|
1. Li, Y., Yu, R., Shah, C., & Liu, Y. (2018). Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations (ICLR). 2. Wei, H., Zheng, G., Yao, H., & Li, Z. (2019). PressLight: Learning Max Pressure Control to Coordinate Traffic Signals. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3. Wang, W., Jiang, W., & Luo, D. (2019). Mining Social Media Data for Traffic Event Detection. Journal of Transportation Technologies, 9(3), 237-251. 4. Mahmassani, H. S. (2016). Traffic Flow Theory: A State-of-the-Art Review. Transportation Science, 50(3), 100–121. 5. Zheng, Z., & Van Zuylen, H. (2019). Urban Traffic Flow Prediction Based on Data Warehouse and Neural Network Integration. International Journal of Transportation Science and Technology. 6. Chen, C., et al. (2017). Freeway Performance Measurement System (PeMS): An Integrated Real-Time and Historical Traffic Data Warehouse. Transportation Research Part C. 7. Golubchik, L., et al. (2013). Data Cube: A Relational Aggregation Operator Generalization for Traffic Analytics. IEEE Transactions on Knowledge and Data Engineering.
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