Lane-specific characteristics, traffic flow, vehicle properties, and atmospheric conditions profoundly impact successive vehicle time intervals. A lane position plays an important role due to the differences in flow properties, such as speed, density, and overtaking maneuvers, which can vary significantly across different lanes. Larger or heavier vehicles, which are assigned to specific lanes, tend to require although time more space from each other due to their dimensions and braking requirements. Driver behavior, as determined by reaction times and compliance with rules, introduces variability, especially in challenging weather or road conditions, such as rain, snow, or icy roads. In addition to geographical location and distance of travel, temporal factors also influence perception and choice, including the day of the week and variations in light intensities. Effective modeling approaches and effective traffic control require a comprehensive analysis of these factors. In the current work, a dataset of independent traffic roads with all the above-mentioned variables was constructed, and time-gap prediction between two consecutive cars driving along every road lane was performed using Support Vector Regression (SVR) and Random Subspace (RSS). In addition, new optimization algorithms, referred to as the Partial Reinforcement Optimizer (PRO) and the Walrus Optimizer (WO), were utilized to enhance predictive capability, resulting in strong hybrid models. In this evaluation approach, we also employed Analysis of Variance (ANOVA’s) sensitivity analysis technique to identify the most contributing feature in our data, providing us with the importance of each variable in the prediction process. The SVPR hybrid model performed the best for lane 1, with the highest R² of 0.997, the lowest Root Mean Square Error (RMSE) of 2.36E+07, and the lowest Ratio of RMSE to Standard deviation (RSR) of 0.059 during testing, thereby reflecting its superior predictive accuracy and minimal error. For lane 2, the hybrid SVWO model emerged as the most effective, achieving the highest R² of 0.989, the lowest RMSE of 3.36×10^7, and the smallest RSR of 0.107, demonstrating its robust capability in capturing lane-specific traffic dynamics. These findings highlight the potential of hybrid optimization techniques to enhance predictive performance and minimize errors in practical traffic management systems.

[1] Abideen Z., Sun X., and Sun C., “Traffic Flow Prediction: A 3D Adaptive Multi-Module Joint Modeling Approach Integrating Spatial-Temporal Patterns to Capture Global Features,” Journal of Forecasting, vol. 43, no. 7, pp. 2766-2791, 2024. DOI:10.1002/for.3147

[2] Ayu Z., Sari P., and Mauramdha G., “Analysis of Traffic Flow Due to Lane Changes by Heavy Vehicles,” in Proceedings of the IOP Conference Series: Earth and Environmental Science, Lagos, pp. 1-11, 2024. DOI:10.1088/1755- 1315/1294/1/012026

[3] Brahimi M., Karatzas S., Theuriot J., and Christoforou Z., “Drones for Traffic Flow Analysis of Urban Roundabouts,” International Journal for Traffic and Transport Engineering, vol. 9, no. 3, pp. 62-71, 2020. DOI: 10.5923/j.ijtte.20200903.02

[4] Das S., Dutta A., Tamakloe R., and Khan M., “Analyzing the Time-Varying Patterns of Contributing Factors in Work Zone-Related Crashes,” Journal of Transportation Safety and Security, vol. 16, no. 4, pp. 655-682, 2023. DOI:10.1080/19439962.2023.2246020

[5] Dey L., Naik B., Poojita O., and Pothireddi K., “SMM4H 2024: 5 Fold Cross Validation for Classification of Tweets Reporting Children’s Disorders,” in Proceedings of the 9th Social Media Mining for Health Research and Applications Workshop and Shared Tasks, Bangkok, pp. 55-57, 2024. DOI: 10.18653/v1/2024.smm4h-1.12

[6] Ding H., Feng P., Chen W., and Lin H., “Identification of Bacteriophage Virion Proteins by the ANOVA Feature Selection and Analysis,” Molecular BioSystems, vol. 10, no. 8, pp. 2229- 2235, 2014. DOI:10.1039/c4mb00316k Optimized Predictive Modeling of Lane-Specific Vehicle Time Gaps for Traffic Flow ... 403

[7] Dunne S. and Ghosh B., “Weather Adaptive Traffic Prediction Using Neurowavelet Models,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 2, pp. 370-379, 2013. DOI:10.1109/TITS.2012.2225049

[8] Faouzi N., Billot R., Nurmi P., and Nowotny B., “Effects of Adverse Weather on Traffic and Safety: State-of-the-Art and a European Initiative,” in Proceedings of the 15th International Road Weather Conference, Quebec City, pp. 1-7, 2010. https://sirwec.org/wp- content/uploads/2022/04/Quebec-D-45.pdf

[9] Ferster C. and Skinner B., Schedules of Reinforcement, Prentice-Hall, 1957. DOI:10.1037/10627-000

[10] Formosa N., Quddus M., Ison S., and Timmis A., “A New Modeling Approach for Predicting Vehicle-Based Safety Threats,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 18175-18185, 2022. DOI:10.1109/TITS.2022.3156763

[11] Gao H., Qu T., Hu Y., and Chen H., “Personalized Driver Car-Following Model-Considering Human’s Limited Perception Ability and Risk Assessment Characteristics,” in Proceedings of the 6th CAA International Conference on Vehicular Control and Intelligence, Beijing, pp. 1- 6, 2022. DOI:10.1109/CVCI56766.2022.9965180

[12] Hjelkrem O. and Ryeng E., “Driver Behaviour Data Linked with Vehicle, Weather, Road Surface, and Daylight Data,” Data Brief, vol. 10, pp. 511-514, 2017. DOI: 10.1016/j.dib.2016.12.036

[13] Ho T., “The Random Subspace Method for Constructing Decision Forests,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, pp. 832-844, 1998. DOI:10.1109/34.709601

[14] Kilimci Z. and Omurca S., “Enhancement of the Heuristic Optimization Based on Extended Space Forests Using Classifier Ensembles,” The International Arab Journal of Information Technology, vol. 17, no. 2, pp. 188-195, 2020, https://doi.org/10.34028/iajit/17/2/6

[15] Kumar M. and Anwar S., “Deep Learning Model for UAV aided Traffic Analysis and Vehicle Classification,” in Proceedings of the 5th International Conference on Data Intelligence and Cognitive Informatics, Tirunelveli, pp. 1219- 1224, 2024. DOI:10.1109/ICDICI62993.2024.10810974

[16] Laarej A., Sansalvador J., Lakouari N., and Ezzahraouy H., “Study of Energy Dissipation and Satisfaction Rates in Mixed Traffic Flow with Lights: A Two-Lane Cellular Automaton Approach,” in Proceedings of E3S Web of Conferences, Les Ulis, pp. 1-12, 2024. DOI:10.1051/e3sconf/202458204001

[17] Liu Z., Lyu C., Wang Z., Wang S., Liu P., and Meng Q., “A Gaussian-Process-Based Data- Driven Traffic Flow Model and its Application in Road Capacity Analysis,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 2, pp. 1544-1563, 2023. DOI:10.1109/TITS.2022.3223982

[18] Lopukhova E., Abdulnagimov A., Voronkov G., and Grakhova E., “Machine Learning-Driven Calibration of Traffic Models Based on a Real- Time Video Analysis,” Applied Sciences, vol. 14, no. 11, pp. 1-22, 2024. DOI: 10.20944/preprints202404.1799.v1

[19] Maraia L., “Data-Driven Traffic Management,” Traffic Technology International, vol. 2023, no. 3, pp. 45, 2023. DOI:10.12968/S1356- 9252(24)40014-2

[20] Meese C., Chen H., Li W., Lee D., and et al., “Adaptive Traffic Prediction at the ITS Edge with Online Models and Blockchain-Based Federated Learning,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 6, pp. 10725- 10740, 2024. DOI:10.1109/TITS.2024.3391053

[21] Mitsakis E., Stamos I., Papanikolaou A., Aifadopoulou G., and Kontoes H., “Assessment of Extreme Weather Events on Transport Networks: Case Study of the 2007 Wildfires in Peloponnesus,” Natural Hazards, vol. 72, no. 1, pp. 87-107, 2013. DOI:10.1007/s11069-013- 0896-3

[22] Moumen I., Abouchabaka J., and Rafalia N., “Adaptive Traffic Lights Based on Traffic Flow Prediction Using Machine Learning Models,” International Journal of Electrical and Computer Engineering, vol. 13, no. 5, pp. 5813-5823, 2023. DOI:10.11591/ijece.v13i5.pp5813-5823

[23] Mwalimo D., Wainaina M., and Kaluki W., “Mixed Vehicular Traffic Flow Model on an Inclined Multilane Road,” International Journal of Innovative Science and Research Technology, vol. 5, no. 7, pp. 331-342, 2020. DOI:10.38124/IJISRT20JUL276

[24] Nan H., Li R., Zhu X., Ma J., and Niyato D., “An Efficient Data-Driven Traffic Prediction Framework for Network Digital Twin,” IEEE Network, vol. 38, no. 1, pp. 22-29, 2024. DOI:10.1109/MNET.2023.3335952

[25] Pi Y., Duffield N., Behzadan A., and Lomax T., “Lane-Specific Speed Analysis in Urban Work Zones with Computer Vision,” Traffic Injury Prevention, vol. 24, no. 3, pp. 242-250, 2023. DOI:10.1080/15389588.2023.217352

[26] Po L., Rollo F., Bachechi C., and Corni A., “From Sensors Data to Urban Traffic Flow Analysis,” in Proceedings of the IEEE International Smart Cities Conference, Casablanca, pp. 478-85, 2019. DOI:10.1109/ISC246665.2019.9071639 404 The International Arab Journal of Information Technology, Vol. 23, No. 3, May 2026

[27] Roy R. and Saha P., “Analysis of Vehicle-Type- Specific Headways on Two-Lane Roads with Mixed Traffic,” Transport, vol. 35, no. 6, pp. 588- 604, 2021. DOI:10.3846/transport.2020.14136

[28] Sathiaraj D., Punkasem T., Wang F., and Seedah D., “Data-Driven Analysis on the Effects of Extreme Weather Elements on Traffic Volume in Atlanta, GA, USA,” Computers Environment and Urban Systems, vol. 72, pp. 212-220, 2018. DOI: 10.1016/j.compenvurbsys.2018.06.012

[29] Taheri A., RahimiZadeh K., Beheshti A., Baumbach J., and et al., “Partial Reinforcement Optimizer: an Evolutionary Optimization Algorithm,” Expert Systems with Applications, vol. 238, no. Part F, pp. 1-20, 2024, DOI:10.1016/j.eswa.2023.122070

[30] Trojovsky P. and Dehghani M., “A New Bio- Inspired Metaheuristic Algorithm for Solving Optimization Problems Based on Walruses Behavior,” Scientific Reports, vol. 13, no. 8770, 2023. https://doi.org/10.1038/s41598-023-35863-5

[31] Tselentis D. and Papadimitriou E., “Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions,” IEEE Open Journal of Intelligent Transportation Systems, vol. 4, no. 1, pp. 83-100, 2023. DOI:10.1109/OJITS.2023.3237177

[32] Vapnik V., “The Nature of Statistical Learning Theory,” Springer Science and Business Media, 2013. DOI: 10.1007/978-1-4757-3264-1

[33] Vasudevan P. and Ekambaram C., “HYAQP: A Hybrid Meta-Heuristic Optimization Model for Air Quality Prediction Using Unsupervised Machine Learning Paradigms,” The International Arab Journal of Information Technology, vol. 21, no. 5, pp. 953-966, 2024. https://doi.org/10.34028/iajit/21/5/15

[34] Wei T., Zhu T., Bai H., Zhao L., and Wang X., “Effects of Driver Gender, Driving Experience, and Visibility on Car-Following Behavior,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2679, no. 1, pp. 2166-2182, 2024. DOI:10.1177/03611981241258988

[35] Yan J., Liu J., and Tseng F., “An Evaluation System Based on the Self-Organizing System Framework of Smart Cities: A Case Study of Smart Transportation Systems in China,” Technological Forecasting and Social Change, vol. 153, no. 1, pp. 119371, 2020. DOI: 10.1016/j.techfore.2018.07.009

[36] Zhang X., Zhao Z., and Li J., “ARDE-N-BEATS: An Evolutionary Deep Learning Framework for Urban Traffic Flow Prediction,” IEEE Internet of Things Journal, vol. 10, no. 3, pp. 2391-2403, 2023. DOI:10.1109/JIOT.2022.3212056