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Found 15 papers, 3 papers with code
TrafPS: A Shapley-based Visual Analytics Approach to Interpret Traffic
Recent achievements in deep learning (DL) have shown its potential for predicting traffic flows.
PlanGPT: Enhancing Urban Planning with Tailored Language Model and Efficient Retrieval
In the field of urban planning, general-purpose large language models often struggle to meet the specific needs of planners.
Hyper-Relational Knowledge Graph Neural Network for Next POI
In addition, prior works overlook the rich structural information inherent in KG, which consists of higher-order relations and can further alleviate the impact of data sparsity. To this end, we propose a Hyper-Relational Knowledge Graph Neural Network (HKGNN) model.
Hybrid Feature Embedding For Automatic Building Outline Extraction
Building outline extracted from high-resolution aerial images can be used in various application fields such as change detection and disaster assessment.
Multitask Weakly Supervised Learning for Origin Destination Travel Time Estimation
Subsequently, a Multitask Weakly Supervised Learning Framework for Travel Time Estimation (MWSL TTE) has been proposed to infer transition probability between roads segments, and the travel time on road segments and intersection simultaneously.
Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout
Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area.
GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation
However, building a model for such a data-driven task requires a large amount of users' travel information, which directly relates to their privacy and thus is less likely to be shared.
Online Trajectory Prediction for Metropolitan Scale Mobility Digital Twin
In the first stage, to encode the daily variation of human mobility at a metropolitan level, we automatically extract citywide mobility trends as crowd contexts and predict long-term and long-distance movements at a coarse level.
Route to Time and Time to Route: Travel Time Estimation from Sparse Trajectories
In this case, this paper aims to resolve the problem of travel time estimation (TTE) and route recovery in sparse scenarios, which often leads to the uncertain label of travel time and route between continuously sampled GPS points.
ST-ExpertNet: A Deep Expert Framework for Traffic Prediction
Recently, forecasting the crowd flows has become an important research topic, and plentiful technologies have achieved good performances.
Domain Adversarial Graph Convolutional Network Based on RSSI and Crowdsensing for Indoor Localization
We also incorporate graph convolutional networks (GCNs) to extract graph-level embeddings, a feature that has been largely overlooked in previous WiFi indoor localization studies.
TrafPS: A Visual Analysis System Interpreting Traffic Prediction in Shapley
TrafPS consists of three layers, from data process to results computation and visualization.
Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction
Forecasting incident occurrences (e. g. crime, EMS, traffic accident) is a crucial task for emergency service providers and transportation agencies in performing response time optimization and dynamic fleet management.
DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction
Based on this idea, a series of methods have been proposed to address grid-based prediction for citywide crowd and traffic.
VLUC: An Empirical Benchmark for Video-Like Urban Computing on Citywide Crowd and Traffic Prediction
In this study, we publish a new aggregated human mobility dataset generated from a real-world smartphone application and build a standard benchmark for such kind of video-like urban computing with this new dataset and the existing open datasets.
