND REAL WORLD MOTION TRAJECTORIEScscpconf
Various mobility models have been proposed to represent the motion behaviour of mobile nodes in the real world. Selection of the most similar mobility model to a given real world environment is a challenging issue which has a significant impact on the quality of performance evaluation
of different network protocols. In this paper we propose a methodology for measurement of similarity between mobility models used in mobile networks simulation and real world mobility scenarios with different transportation modes. We explain our mobility metrics we have used for analysis of motion behavior of mobile nodes and a pre-processing method which makes our trajectories suitable for extraction and calculation of these metrics considering shape of the road networks and GPS noise. Then we use a feature selection method to find the most
discriminative features which are able to distinguish between trajectories with different transportation modes using a supervised learning and feature ranking method. Subsequently,
using our selected feature space we perform Fuzzy C-means Clustering to find the degree of similarity between each of our mobility models and real world trajectories with different
transportation modes. Our methodology can be used to select the most similar mobility model suitable for simulation of mobile network protocols (such as DTN and MANETs protocols) in a
particular real world area.
Measuring similarity between mobility models and real world motion trajectoriescsandit
Various mobility models have been proposed to represent the motion behaviour of mobile nodes
in the real world. Selection of the most similar mobility model to a given real world environment
is a challenging issue which has a significant impact on the quality of performance evaluation
of different network protocols. In this paper we propose a methodology for measurement of
similarity between mobility models used in mobile networks simulation and real world mobility
scenarios with different transportation modes. We explain our mobility metrics we have used for
analysis of motion behavior of mobile nodes and a pre-processing method which makes our
trajectories suitable for extraction and calculation of these metrics considering shape of the
road networks and GPS noise. Then we use a feature selection method to find the most
discriminative features which are able to distinguish between trajectories with different
transportation modes using a supervised learning and feature ranking method. Subsequently,
using our selected feature space we perform Fuzzy C-means Clustering to find the degree of
similarity between each of our mobility models and real world trajectories with different
transportation modes. Our methodology can be used to select the most similar mobility model
suitable for simulation of mobile network protocols (such as DTN and MANETs protocols) in a
particular real world area.
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.
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.
Develop a mobility model for MANETs networks based on fuzzy Logiciosrjce
The study and research in the field of networks MANETs depends alleged understand the protocols
well of the simulation process before they are applied in the real world, so that we create an environment
similar to these networks. The problem of a set of nodes connected with each other wirelessly, this requires the
development of a comprehensive model and full and real emulator for the movement of the contract on behalf of
stochastic models. Many models came to address the problems of random models that restricted the movement
of decade barriers as well as the signals exchanged between them, but these models were not receiving a lot of
light on the movement of the contract, such as direction, speed and path that is going by the node. The main
goal is to get a comprehensive model and simulator for all parts of the environment of the barriers and
obstacles to the movement of the nodes and the mobile signal between them as well as to focus on the movement
transactions for the node of the direction, speed, and best way. . This research aims to provide a realistic
mobility model for MANET networks. It also addresses the problem of imprecision in social relationships and
the location where we apply Fuzzy logic.
This document proposes developing a mobility model for mobile ad hoc networks (MANETs) based on fuzzy logic. It discusses existing mobility models and their limitations in capturing realistic node movement. The proposed model aims to provide a more realistic mobility model for MANETs by incorporating fuzzy logic to address imprecision in social relationships and node locations. It defines mathematical formulas to model social relationships between nodes and calculate the probability of nodes visiting locations based on these relationships and associated weights that vary over time. The model aims to take a more comprehensive approach to mobility modeling in MANETs by considering social, geographical, and temporal factors.
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
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 provides an overview of traffic flow modeling and simulation methods for intelligent transportation systems. It discusses both macroscopic and microscopic modeling approaches. Macroscopic models view traffic as a continuous flow and use partial differential equations involving density, speed, and flow rate over time and space. Microscopic models treat each vehicle individually using ordinary differential equations to model driver behavior and car-following dynamics. The document also reviews several traffic simulation software tools and concludes that modeling and simulation can help design and evaluate new transportation control strategies before implementation.
This document presents a new individual modeling process for more accurately estimating populations' capacity for making journeys by walking and cycling. The method uses spatial microsimulation to account for individual attributes like age, fitness level, and bicycle availability. It improves upon current methods like simple buffer zones that do not consider individual differences. The new approach provides a more detailed understanding of travel capabilities and potential for increasing active transportation.
The Future of Mixed-Autonomy Traffic (AIS302) - AWS re:Invent 2018Amazon Web Services
How will self-driving cars change urban mobility patterns? This talk examines scientific contributions in the field of reinforcement learning, presented in the context of enabling mixed-autonomy mobility—the gradual and complex integration of autonomous vehicles into existing traffic systems. We explore the potential impact of a small fraction of autonomous vehicles on low-level traffic flow dynamics, using novel techniques in model-free deep reinforcement learning. We share examples in the context of a new open-source computational platform and state-of-the-art microsimulation tools with deep-reinforcement libraries.
A Computational Study Of Traffic Assignment AlgorithmsNicole Adams
The document summarizes a study comparing algorithms for solving traffic assignment problems. It classified algorithms as link-based (using link flows), path-based (using path flows), or origin-based (using link flows from origins). It reviewed literature on algorithms like Frank-Wolfe (link-based), path equilibration (path-based), and origin-based algorithm. It chose to implement representative algorithms from each class: Frank-Wolfe, conjugate Frank-Wolfe, bi-conjugate Frank-Wolfe (link-based), path equilibration, gradient projection, projected gradient, improved social pressure (path-based), and Algorithm B (origin-based) to compare their performance on benchmark problems.
A Computational Study Of Traffic Assignment AlgorithmsAlicia Buske
This document summarizes a research study that compares different algorithms for solving traffic assignment problems. The study performs a literature review of prominent traffic assignment algorithms, classifying them based on how the solution is represented (link-based, path-based, origin-based). It then implements representative algorithms from each class and conducts computational tests on benchmark networks of varying sizes. The results are analyzed to compare algorithm performance and identify the impact of different algorithm components on running time.
THRESHOLD RANGE FOR TRAFFIC FLOW PARAMETERS USING FUZZY LOGIC.pdfYMYerima
The document discusses fuzzy logic and its application in determining thresholds for traffic flow parameters. It provides context on fuzzy logic and fuzzy input. It then discusses literature on using fuzzy logic to set thresholds for factors like traffic density, speed, and congestion. The literature emphasizes dynamic adjustment of thresholds based on real-time traffic conditions. The document aims to determine an optimal range of thresholds for traffic flow parameters using fuzzy logic models to handle uncertainty in a way that mimics human reasoning. It reviews recent related studies and discusses challenges and potential future directions for research.
Predictive Analysis of Bike Sharing System Using Machine Learning Algorithmssushantparte
Provided business solutions based on the ethical aspects of data collection and shortcomings of business by visualizing data and forecasting the demand using Ensemble Learning Technique (Random Forest) with an RMSE of 89.09%.
- The document discusses a project in Pisa, Italy to redesign the city's bicycle lanes using a participatory approach. An online survey and GIS tools were used to collect and analyze data on citizen preferences, mobility patterns, and potential bicycle lane routes.
- Data mining techniques like decision trees were applied to the spatial, temporal, socioeconomic and survey data to extract rules about transport choices. Most bicycle use was associated with lunch/afternoon activities, shopping trips under 45 minutes, and bringing things for citizens with low incomes.
- The results provide guidance for the municipality on how to best connect existing bicycle lanes to tourist areas and accommodate citizen preferences in the redesign.
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 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.
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
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
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.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
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.
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.
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.
1. unrAvelLing sLow modE
travelinG and tRaffic
With innOvative data to a new transportation and traffic
theory for pedestrians and bicycles
1
2. The battle for urban space
With increased densification of cities, where can we find the space for
performing those functions that the city was actually built for?
It is clear that active modes can play a major role in making cities liveable!
Car
50 km/h, driver only
Car
Parked
Tram
50 passangers
Cyclist
15 km/h
Bicycle
Parked
Pedestrian
Walking
Pedestrian
Standing140 m2
20 m2
7 m2
5 m2
2 m2
2 m2
0.5 m2
3. Towards greener, healthier, more liveable cities…
How active mode friendly is your city?
What makes people in
your city walk or cycle (or
not!) instead of using car
Can pedestrians and
cyclists find their way
easily through the city?
Can your city / transfer
hub deal with large
numbers of people?
Is your active mode
infrastructure (roads,
control) well designed?
4. Our central proposition…
Science has not yet delivered adequate tools (empirical insights, theory,
models, guidelines) to support planners, designers, and traffic managers…
The ‘science of active mode mobility’ has been hampered by lack of data!
5. Unique large-scale cycling experiments (pilot)
Large-scale experiment in May 2018 in AHOY
Revealed preference route choice data
Collaboration with MoBike, and The Student Hotel
Innovations in data collection for active modes
Active mode monitoring dashboard incl. Social Data
Short-run and long-run household travel dynamics
MPN longitudinal survey active mode “specials”
6. Active Mode
UML
Engineering
Applications
Transportation & Traffic Theory
for Active Modes in Cities
Data collection
and fusion toolbox
Social-media
data analytics
AM-UML app
Simulation
platform
Walking and
Cycling
Behaviour
Traffic Flow
Operations
Route and Mode
Choice and
Scheduling Theory
Planning anddesign guidelines
Organisation of
large-scale
events
Data Insights
Tools
Models Impacts
Network Knowledge Acquisition (learning)
Factors
determining
route choice
Real-timepersonalised
guidance
7. Active Mode
UML
Engineering
Applications
Transportation & Traffic Theory
for Active Modes in Cities
Data collection
and fusion toolbox
Social-media
data analytics
AM-UML app
Simulation
platform
Walking and
Cycling
Behaviour
Traffic Flow
Operations
Route and Mode
Choice and
Scheduling Theory
Planning anddesign guidelines
Organisation of
large-scale
events
Data Insights
Tools
Models Impacts
Network Knowledge Acquisition (learning)
Factors
determining
route choice
Real-timepersonalised
guidance
10. An average cycling day in Amsterdam…
Understanding and
modelling require
access to data…
Which techniques
are available?
11. Advanced SP and Simulators
Field observations
Controlled experiments
Social Data Crawling
12. 0
2
4
6
8
10
Effort
ValidityControllability
Field observations Controlled Experiments
Advanced SP and Simulators Social Data Crawling
Trade-offs in data collection
• Selection of data collection
approach is trade-off between
different factors (e.g. effort,
controllability, data validity)
• In general strive for optimal mix
between different data collection
techniques
• Nevertheless, for young research
fields with limited prior knowledge,
advantage of controlled
conditions are compelling…
12
Ease data collection
Controllability Validity
14. A bit of theory…
• Pedestrians moving with speed v (in m/s) need space A (in m2) to move
• The faster one walks, the more space one needs:
• Density = number of pedestrians / square meter:
• We can thus express the density as a function of speed:
• Equally, we can express speed as a function of density…
14
A(v) = A0 + γ ⋅ v
ρ = 1/A
ρ = ρ(v) =
1
A(v)
=
1
A0 + γ ⋅ v
ρ
15. • We have:
• Given that speed can
not be larger than
maximum speed,
we can rewrite:
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
0.0 1.0 2.0 3.0 4.0 5.0 6.0
Speedv
Density k
A bit of theory…
15
ρ(v) =
1
A0 + γ ⋅ v
ρ(0) =
1
A0
= ρjam
v(ρ) =
{
v0
,
1
γ (
1
ρ
− A0)}
−
ρ
16. F
0
5
10
15
20
25
30
0 200 400 600
Speedv(km/h)
Density k (bike/km)
• Do-It-Yourself bicycle
experiment revealing
fundamental diagram for
bicycle flows
• Single file assumption relaxed
with recently performed
experiments
• Fundamental diagram also
exist for bicycle flows
18. Which fundamental diagram
is bi-directional flow (the
other is uni-directional)?
How many lanes are formed in a bi-directional flow (4 m wide)
3 4
s formed
25 Ped/m2
1 2 3 4
0
0.2
0.4
0.6
Number of lanes formed
Relativefrequency
Density = 0.25 to 0.5 Ped/m2
.75 Ped/m2
0.6
cy
Density = 0.75 to 1 Ped/m2
0 0.2 0.4 0.6 0.8
0.9
1
1.1
1.2
1.3
density (Ped/m2
)
speed(m/s)
1
2
3
4
1 2 3 4
0
0.2
0.4
0.6
Number of lanes formed
Relativefrequency
Density = 0 to 0.25 Ped/m2
1 2 3 4
0
0.2
0.4
0.6
Number of lanes formed
Relativefrequency
Density = 0.25 to 0.5 Ped/m2
1 2 3 4
0
0.2
0.4
0.6
Number of lanes formed
Relativefrequency
Density = 0.5 to 0.75 Ped/m2
1 2 3 4
0
0.2
0.4
0.6
Number of lanes formedRelativefrequency
Density = 0.75 to 1 Ped/m2
Figure (b) shows the data of the bi-
directional flow experiment
Clearly, bi-directional flows are very
efficient!
(a) (b)
19. !19
Example shared-space region
Amsterdam Central Station
Other forms of self-organisation?
Many other forms of self-organisation are found in pedestrian flow
Diagonal stripes in crossing flows, zipper effect in bottlenecks, viscous
fingering when group of pedestrians move through crowd, etc.
21. Understanding by game-theoretic modelling
• Main assumption “pedestrian economicus”
based on principle of least effort:
For all available options (accelerating, changing
direction, do nothing) she chooses option
yielding smallest predicted effort (i.e. predicting
behaviour of others)
• Under specific conditions, the game-theoretic
setting yields emergence of Nash equilibrium
situations in which no pedestrian can
unilaterally improve her situation
21
22. Game-theoretic pedestrian flow model
22
• Considering the following effort or cost components:
- Straying from desired direction and speed
- Walking close to or colliding with other pedestrians
- Frequently slowing down and accelerating
• Using a very simple prediction model for behaviour of others: i
j
Acceleration towards desired velocity Push away from ped j
+ …
1. Introduction
This memo aims at connecting the microscopic modelling principles underlying the
cial-forces model to identify a macroscopic flow model capturing interactions amongst
edestrians. To this end, we use the anisotropic version of the social-forces model pre-
nted by Helbing to derive equilibrium relations for the speed and the direction, given
e desired walking speed and direction, and the speed and direction changes due to
teractions.
2. Microscopic foundations
We start with the anisotropic model of Helbing that describes the acceleration of
edestrian i as influence by opponents j:
) ~ai =
~v0
i ~vi
⌧i
Ai
X
j
exp
Rij
Bi
· ~nij ·
✓
i + (1 i)
1 + cos ij
2
◆
here Rij denotes the distance between pedestrians i and j, ~nij the unit vector pointing
om pedestrian i to j; ij denotes the angle between the direction of i and the postion
j; ~vi denotes the velocity. The other terms are all parameters of the model, that will
⃗n ij
⃗v j
ϕij
23. Characteristics of the simplified model
• Simple model captures macroscopic characteristics of flows well
• Also self-organised phenomena are captured, including dynamic lane formation, formation of diagonal stripes, viscous fingering, etc.
• Does model capture ‘faster is slower effect’?
• If it does not, what would be needed to include it?
Application of differential game theory:
• Pedestrians minimise predicted walking cost, due
to straying from intended path, being too close to
others / obstacles and effort, yielding:
• Simplified model is similar to Social Forces model of Helbing
Face validity?
• Model results in reasonable macroscopic flow characteristics
• What about self-organisation?
23
Characteristics of NOMAD
• Simple model captures some
key relations (e.g. speed-
density curve) reasonable well!
• All self-organised
phenomena are captured,
including dynamic lane
formation, formation of
diagonal stripes, viscous
fingering
• Playing around with model
input and parameters allows
us to understand conditions
for self-organisation better
24. Game-theoretic pedestrian flow model
• Breakdown probability demands on many
factors, including:
- Demand levels (see figure)
- Variability in desired walking speeds (see
figure: low (-), medium (-), high (-)
- Variability in physical size (limited)
- Level of anticipation / delayed response
• Calibration reveals substantial heterogeneity
in parameters (and correlation)
• No empirical basis for threshold values
formed motivation for CrowdLimits
experiment @TUDelft in May 2018
1.2 1.4 1.6 1.8 2.01.0
0
1
Demand (P/s)
Breakdownprob.
Parameter Mean CoV
Free speed (m/s) 1.34 0.23
Relaxation time (s) 0.74 0.23
Interaction strength (m/s2) 11.33 0.64
Interaction radius (m) 0.35 0.11
Reaction time (s) 0.28 0.07
27. Bicycle and mixed flows
Using game theory to model bicycle and mixed flows and
understanding conditions for self-organisation
28. Graphical explanation…
• Data collection for modelling
and capacity estimation
• 25 scenarios (overtaking,
merging, crossing, …)
• Structures in and upstream
b-n determine capacity
• Discovery of capacity drop
phenomenon for cycle flows
Bottleneck width (m)
Capacityflow(cyc/s)
29. Modelling waiting positions
• Capacity and flow operations is determined
by way queue is formed
• Discretisation of area using diamonds
(representing bicycle shape)
• Estimation of discrete choice model to
predict waiting location
• Waiting location choice determined by
various attributes (distance from stop line,
distance to others, distance curb)
• Use approach for other important
processes (waiting passengers on platform)
29
30. Active mode traffic management
Modelling for real-time prediction and control applications
31. Modelling for estimation and prediction
• NOMAD / Social-forces model as starting point:
• Equilibrium relation stemming from model (ai = 0):
• Interpret density as the ‘probability’ of a pedestrian being present, which gives a
macroscopic equilibrium relation (expected velocity), which equals:
31
the desired walking speed and direction, and the speed and direction changes due to
interactions.
2. Microscopic foundations
We start with the anisotropic model of Helbing that describes the acceleration of
pedestrian i as influence by opponents j:
(1) ~ai =
~v0
i ~vi
⌧i
Ai
X
j
exp
Rij
Bi
· ~nij ·
✓
i + (1 i)
1 + cos ij
2
◆
where Rij denotes the distance between pedestrians i and j, ~nij the unit vector pointing
from pedestrian i to j; ij denotes the angle between the direction of i and the postion
of j; ~vi denotes the velocity. The other terms are all parameters of the model, that will
be introduced later.
In assuming equilibrium conditions, we generally have ~ai = 0. The speed / direction
for which this occurs is given by:
(2) ~vi = ~v0
i ⌧iAi
X
j
exp
Rij
Bi
· ~nij ·
✓
i + (1 i)
1 + cos ij
2
◆
Let us now make the transition to macroscopic interaction modelling. Let ⇢(t, ~x)
pedestrian i as influence by opponents j:
(1) ~ai =
~v0
i ~vi
⌧i
Ai
X
j
exp
Rij
Bi
· ~nij ·
✓
i + (1 i)
1 + cos ij
2
◆
where Rij denotes the distance between pedestrians i and j, ~nij the unit vector pointing
from pedestrian i to j; ij denotes the angle between the direction of i and the postion
of j; ~vi denotes the velocity. The other terms are all parameters of the model, that will
be introduced later.
In assuming equilibrium conditions, we generally have ~ai = 0. The speed / direction
for which this occurs is given by:
(2) ~vi = ~v0
i ⌧iAi
X
j
exp
Rij
Bi
· ~nij ·
✓
i + (1 i)
1 + cos ij
2
◆
Let us now make the transition to macroscopic interaction modelling. Let ⇢(t, ~x)
denote the density, to be interpreted as the probability that a pedestrian is present on
location ~x at time instant t. Let us assume that all parameters are the same for all
pedestrian in the flow, e.g. ⌧i = ⌧. We then get:
(3)
~v = ~v0
(~x) ⌧A
ZZ
exp
✓
||~y ~x||
◆ ✓
+ (1 )
1 + cos xy(~v)
◆
~y ~x
⇢(t, ~y)d~y
(1) ~ai =
~vi ~vi
⌧i
Ai
X
j
exp
Rij
Bi
· ~nij · i + (1 i)
1 + cos ij
2
where Rij denotes the distance between pedestrians i and j, ~nij the unit vector pointing
from pedestrian i to j; ij denotes the angle between the direction of i and the postion
of j; ~vi denotes the velocity. The other terms are all parameters of the model, that will
be introduced later.
In assuming equilibrium conditions, we generally have ~ai = 0. The speed / direction
for which this occurs is given by:
(2) ~vi = ~v0
i ⌧iAi
X
j
exp
Rij
Bi
· ~nij ·
✓
i + (1 i)
1 + cos ij
2
◆
Let us now make the transition to macroscopic interaction modelling. Let ⇢(t, ~x)
denote the density, to be interpreted as the probability that a pedestrian is present on
location ~x at time instant t. Let us assume that all parameters are the same for all
pedestrian in the flow, e.g. ⌧i = ⌧. We then get:
(3)
~v = ~v0
(~x) ⌧A
ZZ
~y2⌦(~x)
exp
✓
||~y ~x||
B
◆ ✓
+ (1 )
1 + cos xy(~v)
2
◆
~y ~x
||~y ~x||
⇢(t, ~y)d~y
Here, ⌦(~x) denotes the area around the considered point ~x for which we determine the
Microscopic models
are great for off-line
assessment, but too
slow for real-time
applications….
Can we come up with a
macroscopic version?
32. Modelling for estimation and prediction
• Taylor series approximation:
yields a closed-form expression for the equilibrium velocity , which is given by the
equilibrium speed and direction:
• Equilibrium speed V shows that speed reduces with density / density gradient
• Equilibrium direction is function of desired walking direction and density gradient
(pedestrians move away from dense areas)
• Completing model by including ped. conservation:
32
!
v =
!
e ⋅V
2 SERGE P. HOOGENDOORN
From this expression, we can find both the equilibrium speed and the equilibrium direc-
tion, which in turn can be used in the macroscopic model.
We can think of approximating this expression, by using the following linear approx-
imation of the density around ~x:
(5) ⇢(t, ~y) = ⇢(t, ~x) + (~y ~x) · r⇢(t, ~x) + O(||~y ~x||2
)
Using this expression into Eq. (3) yields:
(6) ~v = ~v0
(~x) ~↵(~v)⇢(t, ~x) (~v)r⇢(t, ~x)
with ↵(~v) and (~v) defined respectively by:
(7) ~↵(~v) = ⌧A
ZZ
~y2⌦(~x)
exp
✓
||~y ~x||
B
◆ ✓
+ (1 )
1 + cos xy(~v)
2
◆
~y ~x
||~y ~x||
d~y
and
(8) (~v) = ⌧A
ZZ
~y2⌦(~x)
exp
✓
||~y ~x||
B
◆ ✓
+ (1 )
1 + cos xy(~v)
2
◆
||~y ~x||d~y
FROM MICROSCOPIC TO MACROSCOPIC INTERACTION MODELING 3
, we see that for ~↵, we find:
~↵(~v) = ↵0 ·
~v
||~v||
ermine this directly from the integrals?)
(6), with ~v = ~e · V we can derive:
V = ||~v0
0 · r⇢|| ↵0⇢
~e =
~v0
0 · r⇢
V + ↵0⇢
=
~v0
0 · r⇢
||~v0
0 · r⇢||
he direction does not depend on ↵0, which implies that the magnitude of
tself has no e↵ect on the direction, while the gradient of the density does
direction.
FROM MICROSCOPIC TO MACROSCOPIC INTERACTION MODEL
Furthermore, we see that for ~↵, we find:
(10) ~↵(~v) = ↵0 ·
~v
||~v||
(Can we determine this directly from the integrals?)
From Eq. (6), with ~v = ~e · V we can derive:
(11) V = ||~v0
0 · r⇢|| ↵0⇢
and
(12) ~e =
~v0
0 · r⇢
V + ↵0⇢
=
~v0
0 · r⇢
||~v0
0 · r⇢||
Note that the direction does not depend on ↵0, which implies that t
the density itself has no e↵ect on the direction, while the gradient of
influence the direction.
2.1. Homogeneous flow conditions. Note that in case of homogen
i.e. r⇢ = ~0, Eq. (11) simplifies to
(13) V = ||~v0|| ↵0⇢ = V 0
↵0⇢
and
@⇢
@t
+ r · (⇢ · ~v) = r s
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⃗e
33. 33
Macroscopic model
yields plausible
results…
• First macroscopic model able to
reproduce self-organised patterns
(lane formation, diagonal stripes)
• Self-organisation breaks downs in
case of overloading
• Continuum model inherits
properties of the microscopic
model underlying it
• Forms solid basis for real-time
prediction module
• First trials in model-based
optimisation and use of model for
state-estimation are promising
34. Centraal Metro Station
access to concourse gate line escalator
stairway railway line control area (PI)
0 20 40 60 80 100 m
Flurin H¨anseler (TU Delft) 20
Model Predictive Crowd Control (MPCC)
34
• Case: controlling turnstiles in
Amsterdam Central Station
• In the MPCC framework, the
macroscopic model is used
to compute predictions
given the current state and
the control signal
• The controller iteratively
determines the control
signal that optimised the
predicted objective function
Controller: Crowd dynamics
Crowd Dynamics Model
Optimizer
Objective
Function
Demand
Prediction Model
predicted
state
performance
control
signal
estimated
state
predicted
demand
historical
data
timetable,
schedule
optimal
control signal
Controller
35. 1. Monitoring
Microscopic data is collected
via video-based sensors, and
combined with smartcard data
Smart station and MPC
2. Estimation
Based on data, current state is
estimated and used as initial
state for prediction
3. Prediction & optimisation
Optimal control signal is
computed, yield a 10%
decrease in crowding cost
36. GPS tracks
Routes based on Instagram
Crowdedness
Social-media activiteit
Micro-posts related to ‘crowdedness’
From – To relations (WiFi + cam)Socio-demographics (Instagram / Twitter)
0
2000
4000
6000
8000
10000
12000
6 8 10 12 14 16 18
Ruijterkade
In Out Total
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
11 12 13 14 15 16 17 18 19
Dichtheden)Veemkade
dichtheid2(ped/m2)
Scaling up!
• Combination of mixed
datasources (counting
cameras, Wifi, social
data, GPS, apps, etc.)
• Reliable picture of
current situation in at
event site by fusion
of data sources
• Sentiment analysis
• Operational web-
based dashboard
• Development of
efficient models for
prediction
37. Cooperative
Bicycle Control
• Application of model-
based stochastic control
• Optimise trade-off between
missing green phase and
consuming energy for
traffic responsive control
• Inform rider using on-board
device (alt. road-side sign)
• Chance to catch green
phase +65%
• Energy consumption -30%
40. Analytical derivation of P-MFD
• Suppose that we have an area that
we partition into subareas
• For each subarea, flow operations
are described by Greenshields FD:
• Then we can easily show that for the
entire area we have the P-MFD:
• where
denotes the spatial variation in density
Area U U
Ui
qi(t) = Q(⇢i) = v0
⇢i (1 ⇢i/⇢jam)
U
¯q(t) = Q(¯⇢(t)) (v0
/⇢jam) · 2
(t)
2
(t) = 1
m
P
m(⇢i ¯⇢)2
Ui
⇢i(t)
*) Illustration only: we consider walking pedestrians
Also for other FDs, we
can show that the P-
MFD exist! It is given
by the FD with a
correction due to
spatial variation!
41. Multi-scale modelling for large areas
• Coarse modelling of network flow operations, where dynamics
of (sub-)area are described via P-MFD:
where
• Requires specification of spatial variation; preliminary data
analysis points towards:
• Approach is equivalent to macroscopic model presented
before development of multi-scale simulation approach
dni
dt =
P
j fji(t) Fi(ni(t), i(t))
fji(t) = ji(t) · Fj(nj(t), j(t))
(¯⇢, ˙¯⇢) = 0.277 · ¯⇢ 0.039 · ˙¯⇢
n1(t)
n2(t) f21(t)
F1(t)
43. Closing remarks
• Lecture provided insight into the
physics of active modes (empirics,
modelling, applications) allowing
efficient design and control active
mode infrastructure
• Only part of the puzzle!
• ALLEGRO also provides insight into
why people choice to walk or cycle
(top figure), or to understand which
are the determinants for route
choice (bottom table)
• Predict impact of policy interventions
Determinant Influence
Distance Negative
Distance in
morning peak hour
Negative
(stronger than other moments)
# Intersections / km Negative
% Separate cycle paths No / slightly positive
Overlap of routes Positive
Rain No
Daylight No
44. The battle for urban space
Should NOT a battle of the modes!
Rethinking the optimal mobility mix and the role of active modes
In the end, the key question is: what type of city do you want to live in
and which mobility mix best suits that desire!
Car
50 km/h, driver only
Car
Parked
Tram
50 passangers
Cyclist
15 km/h
Bicycle
Parked
Pedestrian
Walking
Pedestrian
Standing140 m2
20 m2
7 m2
5 m2
2 m2
2 m2
0.5 m2