DOI: https://doi.org/10.14710/jati.17.2.71-81

SIMULASI SISTEM PARKIR SPASIAL BERBASIS AGEN: SEBUAH PERANCANGAN KONSEP DAN IMPLEMENTASI MODEL

*Ary Arvianto  -  Universitas Diponegoro, Indonesia
Wiwik Budiawan  -  Universitas Diponegoro, Indonesia
Ahmad Karami  -  Universitas Diponegoro, Indonesia
Fachrul Rozi  -  Universitas Diponegoro, Indonesia
Jose Daniel Marthin  -  Universitas Diponegoro, Indonesia

Citation Format:
Abstract

Parkir dapat menimbulkan permasalah transportasi diperkotaan. Rendahnya okupansi ruang parkir sebuah fasilitas umum juga berdampak pada kemacetan lalu lintas di sekitarnya. Studi ini berfokus pada optimalisasi ruang parkir dengan mengukur tingkat okupansinya. Mikro simulasi diterapkan untuk mendapatkan gambaran okupansi secara spasial. Oleh karena itu, pendekatan agent based digunakan untuk dapat menangkap aspek mikro terutama perilaku pengemudi dalam mencari tempat parkir. Simulasi ini menggunakan tiga skenario dalam analisis okupansinya yaitu parkir yang terjadi pada hari kerja, hari akhir minggu, dan hari raya atau masa libur panjang. Hasilnya adalah okupansi tertinggi terjadi pada skenario hari raya khususnya pada area pintu masuk, railway, dan area mesin tiket. Sementara itu, area tunnel adalah area yang paling sedikit diminati oleh pengendara dan memiliki okupansi paling sedikit. Penambahan pintu masuk dan mesin tiket di dekat area tunnel dapat meningkatkan okupansi parkir yang berarti berpotensi dapat manambah okupansi kendarannya.

Fulltext View|Download
Keywords: transportasi; sistem parkir; simulasi; agent-based modeling

Article Metrics:

Article Info
Section: Research Articles
Language : ID
Recent articles
IMPLEMENTASI DAN TANTANGAN PENERAPAN KEBIJAKAN K3 DI SEBUAH PASAR TRADISIONAL, KABUPATEN KLATEN, PADA MASA PANDEMI COVID-19 WORKERS’ MENTAL WORKLOAD IN INDONESIAN SMALL FOOD-PRODUCING BUSINESS: PRELIMINARY FINDINGS AT A CORN CHIPS BUSINESS USULAN STRATEGI PENINGKATAN KEPERCAYAAN PELANGGAN DAN KEPUTUSAN BOOKING PELANGGAN HOTEL X ANALISIS RISIKO DAN STRATEGI MITIGASI RISIKO SUPPLY CHAIN PRODUK CRUDE PALM OIL (CPO) (STUDI KASUS: PT XYZ) More recent articles
Most cited articles
SIMULASI MODEL DINAMIK PENGANGKUTAN CRUDE PALM OIL (CPO) DI PT. XYZ UNTUK MEMINIMALKAN BIAYA TRANSPORTASI PENGADAAN BAHAN ANALISA PEMILIHAN SUPPLIER RAMAH LINGKUNGAN DENGAN METODE ANALYTICAL NETWORK PROCESS (ANP) PADA PT KIMIA FARMA PLANT SEMARANG PERHITUNGAN NILAI OVERALL EQUIPMENT EFFECTIVENESS MESIN MIXER BANBURY 270 L DAN MESIN BIAS CUTTING LINE 2 (STUDI KASUS PT. SURYARAYA RUBBERINDO INDUSTRIES) PENGEMBANGAN TEKNOLOGI RAPID PROTOTYPING UNTUK PEMBUATAN PRODUK-PRODUK MULTI MATERIAL MODEL KONSEPTUAL UNTUK MENGUKUR ADAPTABILITAS BANK SAMPAH DI INDONESIA More cited articles
  1. Amato, G., Carrara, F., Falchi, F., Gennaro, C., Meghini, C., & Vairo, C. (2017). Deep learning for decentralized parking lot occupancy detection. Expert Systems with Applications, 72, 327–334. https://doi.org/10.1016/j.eswa.2016.10.055
  2. Arnott, R., & Rowse, J. (1999). Modeling Parking. Journal of Urban Economics, 45(1), 97–124. https://econpapers.repec.org/RePEc:eee:juecon:v:45:y:1999:i:1:p:97-124
  3. Arvianto, A., Sopha, B. M., Asih, A. M. S., & Imron, M. A. (2021). City logistics challenges and innovative solutions in developed and developing economies: A systematic literature review. International Journal of Engineering Business Management, 13, 184797902110397. https://doi.org/10.1177/18479790211039723
  4. Benenson, I., Martens, K., & Birfir, S. (2008). PARKAGENT: An agent-based model of parking in the city. Computers, Environment and Urban Systems, 32(6), 431–439. https://doi.org/10.1016/j.compenvurbsys.2008.09.011
  5. Bonsall, P., & Palmer, I. (2004). Modelling drivers’ car parking behaviour using data from a travel choice simulator. Transportation Research Part C: Emerging Technologies, 12, 321–347. https://doi.org/10.1016/j.trc.2004.07.013
  6. Farley, A., Ham, H., & Hendra. (2021). Real Time IP Camera Parking Occupancy Detection using Deep Learning. Procedia Computer Science, 179, 606–614. https://doi.org/10.1016/j.procs.2021.01.046
  7. Grimm, V., Berger, U., DeAngelis, D. L., Polhill, J. G., Giske, J., & Railsback, S. F. (2010). The ODD protocol: A review and first update. Ecological Modelling, 221(23), 2760–2768. https://doi.org/10.1016/j.ecolmodel.2010.08.019
  8. Humphreys, J. B., Box, P. C., Sullivan, T. D., & Wheeler, D. J. (1978). SAFETY ASPECTS OF CURB PARKING
  9. Khalid, A., Ahmed, H., Mohamed, M., & Atef, M. (2020). Improved Global Routing By Using A-Star Algorithm. https://doi.org/10.13140/RG.2.2.32240.07684
  10. PJHJ, W., Borgers, A., & Timmermans, H. J. P. (2003). Travelers Micro-Behavior at Parking Lots - A Model of Parking Choice Behvior
  11. Provoost, J. C., Kamilaris, A., Wismans, L. J. J., van der Drift, S. J., & van Keulen, M. (2020). Predicting parking occupancy via machine learning in the web of things. Internet of Things, 12, 100301. https://doi.org/10.1016/j.iot.2020.100301
  12. Saharan, S., Kumar, N., & Bawa, S. (2020). An efficient smart parking pricing system for smart city environment: A machine-learning based approach. Future Generation Computer Systems, 106, 622–640. https://doi.org/10.1016/j.future.2020.01.031
  13. Sklar, E. (2007). NetLogo, a Multi-agent Simulation Environment. Artificial Life, 13, 303–311. https://doi.org/10.1162/artl.2007.13.3.303
  14. Slavova, S., Sebastian Piest, J. P., & van Heeswijk, W. (2022). Predicting truck parking occupancy using machine learning. Procedia Computer Science, 201, 40–47. https://doi.org/10.1016/j.procs.2022.03.008
  15. Sopha, B. M., & Sakti, S. (2020). Pemodelan dan Simulasi Berbasis Agen. Gadjah Mada University Press
  16. Thompson, R. G., & Richardson, A. J. (1998). A Parking Search Model. Transportation Research Part A: Policy and Practice, 32(3), 159–170. https://econpapers.repec.org/RePEc:eee:transa:v:32:y:1998:i:3:p:159-170
  17. van der Waerden, P., Timmermans, H., & da Silva, A. N. R. (2015). The influence of personal and trip characteristics on habitual parking behavior. Case Studies on Transport Policy, 3(1), 33–36. https://doi.org/10.1016/j.cstp.2014.04.001
  18. Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling (Issue January)
  19. Yang, S., Ma, W., Pi, X., & Qian, S. (2019). A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources. Transportation Research Part C: Emerging Technologies, 107, 248–265. https://doi.org/10.1016/j.trc.2019.08.010
  20. Young, W. (1986). PARKSIM 1 A NETWORK MODEL FOR PARKING FACILITY DESIGN. Traffic Engineering and Control, 27, 606–613

Last update:

No citation recorded.

Last update: 2024-12-26 15:37:41

No citation recorded.