---2023---
1. Long sequence time-series forecasting with deep learning: A survey. Zonglei Chen, Minbo Ma, Tianrui Li, Hongjun Wang, Chongshou Li. Information Fusion 2023. [paper]
2. Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey. Jin, Guangyin and Liang, Yuxuan and Fang, Yuchen and Huang, Jincai and Zhang, Junbo and Zheng, Yu. arXiv 2023. [paper]
3. Spatio-Temporal Graph Neural Networks: A Survey. Sahili, Zahraa Al and Awad, Mariette. arXiv 2023. [paper]
4. Urban Computing for Sustainable Smart Cities: Recent Advances, Taxonomy, and Open Research Challenges. Hashem, Ibrahim Abaker Targio and Usmani, Raja Sher Afgun and Almutairi, Mubarak S and Ibrahim, Ashraf Osman and Zakari, Abubakar and Alotaibi, Faiz and Alhashmi, Saadat Mehmood and Chiroma, Haruna. Sustainability 2023. [paper]
---2022---
1. Graph neural network for traffic forecasting: A survey. Jiang, Weiwei and Luo, Jiayun. Expert Systems with Applications 2022. [paper]
2. Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues. Bui, Khac-Hoai Nam and Cho, Jiho and Yi, Hongsuk. Applied Intelligence 2022. [paper)]
3. Transformers in Time Series: A Survey. Wen, Qingsong and Zhou, Tian and Zhang, Chaoli and Chen, Weiqi and Ma, Ziqing and Yan, Junchi and Sun, Liang. arXiv 2022. [paper]
---2021---
1. Applications of deep learning in congestion detection, prediction and alleviation: A survey. Kumar, Nishant and Raubal, Martin. Transportation Research Part C: Emerging Technologies 2021. [paper]
2. Modelling and reasoning techniques for context aware computing in intelligent transportation system. Swarnamugi, M and Chinnaiyan, R. arXiv 2021. [paper]
---2020---
1. A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges. David Alexander Tedjopurnomo, Zhifeng Bao, Baihua Zheng, Farhana Choudhury, AK Qin. IEEE Transactions on Knowledge and Data Engineering 2020. [paper]
2. A Comprehensive Survey on Traffic Prediction. Xueyan Yin, Genze Wu, Jinze Wei, Yanming Shen, Heng Qi, Baocai Yin. CoRR abs/2004.08555 (2020). [paper]
3. How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey. Jiexia Ye, Juanjuan Zhao, Kejiang Ye, Chengzhong Xu. CoRR abs/2005.11691 (2020). [paper]
4. Urban flow prediction from spatiotemporal data using machine learning: A survey. Peng Xie, Tianrui Li, Jia Liu, Shengdong Du, Xin Yang, Junbo Zhang. Inf. Fusion 59: 1-12 (2020). [paper]
5. Urban big data fusion based on deep learning: An overview. Jia Liu, Tianrui Li, Peng Xie, Shengdong Du, Fei Teng, Xin Yang. Inf. Fusion 53: 123-133 (2020). [paper]
---2019---
1. Big data analytics in intelligent transportation systems: A survey. Li Zhu, Fei Richard Yu, Yige Wang, Bin Ning, Tao Tang. IEEE Transactions on Intelligent Transportation Systems 20, no. 1(2019): 383-398. [paper]
2. Deep Learning for Spatio-Temporal Data Mining: A Survey. Senzhang Wang, Jiannong Cao, and Philip S. Yu. arXiv preprint arXiv:1906.04928. [paper]
---2018---
1. Spatio-temporal data mining: A survey of problems and methods. Atluri, Gowtham, Anuj Karpatne, and Vipin Kumar. ACM Computing Surveys (CSUR) 51, no. 4 (2018): 1-41. [paper]
2. Survey on traffic prediction in smart cities. Attila M Nagy, Vilmos Simon. Pervasive and Mobile Computing 50: 148-163 (2018). [paper]