-Abdulhai, B., Porwal, H., & Recker, W. (2002). Short-Term Traffic Flow Prediction Using Neuro-Genetic Algorithms. Journal of Intelligent Transportation Systems, 7(1), 3-41. doi:10.1080/713930748.
-Ai, C., Jia, L., Hong, M., & Zhang, C. (2020). Short-term road speed forecasting based on hybrid RBF neural network with the aid of fuzzy system-based techniques in urban traffic flow. IEEE Access, 8: 69461-69470.
-Aydos, C., Hengst, B. and Uther, W. (2009). Kalman filter process models for urban vehicle tracking, 2009 12th International IEEE Conference on Intelligent Transportation Systems. IEEE, 1-8.
-Beasley, D., Bull, D.R. and Martin, R.R.J.U.c., (1993a). An overview of genetic algorithms: Part 1, fundamentals. 15(2), 58-69.
-Beasley, D., Bull, D.R. and Martin, R.R.J.U.c., (1993b). An overview of genetic algorithms: Part 2, research topics. 15(4): 170-181.
-Chen, X. and Chen, R., (2019). A Review on Traffic Prediction Methods for Intelligent Transportation System in Smart Cities, 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 1-5.
-Chen, Y., Zhang, Y. and Hu, J. (2008). Multi-dimensional traffic flow time series analysis with self-organizing maps. Tsinghua Science and Technology, 13(2), 220-228.
-Dadashova, B., Li, X., Turner, S. and Koeneman, P. (2020). Multivariate time series analysis of traffic congestion measures in urban areas as they relate to socioeconomic indicators.
Socio-Economic Planning Sciences: 100877.
-Ding, (2019). Application of GIS technology in the construction of urban traffic sharing multimedia information platform. Multimedia Tools and Applications, 1-13.
-Djenouri, Y., Belhadi, A., Lin, J.C.-W., Djenouri, D. and Cano, A. (2019). A survey on urban traffic anomalies detection algorithms. IEEE Access, 7: 12192-12205.
-Emami, A., Sarvi, M., & Bagloee, S. A. (2019). Using Kalman filter algorithm for short-term traffic flow prediction in a connected vehicle environment. Journal of Modern Transportation, 27(3): 222-232.
-Guo, J., Liu, Z., Huang, W., Wei, Y. and Cao, J. (2017). Short-term traffic flow prediction using fuzzy information granulation approach under different time intervals. 12(2), 143-150.
-Kamarianakis, Y., Gao, H.O. and Prastacos, P. (2010). Characterizing regimes in daily cycles of urban traffic using smooth-transition regressions. Transportation Research Part C: Emerging Technologies, 18(5): 821-840.
-Kumar. (2020). Video based Traffic Forecasting using Convolution Neural Network Model and Transfer Learning Techniques. Journal of Innovative Image Processing (JIIP), 2(03): 128-134.
-Kusakabe, T., Iryo, T., & Asakura, Y. (2010). Data mining for traffic flow analysis: Visualization approach, Traffic Data Collection and its Standardization. Springer, 57-72.
-Ling, X., Feng, X., Chen, Z., Xu, Y. and Zheng, H. (2017). Short-term traffic flow prediction with optimized multi-kernel support vector machine, 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, 294-300.
-Liu, S. Y., Li, D. W., Xi, Y. G., & Tang, Q. F. (2015). A short-term traffic flow forecasting method and its applications. Journal of Shanghai Jiaotong University, 20(2): 156-163.
-Mei, Z., Zhang, W., Zhang, L. and Wang, D., (2020). Real-time multistep prediction of public parking spaces based on Fourier transform–least squares support vector regression. Journal of Intelligent Transportation Systems, 24(1): 68-80.
-Nakata, T., & Takeuchi, J. I. (2004). Mining traffic data from probe-car system for travel time prediction, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 817-822.
-Ntoutsi, I., Mitsou, N., & Marketos, G. (2008). Traffic mining in a road-network: How does the traffic flow? International Journal of Business Intelligence, 3(1), 82-98.
-Pelekis, N., Kopanakis, I., Kotsifakos, E., Frentzos, E., & Theodoridis, Y. (2009). Clustering trajectories of moving objects in an uncertain world, IEEE International Conference on Data Mining, ICDM'09. Ninth IEEE, 417-427.
-qiao, D.-h., zhang, K.-h. and fan, Y. Z., (2007). The optimizing of many traffic flow forecasting models [J]. Communications Standardization, 4: 066.
-Shekhar, S., Lu, C. T., Chawla, S., & Zhang, P. (2001). Data mining and visualization of twin-cities traffic data, University of Minnesota Minneapolis United States.
-Turner, S., (2004). Defining and measuring traffic data quality: White paper on recommended approaches. Transportation research record, 1870(1), 62-69.
-Vlahogianni, E. I., Karlaftis, M. G., & Golias, J. C. (2005). Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach. Transportation Research Part C: Emerging Technologies, 13(3), 211-234.
-Xiangxue, W., Lunhui, X. and Kaixun, C., (2019). Data-driven short-term forecasting for urban road network traffic based on data processing and LSTM-RNN. Arabian Journal for Science and Engineering, 44(4), 3043-3060.
-Azmoodeh, M. and Haghighi, F., (2017). Land Use Evaluation Based On Transportation Accessibility (Case Study: Zone 6 of Tehran). 8(28), 135-148.
-Kodarahm bazi; Akbar Kiani; Mohammad Sadegh Afrasiabi Rad., (2010). Evaluation of urban traffic and the needs of the disabled and veterans using the Topsis decision-making model (Case study: Shiraz city), Journal of Urban Research and Planning, Article 6, Vol. 1, No. 3, December, 103-130.
-PourAhmad, A., and Imranzadeh, B., (2012). Evaluation and presentation of BRT transportation system development strategies in Tehran metropolis using SWOT model. Journal of Urban Research and Planning, 3 (11), 17-36.
-pourjavan, k., (2019). Explanation of Smart City and Urban Smart Transportation Solutions. Karafan Quarterly Scientific Journal, 16(45), 15-34.
-Hadadi, F. and Shirmohammadi, H., (2017). Evaluation and Prioritization of Urban Decision makers in the Integration of Public Transportation System Using COPRAS method (Case Study: Urmia City). 8(30), 65-82.
-Abbasi, S.H., and Yagobi, M. (2013). A New Method in Studying Urban Traffic Predictability Based on Chaos Theory and Prediction of Mashhad Traffic Flow Based on Multiple ANFIS. Quarterly Journal of Transportation Engineering, 4(3), 233-246.
-Alavi, S.A. and Seyyed Mahdavi Chabok, S.J., (2020). Performance and Reliability Improvement on 2D-NOC Based on Reducing the Number of Passing Links. Computational Intelligence in Electrical Engineering, 11(3), 95-106.
-Ghorbani and Azimi. (2014). Investigating the effect of municipal revenue structure on urban development process using correlation coefficient and factor analysis techniques; Case study of Mashhad. Journal of Urban Research and Planning, 5 (18), 115-132.
-Matkan, A., Mirbagheri, B. and Akbari, K., (2017). A Smart Location Model, Based on Multi-Objective Genetic Algorithms to Find Optimal Routes in the Road Network. Iranian Journal of Remote Sensing & GIS, 9(3), 111-126.
-Manshadi, F. and et al., (2015). Analysis and review of measures necessary for the implementation of integrated urban transport in metropolitan areas; Case study: Tehran. Journal of Urban Research and Planning, 6 (20), 83-98.
-Mahdavian, Z., and Nik Nafs, A. (2015). Predict and control traffic with data mining approaches using GPS data. Spatial Information Technology Engineering, 3 (2), 43-59.
-Yaghfouri and et al., (2016). Investigation of spatial-spatial distribution of public parking lots and its optimal location (Case study: Zones 2 and 8 of Shiraz Municipality). Journal of Urban Research and Planning, 7 (24), 173-190.