IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v129y2019icp287-304.html
   My bibliography  Save this article

Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters

Author

Listed:
  • Zhang, Liye
  • Meng, Qiang
  • Fang Fwa, Tien

Abstract

This study develops a tangible analytical approach to analyze ship traffic demand and the spatial–temporal dynamics of ship traffic in port waters using big AIS data. By applying the developed approach to the Singapore port waters, we find that the origin-to-destination pairs and navigation routes in the Singapore port waters keep stable over time. Furthermore, there are several hotspot areas in the Singapore Strait where ship sailing speeds are relatively high and ship sailing speeds in a few water areas vary greatly. More interestingly, we find that these hotspot areas well coincide with the spatial distribution of ship accidents.

Suggested Citation

  • Zhang, Liye & Meng, Qiang & Fang Fwa, Tien, 2019. "Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 287-304.
    • Handle: RePEc:eee:transe:v:129:y:2019:i:c:p:287-304
    DOI: 10.1016/j.tre.2017.07.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554516309516
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2017.07.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tao Cheng & James Haworth & Jiaqiu Wang, 2012. "Spatio-temporal autocorrelation of road network data," Journal of Geographical Systems, Springer, vol. 14(4), pages 389-413, October.
    2. Ennio Cascetta & Domenico Inaudi & Gérald Marquis, 1993. "Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts," Transportation Science, INFORMS, vol. 27(4), pages 363-373, November.
    3. Nishimura, Etsuko & Imai, Akio & Papadimitriou, Stratos, 2005. "Yard trailer routing at a maritime container terminal," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 41(1), pages 53-76, January.
    4. Qu, Xiaobo & Meng, Qiang, 2012. "The economic importance of the Straits of Malacca and Singapore: An extreme-scenario analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 258-265.
    5. Shelmerdine, Richard L., 2015. "Teasing out the detail: How our understanding of marine AIS data can better inform industries, developments, and planning," Marine Policy, Elsevier, vol. 54(C), pages 17-25.
    6. Yang Yue & Anthony Gar-On Yeh, 2008. "Spatiotemporal Traffic-Flow Dependency and Short-Term Traffic Forecasting," Environment and Planning B, , vol. 35(5), pages 762-771, October.
    7. Petering, Matthew E.H., 2009. "Effect of block width and storage yard layout on marine container terminal performance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(4), pages 591-610, July.
    8. Wang, Shuaian & Meng, Qiang & Sun, Zhuo, 2013. "Container routing in liner shipping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 49(1), pages 1-7.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Michael F. Gorman & John-Paul Clarke & René Koster & Michael Hewitt & Debjit Roy & Mei Zhang, 2023. "Emerging practices and research issues for big data analytics in freight transportation," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 28-60, March.
    2. Liu, Baoli & Li, Zhi-Chun & Wang, Yadong, 2022. "A two-stage stochastic programming model for seaport berth and channel planning with uncertainties in ship arrival and handling times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    3. Rong, H. & Teixeira, A.P. & Guedes Soares, C., 2022. "Maritime traffic probabilistic prediction based on ship motion pattern extraction," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    4. Yang, Zhisen & Yu, Qing & Yang, Zaili & Wan, Chengpeng, 2024. "A data-driven Bayesian model for evaluating the duration of detention of ships in PSC inspections," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    5. Jin, Lianjie & Chen, Jing & Chen, Zilin & Sun, Xiangjun & Yu, Bin, 2022. "Impact of COVID-19 on China's international liner shipping network based on AIS data," Transport Policy, Elsevier, vol. 121(C), pages 90-99.
    6. Bai, Xiwen & Hou, Yao & Yang, Dong, 2021. "Choose clean energy or green technology? Empirical evidence from global ships," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    7. Xin, Xuri & Liu, Kezhong & Loughney, Sean & Wang, Jin & Li, Huanhuan & Ekere, Nduka & Yang, Zaili, 2023. "Multi-scale collision risk estimation for maritime traffic in complex port waters," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    8. Yang, Zhisen & Wan, Chengpeng & Yu, Qing & Yin, Jingbo & Yang, Zaili, 2023. "A machine learning-based Bayesian model for predicting the duration of ship detention in PSC inspection," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
    9. Zhang, Jinfen & Liu, Jiongjiong & Hirdaris, Spyros & Zhang, Mingyang & Tian, Wuliu, 2023. "An interpretable knowledge-based decision support method for ship collision avoidance using AIS data," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    10. Zhang, Mingyang & Zhang, Di & Fu, Shanshan & Kujala, Pentti & Hirdaris, Spyros, 2022. "A predictive analytics method for maritime traffic flow complexity estimation in inland waterways," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    11. Kang, Liujiang & Meng, Qiang & Tan, Kok Choon, 2020. "Tugboat scheduling under ship arrival and tugging process time uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    12. Xin, Xuri & Liu, Kezhong & Yang, Zaili & Zhang, Jinfen & Wu, Xiaolie, 2021. "A probabilistic risk approach for the collision detection of multi-ships under spatiotemporal movement uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    13. Jiang, Meizhi & Lu, Jing, 2020. "The analysis of maritime piracy occurred in Southeast Asia by using Bayesian network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 139(C).
    14. Eisuke Watanabe & Ryuichi Shibasaki, 2023. "Extraction of Bunkering Services from Automatic Identification System Data and Their International Comparisons," Sustainability, MDPI, vol. 15(24), pages 1-19, December.
    15. Sugrue, Dennis & Adriaens, Peter, 2021. "A data fusion approach to predict shipping efficiency for bulk carriers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    16. Fuentes, Gabriel, 2021. "Generating bunkering statistics from AIS data: A machine learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    17. Zhang, Weibin & Feng, Xinyu & Goerlandt, Floris & Liu, Qing, 2020. "Towards a Convolutional Neural Network model for classifying regional ship collision risk levels for waterway risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    18. Sun, Qiuxia & Zhang, Yu & Sun, Lu & Li, Qing & Gao, Peng & He, Hao, 2021. "Spatial–temporal differences in operational performance of urban trunk roads based on TPI data: The case of Qingdao," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 568(C).
    19. Xin, Xuri & Liu, Kezhong & Loughney, Sean & Wang, Jin & Yang, Zaili, 2023. "Maritime traffic clustering to capture high-risk multi-ship encounters in complex waters," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    20. Rong, H. & Teixeira, A.P. & Guedes Soares, C., 2021. "Spatial correlation analysis of near ship collision hotspots with local maritime traffic characteristics," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    21. Leonardo M. Millefiori & Paolo Braca & Dimitris Zissis & Giannis Spiliopoulos & Stefano Marano & Peter K. Willett & Sandro Carniel, 2020. "COVID-19 Impact on Global Maritime Mobility," Papers 2009.06960, arXiv.org, revised Mar 2021.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Shuaian, 2014. "A novel hybrid-link-based container routing model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 61(C), pages 165-175.
    2. D'Acierno, Luca & Cartenì, Armando & Montella, Bruno, 2009. "Estimation of urban traffic conditions using an Automatic Vehicle Location (AVL) System," European Journal of Operational Research, Elsevier, vol. 196(2), pages 719-736, July.
    3. Moussawi-Haidar, Lama & Nasr, Walid & Jalloul, Maya, 2021. "Standardized cargo network revenue management with dual channels under stochastic and time-dependent demand," European Journal of Operational Research, Elsevier, vol. 295(1), pages 275-291.
    4. Huo, Jinbiao & Liu, Chengqi & Chen, Jingxu & Meng, Qiang & Wang, Jian & Liu, Zhiyuan, 2023. "Simulation-based dynamic origin–destination matrix estimation on freeways: A Bayesian optimization approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    5. Osorio, Carolina & Punzo, Vincenzo, 2019. "Efficient calibration of microscopic car-following models for large-scale stochastic network simulators," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 156-173.
    6. A. Stathopoulos & T. Tsekeris, 2003. "Framework for analysing reliability and information degradation of demand matrices in extended transport networks," Transport Reviews, Taylor & Francis Journals, vol. 23(1), pages 89-103, January.
    7. Matthew E. H. Petering & Yong Wu & Wenkai Li & Mark Goh & Robert Souza & Katta G. Murty, 2017. "Real-time container storage location assignment at a seaport container transshipment terminal: dispersion levels, yard templates, and sensitivity analyses," Flexible Services and Manufacturing Journal, Springer, vol. 29(3), pages 369-402, December.
    8. M. Bierlaire & F. Crittin, 2004. "An Efficient Algorithm for Real-Time Estimation and Prediction of Dynamic OD Tables," Operations Research, INFORMS, vol. 52(1), pages 116-127, February.
    9. Qing Luo & Daniel A. Griffith & Huayi Wu, 2019. "Spatial autocorrelation for massive spatial data: verification of efficiency and statistical power asymptotics," Journal of Geographical Systems, Springer, vol. 21(2), pages 237-269, June.
    10. Zhi Heng & Tsz Leung Yip, 2018. "Impacts of Kra Canal and its toll structures on tanker traffic," Maritime Policy & Management, Taylor & Francis Journals, vol. 45(1), pages 125-139, January.
    11. Ryuichi Shibasaki & Takayuki Iijima & Taiji Kawakami & Takashi Kadono & Tatsuyuki Shishido, 2017. "Network assignment model of integrating maritime and hinterland container shipping: application to Central America," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(2), pages 234-273, June.
    12. Kumawat, Govind Lal & Roy, Debjit & De Koster, René & Adan, Ivo, 2021. "Stochastic modeling of parallel process flows in intra-logistics systems: Applications in container terminals and compact storage systems," European Journal of Operational Research, Elsevier, vol. 290(1), pages 159-176.
    13. Hu, Shou-Ren & Peeta, Srinivas & Chu, Chun-Hsiao, 2009. "Identification of vehicle sensor locations for link-based network traffic applications," Transportation Research Part B: Methodological, Elsevier, vol. 43(8-9), pages 873-894, September.
    14. K. Ashok & M. E. Ben-Akiva, 2000. "Alternative Approaches for Real-Time Estimation and Prediction of Time-Dependent Origin–Destination Flows," Transportation Science, INFORMS, vol. 34(1), pages 21-36, February.
    15. Claudia Durán & Ivan Derpich & Raúl Carrasco, 2022. "Optimization of Port Layout to Determine Greenhouse Gas Emission Gaps," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    16. Camus, Roberto & Cantarella, Giulio E. & Inaudi, Domenico, 1997. "Real-time estimation and prediction of origin--destination matrices per time slice," International Journal of Forecasting, Elsevier, vol. 13(1), pages 13-19, March.
    17. Akash Gupta & Debjit Roy & René de Koster & Sampanna Parhi, 2017. "Optimal stack layout in a sea container terminal with automated lifting vehicles," International Journal of Production Research, Taylor & Francis Journals, vol. 55(13), pages 3747-3765, July.
    18. Anselmo Ramalho Pitombeira-Neto & Carlos Felipe Grangeiro Loureiro & Luis Eduardo Carvalho, 2020. "A Dynamic Hierarchical Bayesian Model for the Estimation of day-to-day Origin-destination Flows in Transportation Networks," Networks and Spatial Economics, Springer, vol. 20(2), pages 499-527, June.
    19. Branislav Dragović & Ernestos Tzannatos & Nam Kuy Park, 2017. "Simulation modelling in ports and container terminals: literature overview and analysis by research field, application area and tool," Flexible Services and Manufacturing Journal, Springer, vol. 29(1), pages 4-34, March.
    20. Flurin S. Hänseler & Nicholas A. Molyneaux & Michel Bierlaire, 2017. "Estimation of Pedestrian Origin-Destination Demand in Train Stations," Transportation Science, INFORMS, vol. 51(3), pages 981-997, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transe:v:129:y:2019:i:c:p:287-304. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.