|| M. J. Lighthill and G. B. Whitham. On kinematic waves ii. a theory of traffic|
flow on long crowded roads. Proceedings of the Royal Society of London. Series A.
Mathematical and Physical Sciences, 229(1178):317–345, 1955.
 P. I. Richards. Shock waves on the highway. Operations Research, 4(1):42–51, 1956.
 J.-P. Lebacque. The Godunov scheme and what it means for first order traffic flow
models. In Proceedings of the 13TH International Symposium on Transportation and
Traffic Theory, pages 24–26, 1996.
 F. Van Wageningen-Kessels, H. Van Lint, S. P. Hoogendoorn, and K. Vuik. Implicit
and explicit numerical methods for macroscopic traffic flow models: Efficiency and
 J. Thai, B. Prodhomme, and A. M. Bayen. State estimation for the discretized LWR
PDE using explicit polyhedral representations of the Godunov scheme. In American
Control Conference, pages 2428–2435. IEEE, 2013.
 M. S. Ahmed and A. R. Cook. Analysis of freeway traffic time-series data by using
Box-Jenkins techniques. 1979.
 V. D. V. Mascha, M. Dougherty, and S. Watson. Combining kohonen maps with
ARIMA time series models to forecast traffic flow. Transportation Research Part C:
Emerging technologies, 4(5):307–318, 1996.
 S. Lee and D. Fambro. Application of subset Autoregressive Integrated Moving
Average model for short-term freeway traffic volume forecasting. Transportation
Research Record, 1678(1):179–188, 1999.
 B. Williams and L. Hoel. Modeling and forecasting vehicular traffic flow as a seasonal
ARIMA process: Theoretical basis and empirical results. Journal of Transportation
Engineering, 129(6):664–672, 2003.
 S. Kumar and L. Vanajakshi. Short-term traffic flow prediction using seasonal
ARIMA model with limited input data. European Transport Research Review, 7(3):
 B. L. Smith and M. J. Demetsky. Short-term traffic flow prediction models-a comparison
of neural network and nonparametric regression approaches. In Proceedings of
IEEE International Conference on Systems, Man and Cybernetics, pages 1706–1709.
 C.-H. Wu, J.-M. Ho, and D.-T. Lee. Travel-time prediction with Support Vector
Regression. IEEE Transactions on Intelligent Transportation Systems, 5(4):276–281,
 Y. Lv, Y. Duan, W. Kang, Z. Li, and F.-Y. Wang. Traffic flow prediction with big
data: A deep learning approach. IEEE Transactions on Intelligent Transportation
Systems, 16(2):865–873, 2014.
 X. Ma, Z. Tao, Y. Wang, H. Yu, and Y. Wang. Long short-term memory neural network
for traffic speed prediction using remote microwave sensor data. Transportation
Research Part C: Emerging Technologies, 54:187–197, 2015.
 Y. Tian and L. Pan. Predicting short-term traffic flow by Long Short-Term Memory
Recurrent Neural Network. In IEEE international conference on smart city/
SocialCom/SustainCom (SmartCity), pages 153–158. IEEE, 2015.
 R. Fu, Z. Zhang, and L. Li. Using LSTM and GRU neural network methods for
traffic flow prediction. In Youth Academic Annual Conference of Chinese Association
of Automation (YAC), pages 324–328. IEEE, 2016.
 Y. Jia, J. Wu, and M. Xu. Traffic flow prediction with rainfall impact using a deep
learning method. Journal of Advanced Transportation, 2017.
 Y. Liu, H. Zheng, X. Feng, and Z. Chen. Short-term traffic flow prediction with
Conv-LSTM. In International Conference on Wireless Communications and Signal
Processing (WCSP), pages 1–6. IEEE, 2017.
 X. Ran, Z. Shan, Y. Fang, and C. Lin. An LSTM-Based method with attention
mechanism for travel time prediction. Sensors, 19(4):861, 2019.
 I. Okutani and Y. J. Stephanedes. Dynamic prediction of traffic volume through
Kalman filtering theory. Transportation Research Part B: Methodological, 18(1):
 S. I.-J. Chien and C. M. Kuchipudi. Dynamic travel time prediction with real-time
and historic data. Journal of Transportation Engineering, 129(6):608–616, 2003.
 J. W. C. Van Lint. Online learning solutions for freeway travel time prediction. IEEE
Transactions on Intelligent Transportation Systems, 9(1):38–47, 2008.
 T. Schreiter, C. P. I. J. Van Hinsbergen, F. S. Zuurbier, J. W. C. Van Lint, and S. P.
Hoogendoorn. Data-model synchronization in extended Kalman filters for accurate
online traffic state estimation. In TFTC Summer Meeting, 2010.
 S. V. Kumar. Traffic flow prediction using Kalman filtering technique. Procedia
Engineering, 187:582–587, 2017.
 S. Kim and H. Kim. A new metric of absolute percentage error for intermittent
demand forecasts. International Journal of Forecasting, 32(3):669–679, 2016.