Main problem addressed:

Growth in international trade and the construction of larger container ships have had a major impact on road operations for the entry and exit of trucks to/from ports, sometimes causing significant levels of congestion, which in turn have led to high levels of pollution.

Current situation:

Although ports know the containers that are unloaded from a vessel, they do not know the real date of departure of those containers from their facilities since this date depends on whether these containers will be inspected by Customs, as well as on their final destination, days off without terminal storage costs, type of cargo (e.g. dangerous goods), among others.

Despite the cruciality of this information to help port managers with their planning processes, there is no AI tool that predicts the number of trucks that are going to come or depart from the port on a given day. This lack of valuable information means that appropriate measures cannot be taken to mitigate congestion and the levels of pollution and accidents caused by it.

Outcome:

This pilot case study intends to integrate different platforms, sensor networks and sources of information to predict the date and time of entry and departure of trucks using predictive analytics and business intelligence tools. In this way, and by achieving high accuracy in the predictions made, it will be possible to determine how many trucks/hour will leave and enter the port at a certain future date and time. With this knowledge, port managers will be able to anticipate possible congestion problems in the port, making smart decisions (e.g., enabling more exit/entry lanes, allocating more resources, spreading transport requests across a time period, carrying out operations such as massive transfers at a certain time of day, etc.) that will avoid congestion as well as the consequences that it generates: pollution increase, higher accident probability, and more.

Main problem addressed:

The ports of Venice and Chioggia are located inside a lagoon with shallow water and low visibility mainly due to fog. Fog in the lagoon area is quite common and has caused in recent years the closure of the core port of Venice for 10 days/year on average. In addition, in the near future the MoSE barrier will start being operative to protect the city of Venice from high tides and this will cause the physical closure of the port entrance.

Current situation:

Available network of sensors from many public bodies including North Adriatic Sea Port Authority (NASPA). They locally provide port users with a clear picture of real-time weather conditions. Notwithstanding this currently available service, the need to further improve and update the sensor network is clear in order to fully cover the whole ship channel system, thus increasing its effectiveness; to provide a geographically complete service for the safety of navigation and to increase the efficiency of port operations.

Outcome:

In this pilot case study, the PEP Platform will integrate different platforms, sensor networks and sources of information to predict the closure of the Port of Venice due to tide, wind, fog, and consequently to optimize date and time of entry and departure of ships using predictive analytics and big data tools.

This prediction system will allow the ships a safer and more efficient organisation of their trip, avoiding the only solution currently put in place: to close the port with very short notice entailing consequent changes of navigation routes or long waiting times for vessels that were about to call at it and as a result, economic and port performance loss.

As a result of the pilot, NASPA will provide real-time information to the navigation sector and will create a predictive model for the port traffic optimization. The goal is to allow ships to have real-time knowledge of the weather condition and visibility, and thus take measures about the single trip; and to intervene in the port efficiency level, to effectively improve it by means of the assumption provided by the new forecasting model.

Main problem addressed:

Port authorities and city councils in port cities will need to make sure that ships calling at their ports will be complying with international and European environmental regulation after 1 January 2020 by having adapted one of the possible technological compliance solutions.

Current situation:

Several Port Authorities would be interested in controlling that ships are complying with environmental regulation and would be ready to define incentive schemes and financial bonuses to be applied to those shipping companies whose vessels are truly less polluting (i.e. Environmental Ship Index). At the moment, it is not possible to do so as ports lack the equipment that could monitor specifically and individually each vessel and report in a reliable and consistent way on the emissions generated by each individual ship calling at the port.

Outcome:

This pilot case study will integrate information from highly-innovative measurement cameras of the different gases emitted by vessels operating in the port of Valencia (first pilot in Europe with this type of technology), air quality measurement sensors (new and existing) both in Valencia and in Piraeus, wind sensors in Valencia and Piraeus, information from the requested port calls made by the shipping companies in Valencia and Piraeus and other shipping information databases.

A highly-innovative optical imaging camera system will be piloted in this case study in the core port of Valencia.

With the piloted technologies, port managers will be able to know the exact quantity of emissions generated by a particular ship (depending on its gas composition and the total number of hours of stay) through selected queries to the PEP platform. In addition, by modelling three datasets: 1) information on the emissions of vessels that call in the port, 2) the planned windows for the vessels to arrive and operate in the port in the next days / weeks and 3) weather forecast (e.g. wind factor and direction); the PEP platform will be able to predict air quality levels in a near future date and time. These air quality predictions will be of great interest to the port authority, city council and other government institutions.

Main problem addressed:

Port infrastructures, particularly in the Mediterranean area territories, are often an integral part of coastal cities, deeply merged in the urban texture. Harbours contain several noise sources, including ferries, cruise ships, containerships, other types of ships, industries and shipyards as well as auxiliary services. Such activities strongly impact the environment of the surrounding area and, as a consequence, local population, port workers and tourists as well as both terrestrial and marine ecosystems.

Current situation:

Ports, as industrial activities, are subject to the EU Environmental Noise Directive (2002/49/EC), which provides a basis for measuring actions aimed to reduce noise emitted by the major sources. Along with the typical port activities generating noise, many other factors contribute to increase sound pressure level such as the behaviour of the drivers within the port area, traffic jams mainly on quays, the age of port vehicles and cargo handling on vehicles.

Outcome:

The pilot case study will integrate noise sensors that will be able to measure how much noise is generated by ships calling at specific berths at the port and due to the loading and unloading operations of certain vessels. The pilot case study will include effective predictive models capable of indicating which would be the worst and best scenarios that could occur depending on the expected port calls, the weather conditions foreseen, the berths where the vessels will be moored, the handling means (cranes and other equipment) foreseen for each operation and the number of trucks/trains foreseen to collect the containers unloaded by the vessel.

Thanks to the measurement of the different levels of noise generated by operations on specific vessels, port equipment and the different areas/activities of the port, the Port Authority will be able to have more data to give incentives to those shipping companies and terminals that opt for less noisy and less polluting ships and machinery.

Main problem addressed:

Maritime ports are affected by waves’ movements and currents that make difficult the loading and unloading operations of vessels at times. In the case of a containership, loading and unloading operations are done with ship-to-shore (STS) gantry cranes, which use a special mechanism called spreader to hook the corners of the boxes. Consequently, when there are movements of the ship due to bad weather conditions, it gets harder to be able to hook the spreader into the container and, therefore, productivity decreases notably. In addition, when a port has lower productivity levels, vessels are forced to spend more time at berth, increasing the quantity of emissions, noise and other environmental externalities.

Current situation:

Currently operating systems focus on meteorological information only and the link with port operations systems has not been created yet. Therefore, even though port managers are aware of the impact that the state of the sea has on port operations, they do not have the appropriate tool that helps them to understand what type of movements are those that reduce productivity to a greater or lesser extent. In this regard, the lack of systems integration means that port managers cannot alert shipping companies when lower productivity levels are expected due to bad weather conditions.

Outcome:

This pilot case study will use the PEP platform to link the datasets coming from meteorological existing systems, from the Terminal Operating System in use in the container terminals in Bremerhaven and Wilhemshaven and from Port Community System of dbh and Port Management Information Systems already operational for the port communities in both ports (Bremerhaven and Wilhemshaven). By being able to model together big data originated by all these systems, a predictive model on how ship-to-shore crane productivity is affected by wave agitation, currents and wind will be estimated, and warnings on expected reductions in port productivity up to 48h prior to the event occurrence will be sent to affected parties such as terminal operators and sea carriers.

Once this information is reported, shipping companies will be able to adjust the “berth window” in which they call at the ports, reducing as far as possible the length of the ship’s stay in port and the number of polluting emissions that these ships generate.

Main problem addressed:

Transport emissions in a multimodal transport chain are not currently being monitored from the point of origin to their final destination. Therefore, shippers do not have real information on the environmental impact of transporting their products using different modes of transport.

Current situation:

The reality is that at present, information on complete real measurements of a transport chain combining different modes of transports is not available for shippers. Although a trading company may know the carbon footprint generated at some point of the supply chain, it is not possible for shippers to know the impact in terms of emissions of transporting a specific product from the point of origin to its final destination (door-to-door carbon footprint) for different transport alternatives. As a result, they cannot assess this info at the time of choosing their transport solution and labelling products for consumers to evaluate this information as part of their purchasing decision is obviously not possible either.

Outcome:

This pilot case study will evaluate the impact in terms of emissions of a series of goods from the time they are loaded in the warehouse of origin to the time they are unloaded in the warehouse of destination. A series of sensors and emission cameras will be installed so that carbon emissions can be determined for each of the products that are transported. Thanks to this pilot case study, companies in the retailing sector will be able to inform their customers about the door-to-door carbon footprint of the products to be purchased in the company’s supermarkets. The notification form will be made through special stickers (i.e. green labelling) that will be downloaded from the PEP Platform and attached to the final products that will be sold in the supermarket.

Additionally, by measuring the carbon footprint of a given product, shippers will be able to compare different multimodal transport chains, being able to select those that provide greater efficiency and lower environmental impact.