-Al Mallah, R., Quintero, A., and Farooq, B. (2017). Distributed Classification of Urban Congestion Using VANET. IEEE Transactions on Intelligent Transportation Systems, 18(9), 2435–2442. doi.org/10.1109/TITS.2016.2641903
-Arifin, A. S., and Zulkifli, F. Y. (2021). Recent development of smart traffic lights. IAES International Journal of Artificial Intelligence, 10(1), 224. doi.org/10.11591/ijai.v10.i1.pp224-233
-Banu, A. S. M., Lakchida, S. A. S., Shanthini, V. S., and Stinsha, S. L. (2022). Smart Traffic Light Control System Using Image Processing. Proceedings - International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022, 700–706. doi.org/10.1109/ICAISS55157.2022.10010764
-Barhmaiah, B., Chandrasekar, A., Ramya, T., and Moses Santhakumar, S. (2022). Delay models for Signalised Intersections with Vehicle Actuated Controlled system in Heterogeneous Traffic Conditions. IOP Conference Series: Earth and Environmental Science, 1084(1), 012038.doi.org/10.1088/1755-1315/1084/1/012038
-Erdmann, J. (2015). SUMO’s Lane-Changing Model. Lecture Notes in Mobility, 105–123.
doi.org/10.1007/978-3-319-15024-6_7
-Gawron, C. (2011). An Iterative Algorithm to Determine the Dynamic User Equilibrium in a Traffic Simulation Model. 9(3), 393–407. doi.org/10.1142/S0129183198000303
-Goel, A., and Kumar, P. (2014). A review of fundamental drivers governing the emissions, dispersion and exposure to vehicle-emitted nanoparticles at signalised traffic intersections. Atmospheric Environment, 97, 316–331.
doi.org/10.1016/J.ATMOSENV.2014.08.037
-Hennessy, D. A., and Wiesenthal, D. L. (1999). Traffic congestion, driver stress, and driver aggression. Aggressive Behavior: Official Journal of the International Society for Research on Aggression, 25(6), 409–423.
-Hoogendoorn, S. P., and Hoogendoorn, R. (2010). Generic calibration framework for joint estimation of car-following models by using microscopic data. Transportation Research Record, 2188, 37–45.
doi.org/10.3141/2188-05
-Jiang, C. Y., Hu, X. M., and Chen, W. N. (2021). An Urban Traffic Signal Control System Based on Traffic Flow Prediction. 2021 13th International Conference on Advanced Computational Intelligence, ICACI 2021, 259–265.
doi.org/10.1109/ICACI52617.2021.9435905
-Keyvan-Ekbatani, M., Carlson, R. C., Knoop, V. L., and Papageorgiou, M. (2021). Optimizing distribution of metered traffic flow in perimeter control: Queue and delay balancing approaches. Control Engineering Practice, 110, 104762.
doi.org/10.1016/J.CONENGPRAC.2021.104762
-Krajzewicz, Daniel, Georg Hertkorn, C. Rössel, and P. Wagner (2002). SUMO (Simulation of Urban Mobility) - an Open-Source Traffic Simulation.
-Krauss, S. (1998). Microscopic modeling of traffic flow: Investigation of collision free vehicle dynamics.
-Kuang, L., Zheng, J., Li, K., and Gao, H. (2021). Intelligent Traffic Signal Control Based on Reinforcement Learning with State Reduction for Smart Cities. ACM Transactions on Internet Technology (TOIT), 21(4). doi.org/10.1145/3418682
-Li, R., Zheng, C., Wang, H., Zhao, D., Ran, B., and Xue, X. (2020). Intersection traffic signal optimisation considering the impact of upstream curbside bus stops. IET Intelligent Transport Systems, 14(8), 880–888.
doi.org/10.1049/IET-ITS.2019.0660
-Lochrane, T. (2014). a New Multidimensional Psycho-Physical Framework for Modeling Car-Following in a Freeway Work Zone. 2004–2019.
-Lücken, L., Mintsis, E., Porfyri, K. N., Alms, R., Flötteröd, Y. P., and Koutras, D. (2019). From automated to manual-modeling control transitions with sumo. EPiC Series in Computing, 62, 124–144.
doi.org/10.29007/sfgk
-Mortazavi Azad, S. M., and Ramazani, A. (2023). Smart control of traffic lights based on traffic density in the multi-intersection network by using Q learning. Discover Artificial Intelligence 2023 3:1, 3(1), 1–11.
doi.org/10.1007/S44163-023-00087-Z
-Ostrowski, K., and Budzynski, M. (2019). Modelling Signalised Intersections Reliability of Functioning. IOP Conference Series: Materials Science and Engineering, 471(6), 062028.doi.org/10.1088/1757-899X/471/6/062028
-Shahid, S., Minhans, A., and Puan, O. C. (2014). Assessment of Greenhouse Gas Emission Reduction Measures in Transportation Sector of Malaysia. Jurnal Teknologi, 70(4), 1–8. doi.org/10.11113/JT.V70.3481
-Song, J., Wu, Y., Xu, Z., and Lin, X. (2015). Research on car-following model based on SUMO. Proceedings of 2014 IEEE 7th International Conference on Advanced Infocomm Technology, IEEE/ICAIT 2014, 47–55.
doi.org/10.1109/ICAIT.2014.7019528
-Song, M., Yin, M., Chen, X. (Michael), Zhang,L., and Li, M. (2013). A Simulation-based Approach for Sustainable Transportation Systems Evaluation and Optimization: Theory, Systematic Framework and Applications. Procedia - Social and Behavioral Sciences, 96(Cictp), 2274–2286. doi.org/10.1016/j.sbspro.2013.08.257
-Stephens, A. N., Beanland, V., Candappa, N., Mitsopoulos-Rubens, E., Corben, B., and Lenné, M. G. (2017). A driving simulator evaluation of potential speed reductions using two innovative designs for signalised urban intersections. Accident Analysis and Prevention, 98, 25–36. doi.org/10.1016/J.AAP.2016.09.022
-World’s Most Polluted Countries in 2022 - PM2.5 Ranking | IQAir. (n.d.). Retrieved February 15, 2024, from https://www.iqair.com/world-most-polluted-countries
-Zerroug, R., Zerroug, R., Aliouat, Z., Aliouat, M., and Alti, A. (2023). Smart Cities Trafic Light Management Systems Review: Models and Approaches. doi.org/10.21203/RS.3.RS-3257486/V1
-Zhou, B., Ma, D., Song, X., and Dai, H. (2022). A Deep Reinforcement Learning Approach to Traffic Signal Control with Temporal Traffic Pattern Mining. IEEE Transactions on Intelligent Transportation Systems, 23(8), 11789–11800.
doi.org/10.1109/TITS.2021.3107258
