Predbench: Benchmarking spatio-temporal prediction across diverse disciplines
In this paper, we introduce PredBench, a benchmark tailored for the holistic evaluation of
spatio-temporal prediction networks. Despite significant progress in this field, there remains …
spatio-temporal prediction networks. Despite significant progress in this field, there remains …
Metropolitan segment traffic speeds from massive floating car data in 10 cities
Traffic analysis is crucial for urban operations and planning, while the availability of dense
urban traffic data beyond loop detectors is still scarce. We present a large-scale floating …
urban traffic data beyond loop detectors is still scarce. We present a large-scale floating …
Lane segmentation refinement with diffusion models
The lane graph is a key component for building high-definition (HD) maps and crucial for
downstream tasks such as autonomous driving or navigation planning. Previously, He et …
downstream tasks such as autonomous driving or navigation planning. Previously, He et …
Physics-Informed Graph Neural Networks for Water Distribution Systems
I Ashraf, J Strotherm, L Hermes… - Proceedings of the AAAI …, 2024 - ojs.aaai.org
Water distribution systems (WDS) are an integral part of critical infrastructure which is pivotal
to urban development. As 70% of the world's population will likely live in urban …
to urban development. As 70% of the world's population will likely live in urban …
Spatial graph convolution neural networks for water distribution systems
We investigate the task of missing value estimation in graphs as given by water distribution
systems (WDS) based on sparse signals as a representative machine learning challenge in …
systems (WDS) based on sparse signals as a representative machine learning challenge in …
Transfer Learning for Transportation Demand Resilience Pattern Prediction Using Floating Car Data
Understanding the response of a transportation system to disruptive events is significant for
evaluating the resilience of the system. However, data collection during such events is …
evaluating the resilience of the system. However, data collection during such events is …
ADDGCN: A Novel Approach with Down-Sampling Dynamic Graph Convolution and Multi-Head Attention for Traffic Flow Forecasting
Z Li, S Wei, H Wang, C Wang - Applied Sciences, 2024 - mdpi.com
An essential component of autonomous transportation system management and decision-
making is precise and real-time traffic flow forecast. Predicting future traffic conditionsis a …
making is precise and real-time traffic flow forecast. Predicting future traffic conditionsis a …
Spatial Graph Convolution Neural Networks for Water Distribution Systems
We investigate the task of missing value estimation in graphs as given by water distribution
systems (WDS) based on sparse signals as a representative machine learning challenge in …
systems (WDS) based on sparse signals as a representative machine learning challenge in …
Uncertainty Quantification for Image-based Traffic Prediction across Cities
Despite the strong predictive performance of deep learning models for traffic prediction, their
widespread deployment in real-world intelligent transportation systems has been restrained …
widespread deployment in real-world intelligent transportation systems has been restrained …
Enhancing Deep Learning-Based City-Wide Traffic Prediction Pipelines Through Complexity Analysis
Deep learning models can effectively capture the non-linear spatiotemporal dynamics of city-
wide traffic forecasting. Evidence of varying deep learning model performance between …
wide traffic forecasting. Evidence of varying deep learning model performance between …