An efficient safest route prediction-based route discovery mechanism for drivers using improved golden tortoise beetle optimizer

References

• Ardeshiri, A., Jeihani, M., & Peeta, S. (2015). Driving simulator‐based study of compliance behaviour with dynamic message sign route guidance. IET Intelligent Transport Systems, 9(7), 765–772. https://doi.org/10.1049/iet-its.2014.0164
   Web of Science ®Google Scholar
• Beed, R. S., Sarkar, S., & Roy, A. (2021). Bayesian hierarchical multi-objective optimization for vehicle parking route discovery. Innovations in Systems and Software Engineering, 17(2), 109–120. https://doi.org/10.1007/s11334-020-00373-4
   Web of Science ®Google Scholar
• Bello-Salau, H., Onumanyi, A. J., Abu-Mahfouz, A. M., Adejo, A. O., & Mu’azu, M. B. (2020). New discrete cuckoo search optimization algorithms for effective route discovery in IoT-based vehicular ad-hoc networks. IEEE Access, 8, 145469–145488. https://doi.org/10.1109/ACCESS.2020.3014736
   Google Scholar
• Bucar, R. C., & Hayeri, Y. M. (2022). Quantitative flood risk evaluation to improve drivers’ route choice decisions during disruptive precipitation. Reliability Engineering & System Safety, 219, 108202. https://doi.org/10.1016/j.ress.2021.108202
   Web of Science ®Google Scholar
• Dovgan, E., Gams, M., & Filipič, B. (2019). A real-time multiobjective optimization algorithm for discovering driving strategies. Transportation science, 53(3), 695–707. https://doi.org/10.1287/trsc.2018.0872
   Web of Science ®Google Scholar
• Feng, L., Zhou, L., Gupta, A., Zhong, J., Zhu, Z., Tan, K. C., & Qin, K. (2019). Solving generalized vehicle routing problem with occasional drivers via evolutionary multitasking. IEEE Transactions on Cybernetics, 51(6), 3171–3184. https://doi.org/10.1109/TCYB.2019.2955599
   Web of Science ®Google Scholar
• Ferreira, J. C., de Almeida, J., & da Silva, A. R. (2015). The impact of driving styles on fuel consumption: A data-warehouse-and-data-mining-based discovery process. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2653–2662. https://doi.org/10.1109/TITS.2015.2414663
   Web of Science ®Google Scholar
• Garg, N., & Ranu, S. (2018, July). Route recommendations for idle taxi drivers: Find me the shortest route to a customer! Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1425–1434).
   Google Scholar
• He, Z., Chen, K., & Chen, X. (2017). A collaborative method for route discovery using taxi drivers’ experience and preferences. IEEE Transactions on Intelligent Transportation Systems, 19(8), 2505–2514. https://doi.org/10.1109/TITS.2017.2753468
   Web of Science ®Google Scholar
• Hiremath, S. C., & Mallapur, J. D. (2022). Fractional-social ski driver optimization-driven routing protocol for routing electric vehicle under server hosted VANET. Multimedia Tools and Applications, 81(12), 17437–17456. https://doi.org/10.1007/s11042-022-12543-6
   Web of Science ®Google Scholar
• Jiang, S., Jafari, M., Kharbeche, M., Jalayer, M., & Al-Khalifa, K. N. (2020). Safe route mapping of roadways using multiple sourced data. IEEE transactions on intelligent transportation systems, 23(4), 3169–3179. https://doi.org/10.1109/TITS.2020.3032643
   Web of Science ®Google Scholar
• Jose, C., & Vijula Grace, K. S. (2022). Optimization based routing model for the dynamic path planning of emergency vehicles. Evolutionary Intelligence, 15(2), 1425–1439. https://doi.org/10.1007/s12065-020-00448-y
   Web of Science ®Google Scholar
• Jou, R. C., Lam, S. H., Weng, M. C., & Chen, C. C. (2010). Real time traffic information and its impacts on route switching behavior of expressway drivers. Journal of Advanced Transportation, 38(2), 187–223. https://doi.org/10.1002/atr.5670380206
   Google Scholar
• Krishnan, P. S., & Manimala, K. (2020). Implementation of optimized dynamic trajectory modification algorithm to avoid obstacles for secure navigation of UAV. Applied Soft Computing, 90, 106168. https://doi.org/10.1016/j.asoc.2020.106168
   Web of Science ®Google Scholar
• Li, L., Wang, S., & Wang, F. Y. (2018). An analysis of taxi driver’s route choice behavior using the trace records. IEEE Transactions on Computational Social Systems, 5(2), 576–582. https://doi.org/10.1109/TCSS.2018.2831285
   Web of Science ®Google Scholar
• Manogaran, G., Shakeel, P. M., Priyan, R. V., Chilamkurti, N., & Srivastava, A. (2019). Ant colony optimization-induced route optimization for enhancing driving range of electric vehicles. International Journal of Communication Systems, 35(12). https://doi.org/10.1002/dac.3964
   Google Scholar
• Morikawa, T., Miwa, T., Kurauchi, S., Yamamoto, T., & Kobayashi, K. (2005). Driver’s route choice behavior and its implications on network simulation and traffic assignment. Simulation Approaches in Transportation Analysis: Recent Advances and Challenges, 341–369. https://doi.org/10.1007/0-387-24109-4_13
   Google Scholar
• Mukund, B. W., & Gomathi, N. (2018). Quantitative and qualitative correlation analysis of optimal route discovery for vehicular ad-hoc networks. Journal of Central South University, 25(7), 1732–1745.
   Web of Science ®Google Scholar
• Parrado, N., & Donoso, Y. (2015). Congestion based mechanism for route discovery in a V2I-V2V system applying smart devices and IoT. Sensors, 15(4), 7768–7806. https://doi.org/10.3390/s150407768
   PubMed Web of Science ®Google Scholar
• Premanand, R. P., & Rajaram, A. (2020). Enhanced data accuracy based PATH discovery using backing route selection algorithm in MANET. Peer-To-Peer Networking and Applications, 13(6), 2089–2098. https://doi.org/10.1007/s12083-019-00824-1
   Web of Science ®Google Scholar
• Ran, X., Shan, Z., Fang, Y., & Lin, C. (2019). An LSTM-based method with attention mechanism for travel time prediction. Sensors, 19(4), 861. https://doi.org/10.3390/s19040861
   PubMed Web of Science ®Google Scholar
• Schwemmer, P., Güpner, F., Adler, S., Klingbeil, K., & Garthe, S. (2016). Modeling small-scale foraging habitat use in breeding Eurasian oystercatchers (haematopus ostralegus) in relation to prey distribution and environmental predictors. Ecological Modelling, 320, 322–333. https://doi.org/10.1016/j.ecolmodel.2015.10.023
   Web of Science ®Google Scholar
• Ta, N., Li, G., Zhao, T., Feng, J., Ma, H., & Gong, Z. (2018). An efficient ride-sharing framework for maximizing shared route. IEEE Transactions on Knowledge and Data Engineering, 30(2), 219–233. https://doi.org/10.1109/TKDE.2017.2760880
   Web of Science ®Google Scholar
• Tarkhaneh, O., Alipour, N., Chapnevis, A., & Shen, H. (2021). Golden tortoise beetle optimizer: A novel nature-inspired meta-heuristic algorithm for engineering problems. Journal: Neural and Evolutionary Computing, 1–19. https://doi.org/10.48550/arXiv.2104.01521.
   Google Scholar
• Wang, Z., Lin, W. H., & Xu, W. (2021). A data driven approach to assessing the reliability of using taxicab as probes for real-time route selections. Journal of Intelligent Transportation Systems, 25(4), 331–342. https://doi.org/10.1080/15472450.2019.1617142
   Web of Science ®Google Scholar
• Wang, J., Qiu, H., Wang, J., Fang, W., & Zhou, G. (2021). Train route information visual distance extension and analysis of driver cognitive reliability. IEEE Transactions on Reliability, 70(2), 602–612. https://doi.org/10.1109/TR.2020.3025827
   Web of Science ®Google Scholar
• Xu, D., & Yin, J. (2023). An improved black widow optimization algorithm for engineering constrained optimization problems. IEEE Access, 11, 32476–32495. https://doi.org/10.1109/ACCESS.2023.3262600
   Web of Science ®Google Scholar
• Yamamoto, T., Kitamura, R., & Fujii, J. (2002). Drivers’ route choice behavior: Analysis by data mining algorithms. Transportation Research Record: Journal of the Transportation Research Board, 1807(1), 59–66. https://doi.org/10.3141/1807-08
   Google Scholar
• Yao, Y., Peng, Z., & Xiao, B. (2018). Parallel hyper-heuristic algorithm for multi-objective route planning in a smart city. IEEE Transactions on Vehicular Technology, 67(11), 10307–10318. https://doi.org/10.1109/TVT.2018.2868942
   Web of Science ®Google Scholar
• Zhao, D., Yu, H., Fang, X., Tian, L., & Han, P. (2020). A path planning method based on multi-objective cauchy mutation cat swarm optimization algorithm for navigation system of intelligent patrol car. IEEE Access, 8, 151788–151803. https://doi.org/10.1109/ACCESS.2020.3016565
   Web of Science ®Google Scholar