1. Ulmer, M.W., Approximate dynamic programming for dynamic vehicle routing, Springer, 2017.
2. Helsgaun, K., "An extension of the lin-kernighan-helsgaun tsp solver for constrained traveling salesman and vehicle routing problems", Technical Report, Roskilde: Roskilde University. (2017).
3. Badjonski, M., Ivanović, M. and Budimac, Z., Agent oriented programming language lass, Object-oriented technology and computing systems re-engineering, 1999.
4. Ferber, J. and Weiss, G., Multi-agent systems: An introduction to distributed artificial intelligence, Addison-Wesley Reading, Vol. 1, 1999.
5. Lemessi, M., "An slx-based microsimulation model for a two-lane road section", in Proceeding of the 2001 Winter Simulation Conference (Cat. No. 01CH37304), IEEE. Vol. 2, 1064-1071, (2001), doi: 10.1109/WSC.2001.977415.
6. Owen, L.E., Zhang, Y., Rao, L. and McHale, G., "Traffic flow simulation using corsim", in 2000 Winter Simulation Conference Proceedings, IEEE. Vol. 2, Issue, 1143-1147, (2000), doi: 10.1109/WSC.2000.899077.
7. Schulze, T. and Fliess, T., "Urban traffic simulation with psycho-physical vehicle-following models", Proceedings of the 29th conference on Winter simulation. 1222-1229, (1997), doi: 10.1145/268437.268764.
8. Lansdowne, A., Traffic simulation using agent-based modelling, University of the West England Bristol, (2006).
9. Nagel, K. and Schreckenberg, M., "A cellular automaton model for freeway traffic", Journal de Physique I, Vol. 2, No. 12, (1992), 2221-2229, doi:10.1051/jp1:1992277.
10. Google apps. 2021, URL: https://www.google.com/maps.
11. Waze, Free community-based gps, maps & traffic navigation app. 2021, URL: https://www.waze.com/.
12. Younes, M.B. and Boukerche, A., "An efficient dynamic traffic light scheduling algorithm considering emergency vehicles for intelligent transportation systems", Wireless Networks, Vol. 24, No. 7, (2018), 2451-2463, doi: https://doi.org/10.1007/s11276-017-1482-5.
13. Davydov, I., Tolstykh, D., Kononova, P. and Legkih, I., "Genetic based approach for novosibirsk traffic light scheduling", in 2019 15th International Asian School-Seminar Optimization Problems of Complex Systems (OPCS), IEEE. Vol., No. 31-36, (2019), doi: 10.1109/OPCS.2019.8880158.
14. Wahle, J., Bazzan, A.L.C., Klügl, F. and Schreckenberg, M., "The impact of real-time information in a two-route scenario using agent-based simulation", Transportation Research Part C: Emerging Technologies, Vol. 10, No. 5-6, (2002), 399-417, doi: https://doi.org/10.1016/S0968-090X(02)00031-1.
15. Gao, B., Zhang, Q.-Y., Liang, Y.-S., Liu, N.-N., Huang, C.-B. and Zhang, N.-T., "Predicting self-similar networking traffic based on emd and arma", Journal of China Institute of Communications, Vol. 32, No. 4, (2011), 47-56.
16. Bie, Y., Yang, M. and Pei, Y., "Development of short-term traffic volume prediction models for adaptive traffic control", in International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2015), (2015).
17. Ma, X., Yu, H., Wang, Y. and Wang, Y., "Large-scale transportation network congestion evolution prediction using deep learning theory", PloS One, Vol. 10, No. 3, (2015), e0119044, doi: 10.1371/journal.pone.0119044.
18. Ma, Y., Feng, C., Zhang, J. and Yan, F., "Judging customer satisfaction by considering fuzzy random time windows in vehicle routing problems", in International Conference on Management Science and Engineering Management, Springer. 204-211, (2017), doi: https://doi.org/10.1007/978-3-319-59280-0_16.
19. Ando, Y., Masutani, O., Sasaki, H., Iwasaki, H., Fukazawa, Y. and Honiden, S., "Pheromone model: Application to traffic congestion prediction", in International Workshop on Engineering Self-Organising Applications, Springer, 182-196, (2005), doi: https://doi.org/10.1007/11734697_14.
20. Jiang, S., Zhang, J. and Ong, Y.-S., "A multiagent evolutionary framework based on trust for multiobjective optimization", in AAMAS. , 299-306, (2012).
21. Kurihara, S., Tamaki, H., Numao, M., Yano, J., Kagawa, K. and Morita, T., "Traffic congestion forecasting based on pheromone communication model for intelligent transport systems", in 2009 IEEE Congress on Evolutionary Computation, IEEE. 2879-2884, (2009), doi: 10.1109/CEC.2009.4983304.
22. AbdAllah, A.M.F., Essam, D.L. and Sarker, R.A., "On solving periodic re-optimization dynamic vehicle routing problems", Applied Soft Computing, Vol. 55, (2017), 1-12, doi: https://doi.org/10.1016/j.asoc.2017.01.047.
23. Grzybowska, H. and Barceló, J., "Decision support system for real-time urban freight management", Procedia-Social and Behavioral Sciences, Vol. 39, (2012), 712-725, doi: https://doi.org/10.1016/j.sbspro.2012.03.142.
24. Li, J., Wu, Q. and Zhu, D., "Route guidance mechanism with centralized information control in large-scale crowd's activities", in 2009 International Joint Conference on Artificial Intelligence, IEEE. 7-11, (2009), doi: 10.1109/JCAI.2009.41.
25. Yamashita, T., Izumi, K., Kurumatani, K. and Nakashima, H., "Smooth traffic flow with a cooperative car navigation system", in Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems. 478-485, (2005), doi:10.1145/1082473.1082546.
26. Chiu, Y.-C., Bottom, J., Mahut, M., Paz, A., Balakrishna, R., Waller, S. and Hicks, J., Dynamic traffic assignment: A primer (transportation research circular e-c153), 2011.
27. Cao, Z., Guo, H. and Zhang, J., "A multiagent-based approach for vehicle routing by considering both arriving on time and total travel time", ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 9, No. 3, (2017), 1-21, doi: https://doi.org/10.1145/3078847.
28. Younes, M.B. and Boukerche, A., "An intelligent traffic light scheduling algorithm through vanets", in 39th Annual IEEE Conference on Local Computer Networks Workshops, IEEE. 637-642, (2014), doi: 10.1109/LCNW.2014.6927714.
29. Faye, S., Chaudet, C. and Demeure, I., "A distributed algorithm for adaptive traffic lights control", in 2012 15th International IEEE Conference on Intelligent Transportation Systems, IEEE, 1572-1577, (2012), doi: 10.1109/ITSC.2012.6338671.
30. Rezgui, J., Barri, M. and Gayta, R., "Smart traffic light scheduling algorithms", in 2019 International Conference on Smart Applications, Communications and Networking (SmartNets), IEEE. 1-7, (2019), doi: 10.1109/SmartNets48225.2019.9069760.
31. Sharma, K.K. and Indu, S., "Gps based adaptive traffic light timings and lane scheduling", in 2019 IEEE Intelligent Transportation Systems Conference, IEEE. 4267-4274, (2019), doi:10.1109/ITSC.2019.8917153.
32. Razavi, M., Hamidkhani, M. and Sadeghi, R., "Smart traffic light scheduling in smart city using image and video processing", in 2019 3rd International Conference on Internet of Things and Applications (IoT), IEEE, 1-4, (2019), doi: 10.1109/IICITA.2019.8808836.
33. Hu, W., Wang, H., Yan, L. and Du, B., "A hybrid cellular swarm optimization method for traffic-light scheduling", Chinese Journal of Electronics, Vol. 27, No. 3, (2018), 611-616, doi: 10.1049/cje.2018.02.002.
34. Habibi, M., Broumandnia, A. and Harounabadi, A., "An intelligent traffic light scheduling algorithm by using fuzzy logic and gravitational search algorithm and considering emergency vehicles", International Journal of Nonlinear Analysis and Applications, Vol. 11, Special Issue, (2020), 475-482, doi: 10.22075/IJNAA.2020.4706.