The increasing severity of river drought poses a potential threat to the operations of ports using rivers as hinterland connections. River droughts have impacted navigation in various rivers worldwide, including the Rhine river where instances of low water levels in 2018 and 2022 caused disruptions in navigation. As a consequence, inland vessels were forced to reduce their cargo loads. Seaport terminals that serve as crucial links between sea vessels and inland vessels may be impacted by changes in fleet composition, thereby affecting a terminal's cargo handling operations and storage. However, there is a lack of research quantifying the effects of low river levels on seaport processes and determining suitable methods to assess this impact using vessel movement data.

To address this research gap, this study aims to quantify the impact of low river levels on seaport processes by focusing on the EMO dry bulk terminal within the Port of Rotterdam as a case study. Given the connection between this port and the Rhine, which is known for its vulnerability to drought and serves as a vital route for inland navigation, the analysis begins by examining the impact of reduced river discharge on the dry bulk fleet sailing from Rotterdam to Germany utilising Information and Tracking System for Shipping (IVS) data. Additionally, Automatic Identification System (AIS) data is employed to assess the service time, the number of vessels and the berth occupancy within the terminal to quantify the impact of low river discharge on the cargo handling process within the terminal. Specifically investigating the effects on vessels being loaded and bound to the hinterland and those being unloaded after arriving from the sea. Finally, the study analyses the impact on the storage capacity of the EMO terminal using monthly cargo data provided by the terminal, by studying the balance between the amount of cargo being unloaded and loaded.

The findings show that vessels sailing along the Rhine are required to reduce their load as discharge decreases, leading to an increased number of vessels. However, this compensation by more vessels does not fully offset the load losses, resulting in a decrease in the total load carried per day when discharge decreases. Consequently, more vessels arrive at the EMO loading berths during low discharge periods to compensate for the reduced load and therefore loading time for vessels at these berths is shortened. Due to the increased vessel arrivals, the berth occupancy at the loading berths increases to a maximum of 65%. On the contrary, the berth occupancy at the unloading berths remains unaffected. Cargo flow analysis shows that the lowest cargo loading into inland vessels occurs during months without low discharge, rather than during the months with the lowest discharge. Additionally, the share of rail transport increases during low discharge months but remains below maximum capacity, indicating sufficient slack to handle all cargo. The stockpile analysis reveals a small surplus during low discharge months, although not as significant as in months with normal discharge levels.

Overall, the findings indicate that the large size of the EMO terminal allows it to withstand the impacts of past periods of low river discharge. As the loading berth's occupancy is not at its maximum capacity and the unloading berths remain unaffected, there is sufficient slack to accommodate additional vessels. Accordingly, adequate vessels are loaded to transport cargo to the hinterland, ensuring no impact on the stockpile and thus maintaining the storage operations at the EMO terminal without disruption.

While this research provides valuable insights, it is important to acknowledge that terminals of varying sizes or handling different cargo types may experience different impacts and should be subject to further investigation. Additionally, the limited availability of AIS and cargo data, with only one year of extreme drought (2022) for analysis, prevents drawing definitive trends and making long-term assumptions for future scenarios. Nevertheless, this study's methodology can be adopted in related studies to analyse more terminals and gain further insights.

Lock passage can potentially make up a third of a vessel’s travel time for inland waterway transport and therefore has a large impact on inland waterway and multimodal transport networks.
To reduce the overall environmental and economical impact of transport, there is an increasing interest in inland waterway transport relative to road and rail transport. To keep up with this shift towards inland waterway transport, models are created for the optimal arrangement of multimodal transport and transport on inland waterway networks. Hence, lock operations need to be carefully modelled.
The modelling of lock passage is often based on historical data and generalised for all the locks on the network. However, there are uncertainties in historical data, especially with the prospect of an increasing fleet size and more extreme seasonal changes. Additionally, locks can vary in functionality (e.g. recreational or professional use), operability and strategy (e.g. filling and emptying), structural design (e.g. capacity of lock chamber), and environmental conditions (e.g. water level differences).
These variations impact the passage time of phases in the locking cycle. An overgeneralised validation for a simulation model can result in inaccurate simulations for a specific lock.
Therefore, it is desired to use more lock specific data, which can be achieved by applying vessel specific data in the form of AIS (Automatic Identification Systems). AIS, live location data of professional vessels, is used in waterway traffic management, but is also collected for research purposes. The data is easily accessible for a desired period of time and area.
Literature studies show that analyses and validations of simulation models is a common practice with the combination of GPS based data. Some studies use AIS data around locks, only to find the total passage time of the vessel. A lock passage can be divided in more phases that impact the total passage and waiting time of a vessel, examples are the entering and exiting of the vessel and the
operating time of the lock itself.
The objective of this study is to find a generic method to derive validation parameters for simulation models of locks. Data derived from this study can further be used in the optimisation of lock passages in simulation models. In this way, any desired lock and for any circumstances (e.g. seasonal changes or periods of maintenance) a validation can be performed.
A method was created to translate AIS data to information that is relevant for a lock. This method enabled us to analyse the total passage time and the phases of a lock passage that impact this passage time. Based on the AIS data over a certain time period and considering the geometry of the lock, trajectories of passing vessels were derived. These movements were combined into lock cycles
and therefore the corresponding validation parameters could be identified. One example of these validation parameters was the lock operating time, which is estimated based on the principle that all vessels in the lock chamber stopped moving.
The lock specific data can be used to compare the data with the performance of a simulation model of a lock and period in time. This applies for models that only use the total passage time of a vessel, which in some situations might be sufficient. This also applies to models that consider more detailed lock passage. Since the method is based on positional data, the boundaries of the lock sections can be adjusted to the definitions used in the model.
The method is applied on a cases study of the Volkerak and Kreekrak locks. Firstly, the AIS data translation is performed for a lock to create data sets of the lock passages. Also, data of the water level at the lock is linked to each locking cycle. Secondly, this data is compared to a lock simulation in the same situation. The arrival rates, arrival speed, vessel dimensions and water level difference
are used as input to create a base simulation for various configurations. The lock simulation module of OpenTNSim is used because this is an open-source transport network simulation model developed at TU Delft.
The simulations are compared to the collected data, for a total of 18 segments on the trajectory passing the lock. The segments correspond to the phases of a locking cycle. The average passage time of each segment is compared between the vessels of the simulated and the collected data. Because of the large sample sizes, these comparisons created a view on the overall performance of the simulation for each segment.
The information found in the base simulations was further used to calibrate the OpenTNSim package. The vessel speed was adjusted on the approach and leaving of the lock chamber to match the trajectory of the arriving vessels. Also, the filling time of the the lock chamber was optimised for the simulation.
The Volkerak had a total of 24,446 vessels passing over the time period, this is comparable to the 24,570 vessels that were counted by Rijkswaterstaat in the same period. Three cases were selected to compare the simulation model, one vessel passing without waiting, one vessel passing with waiting and two vessels passing. These resulted in sample sizes of 943, 89 and 561 lock cycles respectively. The case study shows that, with a small sample size of 89 locking cycles, there can be large variability in passage times.
The largest deviations of the simulations relative to the collected data were found in the passage time of the segments between the waiting area and the lock chamber. Another deviation was found in the lock operating time for the case of two vessels passing.
The method used can give a large and useful data set of vessels passing a certain lock. The lock data can be used for statistical analyses. An example includes the number of vessels per locking cycle or the entering time or speed. Also, each locking cycle can be assessed and visualised individually or for a desired time span.
When the data is used for the validation of a simulation model, a large data set is needed to give reliable results. After all there can be large outliers and the performance of the lock is dependent on human interaction, the lock operator and the vessel’s captain. A combination of the AIS based data with other additional data can expand the method.
In conclusion, a highly suitable method was found that enables to use AIS data for the validation of a simulation model of a lock. The method is sufficient for most applications of a lock simulation model, despite being limited to just the movement of the vessels. However, when an accurate simulation of a lock phase like the closing of the gate is desired, other data sources might be more suitable. While this research has a focus on the comparison of the collected data to a lock simulation, the data can also be used for a statistical analysis of the lock when looking at fleet composition, arrival rates and stopping distances.

The pressure to reduce emissions in the sector of inland shipping is increasing. Especially considering emissions of environmental pollutants, the increasing pressure gives rise to the question how to get insight into emissions distributions along an inland waterway network. In this research, a bottom-up method is developed that is able to map the potential CO2, PM10 and NOx emissions levels of a single inland vessel on a certain waterway network, as a function of time and space. This method uses the dimensions of the ship, its speed and the waterway characteristics to estimate the energy consumption and corresponding emissions of a vessel. This method can be used in the support of development and evaluation of emission reduction policies. To illustrate the potential of this method, it is applied to a case study: the inland fleet on the Rotterdam-Antwerp corridor, one of the main transport axes in the Netherlands. The developed method is applied to observed AIS data, to map the current potential emission patterns (‘t0 emission scenario’). In addition, a model has been developed that simulates the ‘t0’ case. This model serves as a tool to assess alternative measures to reduce emissions.