Optimization of Intercity Bus Terminal Operations: Capacity and Frequency Analysis Based on Production Data to Improve Efficiency in Cirebon Regency
DOI:
https://doi.org/10.52920/jttl.v6i2.489Keywords:
capacity analysis, frequency of service, production data, public transportation, terminal optimizationAbstract
Optimizing the operation of intercity bus terminals is a crucial step to improve the efficiency of the public transportation system. This study analyzes and optimizes the performance of terminals in Cirebon Regency through a data-based approach on capacity and frequency. Using a quantitative method with a case study in August-November 2025, route production data was analyzed descriptively and inferentially, followed by integer programming optimization modeling. The results showed a structural imbalance: route utilization varied from 45% to 90%, leading to an average queue of 22 minutes. The optimization model results in a scenario of redistributing 8 trips from peak hours and consolidating three low-frequency routes. The simulation proves that this scenario is able to reduce queue time by 35% and increase system utilization to 84.1% with lower disparity. The findings confirm that the analysis of production data is effective for formulating scalable terminal operational optimization solutions.
References
Adochiei, I. R., Tirbu, O. I., Adochiei, N. I., Pericle-Gabriel, M., Larco, C. M., Mustata, S. M., & Costin, D. (2020). Drivers’ drowsiness detection and warning systems for critical infrastructures. 2020 8th E-Health and Bioengineering Conference, EHB 2020, 14–17. https://doi.org/10.1109/EHB50910.2020.9280165
Akrout, B., & Mahdi, W. (2023). A novel approach for driver fatigue detection based on visual characteristics analysis. Journal of Ambient Intelligence and Humanized Computing, 14(1), 527–552. https://doi.org/10.1007/s12652-021-03311-9
Alkinani, M. H., Khan, W. Z., & Arshad, Q. (2020a). Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges. IEEE Access, 8, 105008–105030. https://doi.org/10.1109/ACCESS.2020.2999829
Alkinani, M. H., Khan, W. Z., & Arshad, Q. (2020b). Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges. IEEE Access, 8, 105008–105030. https://doi.org/10.1109/ACCESS.2020.2999829
Alkishri, W., Abualkishik, A., & Al-Bahri, M. (2022). Enhanced Image Processing and Fuzzy Logic Approach for Optimizing Driver Drowsiness Detection. Applied Computational Intelligence and Soft Computing, 2022. https://doi.org/10.1155/2022/9551203
Biswal, A. K., Singh, D., Pattanayak, B. K., Samanta, D., & Yang, M. H. (2021). IoT-based smart alert system for drowsy driver detection. Wireless Communications and Mobile Computing, 2021. https://doi.org/10.1155/2021/6627217
Budiarti, R. P. N., Nugroho, B. W., Ayunda, N., & Sukaridhoto, S. (2023). Drowsy Eyes and Face Mask Detection for Car Drivers using the Embedded System. Register: Jurnal Ilmiah Teknologi Sistem Informasi, 9(1), 86–94. https://doi.org/10.26594/register.v9i1.2612
Chakraborty, M., & Aoyon, A. N. H. (2014). Implementation of Computer Vision to detect driver fatigue or drowsiness to reduce the chances of vehicle accident. 1st International Conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2014. https://doi.org/10.1109/ICEEICT.2014.6919054
Cui, Z., Sun, H. M., Yin, R. N., Gao, L., Sun, H. Bin, & Jia, R. S. (2021). Real-time detection method of driver fatigue state based on deep learning of face video. Multimedia Tools and Applications, 80(17), 25495–25515. https://doi.org/10.1007/s11042-021-10930-z
Dey, S., Chowdhury, S. A., Sultana, S., Hossain, M. A., Dey, M., & Das, S. K. (2019a). Real Time Driver Fatigue Detection Based on Facial Behaviour along with Machine Learning Approaches. 2019 IEEE International Conference on Signal Processing, Information, Communication and Systems, SPICSCON 2019, 135–140. https://doi.org/10.1109/SPICSCON48833.2019.9065120
Dey, S., Chowdhury, S. A., Sultana, S., Hossain, M. A., Dey, M., & Das, S. K. (2019b). Real Time Driver Fatigue Detection Based on Facial Behaviour along with Machine Learning Approaches. 2019 IEEE International Conference on Signal Processing, Information, Communication and Systems, SPICSCON 2019, 135–140. https://doi.org/10.1109/SPICSCON48833.2019.9065120
Ed-Doughmi, Y., Idrissi, N., & Hbali, Y. (2020). Real-time system for driver fatigue detection based on a recurrent neuronal network. Journal of Imaging, 6(3). https://doi.org/10.3390/jimaging6030008
Elleuch, H., Wali, A., & Alimi, A. M. (2015). Smart tablet monitoring by a real-time head movement and eye gestures recognition system. International Journal of Computing and Digital Systems, 4(3), 183–192. https://doi.org/10.12785/IJCDS/040305
Gawande, R., & Badotra, S. (2022). Deep-Learning Approach for Efficient Eye-blink Detection with Hybrid Optimization Concept. International Journal of Advanced Computer Science and Applications, 13(6), 782–795. https://doi.org/10.14569/IJACSA.2022.0130693
Hollósi, J., Ballagi, Á., Kovács, G., Fischer, S., & Nagy, V. (2024). Bus Driver Head Position Detection Using Capsule Networks under Dynamic Driving Conditions. Computers, 13(3). https://doi.org/10.3390/computers13030066
Junaedi, S., & Akbar, H. (2018). Driver Drowsiness Detection Based on Face Feature and PERCLOS. Journal of Physics: Conference Series, 1090(1). https://doi.org/10.1088/1742-6596/1090/1/012037
Kim, W., Jung, W. S., & Choi, H. K. (2019). Lightweight driver monitoring system based on multi-task mobilenets. Sensors (Switzerland), 19(14). https://doi.org/10.3390/s19143200
Li, T., Zhang, T., & Li, Q. (2022). Train Driver Fatigue Detection Using Eye Feature Vector and Support Vector Machine. International Journal of Circuits, Systems and Signal Processing, 16, 1007–1017. https://doi.org/10.46300/9106.2022.16.123
Li, Z., Chen, C., Ci, Y., Zhang, G., Wu, Q., Liu, C., & Qian, Z. (Sean). (2018). Examining driver injury severity in intersection-related crashes using cluster analysis and hierarchical Bayesian models. Accident Analysis and Prevention, 120, 139–151. https://doi.org/10.1016/j.aap.2018.08.009
Mangshor, N. N. A., Majid, I. A. A., Ibrahim, S., & Sabri, N. (2020). A real-time drowsiness and fatigue recognition using support vector machine. IAES International Journal of Artificial Intelligence, 9(4), 584–590. https://doi.org/10.11591/ijai.v9.i4.pp584-590
Murthy, K. S. R., Siddineni, B., Kompella, V. K., Aashritha, K., Sri Sai, B. H., & Manikandan, V. M. (2022). An Efficient Drowsiness Detection Scheme using Video Analysis. International Journal of Computing and Digital Systems, 11(1), 573–581. https://doi.org/10.12785/ijcds/110146
ÖZTÜRK, M., KÜÇÜKMAN?SA, A., & URHAN, O. (2022). Drowsiness Detection System Based on Machine Learning Using Eye State. Balkan Journal of Electrical and Computer Engineering, 10(3), 258–263. https://doi.org/10.17694/bajece.1028110
Reddy Chirra, V. R., Uyyala, S. R., & Kishore Kolli, V. K. (2019). Deep CNN: A machine learning approach for driver drowsiness detection based on eye state. Revue d’Intelligence Artificielle, 33(6), 461–466. https://doi.org/10.18280/ria.330609
Rudrusamy, B., Teoh, H. C., Pang, J. Y., Lee, T. H., & Chai, S. C. (2023). IoT-Based Vehicle Monitoring and Driver Assistance System Framework for Safety and Smart Fleet Management. International Journal of Integrated Engineering, 15(1), 391–403. https://doi.org/10.30880/ijie.2023.15.01.035
Said, S., AlKork, S., Beyrouthy, T., Hassan, M., Abdellatif, O. E., & Fayek Abdraboo, M. (2018). Real time eye tracking and detection- A driving assistance system. Advances in Science, Technology and Engineering Systems, 3(6), 446–454. https://doi.org/10.25046/aj030653
Shang, Y., Yang, M., Cui, J., Cui, L., Huang, Z., & Li, X. (2023). Driver Emotion and Fatigue State Detection Based on Time Series Fusion. Electronics (Switzerland), 12(1). https://doi.org/10.3390/electronics12010026
Sharan, S. S., Viji, R., Pradeep, R., & Sajith, V. (2019). Driver Fatigue Detection Based on Eye State Recognition Using Convolutional Neural Network. Proceedings of the 4th International Conference on Communication and Electronics Systems, ICCES 2019, Icces, 2057–2063. https://doi.org/10.1109/ICCES45898.2019.9002215
Shariff, W., Dilmaghani, M. S., Kielty, P., Lemley, J., Farooq, M. A., Khan, F., & Corcoran, P. (2023). Neuromorphic Driver Monitoring Systems: A Computationally Efficient Proof-of-Concept for Driver Distraction Detection. IEEE Open Journal of Vehicular Technology, 4(October), 836–848. https://doi.org/10.1109/OJVT.2023.3325656
Sharma, V. P., Yadav, J. S., & Sharma, V. (2022). Deep convolutional network based real time fatigue detection and drowsiness alertness system. International Journal of Electrical and Computer Engineering, 12(5), 5493–5500. https://doi.org/10.11591/ijece.v12i5.pp5493-5500
Shen, J., Li, G., Yan, W., Tao, W., Xu, G., Diao, D., & Green, P. (2018). Nighttime driving safety improvement via image enhancement for driver face detection. IEEE Access, 6, 45625–45634. https://doi.org/10.1109/ACCESS.2018.2864629
Shofiah, S., Pradana, B., Bunga, S., & Ayu, R. (2024). Pengenalan Wajah dengan Viola Jones. 3(4).
Shofiah, S., Sediyono, E., Hasibuan, Z. A., Kristianto, B., Setiawan, S., Pratindy, R., Hakim, M. I. N., & Humami, F. (2024). Driver Facial Detection Across Diverse Road Conditions. ILKOM Jurnal Ilmiah, 16(2), 108–114.
Sowmyashree, P., & Sangeetha, J. (2023). Multistage End-to-End Driver Drowsiness Alerting System. International Journal of Advanced Computer Science and Applications, 14(4), 464–473. https://doi.org/10.14569/IJACSA.2023.0140452
Sun, Y., Yan, P., Li, Z., Zou, J., & Hong, D. (2020). Driver fatigue detection system based on colored and infrared eye features fusion. Computers, Materials and Continua, 63(3), 1563–1574. https://doi.org/10.32604/CMC.2020.09763
Valsan A, V., Mathai, P. P., & Babu, I. (2021). Monitoring driver’s drowsiness status at night based on computer vision. Proceedings - IEEE 2021 International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2021, 989–993. https://doi.org/10.1109/ICCCIS51004.2021.9397180
Wan, Y., Liu, M., Fan, J., Zhao, Y., Jiang, J., & Yao, L. (2019). Multi-feature analysis fatigue driving based on Conditional Local Neural Fields algorithm detection method. Journal of Physics: Conference Series, 1237(2). https://doi.org/10.1088/1742-6596/1237/2/022157
Wang, F., Chen, X., Wang, D., & Yang, B. (2017). An improved image-based iris-tracking for driver fatigue detection system. Chinese Control Conference, CCC, 11521–11526. https://doi.org/10.23919/ChiCC.2017.8029198
Yu, B., Bao, S., Zhang, Y., Sullivan, J., & Flannagan, M. (2021). Measurement and prediction of driver trust in automated vehicle technologies: An application of hand position transition probability matrix. Transportation Research Part C: Emerging Technologies, 124. https://doi.org/10.1016/j.trc.2020.102957
Zaman, K., Zhaoyun, S., Shah, B., Hussain, T., Shah, S. M., Ali, F., & Khan, U. S. (2023). A novel driver emotion recognition system based on deep ensemble classification. Complex and Intelligent Systems, 9(6), 6927–6952. https://doi.org/10.1007/s40747-023-01100-9
Zhao, X., Zhao, X., Yu, Q., Ye, Y., & Yu, M. (2020). Development of a representative urban driving cycle construction methodology for electric vehicles: A case study in Xi’an. Transportation Research Part D: Transport and Environment, 81. https://doi.org/10.1016/j.trd.2020.102279.
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