Articles with "demand prediction" as a keyword



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Unifying Uber and taxi data via deep models for taxi passenger demand prediction

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Published in 2020 at "Personal and Ubiquitous Computing"

DOI: 10.1007/s00779-020-01426-y

Abstract: Taxi passenger demand prediction is of great significance to perceive citywide human mobility and make a lot of urban sensing applications more convenient. There are two major challenges to develop accurate predictive models, i.e., the… read more here.

Keywords: passenger demand; demand prediction; taxi; prediction ... See more keywords
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Graph Neural Network for Robust Public Transit Demand Prediction

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Published in 2020 at "IEEE Transactions on Intelligent Transportation Systems"

DOI: 10.1109/tits.2020.3041234

Abstract: Understanding and forecasting mobility patterns and travel demand are fundamental and critical to efficient transport infrastructure planning and service operation. However, most existing studies focused on deterministic demand estimation/prediction/analytics. Differently, this study provides confidence interval… read more here.

Keywords: network; demand prediction; public transit; prediction ... See more keywords
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Exploring the Impact of Spatiotemporal Granularity on the Demand Prediction of Dynamic Ride-Hailing

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Published in 2022 at "IEEE Transactions on Intelligent Transportation Systems"

DOI: 10.1109/tits.2022.3216016

Abstract: Dynamic demand prediction is a key issue in ride-hailing dispatching. Many methods have been developed to improve the demand prediction accuracy of an increase in demand-responsive, ride-hailing transport services. However, the uncertainties in predicting ride-hailing… read more here.

Keywords: demand prediction; ride hailing; demand; spatiotemporal granularity ... See more keywords
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Dynamic Auto-Structuring Graph Neural Network: A Joint Learning Framework for Origin-Destination Demand Prediction

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Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2021.3135898

Abstract: Solving the demand prediction problem is an important part of improving the efficiency and reliability of ride-hailing services. Spatial-temporal graph learning methods have shown potential in modeling the spatial-temporal dependencies of ride-hailing demand data, but… read more here.

Keywords: demand prediction; destination demand; origin destination; graph ... See more keywords
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Short-Term Demand Prediction Using an Ensemble of Linearly-Constrained Estimators

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Published in 2021 at "IEEE Transactions on Power Systems"

DOI: 10.1109/tpwrs.2021.3050150

Abstract: The benefits of forecasting power demand can bring increased stability to any power grid. Between optimizing the production and control of grid resources and interacting with energy markets, there is a strong motivation for generation,… read more here.

Keywords: short term; power; prediction; demand ... See more keywords
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A quantitative analysis of the impact of explicit incorporation of recency, seasonality and model specialization into fine-grained tourism demand prediction models

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Published in 2022 at "PLOS ONE"

DOI: 10.1371/journal.pone.0278112

Abstract: Forecasting is of utmost importance for the Tourism Industry. The development of models to predict visitation demand to specific places is essential to formulate adequate tourism development plans and policies. Yet, only a handful of… read more here.

Keywords: demand prediction; prediction; fine grained; tourism demand ... See more keywords
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A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information

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Published in 2023 at "Sustainability"

DOI: 10.3390/su15097096

Abstract: With the development of smart cities and smart transportation, cities can gradually provide people with more information to facilitate their life and travel, and parking is also inseparable from both of them. Accurate on-street parking… read more here.

Keywords: demand prediction; information; parking demand; demand ... See more keywords