Urban-computing-papers

Introduction

This project is a collection of recent research in areas such as new infrastructure and urban computing, including white papers, academic papers, AI lab and dataset etc.

Content

新基建
白皮书
expert
AI Lab
Dataset

Relevant papers

survey
GNN
1. Trafic forecasting
- 1.1 GNN methods on Traffic forecasting
- 1.2 Other methods on Traffic forecasting
2. Flows Prediction
3. Demand Prediction

新基建

[1] 什么是新基建

[2] 百度AI新基建版图

[3] 新基建项目盘点

白皮书

[6] 赛迪白皮书：2020城市新基建布局与发展白皮书

[7] 赛迪报告：**“新基建”发展研究报告

[8] 新基建，新机遇：**智能经济发展白皮书

expert

[1] Yu Zheng: link

[2] Yanhua Li: link

[3] Xun Zhou: link

[4] YaGuang Li: link

[5] Zhenhui Jessie Li: link

[6] David S. Rosenblum: link

[7] Huaiyu Wan: link

[8] Junbo Zhang: link

AI Lab

[1] iFly Tek: link

[2] JD city : link

[3] alibaba: link

[4] Huawei: link

[5] ByteDance: link

[6] alibaba damo academy: link

[7] Tencent: link

[8] Microsoft: link

[9] intel: link

[10] FACEBOOK: link

[11] Google: link

[12] National Laboratory of Pattern Recognition: link

[13] Baidu: link

[14] JD cloud: link

Dataset

[1] GAIA Open Dataset: link

[2] 智慧足迹: link

[3] NYC OpenData: link

[4] METR-LA: link, 百度网盘密码:xsz5

[5] PEMS: link, 百度网盘密码:jutw

[6] TaxiBJ: link, 百度网盘密码:sg4n

[7] BikeNYC: link, 百度网盘密码:lmwj

[8] NYC-Taxi-Bike: link, 百度网盘密码:022y

Relevant papers

survey

[1] Urban Computing: Concepts, Methodologies, and Applications. TIST 2014. paper

YU ZHENG, LICIA CAPRA, OURI WOLFSON, HAI YANG

[2] A Comprehensive Survey on Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems 2020. paper

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu

[3] Batman or the Joker? The Powerful Urban Computing and its Ethics Issues. SIGSPATIAL 2019. paper

Kaiqun Fu, Abdulaziz Alhamadani, Taoran Ji, Chang-Tien Lu

[4] Deep Learning for Spatio-Temporal Data Mining: A Survey. paper

Senzhang Wang, Jiannong Cao, Fellow, Philip S. Yu

[5] Urban flows prediction from spatial-temporal data using machine learning: A survey. Information Fusion 2020. paper

Peng Xie, Tianrui Li, Jia Liu, Shengdong Du, Xin Yang, Junbo Zhang

[6] How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey. paper

Jiexia Ye, Juanjuan Zhao, Kejiang Ye, Chengzhong Xu

[7] A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges. IEEE Transactions on Knowledge and Data Engineering 2020. paper

David Alexander Tedjopurnomo, Zhifeng Bao, Baihua Zheng, Farhana Murtaza Choudhury, Kai Qin

GNN

More graph neural network contents

[1] GRAPH ATTENTION NETWORKS. ICLR 2018. paper

Petar Veliˇckovi´, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li`, Yoshua Bengio

[2] AM-GCN: Adaptive Multi-channel Graph Convolutional Networks. KDD 2020. paper

Xiao Wang, Meiqi Zhu, Deyu Bo, Peng Cui, Chuan Shi, Jian Pei

[3] Heterogeneous Graph Neural Network. SIGKDD 2019. paper

Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, Nitesh V. Chawla

Traffic forecasting

GNN papers on Traffic forecasting

[1] Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. IJCAI 2018. paper, code.

Detailed analysis

Bing Yu, Haoteng Yin, Zhanxing Zhu

[2] Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting. AAAI 2019. paper.

Xu Geng, Yaguang Li, Leye Wang, Lingyu Zhang, Qiang Yang, Jieping Ye, Yan Liu

[3] Spatio-Temporal Graph Structure Learning for Traffic Forecasting. AAAI 2020. paper.

Qi Zhang, Jianlong Chang, Gaofeng Meng, Shiming Xiang, Chunhong Pan

[4] GMAN: A Graph Multi-Attention Network for Traffic Prediction. AAAI 2020. paper, code.

Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, Jianzhong Qi

[5] Graph WaveNet for Deep Spatial-Temporal Graph Modeling. IJCAI 2019. paper, code.

Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang

[6] Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting. AAAI 2020. paper, code.

Chao Song, Youfang Lin, Shengnan Guo, Huaiyu Wan

[7] DIFFUSION CONVOLUTIONAL RECURRENT NEURAL NETWORK: DATA-DRIVEN TRAFFIC FORECASTING. ICLR 2018. paper.

Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu

[8] Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. AAAI 2019. paper code.

Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, Huaiyu Wan

[9] STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting. paper

Cheonbok Park , Chunggi Lee , Hyojin Bahng, Taeyun won

[10] Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction. IEEE Transactions on Intelligent Transportation Systems 2020. paper

Mingqi Lv , Zhaoxiong Hong, Ling Chen , Tieming Chen, Tiantian Zhu , and Shouling Ji

[11] Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data. KDD 2020. paper

Rui Dai, Shenkun Xu, Qian Gu, Chenguang Ji, Kaikui Liu

[12] Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting. AAAI 2020. paper

Weiqi Chen, Ling Chen, Yu Xie, Wei Cao, Yusong Gao, Xiaojie Feng

Other method on Traffic forecasting

[1] Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning. SIGKDD 2019. paper

Zheyi Pan , Yuxuan Liang , Weifeng Wang, Yong Yu, Yu Zheng, Junbo Zhang

[2] Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction. AAAI 2019. paper

Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, Zhenhui Li*

[3] Deep Spatial–Temporal 3D Convolutional Neural Networks for Traffic Data Forecasting. EEE Transactions on Intelligent Transportation Systems 2019. paper

Shengnan Guo, Youfang Lin, Shijie Li, Zhaoming Chen, and Huaiyu Wan

Flows Prediction

[1] Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. AAAI 2017. paper

Junbo Zhang, Yu Zheng, Dekang Qi

[2] UrbanFM: Inferring Fine-Grained Urban Flows. SIGKDD 2019. paper

Yuxuan Liang, Kun Ouyang, Lin Jing, Sijie Ruan, Ye Liu1 Junbo Zhang, David S. Rosenblum, Yu Zheng

[3] DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction. IEEE Transactions on Intelligent Transportation Systems 2019. paper

Chuanpan Zheng, Xiaoliang Fan, Chenglu Wen, Longbiao Chen, Cheng Wang, Jonathan Li

[4] Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction. IEEE Transactions on Intelligent Transportation Systems 2020. paper

Lingbo Liu, Jiajie Zhen, Guanbin Li , Geng Zhan, Zhaocheng He,Bowen Du,Liang Lin

[5] AutoST: Efficient Neural Architecture Search for Spatio-Temporal Prediction. KDD 2020. paper

Ting Li, Junbo Zhang, Kainan Bao, Yuxuan Liang, Yexin Li, Yu Zheng

Demand Prediction

[1] Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction. AAAI 2018. paper

Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, Zhenhui Li

[2] Origin-Destination Matrix Prediction via Graph Convolution: a New Perspective of Passenger Demand Modeling. SIGKDD 2019. paper

Yuandong Wang, Hongzhi Yin, Hongxu Chen, Tianyu Wo, Jie Xu

[3] STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting. IJCAI 2019. paper

Lei Bai, Lina Yao , Salil.S Kanhere, Xianzhi Wang, Quan.Z Sheng

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