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Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Joint Short-Term Origin-Destination Demand Prediction for Multimodal Transport Systems.

Jinlei Zhang, Yongjie Yang, Lixing Yang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
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    Summary
    This summary is machine-generated.

    Accurate short-term multimodal transportation demand prediction is challenging due to data limitations and intermodal influences. The proposed PD-MTSOD model effectively forecasts origin-destination demand by analyzing spatiotemporal features and intermodal correlations.

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    Area of Science:

    • Transportation Science
    • Data Science
    • Applied Mathematics

    Background:

    • Short-term origin-destination (OD) demand prediction is vital for multimodal transportation systems.
    • Existing methods struggle with real-time data availability, demand sparsity, high dimensionality, and intermodal influences.

    Purpose of the Study:

    • To develop a novel model for accurate short-term multimodal transportation OD demand prediction.
    • To address challenges of data availability, sparsity, high dimensionality, and intermodal correlations.

    Main Methods:

    • Proposed a multitask learning and Partial-Differential-based model (PD-MTSOD).
    • Incorporated an OD demand learner for real-time demand estimation.
    • Utilized hypergraph attention for spatiotemporal feature aggregation.
    • Decomposed OD demand and employed partial differential methods to model intermodal correlations.

    Main Results:

    • PD-MTSOD demonstrated superior performance compared to baseline models in tests on Beijing and New York City multimodal systems.
    • Validated the benefits of jointly considering multiple transportation modes.
    • Revealed significant correlations between different transportation modes.

    Conclusions:

    • The PD-MTSOD model offers a reliable approach for short-term multimodal transportation OD demand prediction.
    • Jointly analyzing multiple transportation modes enhances prediction accuracy.
    • Understanding intermodal correlations is crucial for effective transportation management.