Parking Route Modeling Using the A* Algorithm for Density Reduction at the Faculty of Science and Technology, State Islamic University of North Sumatra

Authors

  • Asro Hayati Berutu Universitas Islam Negeri Sumatera Utara Author
  • Salsabila Nasution Universitas Islam Negeri Sumatera Utara Author
  • Suci Rahmadani Universitas Syiah Kuala Author

DOI:

https://doi.org/10.65230/jitcos.v1i2.46

Keywords:

Parking Route, A* Algorithm, Density Reduction, State Islamic University of North Sumatra

Abstract

The increasing number of vehicles on university campuses has led to significant congestion, particularly around parking areas. This study aims to design an intelligent parking route model using the Density-Aware A* algorithm to minimize vehicle congestion within the Faculty of Science and Technology (FST) at UIN North Sumatra. The proposed approach represents the internal campus network as a weighted graph, where each edge integrates both spatial distance and a density penalty that reflects the occupancy-to-capacity ratio of each parking area. The algorithm was implemented and simulated using Python and the NetworkX library within Google Colab. The results show that the system accurately identifies the optimal parking route based on vehicle type and real-time occupancy data. For motorcycles, the optimal path is A > B > F with a total cost of 23.06, while for cars, the most efficient path is A > B > H with a total cost of 18.21. The findings indicate that incorporating density-based cost adjustments effectively balances travel efficiency and vehicle distribution, contributing to overall congestion reduction in the FST–FKM corridor. Future research should focus on integrating live sensor data and adaptive feedback mechanisms to support large-scale deployment across diverse campus environments.

References

Alansyah, E., & Susanti, A. (2025). Identifikasi Kebutuhan Fasilitas Ruang Parkir ( Studi Kasus : Fakultas Vokasi Universitas Negeri Surabaya ) Identification of Parking Space Facility Needs ( Case Study : Vocational Faculty of State University of Surabaya ). Mitrans: Jurnal Media Publikasi Terapan Transportasi, 3(2), 176–183. https://doi.org/10.26740/mitrans.v3n2.p176-183

Betkier, I., Zak, J. K., & Mitkow, S. (2021). Parking Lots Assignment Algorithm for Vehicles Requiring Specific Parking Conditions in Vehicle Routing Problem. IEEE Access, 9, 161469–161487. https://doi.org/10.1109/ACCESS.2021.3131480

Dalla Chiara, G., Krutein, K. F., Ranjbari, A., & Goodchild, A. (2022). Providing curb availability information to delivery drivers reduces cruising for parking. Scientific Reports, 12(1), 1–11. https://doi.org/10.1038/s41598-022-23987-z

Deng, Z., & Wang, D. (2023). Research on Parking Path Planing Based on A-Star Algorithm. Journal of New Media, 5(1), 55–64. https://doi.org/10.32604/jnm.2023.040252

Elfaki, A. O., Messoudi, W., Bushnag, A., Abuzneid, S., & Alhmiedat, T. (2023). A Smart Real-Time Parking Control and Monitoring System. Sensors. https://doi.org/10.3390/s23249741

Gu, Z., Safarighouzhdi, F., Saberi, M., & Rashidi, T. H. (2021). A macro-micro approach to modeling parking. Transportation Research Part B: Methodological, 147, 220–244. https://doi.org/10.1016/j.trb.2021.03.012

Han, G. L. (2021). Automatic Parking Path Planning Based on Ant Colony Optimization and the Grid Method. Sensors, 2021(1), 1–10. https://doi.org/10.1155/2021/8592558

Han, I. (2022). Geometric Path Plans for Perpendicular / Parallel Reverse Parking in a Narrow Parking Spot with Surrounding Space. Vehicles, 4, 1195–1208. https://doi.org/10.3390/vehicles4040063

Han, Z., Sun, H., Huang, J., Xu, J., Tang, Y., & Liu, X. (2024). Path Planning Algorithms for Smart Parking: Review and Prospects. World Electric Vehicle Journal, 15(7). https://doi.org/10.3390/wevj15070322

Li, X., Li, G., & Bian, Z. (2024). Research on Autonomous Vehicle Path Planning Algorithm Based on Improved RRT * Algorithm and Artificial Potential Field Method. Sensors, 24, 3899. https://doi.org/10.3390/s24123899

Liu, K., Wang, H., Fu, Y., Wen, G., & Wang, B. (2023). A Dynamic Path-Planning Method for Obstacle Avoidance Based on the Driving Safety Field. Sensors, 23(22). https://doi.org/10.3390/s23229180

Millard-Ball, A., Hampshire, R. C., & Weinberger, R. (2021). Parking behaviour: The curious lack of cruising for parking in San Francisco. Land Use Policy, 91. https://doi.org/10.1016/j.landusepol.2019.03.031

Mo, B., Kong, H., Wang, H., Wang, X. (Cara), & Li, R. (2021). Impact of pricing policy change on on-street parking demand and user satisfaction: A case study in Nanning, China. Transportation Research Part A: Policy and Practice, 148, 445–469. https://doi.org/https://doi.org/10.1016/j.tra.2021.04.013

Paudel, S., Vechione, M., & Gurbuz, O. (2024). Predicting University Campus Parking Demand Using Machine Learning Models. Transportation Research Record, 2678(6), 14–26. https://doi.org/10.1177/03611981231193417

Setiawan, A., Widodo, B., Novianty, I., Siskandar, R., Parasti, G., Marcellita, F., Ariyanto, D., & Fathonah, L. (2024). Implementation of Smart Parking System with RFID Technology in IPB Vocational School Campus. International Journal of Progressive Sciences and Technologies (IJPSAT), 46(2), 559–564. https://doi.org/10.52155/ijpsat.v46.2.6621

Sui, X., Ye, X., Wang, T., Yan, X., & Chen, J. (2022). Microscopic Simulating the Impact of Cruising for Parking on Traffic Efficiency and Emission with Parking-and-Visit Test Data. International Journal of Environmental Research and Public Health Article, 19, 9127. https://doi.org/10.3390/ijerph19159127

Wang, Z., Zhang, C., Xue, S., Luo, Y., Chen, J., Wang, W., & Yan, X. (2024). Dynamic coordinated strategy for parking guidance in a mixed driving parking lot involving human-driven and autonomous vehicles. Electronic Research Archive, 32, 523–550. https://doi.org/10.3934/era.2024026

Wangi, I. P., Lenggogeni, & Hadi, W. (2024). Journal of Green Science and Technology Car Parking Needs Analysis At Campus a Jakarta State. Journal of Green Science and Technology, 8(1), 1–10. https://doi.org/10.33603/jgst.v8i1.123

Wei, X., Qiu, R., Yu, H., Yang, Y., Tian, H., & Xiang, X. (2021). Entropy-based optimization via A* algorithm for parking space recommendation. ArXiv, 1–6. https://doi.org/10.1117/12.2619645

Zhang, X., Pitera, K., & Wang, Y. (2023). Parking reservation techniques : A review of research topics , considerations , and optimization methods. Journal of Traffic and Transportation Engineering, 6, 1099–1117. https://doi.org/10.1016/j.jtte.2023.07.009

Zhao, X., & Zhang, M. (2024). Enhancing Predictive Models for On-Street Parking Occupancy : Integrating Adaptive GCN and GRU with Household Categories and POI Factors. Mathematics, 12, 2823. https://doi.org/10.3390/math12182823

Zhao, Y., Mo, L., & Liu, J. (2023). Path Planning Based on Traffic Flow Prediction for Vehicle Scheduling. 2023 IEEE/CIC International Conference on Communications in China (ICCC), 1–5. https://doi.org/10.1109/ICCC57788.2023.10233352

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Published

31-12-2025

How to Cite

Berutu, A. H., Nasution, S., & Rahmadani, S. (2025). Parking Route Modeling Using the A* Algorithm for Density Reduction at the Faculty of Science and Technology, State Islamic University of North Sumatra. JITCoS : Journal of Information Technology and Computer System, 1(2), 101-110. https://doi.org/10.65230/jitcos.v1i2.46

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