The road freight transport market accounts for 40 \% of the carbon dioxide emissions of the transport sector globally. To reduce these emissions, battery electric trucks (BETs) have the greatest potential as they are zero-emission vehicles. However, compared to the private electric vehicle market, this market faces significant implementation barriers, as seen from the low BET sales. This is mainly due to the optimized market and low profit margins limiting freight carriers from investing in BETs. Furthermore, BETs face serious operational challenges due to long charging times and low operational flexibility. The way to charge the BET plays a big role in finding solutions to these challenges. In recent years, new technologies such as Electric Road Systems (ERS) and battery swapping have been developed next to depot charging and on-road charging. This study gains insight into the preferences of different freight carriers for charging alternatives, using a scenario-based multi-criteria decision analysis(MCDA). Four charging alternatives: depot charging, on-road charging, ERS, and battery swapping, are compared from a freight carrier's perspective on ten criteria identified through literature review, expert interviews, and a survey. The criteria are: Investment costs, operational costs, lifetime of the battery, charging time, operational flexibility, payload capacity, emission reduction, pioneering, complexity of implementation, and strategic policy alignment. To compare the charging alternatives, the criteria weights were obtained through the best-worst method and the analytic hierarchy process, and the performance of the alternatives on the criteria was calculated for daily distance use cases. These performances and weights were combined to arrive at a final ranking of alternatives. This was done for different freight carrier groups based on daily distance, sectors, company sizes, and countries of operation. The results show that financial and operational criteria were considered most important by all the segments of freight carriers. Furthermore, depot charging appeared to be the preferred alternative in most cases, and battery swapping scored best for larger daily distances. However, in almost all freight carrier groups, depot charging, ERS, and battery swapping had similar scores. This result advocates for stakeholders to consider investing in the charging alternatives to give the freight carrier the possibility to choose the charging alternative that is best for them. On-road charging had the lowest score for all the freight carrier groups. This result mismatches with the current investments in the charging alternative and showcases the need to reconsider these investments. Due to the number of criteria and the difficulties in obtaining many responses from freight carriers, the criteria performance and weights can be studied in more detail in future research. Also, using better weighing and evaluation methods and improved research on the four alternatives in a similar scenario can improve the quality of the results in future studies. Also, looking at the problem from different perspectives in future studies can provide great insight into which charging alternatives will shape the future of BETs.

Container terminals are vital hubs in global trade, facilitating the seamless transfer of goods between maritime and inland transportation networks. Truck turnaround time serves as a critical performance metric for container terminals, provides direct feedback on port congestion and efficiency. This study focuses on developing a predictive framework for truck turnaround time (TTT) at the Port of Rotterdam by integrating multi-source data and employing advanced machine learning techniques.
Previous studies on truck turnaround time prediction have largely relied on limited datasets and methodologies, such as utilizing historical truck arrival flows or terminal operation logs and using statistical methods. This research employs diverse datasets, including Bluetooth detection records, container arrival information, and environmental condition. By combining these data sources, a harmonized dataset was constructed to represent the complexities of port operations. A stacked Long Short-Term Memory (LSTM) network was employed as the predictive model, utilizing its ability to capture temporal dependencies and nonlinear interactions between variables. This approach allows for more comprehensive and accurate TTT predictions compared to conventional methods.
To process the noisy and incomplete Bluetooth data, a robust trip identification pipeline was developed. The pipeline employed spatial clustering, temporal filtering, and dual verification to accurately identify container truck trips, achieving an accuracy exceeding 90%. Using this processed data, the stacked LSTM model demonstrated superior predictive performance, effectively capturing periodic trends and long-term dependencies. Benchmarking results showed that the stacked LSTM outperformed traditional methods, including Random Forest and XGBoost. Sensitivity analysis highlighted the critical role of truck arrival flows and wind conditions in determining truck turnaround time variability.
In summary, this study provides a novel and scalable framework for TTT prediction, integrating multi-source data and advanced modeling techniques to address key limitations of existing approaches. The findings offer actionable insights for optimizing terminal operations and reducing congestion. Future research could focus on expanding data sources, enhancing model interpretability, and validating the framework across diverse port environments to ensure broader applicability.

The Automatic Identification System (AIS) is used in the maritime domain to improve sea traffic safety by requiring vessels to broadcast real-time information such as identity, speed, location, and course. As it allows global monitoring of almost any larger vessel and has the potential to considerably improve vessel traffic services and collision risk assessment, AIS has been used in an increasing number of applications. This emphasizes why the quality of data transmitted is critical. We are also becoming more aware of the possibilities of spoofing or fabrication of AIS data, which has a direct impact on the dependability of AIS data. This study looks into comparing video surveillance with Automatic Identification System (AIS) data to detect data overlap or spoofing in the maritime environment. An object detection model was proposed and trained to fill the research gap mentioned above. Data from video surveillance and AIS were collected. A comparison of the results of object detection and AIS data was performed using a Python script. The analysis assisted in identifying data variations and understanding the potential and constraints of the current Algorithm. Work for the future is suggested to tackle the current constraints.