Travel demand forecasting
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This article uses Random Forest (RF) and k-fold cross-validation to predict the hourly count of rental bikes (cnt/h) in the city of Seoul (Korea) using information related to rental hour, temperature, humidity, wind speed, visibility, dewpoint, solar radiation, snowfall, and rainfall.
9p 
viengfa
28-10-2024
3
2
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This paper concisely overviews recent advancements and challenges associated with applying activity-based models in travel demand forecasting. Additionally, the article explores the operational process of the ActivitySIM model, a specific ABM tool for traffic demand forecasting, by detailing the required input data and parameters.
5p vibenya
31-12-2024
6
2
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