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1.225J Transportation Flow Systems (MIT) 1.225J Transportation Flow Systems (MIT)

Description

Design, operation, and management of traffic flows over complex transportation networks are the foci of this course. It covers two major topics: traffic flow modeling and traffic flow operations. Sub-topics include deterministic and probabilistic models, elements of queuing theory, and traffic assignment. Concepts are illustrated through various applications and case studies. This is a half-term subject offered during the second half of the semester. Design, operation, and management of traffic flows over complex transportation networks are the foci of this course. It covers two major topics: traffic flow modeling and traffic flow operations. Sub-topics include deterministic and probabilistic models, elements of queuing theory, and traffic assignment. Concepts are illustrated through various applications and case studies. This is a half-term subject offered during the second half of the semester.Subjects

transportation | transportation | transportation flow systems | transportation flow systems | traffic | traffic | traffic flow | traffic flow | networks | networks | transportation networks | transportation networks | flow modeling | flow modeling | flow operations | flow operations | deteministic models | deteministic models | probabilistic models | probabilistic models | queuing theory | queuing theory | queues | queues | traffic assignment | traffic assignment | case studies | case studies | cumulative plots | cumulative plots | airport runway capacity | airport runway capacity | runway capacity | runway capacity | road traffic | road traffic | shortest paths | shortest paths | optimizations | optimizations | highway control | highway control | ramp metering | ramp metering | simulation models | simulation models | isolated signals | isolated signals | operations | operations | operational problems | operational problems | air traffic operation | air traffic operation | air | air | road | road | component | component | 1.225 | 1.225 | ESD.205 | ESD.205License

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In this class, students use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Students will enhance their model-building skills, through review and extension of functions of random variables, Poisson processes, and Markov processes; move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables; and review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. A class project is required. In this class, students use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Students will enhance their model-building skills, through review and extension of functions of random variables, Poisson processes, and Markov processes; move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables; and review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. A class project is required.Subjects

statistics | statistics | statistical model | statistical model | modelling | modelling | probability | probability | probabilistic model | probabilistic model | risk assessment | risk assessment | system analysis | system analysis | system design | system design | systems engineering | systems engineering | distributions | distributions | poisson | poisson | markov | markov | queuing theory | queuing theory | congestion | congestion | traffic | traffic | regression | regression | hypothesis testing | hypothesis testing | inference | inference | operations research | operations research | Weibull analysis | Weibull analysisLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata1.225J Transportation Flow Systems (MIT)

Description

Design, operation, and management of traffic flows over complex transportation networks are the foci of this course. It covers two major topics: traffic flow modeling and traffic flow operations. Sub-topics include deterministic and probabilistic models, elements of queuing theory, and traffic assignment. Concepts are illustrated through various applications and case studies. This is a half-term subject offered during the second half of the semester.Subjects

transportation | transportation flow systems | traffic | traffic flow | networks | transportation networks | flow modeling | flow operations | deteministic models | probabilistic models | queuing theory | queues | traffic assignment | case studies | cumulative plots | airport runway capacity | runway capacity | road traffic | shortest paths | optimizations | highway control | ramp metering | simulation models | isolated signals | operations | operational problems | air traffic operation | air | road | component | 1.225 | ESD.205License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadataESD.86 Models, Data and Inference for Socio-Technical Systems (MIT)

Description

In this class, students use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Students will enhance their model-building skills, through review and extension of functions of random variables, Poisson processes, and Markov processes; move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables; and review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. A class project is required.Subjects

statistics | statistical model | modelling | probability | probabilistic model | risk assessment | system analysis | system design | systems engineering | distributions | poisson | markov | queuing theory | congestion | traffic | regression | hypothesis testing | inference | operations research | Weibull analysisLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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