for this assignment, we'll be using this data to study bike usage in washington d.c. based on the granularity and the variables present in the data, what might some limitations of using this data be? what are two additional data categories/variables that you can collect to address some of these limitations?

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12-10-2022
Computers and Technology

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for this assignment, we'll be using this data to study bike usage in washington d.c. based on the granularity and the variables present in the data, what might some limitations of using this data be? what are two additional data categories/variables that you can collect to address some of these limitations?

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rekharaypriti8910 rekharaypriti8910 · Answer 1 · 2022-10-14T19:44:23+02:00

For managing transportation infrastructure, particularly during disruptions or around new developments, short-term demand forecasting is crucial.

Due to "tidal flows" of travel and use, many bike-sharing programs struggle to manage service provision and bike fleet rebalancing. Although short-term traffic demand estimates and machine learning techniques like deep neural networks have recently advanced, relatively few studies have looked into this issue utilizing a feature engineering approach to guide model selection. From real-world bike usage records, this study extracts unique time-lagged variables, such as network node Out-strength, In-strength, Out-degree, In-degree, and PageRank, that describe graph topologies and flow interactions. According to the experiment's findings, graph-based features are more crucial for demand forecasting than more widely used meteorological data.

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