Unifying Uber and taxi data via deep models for taxi passenger demand prediction
Sign Up to like & getrecommendations! 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
An XGBoost-SHAP Model for Energy Demand Prediction With Boruta–Lasso Feature Selection
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Access"
DOI: 10.1109/access.2025.3593558
Abstract: Energy demand prediction is essential in ensuring national energy security, promoting high-quality economic development, advancing sustainable development, optimizing the energy structure, and achieving dual carbon goals. In recent years, machine learning (ML) algorithms have been… read more here.
Keywords: demand prediction; energy demand; model; energy ... See more keywords
Domain Adaptation for Retail Demand Prediction
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Access"
DOI: 10.1109/access.2025.3600468
Abstract: Predicting the demand of products in the retail industry is a complex task, especially when there are changes in the market. This paper examines three such market shifts in the retail industry: the COVID-19 pandemic,… read more here.
Keywords: retail demand; demand prediction; demand; domain adaptation ... See more keywords
Graph Neural Network for Robust Public Transit Demand Prediction
Sign Up to like & getrecommendations! 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
Exploring the Impact of Spatiotemporal Granularity on the Demand Prediction of Dynamic Ride-Hailing
Sign Up to like & getrecommendations! 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
Spatial-Temporal Correlation Learning for Traffic Demand Prediction
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Intelligent Transportation Systems"
DOI: 10.1109/tits.2024.3443341
Abstract: Traffic demand prediction has been drawing increasing research interest due to its critical role in intelligent transportation systems. However, conventional deep learning methods for traffic demand forecast ignore the correlations between the pick-up and drop-off… read more here.
Keywords: traffic demand; demand prediction; demand; attention ... See more keywords
Bike-Sharing Demand Prediction Based on Dynamic Time Warping and Spatio-Temporal Graph Attention Network
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Intelligent Transportation Systems"
DOI: 10.1109/tits.2025.3570009
Abstract: Bike-sharing demand prediction involves complex, dynamic spatio-temporal dependencies and various influencing factors, thus becomes one of technical challenges in intelligent transportation systems. Existing methods often rely on predefined adjacency matrices based on distance or road… read more here.
Keywords: sharing demand; attention; bike sharing; spatio temporal ... See more keywords
Dynamic Auto-Structuring Graph Neural Network: A Joint Learning Framework for Origin-Destination Demand Prediction
Sign Up to like & getrecommendations! 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
Short-Term Demand Prediction Using an Ensemble of Linearly-Constrained Estimators
Sign Up to like & getrecommendations! 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
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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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
FF-STGCN: A usage pattern similarity based dual-network for bike-sharing demand prediction
Sign Up to like & getrecommendations! Published in 2024 at "PLOS ONE"
DOI: 10.1371/journal.pone.0298684
Abstract: Accurate bike-sharing demand prediction is crucial for bike allocation rebalancing and station planning. In bike-sharing systems, the bike borrowing and returning behavior exhibit strong spatio-temporal characteristics. Meanwhile, the bike-sharing demand is affected by the arbitrariness… read more here.
Keywords: demand prediction; bike sharing; sharing demand;