Abstract
Emission production in maritime ports has received a lot attention in the last few years. Under the current social and political trend, ports are likely to receive even more pressure in their endeavor to reduce emissions. Monitoring and reporting of emissions enable port authorities to formulate and track the progress of effective emission-reduction measures. As a means, emission inventories are created annually. In this paper, we explore how real-time data can be used to reduce emissions in the port. Applications utilize real-time data for improving the operational efficiency on the landside and seaside while reducing energy consumption and emissions. Hereby, we concentrate on the area of planning and optimization as one area of environmental sustainability in maritime ports. We present applications that deal with inter-terminal truck routing, berth allocation planning, and assistant systems for economic driving. These applications depend on optimization models, machine learning (ML), and gamification. Our research indicates the need for cooperation between the various port stakeholders to share the relevant data as well as proper visualization techniques.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
For instance, the 2018 Los Angeles port EI was released in September 2019.
References
Current data - San Pedro bay ports clean air action plan. https://monitoring.cleanairactionplan.org/current-data/. Accessed 24 Jan 2020
dashPORT. https://www.offis.de/en/offis/project/dashport.html. Accessed 15 Jan 2020
Environmental report 2018: Ports of Bremen/Bremerhaven. https://bremenports.de/greenports/wp-content/uploads/sites/3/2017/04/PERS-Rezertifizierung_Report_2018_en.pdf. Accessed 15 Jan 2020
News 4.19. https://www.cml.fraunhofer.de/content/dam/cml/de/documents/Newsletter/Newsletter. Accessed 15 Jan 2020
Emissions estimation methodology for ocean-going vessels (2008). https://ww3.arb.ca.gov/regact/2008/fuelogv08/appdfuel.pdf. Accessed 23 Jan 2020
Current methodologies in preparing mobile source port-related emission inventories: final report (2009). https://www.epa.gov/sites/production/files/2016-06/documents/2009-port-inventory-guidance.pdf. Accessed 12 Jan 2020
The port of Los Angeles community-based air toxics exposure study (2009). https://monitoring.cleanairactionplan.org/wp-content/uploads/2019/07/POLA_PAH_Final_Report_092309.pdf. Accessed 24 Jan 2020
Air quality monitoring program at the port of Los Angeles: year thirteen data summary May 2017–April 2018 (2018). https://monitoring.cleanairactionplan.org/wp-content/uploads/2019/07/POLA-13th-Annual-Monitoring-Report-May-2017-April-2018.pdf. Accessed 23 Jan 2020
Air quality monitoring program at the port of Long Beach: annual summary report calendar year 2018 (2019). https://monitoring.cleanairactionplan.org/wp-content/uploads/2019/07/POLB-Summary-Annual-Report-for-2018-PDF.pdf. Accessed 23 Jan 2020
Inventory of air emissions for calendar year 2018 [port of Los Angeles] (2019). https://kentico.portoflosangeles.org/getmedia/0e10199c-173e-4c70-9d1d-c87b9f3738b1/2018_Air_Emissions_Inventory. Accessed 18 Oct 2019
Port of Long Beach - 2018 air emissions inventory (2019). http://www.polb.com/civica/filebank/blobdload.asp?BlobID=15271. Accessed 10 Oct 2019
San Pedro Bay ports emissions inventory methodology report - Version 1 (2019). http://www.polb.com/civica/filebank/blobdload.asp?BlobID=15032. Accessed 26 Aug 2019
Acciaro, M., et al.: Environmental sustainability in seaports: a framework for successful innovation. Marit. Policy Manag. 41(5), 480–500 (2014). https://doi.org/10.1080/03088839.2014.932926
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the 5th Annual Workshop on Computational Learning Theory - COLT 1992, pp. 144–152. ACM Press (1992). https://doi.org/10.1145%2F130385.130401
Brandner, H., Lessmann, S., Voß, S.: A memetic approach to construct transductive discrete support vector machines. Eur. J. Oper. Res. 230(3), 581–595 (2013). https://doi.org/10.1016/j.ejor.2013.05.010
Browning, L., Bailey, K.: Current methodologies and best practices for preparing port emission inventories. https://www3.epa.gov/ttnchie1/conference/ei15/session1/browning.pdf. Accessed 30 Dec 2019
Budipriyanto, A., Wirjodirdjo, B., Pujawan, N., Gurning, S.: Berth allocation problem under uncertainty: a conceptual model using collaborative approach. Procedia Manuf. 4, 429–437 (2015). https://doi.org/10.1016/j.promfg.2015.11.059
Daun, T.J., Braun, D.G., Frank, C., Haug, S., Lienkamp, M.: Evaluation of driving behavior and the efficacy of a predictive eco-driving assistance system for heavy commercial vehicles in a driving simulator experiment. In: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pp. 2379–2386. IEEE (2013). https://doi.org/10.1109/ITSC.2013.6728583
Daun, T.J., Lienkamp, M.: Spielend Fahren: Gamification-Konzept für Fahrerassistenzsysteme (2012). https://nbn-resolving.org/urn/resolver.pl?urn:nbn:de:bvb:91-epub-20120000-1129558-0-3
Dorrer, C.: Effizienzbestimmung von Fahrweisen und Fahrerassistenz zur Reduzierung des Kraftstoffverbrauchs unter Nutzung telematischer Informationen: Stuttgart, Univ., Diss., 2003, Schriftenreihe des Instituts für Verbrennungsmotoren und Kraftfahrwesen der Universität Stuttgart, vol. 24. Expert-Verl., Renningen (2004)
Eglese, R., Bektas, T.: Green vehicle routing. In: Toth, P., Vigo, D. (eds.) Vehicle Routing: Problems, Methods, and Applications, 2nd edn., pp. 437–459. SIAM, Philadelphia (2014)
Gatta, V., Marcucci, E., Sorice, F., Tretola, G.: A gamification approach to promote positive behaviours in urban logistics (2015). https://www.researchgate.net/publication/282609851_A_Gamification_approach_to_promote_positive_behaviours_in_Urban_Logistics. Accessed 5 Jan 2020
Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L.A. (eds.): Feature Extraction. Springer, Heidelberg (2006). https://doi.org/10.1007/978-3-540-35488-8
Heilig, L., Lalla-Ruiz, E., Voß, S.: Multi-objective inter-terminal truck routing. Transp. Res. Part E Logist. Transp. Rev. 106, 178–202 (2017)
Heilig, L., Stahlbock, R., Voß, S.: From digitalization to data-driven decision making in container terminals. arXiv preprint arXiv:1904.13251
Heilig, L., Voß, S.: Inter-terminal transportation: an annotated bibliography and research agenda. Flex. Serv. Manuf. J. 29(1), 35–63 (2016). https://doi.org/10.1007/s10696-016-9237-7
Lalla-Ruiz, E., Heilig, L., Voß, S.: Environmental sustainability in ports. In: Sustainable Transportation and Smart Logistics, pp. 65–89. Elsevier (2019). https://doi.org/10.1016/B978-0-12-814242-4.00003-X
Lam, J.S.L., Van de Voorde, E.: Green port strategy for sustainable growth and development. In: Proceedings of the International Forum on Shipping, Ports and Airports (IFSPA), pp. 27–30 (2012)
Lessmann, S., Voß, S.: Feature selection in marketing applications. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds.) ADMA 2009. LNCS (LNAI), vol. 5678, pp. 200–208. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03348-3_21
Mollick, E.R., Rothbard, N.: Mandatory fun: gamification and the impact of games at work. SSRN Electron. J. (2013). https://doi.org/10.2139/ssrn.2277103
O’Reilly, C.A., Cromarty, A.S.: Fast is not real-time: Designing effective real-time AI systems. In: Gilmore, J.F. (ed.) Applications of Artificial Intelligence II. SPIE Proceedings, pp. 249–257. SPIE (1985). https://doi.org/10.1117/12.948443
Parolas, I.: ETA predictions for container ships at the port of Rotterdam using machine learning techniques (2016). https://repository.tudelft.nl/islandora/object/uuid%3A9e95d11f-35ba-4a12-8b34-d137c0a4261d
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Rommerskirchen, C.: Verbrauchsreduzierung durch Fahrerassistenz unter dem Einfluss von Langzeitnutzung und Situationskomplexität. Dissertation, Technische Universität München, München (2018)
Umang, N., Bierlaire, M., Erera, A.L.: Real-time management of berth allocation with stochastic arrival and handling times. J. Sched. 20(1), 67–83 (2016). https://doi.org/10.1007/s10951-016-0480-2
van Mierlo, J., Maggetto, G., van de Burgwal, E., Gense, R.: Driving style and traffic measures-influence on vehicle emissions and fuel consumption. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 218(1), 43–50 (2004). https://doi.org/10.1243/095440704322829155
Werbach, K.: (Re)defining gamification: a process approach. In: Spagnolli, A., Chittaro, L., Gamberini, L. (eds.) PERSUASIVE 2014. LNCS, vol. 8462, pp. 266–272. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07127-5_23
Yu, J., Tang, G., Song, X., Yu, X., Qi, Y., Li, D., Zhang, Y.: Ship arrival prediction and its value on daily container terminal operation. Ocean Eng. 157, 73–86 (2018). https://doi.org/10.1016/j.oceaneng.2018.03.038
Zhen, L., Chang, D.F.: A bi-objective model for robust berth allocation scheduling. Comput. Ind. Eng. 63(1), 262–273 (2012). https://doi.org/10.1016/j.cie.2012.03.003
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Cammin, P., Sarhani, M., Heilig, L., Voß, S. (2020). Applications of Real-Time Data to Reduce Air Emissions in Maritime Ports. In: Marcus, A., Rosenzweig, E. (eds) Design, User Experience, and Usability. Case Studies in Public and Personal Interactive Systems. HCII 2020. Lecture Notes in Computer Science(), vol 12202. Springer, Cham. https://doi.org/10.1007/978-3-030-49757-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-49757-6_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49756-9
Online ISBN: 978-3-030-49757-6
eBook Packages: Computer ScienceComputer Science (R0)