- نواداد, و. کاردان حلوایی, سیستم حمل و نقل هوشمند, سومین کنگره ملی مهندسی عمران. 1386, دانشگاه تبریز.
- پور باقر, شریف طهرانی، حاجی جعفری, بررسی و تحلیل اثرات اجرای ITS درکارایی سیستم حمل و نقل همگانی شهر مشهد، دومین همایش سیستم های حمل و نقل هوشمند جادهای. 1395، سازمان راهداری و حمل و نقل جاده ای.
- معاضدی, اخلاصی نیا، بررسی عملکرد و سطح سرویس تقاطعات هوشمند مجهز به سیستم کنترل مرکزی SCATS مطالعه موردی: تقاطع گلستان نور، کلانشهر اهواز، دومین همایش سیستمهای حملونقل هوشمند جاده ای. 1395، سازمان راهداری و حملونقل جاده ای.
- Bauza, R., J. Gozalvez, and J. Sanchez-Soriano. Road traffic congestion detection through cooperative vehicle-to-vehicle communications. in IEEE Local Computer Network Conference. 2010. IEEE.
- Fukumoto, J., et al. Analytic method for real-time traffic problems by using Contents Oriented Communications in VANET. in 2007 7th International Conference on ITS Telecommunications. 2007. IEEE.
- Dornbush, S. and A. Joshi. StreetSmart traffic: Discovering and disseminating automobile congestion using VANET's. in 2007 IEEE 65th Vehicular Technology Conference-VTC2007-Spring. 2007. IEEE.
- Morla, R., Vision of congestion-free road traffic and cooperating objects. Sentient Future Competition, 2005.
- Huang, D., S. Shere, and S. Ahn. Dynamic highway congestion detection and prediction based on shock waves. in Proceedings of the seventh ACM international workshop on VehiculAr InterNETworking. 2010. ACM.
- Pongpaibool, P., P. Tangamchit, and K. Noodwong. Evaluation of road traffic congestion using fuzzy techniques. in TENCON 2007-2007 IEEE Region 10 Conference. 2007. IEEE.
- Okutani, I. and Y.J. Stephanedes, Dynamic prediction of traffic volume through Kalman filtering theory. Transportation Research Part B: Methodological, 1984. 18(1): p. 1-11.
- Nicholson, H. and C. Swann, The prediction of traffic flow volumes based on spectral analysis. Transportation Research, 1974. 8(6): p. 533-538.
- Vlahogianni, E.I., M.G. Karlaftis, and J.C. Golias, Short-term traffic forecasting: Where we are and where we’re going. Transportation Research Part C: Emerging Technologies, 2014. 43: p. 3-19.
- Vlahogianni, E.I., J.C. Golias, and M.G. Karlaftis, Short‐term traffic forecasting: Overview of objectives and methods. Transport reviews, 2004. 24(5): p. 533-557.
- Chen, M. and S. Chien, Dynamic freeway travel-time prediction with probe vehicle data: Link based versus path based. Transportation Research Record: Journal of the Transportation Research Board, 2001(1768): p. 157-161.
- Yang, F., et al., Online recursive algorithm for short-term traffic prediction. Transportation Research Record: Journal of the Transportation Research Board, 2004(1879): p. 1-8.
- Guo, J., W. Huang, and B.M. Williams, Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transportation Research Part C: Emerging Technologies, 2014. 43: p. 50-64.
- Chang, G., et al., A summary of short-term traffic flow forecasting methods, in ICCTP 2011: Towards Sustainable Transportation Systems. 2011. p. 1696-1707.
- Lippi, M., M. Bertini, and P. Frasconi, Short-term traffic flow forecasting: An experimental comparison of time-series analysis and supervised learning. IEEE Transactions on Intelligent Transportation Systems, 2013. 14(2): p. 871-882.
- Lopez-Garcia, P., et al., A hybrid method for short-term traffic congestion forecasting using genetic algorithms and cross entropy. IEEE Transactions on Intelligent Transportation Systems, 2016. 17(2): p. 557-569.
- Shang, Q., et al., A hybrid short-term traffic flow prediction model based on singular spectrum analysis and kernel extreme learning machine. PLoS one, 2016. 11(8): p. e0161259.
- Lopez-Garcia, P., et al. Short-term traffic congestion forecasting using hybrid metaheuristics and rule-based methods: A comparative study. in Conference of the Spanish Association for Artificial Intelligence. 2016. Springer.
- Zheng, Z. and D. Su, Short-term traffic volume forecasting: A k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm. Transportation Research Part C: Emerging Technologies, 2014. 43: p. 143-157.
- Polson, N.G. and V.O. Sokolov, Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies, 2017. 79: p. 1-17.
- Lin, L., J.C. Handley, and A.W. Sadek, Interval Prediction of Short-Term Traffic Volume Based on Extreme Learning Machine and Particle Swarm Optimization. 2017.
- Yang, S., et al., Ensemble Learning for Short-Term Traffic Prediction Based on Gradient Boosting Machine. Journal of Sensors, 2017. 2017.
- Ling, X., et al. Short-term traffic flow prediction with optimized Multi-kernel Support Vector Machine. in Evolutionary Computation (CEC), 2017 IEEE Congress on. 2017. IEEE.
- Elhenawy, M., H.A. Rakha, and H. Chen. Traffic Stream Short-term State Prediction using Machine Learning Techniques. in VEHITS. 2016.
- Haykin, S., Neural networks: a comprehensive foundation. 1994: Prentice Hall PTR.
- Zhao, Z., et al., LSTM network: a deep learning approach for short-term traffic forecast. IET Intelligent Transport Systems, 2017. 11(2): p. 68-75.