Transportation forecasting

Résumé

Transportation forecasting is the attempt of estimating the number of vehicles or people that will use a specific transportation facility in the future. For instance, a forecast may estimate the number of vehicles on a planned road or bridge, the ridership on a railway line, the number of passengers visiting an airport, or the number of ships calling on a seaport. Traffic forecasting begins with the collection of data on current traffic. This traffic data is combined with other known data, such as population, employment, trip rates, travel costs, etc., to develop a traffic demand model for the current situation. Feeding it with predicted data for population, employment, etc. results in estimates of future traffic, typically estimated for each segment of the transportation infrastructure in question, e.g., for each roadway segment or railway station. The current technologies facilitate the access to dynamic data, big data, etc., providing the opportunity to develop new algorithms to im

Source officielle

https://en.wikipedia.org/wiki/Transportation_forecasting

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Production management deals with producing goods sustainably at the right time, quantity, and quality with the minimum cost. This course equips students with practical skills and tools for manufacturing companies' demand management, supply management, and advanced supply chain analytics.

Introduce the major elements of transportation systems and create awareness of the broader context
Develop basic skills in applying the fundamentals of the transportation field
Understand the key concepts and physics of the transport phenomena
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Transportation forecasting

Generating daily activity schedules using machine learning Master Thesis Sergej Gasparovich June 19, 2020 Prof. M. Bierlaire, J. Pougala, T. Hillel Transport and Mobility Laboratory, EPFL Y. Liu Visual Intelligence

Activity-based Travel Demand Forecasting: Extensions to the SBB Nationwide Model

Data-driven traffic and experienced travel time forecasting method for freeways with a localized linear regression model

Generating daily activity schedules using machine learning Master Thesis Sergej Gasparovich June 19, 2020 Prof. M. Bierlaire, J. Pougala, T. Hillel Transport and Mobility Laboratory, EPFL Y. Liu Visual Intelligence

Activity-based Travel Demand Forecasting: Extensions to the SBB Nationwide Model

Data-driven traffic and experienced travel time forecasting method for freeways with a localized linear regression model