(AFRICA), N. S. 2023. In Wikipedia [Online]. Available: https://en.wikipedia.org/wiki/N1_(South_Africa)) [Accessed 10th March 2023].
Cedillo-Campos, 2019, Measurement of travel time reliability of road transportation using GPS data: a freight fluidity approach, Transp. Res. Part A: Policy Pract., 130, 240
Chang, 2012, Dynamic near-term traffic flow prediction: system-oriented approach based on past experiences, IET Intel. Transport Syst., 6, 292, 10.1049/iet-its.2011.0123
Clerc, 2002, The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Trans. Evol. Comput., 6, 58, 10.1109/4235.985692
Duan, 2014, Characteristics of traffic flow on 8-lane freeway under lane restriction strategy, J. Highway Transp. Res. Dev., 31, 125
Gingerich, 2016, Classifying the purpose of stopped truck events: an application of entropy to GPS data, Transp. Res. Part C: Emerging Technol., 64, 17, 10.1016/j.trc.2016.01.002
Gordan, 2016, Prediction of seismic slope stability through combination of particle swarm optimization and neural network, Eng. Computers, 32, 85, 10.1007/s00366-015-0400-7
Gu, 2019, An improved Bayesian combination model for short-term traffic prediction with deep learning, IEEE Trans. Intell. Transp. Syst., 21, 1332, 10.1109/TITS.2019.2939290
Hasanipanah, 2016, Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling, Eng. Comput., 32, 705, 10.1007/s00366-016-0447-0
Hobeika, A.G., Kim, C.K., 1994. Traffic-flow-prediction systems based on upstream traffic. In: Proceedings of VNIS'94-1994 Vehicle Navigation and Information Systems Conference, IEEE, pp. 345-350.
Iliopoulou, 2019, Metaheuristics for the transit route network design problem: a review and comparative analysis, Public Transport, 11, 487, 10.1007/s12469-019-00211-2
Isaac, 2021, Prediction and modelling of traffic flow of human-driven vehicles at a signalized road intersection using artificial neural network model: a South Africa road transportation system scenario, Transp. Eng., 100095
Junevičius, 2009, Mathematical modelling of network traffic flow, Transport, 24, 333, 10.3846/1648-4142.2009.24.333-338
Kalatehjari, 2014, The effects of method of generating circular slip surfaces on determining the critical slip surface by particle swarm optimization, Arab. J. Geosci., 7, 1529, 10.1007/s12517-013-0922-5
Kong, 2016, Analysis of vehicle headway distribution on multi-lane freeway considering car–truck interaction, Adv. Mech. Eng., 8, 10.1177/1687814016646673
Kong, 2016, Analyzing the impact of trucks on traffic flow based on an improved cellular automaton model, Discrete Dyn Nat. Soc., 10.1155/2016/1236846
Kwon, 2000, Day-to-day travel-time trends and travel-time prediction from loop-detector data, Transp. Res. Rec., 1717, 120, 10.3141/1717-15
Macioszek, 2020, Oversize cargo transport in road transport–problems and issues, Zeszyty Naukowe Transport/Politechnika Śląska
Macioszek, 2021, Extracting road traffic volume in the city before and during COVID-19 through video remote sensing, Remote Sens. (Basel), 13, 2329, 10.3390/rs13122329
Mendes, R., Cortez, P., Rocha, M., Neves, J., 2002. Particle swarms for feedforward neural network training. In: Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No. 02CH37290), IEEE, 1895-1899.
Moretti, 2015, Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling, Neurocomputing, 167, 3, 10.1016/j.neucom.2014.08.100
Okawa, M., Kim, H., Toda, H., 2017. Online traffic flow prediction using convolved bilinear poisson regression. In: 2017 18th IEEE International Conference on Mobile Data Management (MDM). IEEE, pp. 134-143.
Olayode, I.O., Tartibu, L.K., Okwu, M.O. Application of Fuzzy Mamdani Model for Effective Prediction of Traffic Flow of Vehicles at Signalized Road Intersections. In: 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT), 2021a. IEEE, pp. 219-224.
Olayode, I.O., Tartibu, L.K., Okwu, M.O., 2021. Traffic flow Prediction at Signalized Road Intersections: A case of Markov Chain and Artificial Neural Network Model. 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT), 2021b. IEEE, pp. 287-292.
Olayode, O.I., Tartibu, L.K., Okwu, M.O., 2021. Application of adaptive neuro-fuzzy inference system model on traffic flow of vehicles at a signalized road intersections. In: ASME 2021 International Mechanical Engineering Congress and Exposition, 2021e. V009T09A015.
Olayode, 2021, Comparative traffic flow prediction of a heuristic ANN model and a hybrid ANN-PSO Model in the traffic flow modelling of vehicles at a four-way signalized road intersection, Sustainability, 13, 10704, 10.3390/su131910704
Olayode, 2021, Development of a hybrid artificial neural network-particle swarm optimization model for the modelling of traffic flow of vehicles at signalized road intersections, Appl. Sci., 11, 8387, 10.3390/app11188387
Olayode, 2022, Prediction of vehicular traffic flow using levenberg-marquardt artificial neural network model: Italy road transportation system, Commun.-Sci. Lett. Univ. Zilina, 24, E74
Sarvi, 2013, Heavy commercial vehicles-following behavior and interactions with different vehicle classes, J. Adv. Transp., 47, 572, 10.1002/atr.182
Tan, 2009, An aggregation approach to short-term traffic flow prediction, IEEE Trans. Intell. Transp. Syst., 10, 60, 10.1109/TITS.2008.2011693
Ukaegbu, 2021, Development of a light-weight unmanned aerial vehicle for precision agriculture, Sensors, 21, 4417, 10.3390/s21134417
Vanajakshi, 2004, A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed, IEEE Intell. Veh. Symposium, IEEE, 194
Vapnik, 2013
Vlahogianni, 2005, Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach, Transp. Res. Part C: Emerging Technol., 13, 211, 10.1016/j.trc.2005.04.007
Vlahogianni, 2006, Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traffic volume, Transp. Res. Part C: Emerging Technol., 14, 351, 10.1016/j.trc.2006.09.002
Wang, 2020, Truck traffic flow prediction based on LSTM and GRU methods with sampled GPS data, IEEE Access, 8, 208158, 10.1109/ACCESS.2020.3038788
Yang, 2019, Urban rail transit passenger flow forecast based on LSTM with enhanced long-term features, IET Intel. Transport Syst., 13, 1475, 10.1049/iet-its.2018.5511
Zhang, 2003, Short-term travel time prediction, Transp. Res. Part C: Emerging Technol., 11, 187, 10.1016/S0968-090X(03)00026-3