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.
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.