UrbComp/DeepTTE

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DeepTTE/utils.py at master · UrbComp/DeepTTE · GitHub

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When Will You Arrive? Estimating Travel Time Based on Deep ...

https://github.com/UrbComp/DeepTTE. • Chengdu Dataset: Chengdu Dataset ... Travel time esti- mation for urban road networks using low frequency probe vehicle data ...

Urban Traffic Prediction from Spatio-Temporal Data Using Deep ...

Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta. Learning. In The 25th ACM SIGKDD Conference on Knowledge Discovery &. Data Mining (KDD'19) ...

Urban Traffic Prediction from Spatio-Temporal Data Using Deep ...

We proposed a deep-meta-learning based model, entitled ST-MetaNet, to collectively predict traffic in all location at once.

Review and comparison of prediction algorithms for the estimated ...

DeepTTE. https://github.com/UrbComp/DeepTTE . Google Scholar. 5. Essien A., Petrounias I., Sampaio P., Sampaio S. The impact of rainfall and temperature on ...

Raw Data — Bigscity-LibCity documentation - Read the Docs

Chengdu_Taxi_Sample1¶. Place: Chengdu, China. Duration: Aug. 03, 2014 - Aug. 30, 2014. Link: https://github.com/UrbComp/DeepTTE/tree/master/data. Description ...

UrbComp 2023 - Urban Computing

Schedule · Learning to Rank the Importance of Nodes in Road Networks: New Dataset and Model Ming Xu and Jing Zhang. · Time-Aware Trip Generation for Bike Sharing ...

Deep Learning for Spatio-Temporal Data Mining: A Survey - arXiv

Spatial maps can be both represented as graphs and matrices, depending on different applications. For example, in urban traffic flow prediction, the traffic ...

(PDF) Urban Traffic Prediction from Spatio-Temporal Data Using ...

Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning ... DeepTTE that estimates the travel time of the whole path directly. More ...

UrbComp 2019

Bio: Dr. Yu Zheng is a Vice President of JD.COM and the Chief Data Scientist of JD Digits, passionate about using big data and AI technology to tackle urban ...

UCDNet: A Deep Learning Model for Urban Change Detection From ...

This article introduces a deep learning model called urban CD network (UCDNet) for urban CD from bi-temporal multispectral Sentinel-2 satellite images.

A Deep Learning Approach to Urban Street Functionality Prediction ...

In this study, nine centrality measures are used to classify the urban roads in four cities evaluating the structural importance of street ...

A Survey on Transfer Learning for Urban Spatio-temporal Machine ...

within the data? Q: How to formulate urban computing tasks into machine learning problems? Page 10 ...

UrbanCLIP: Learning Text-enhanced Urban Region Profiling with ...

The first-ever LLM-enhanced framework that integrates the knowledge of text modality into urban imagery, named LLM-enhanced Urban Region Profiling with ...

A Study on ETA Prediction using Machine Learning and Recovered ...

8https://github.com/UrbComp/DeepTTE the best result despite having sparse input features com- pared to deep learning models. In contrast ...

Multimodal deep learning from satellite and street-level imagery for ...

We propose two deep learning-based methods for jointly utilizing satellite and street level imagery for measuring urban inequalities.

enabling urban intelligence with big data,” - Frontiers of Computer ...

Urban computing is an interdisciplinary field fusing computer science and information technology with traditional city-related fields, like urban planning, ...

When Transfer Learning Meets Cross-City Urban Flow Prediction

Urban flow prediction is a fundamental task to build smart cities, where neural networks have become the most popular method. However, the deep learning ...

DEEP LEARNING APPROACH FOR URBAN MAPPING

DeepResUnet as a Deep Convolutional Neural Network of Urban. Buildings from VHR Remote Sensing Imagery. Recently, Heng Liu in 2020 [6] from china prefer to ...