Abstract
In this paper, we present a method, based on model predictive control (MPC), to reduce the impact of pollutant emissions in contexts where a port is located within a city. To this purpose, we first introduce a dynamic model of the interactions between truck flows generated by the port and general mobility traffic in the shared urban infrastructure at the port-city interface. In order to keep track of the multiclass and complex nature of the system, the model takes advantage of microsimulation and deep learning for the prediction of road network traffic and related pollutant emissions. Then, we define a MPC control scheme exploiting the proposed model, to be used in real time to maintain the emissions levels below a certain threshold by appropriately adjusting traffic inflows from the port to the city, which represent the controls optimized by the MPC procedure. A simulation case study, involving the port of Genova in north-west Italy, is presented to showcase the ability of the proposed MPC scheme to control emissions in the shared area, also in complex situations such as transitions to mobility rush hours.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The amount of trucks and general mobility vehicles are generically expressed throughout the paper in equivalent large vehicle units [lveh] and general vehicle units [gveh], respectively, where “equivalent” refers to an arbitrary standard reference for both categories.
- 2.
References
Alessandri, A., Cervellera, C., Gaggero, M.: Predictive control of container flows in maritime intermodal terminals. IEEE Trans. Control Syst. Technol. 21(4), 1423–1431 (2013)
Caballini, C., Gracia, M., Mar-Ortiz, J., Sacone, S.: A combined data mining-optimization approach to manage trucks operations in container terminals with the use of a TAS: application to an Italian and a Mexican port. Transp. Res. Part E Logistics Transp. Rev. 142, 102054 (2020)
Cervellera, C., Gaggero, M., Macció, D.: A receding horizon approach for berth allocation based on random search optimization. In: Advances in Optimization and Decision Science for Society, Services and Enterprises, pp. 1–12. Springer, Berlin (2019)
Cervellera, C., Macciò, D., Parisini, T.: Learning robustly stabilizing explicit model predictive controllers: a non-regular sampling approach. IEEE Control Syst. Lett. 4(3), 737–742 (2020)
Cervellera, C., Macciò, D., Rebora, F.: Copula-based scenario generation for urban traffic models. Expert Syst. Appl. 210, 118389 (2022)
Cervellera, C., Macciò, D., Rebora, F.: Receding-horizon dynamic optimization of port-city traffic interactions over shared urban infrastructure. In: Optimization and Decision Science: Operations Research, Inclusion and Equity. Springer International Publishing, Cham (2022, to appear)
Cervellera, C., Macció, D., Rebora, F.: Deep learning and low-discrepancy sampling for surrogate modeling with an application to urban traffic simulation. In: Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2021)
Cervellera, C., Macció, D., Rebora, F.: Improving the variability of urban traffic microsimulation through the calibration of generative parameter models. J. Intell. Transp. Syst. 1–13 (2021). https://doi.org/10.1080/15472450.2021.1932496
Do, L., Taherifar, N., Vu, H.: Survey of neural network-based models for short-term traffic state prediction. WIREs Data Min. Knowl. Discov. 9(1), e1285 (2019)
Dorokhin, S., Artemov, A., Likhachev, D., Novikov, A., Starkov, E.: Traffic simulation: an analytical review. IOP Conf. Ser. Mater. Sci. Eng. 918(1) (2020)
Fialkin, V., Veremeenko, E.: Characteristics of traffic flow management in multimodal transport hub (by the example of the seaport). Transp. Res. Procedia 20, 205–211 (2017)
Kong, Y., Liu, J.: Sustainable port cities with coupling coordination and environmental efficiency. Ocean Coast. Manag. 205(105534), 1–13 (2021)
Loder, A., Ambühl, L., Menendez, M., Axhausen, K.: Understanding traffic capacity of urban networks. Sci. Rep. 9(16283), 1–10 (2019). https://doi.org/10.1038/s41598-019-51539-5
Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: The 21st IEEE International Conference on Intelligent Transportation Systems. IEEE (2018)
Nugraha, A., Waterson, B., Blainey, S., Nash, F.: Unravelling the dynamics behind the urban morphology of port-cities using a LUTI model based on cellular automata. Comput. Environ. Urban Syst. 92(101733), 1–13 (2022)
Preston, G., Horne, P., Scaparra, M., O’Hanley, J.: Masterplanning at the port of Dover: the use of discrete-event simulation in managing road traffic. Sustainability 12(1067), 1–19 (2020)
Shankar, R., Choudhary, D., Jharkharia, S.: An integrated risk assessment model: a case of sustainable freight transportation systems. Transp. Res. Part D 63, 662–676 (2018)
Sirmatel, I., Geroliminis, N.: Economic model predictive control of large-scale urban road networks via perimeter control and regional route guidance. IEEE Trans. Intell. Transp. Syst. 19(4), 1112–1121 (2018)
Zhang, X., Zeng, Q., Yang, Z.: Optimization of truck appointments in container terminals. Marit. Econ. Logistics 21(1), 125–145 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Cervellera, C., Macciò, D. (2024). Model-Predictive Control of Traffic Emissions in Port-City Environments. In: Bruglieri, M., Festa, P., Macrina, G., Pisacane, O. (eds) Optimization in Green Sustainability and Ecological Transition. ODS 2023. AIRO Springer Series, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-031-47686-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-47686-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47685-3
Online ISBN: 978-3-031-47686-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)