Urban traffic modeling and analysis is part of the advanced traffic intelligent management technologies that has become a crucial sector of Traffic management and control. Its main purpose is to predict congestion states of a specific urban transport network and propose improvements in the traffic network. Researches rely on three different informations. Historical and recent information of a traffic network about its density and flow, a model of the transport network infrastructure and algorithms referring to both spatial and temporal dimensions. The final objective is to provide a better optimization of the traffic infrastructure such as traffic lights. Those optimizations should result into a decrease of the travel times, pollution and fuel consumption. To survey and manage traffic infrastructures, cities can provide themselves with Intelligent transportation system (ITS) which are especially meaningful in densely urbanized areas. They provide the possibility to better analyze and manage a transport network impact of external factors within a short-term vision, with the daily fluctuate density of the transport network. And over a long term vision, with changes as th increase of motorization, urbanization, population growth and changes in population density. At another end, motorists can use Advanced traveler information system (ATIS) which bring processed data to the end user to help him taking the best directions. Researchers work on different level to make progress into traffic analysis, by collecting traffic data from different sources, modeling traffic flows and network, and developing algorithms to either predict traffic states in a far or a short-term future. (Wikipedia).
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
Simone Göttlich: Traffic flow models with non-local flux and extensions to networks
We present a Godunov type numerical scheme for a class of scalar conservation laws with nonlocal flux arising for example in traffic flow modeling. The scheme delivers more accurate solutions than the widely used Lax-Friedrichs type scheme and also allows to show well-posedness of the mode
From playlist Numerical Analysis and Scientific Computing
Data Modeling Tutorial | Data Modeling for Data Warehousing | Data Warehousing Tutorial | Edureka
***** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi ***** Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, th
From playlist Data Warehousing Tutorial Videos
Urbanism. By Design: Ease of Movement
An Urban Design London Lecture Presented by Peter Stewart of Peter Stewart Consultancy.
From playlist Urban Design Group: Introduction to Urban Design
The Control of Traffic Speed. Part One. A UDL Presentation
The evolution and impact of measures to produce traffic calming, particularly via design, and prospects for the future, from a city wide to local, intimate scale. Tim Pharoah MSc, MRTPI, MCILT, MCIHT, is a freelance transport and town planning consultant, having had a seminal role in polic
From playlist Urban Design Group: Introduction to Urban Design
The Problem of Traffic: A Mathematical Modeling Journey
How can we mathematically model traffic? Specifically we will study the problem of a single lane of cars and the perturbation from equilibrium that occurs when one car brakes, and that braking effect travels down the line of cars, amplifying as it goes along, due to the delayed reaction ti
From playlist Cool Math Series
10b Data Analytics: Spatial Continuity
Lecture on the impact of spatial continuity to motivate characterization and modeling of spatial continuity.
From playlist Data Analytics and Geostatistics
Benjamin Seibold: "Basic Traffic Models and Traffic Waves" (Part 2/2)
Watch part 1/2 here: https://youtu.be/9_1cEtimRNE Mathematical Challenges and Opportunities for Autonomous Vehicles Tutorials 2020 "Basic Traffic Models and Traffic Waves" (Part 2/2) Benjamin Seibold - Temple University Institute for Pure and Applied Mathematics, UCLA September 17, 2020
From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020
Making sense of mobile network traffic using deep learning– Paul Patras, Edinburgh
Committing to smart cities The city is the future. By 2050, more than two-thirds of the planet’s 10 billion people will live in urban centres, according to the United Nations. The COVID-19 pandemic has shone a spotlight on some of the issues faced in the cities of today. So, we had better
From playlist AI UK Smart Cities
Session 3 - Climate and environmental change: what’s the cost to our health?
Climate and environmental change: what’s the cost to our health Our health and well-being are intrinsically linked to the environment in which we live and, as our climate changes, living healthily may be increasingly more challenging. In this session at AI UK, we took a deep dive into the
From playlist AIUK 2022 - CLIMATE ACTION DAY 2
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
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
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
Writing: Informative — How-to example | Reading & Writing | SAT | Khan Academy
Watch Sal work through an SAT Writing: Informative passage. Watch the next lesson: https://www.khanacademy.org/test-prep/new-sat/new-sat-reading-writing/new-sat-writing-passages/v/sat-writing-narrative?utm_source=YT&utm_medium=Desc&utm_campaign=NewSAT Missed the previous lesson? https:/
From playlist Reading & Writing | New SAT | Khan Academy
Tony Lelievre (DDMCS@Turing): Coarse-graining stochastic dynamics
Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp
From playlist Data driven modelling of complex systems
Distribution of waiting time in traffic congestion by Subinay Dasgupta
Indian Statistical Physics Community Meeting 2018 16 February 2018 to 18 February 2018 VENUE:Ramanujan Lecture Hall, ICTS Bangalore This is an annual discussion meeting of the Indian statistical physics community which is attended by scientists, postdoctoral fellows, and graduate studen
From playlist Indian Statistical Physics Community Meeting 2018
01b Spatial Data Analytics: Subsurface Data
Lecture of the data available for subsurface modeling.
From playlist Spatial Data Analytics and Modeling
Simulating the city with AI– Nick Malleson, Leeds
Committing to smart cities The city is the future. By 2050, more than two-thirds of the planet’s 10 billion people will live in urban centres, according to the United Nations. The COVID-19 pandemic has shone a spotlight on some of the issues faced in the cities of today. So, we had better
From playlist AI UK Smart Cities