Traffic simulation

Scalable Urban Traffic Control

Scalable Urban Traffic Control (Surtrac) is an adaptive traffic control system developed by researchers at the Robotics Institute, Carnegie Mellon University. Surtrac dynamically optimizes the control of traffic signals to improve traffic flow for both urban grids and corridors; optimization goals include less waiting, reduced traffic congestion, shorter trips, and less pollution. The core control engine combines schedule-driven intersection control with decentralised coordination mechanisms. Since June 2012, a pilot implementation of the Surtrac system has been deployed on nine intersections in the East Liberty neighbourhood of Pittsburgh, Pennsylvania. Surtrac reduced travel times by more than 25% on average, and wait times were reduced by an average of 40%. A second phase of the pilot program for the Bakery Square district has been running since October 2013. In 2015, Rapid Flow Technologies was formed to commercialise the Surtrac technology. The lead inventor of this technology, Dr. Xiao-Feng Xie, states that he has no association with and does not provide technical supports for this company. (Wikipedia).

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How Do Traffic Signals Work?

Traffic management in dense urban areas is an extremely complex problem with a host of conflicting goals and challenges. One of the most fundamental of those challenges happens at an intersection, where multiple streams of traffic - including vehicles, bikes and pedestrians - need to safel

From playlist Civil Engineering

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Yafeng Yin: "Rhythmic Traffic Management and Control in Fully Automated Vehicle Environment"

Mathematical Challenges and Opportunities for Autonomous Vehicles 2020 Workshop III: Large Scale Autonomy: Connectivity and Mobility Networks "Rhythmic Traffic Management and Control in Fully Automated Vehicle Environment" Yafeng Yin - University of Michigan Abstract: In this talk, we pr

From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020

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Talk Andrea Tosin: Kinetic modelling of traffic flow control

The lecture was held within the of the Hausdorff Trimester Program: Kinetic Theory Abstract: In this talk, we present a hierarchical description of control problems for vehicular traffic, which aim to mitigate speed-dependent risk factors and to dampen structural uncertainties responsible

From playlist Summer School: Trails in kinetic theory: foundational aspects and numerical methods

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Data-Driven Control: The Goal of Balanced Model Reduction

In this lecture, we discuss the overarching goal of balanced model reduction: Identifying key states that are most jointly controllable and observable, to capture the most input—output energy. https://www.eigensteve.com/

From playlist Data-Driven Control with Machine Learning

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Desktop to Real-Time Testing with EMS Hardware - Microgrid System Development and Analysis, Part 2

In the second video on microgrid systems, you explore different concepts required to design control strategies for distributed power systems. The focus is to introduce a microgrid example with a utility-scale energy storage system (ESS). This ESS provides peak shaving for the local microgr

From playlist Microgrid System Development and Analysis

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Stanford Seminar - Zhang Lin on MobileUrban Sensing in Beijing

"Crowd-Sources MobileUrban Sensing as Deployed in Beijing" - Zhang Lin, Tsinghua University Topics in International Technology Management: "Green Technologies in Transportation: Recent Developments from Asia." In this seminar series, learn about technology and business trends, innovations,

From playlist EE402A - Topics in International Technology Management Seminar Series

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Cathy Wu: "Mixed Autonomy Traffic: A Reinforcement Learning Perspective"

Mathematical Challenges and Opportunities for Autonomous Vehicles 2020 Workshop II: Safe Operation of Connected and Autonomous Vehicle Fleets "Mixed Autonomy Traffic: A Reinforcement Learning Perspective" Cathy Wu - Microsoft Research AI, MIT Abstract: How might self-driving cars change

From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020

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Fuzzy control of inverted pendulum

Fuzzy control of inverted pendulum, State-feedback controller is designed based on T-S fuzzy model with the consideration of system stability and performance.

From playlist Demonstrations

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Design and Deployment of an Automated Parking Valet on ROS and ROS2 Networks

The development of an autonomous system requires an increase in the number of sensors and actuators to perceive its environment and control every element in the system dexterously. The challenge is to control autonomous systems to ensure their safety results in more diverse components and

From playlist Tips and Tricks from MATLAB and Simulink Developers

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Benjamin Seibold: "Energy Impact of Automated Vehicles used as Sparse Traffic Controllers"

Mathematical Challenges and Opportunities for Autonomous Vehicles 2020 Workshop II: Safe Operation of Connected and Autonomous Vehicle Fleets "Energy Impact of Automated Vehicles used as Sparse Traffic Controllers" Benjamin Seibold - Temple University, Mathematics Abstract: It is a popul

From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020

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Uber’s Electric Flying Taxis | NEW Battery Breakthroughs!

Subscribe here: https://goo.gl/9FS8uF Link to full talk: https://youtu.be/wpG6XcBNcW8?t=1h21m45s Video link about my company http://Electro.Aero: https://www.youtube.com/watch?v=L2fH2P7QhEI Become a Patreon!: https://www.patreon.com/ColdFusion_TV Hi, welcome to ColdFusion (formerly know

From playlist Energy Videos

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Juliana Freire: "Dataset Search and Augmentation"

Mathematical Challenges and Opportunities for Autonomous Vehicles 2020 Workshop III: Large Scale Autonomy: Connectivity and Mobility Networks "Dataset Search and Augmentation" Juliana Freire - New York University Abstract: The growing number of available structured datasets, from Web tab

From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020

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Optimising flow within mobility systems with AI: Neil Walton and Damon Wischik

This workshop is held in collaboration with the Toyota Mobility Foundation, who are sponsoring the Turing’s research into Optimising flow within mobility systems with AI. It forms part of the Turing’s research programme on AI. The workshop is aimed at identifying solutions for future urb

From playlist AI for traffic control

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Improving the Quality of Life through Street Design: Lessons from Germany

Graham Smith, formerly Principal Lecturer at the Joint Centre for Urban Design at Oxford Brookes and co-author of Responsive Environments.

From playlist Urban Design Group: Introduction to Urban Design

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Lillian Ratliff: "Integrating Automation into Curbside Management: Case Studies, Challenges, & O..."

Mathematical Challenges and Opportunities for Autonomous Vehicles 2020 Workshop II: Safe Operation of Connected and Autonomous Vehicle Fleets "Integrating Automation into Curbside Management: Case Studies, Challenges, and Opportunities" Lillian Ratliff - University of Washington Abstract

From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020

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RailsConf 2021: Scaling Rails API to Write-Heavy Traffic - Takumasa Ochi

Tens of millions of people can download and play the same game thanks to mobile app distribution platforms. Highly scalable and reliable API backends are critical to offering a good game experience. Notable characteristics here is not only the magnitude of the traffic but also the ratio of

From playlist RailsConf 2021

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Saif Jabari: "Decentralized Network Control Using Continuum Traffic Models"

Mathematical Challenges and Opportunities for Autonomous Vehicles 2020 Workshop III: Large Scale Autonomy: Connectivity and Mobility Networks "Decentralized Network Control Using Continuum Traffic Models" Saif Jabari - New York University Abu Dhabi Abstract: The control of signalized int

From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020

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Dirk Helbing: "Towards Digital Democracies & Societal Resilience: Upgrading Smart Cities with Co..."

Mathematical Challenges and Opportunities for Autonomous Vehicles 2020 Workshop IV: Social Dynamics beyond Vehicle Autonomy "Towards Digital Democracies and Societal Resilience: Upgrading Smart Cities with Collective Intelligence, and More" Dirk Helbing - ETH Zurich Abstract: Given the o

From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020

Related pages

Single-machine scheduling | Scalability | Traffic flow