Traffic4cast at neurips 2020-yet more on the unreasonable effectiveness of gridded geo-spatial processes
The IARAI Traffic4cast competition at NeurIPS 2019 showed that neural networks can
successfully predict future traffic conditions 15 minutes into the future on simply aggregated …
successfully predict future traffic conditions 15 minutes into the future on simply aggregated …
The surprising efficiency of framing geo-spatial time series forecasting as a video prediction task–insights from the iarai traffic4cast competition at neurips 2019
Abstract Deep Neural Networks models are state-of-the-art solutions in accurately
forecasting future video frames in a movie. A successful video prediction model needs to …
forecasting future video frames in a movie. A successful video prediction model needs to …
Graph-ResNets for short-term traffic forecasts in almost unknown cities
The 2019 IARAI traffic4cast competition is a traffic forecasting problem based on traffic data
from three cities that are encoded as images. We developed a ResNet-inspired graph …
from three cities that are encoded as images. We developed a ResNet-inspired graph …
Traffic4cast 2020--Graph Ensemble Net and the Importance of Feature And Loss Function Design for Traffic Prediction
Q Qi, PH Kwok - arXiv preprint arXiv:2012.02115, 2020 - arxiv.org
This paper details our solution to Traffic4cast 2020. Similar to Traffic4cast 2019, Traffic4cast
2020 challenged its contestants to develop algorithms that can predict the future traffic states …
2020 challenged its contestants to develop algorithms that can predict the future traffic states …
[PDF][PDF] 基于无标签视频数据的深度预测学习方法综述
潘敏婷， 王韫博， 朱祥明， 高思宇， 龙明盛， 杨小康 - 电子学报, 2022 - ejournal.org.cn
基于视频数据的深度预测学习(以下简称“深度预测学习”) 属于深度学习, 计算机视觉和强化学习
的交叉融合研究方向, 是气象预报, 自动驾驶, 机器人视觉控制等场景下智能预测与决策系统的 …
的交叉融合研究方向, 是气象预报, 自动驾驶, 机器人视觉控制等场景下智能预测与决策系统的 …
Traffic4cast--Large-scale Traffic Prediction using 3DResNet and Sparse-UNet
The IARAI competition Traffic4cast 2021 aims to predict short-term city-wide high-resolution
traffic states given the static and dynamic traffic information obtained previously. The aim is …
traffic states given the static and dynamic traffic information obtained previously. The aim is …
Solving Traffic4Cast competition with U-Net and temporal domain adaptation
In this technical report, we present our solution to the Traffic4Cast 2021 Core Challenge, in
which participants were asked to develop algorithms for predicting a traffic state 60 minutes …
which participants were asked to develop algorithms for predicting a traffic state 60 minutes …
A variational u-net for weather forecasting
PH Kwok, Q Qi - arXiv preprint arXiv:2111.03476, 2021 - arxiv.org
Not only can discovering patterns and insights from atmospheric data enable more accurate
weather predictions, but it may also provide valuable information to help tackle climate …
weather predictions, but it may also provide valuable information to help tackle climate …
Computational Methods for Sustainable Mobility-Interpretation and Prediction of Tracking Data using Graphs and Machine Learning
H Martin - 2023 - research-collection.ethz.ch
Many of today's urgent challenges, such as greenhouse gas emissions and climate change,
air quality and health, or traffic and congestion, are closely linked to the movement of people …
air quality and health, or traffic and congestion, are closely linked to the movement of people …
Traffic forecasting on traffic moving snippets
N Wiedemann, M Raubal - arXiv preprint arXiv:2110.14383, 2021 - arxiv.org
Advances in traffic forecasting technology can greatly impact urban mobility. In the
traffic4cast competition, the task of short-term traffic prediction is tackled in unprecedented …
traffic4cast competition, the task of short-term traffic prediction is tackled in unprecedented …