Microscopic traffic simulators are important tools for studying transportation systems as they describe the evolution of traffic to the highest level of detail. A major challenge to microscopic simulators is the slow simulation speed due to the complexity of traffic models. We have developed the Scalable Microscopic Adaptive Road Traffic Simulator (SMARTS), a distributed microscopic traffic simulator that can utilize multiple independent processes in parallel. SMARTS can perform fast large-scale simulations. For example, when simulating 1 million vehicles in an area the size of Melbourne, the system runs 1.14 times faster than real time with 30 computing nodes and 0.2s simulation timestep. SMARTS supports various driver models and traffic rules, such as the car-following model and lane-changing model, which can be driver dependent. It can simulate multiple vehicle types, including bus and tram. The simulator is equipped with a wide range of features that help to customize, calibrate, and monitor simulations. Simulations are accurate and confirm with real traffic behaviours. For example, it achieves 79.1% accuracy in predicting traffic on a 10km freeway 90 minutes into the future. The simulator can be used for predictive traffic advisories as well as traffic management decisions as simulations complete well ahead of real time. SMARTS can be easily deployed to different operating systems as it is developed with the standard Java libraries.

Running large-scale behavioral simulations requires high computational power, which can be acquired by distributing computation workload to multiple computing nodes (i.e., workers) that run in parallel. The implementations of such systems commonly follow the Bulk Synchronous Parallel (BSP) model. However, implementations using BSP usually suffer from the straggler problem, where the delay of any worker slows down the entire simulation. The problem usually occurs due to communication delays or imbalanced workload among workers. To mitigate the straggler problem, we propose a novel parallel computational model, called Priority Synchronous Parallel (PSP) model. PSP exploits data dependencies of parallel processes to determine high priority data to be computed and synchronized while computing the remaining data. PSP is implemented and evaluated using traffic simulations for three large cities. The proposed technique shows significant performance improvements over the BSP model.

Spatial simulations have been widely used to study real-world environments, such as transportation systems. Applications like prediction and analysis of transportation require the simulation to handle millions of objects while running faster than real time. Running such large-scale simulation requires high computational power, which can be provided through parallel distributed computing. Implementations of parallel distributed spatial simulations usually follow a bulk synchronous parallel (BSP) model to ensure the correctness of simulation. The processing in BSP is divided into iterations of computation and communication, running on multiple workers, followed by a global barrier synchronisation to ensure that all communications are concluded. Unfortunately, the BSP model is plagued by the straggler problem, where a delay in any worker slows down the entire simulation. Stragglers may occur for many reasons, including imbalanced workload distribution or communication and synchronisation delays. The straggler problem can become more severe with increasing parallelism and continuous change of workload distribution among workers. This article proposes methods to dynamically mitigate stragglers and tackle communication delays. The proposed strategies can rebalance the workload distribution during simulation. These methods employ the spatial properties of the simulated environments to combine a flexible synchronisation model with decentralised dynamic load balancing and on-demand resource allocation. All proposed methods are implemented and evaluated using a microscopic traffic simulator as an example of large-scale spatial simulations. We run traffic simulations for Melbourne, Beijing and New York with different straggler scenarios. Our methods significantly improve simulation performance compared to advanced methods such as global dynamic load balancing.

Underwater object detection faces significant challenges including uneven lighting, low contrast, and scattering-induced distortions. Existing underwater datasets are limited in scale, class diversity, and annotation consistency, which hinders robust model development. This work addresses these limitations by creating the UWOD dataset through the integration of seven publicly available underwater datasets, comprising over 107 K images with approximately 374 K annotations across 39 classes. We employ a semi-automatic annotation pipeline that combines manual labeling with iterative model-in-the-loop training to ensure high-quality ground truth, As a final verification step, all auto-generated labels were manually inspected and corrected as needed. We benchmark state-of-the-art object detectors—including YOLOv8, YOLOv7, YOLOv5, FCOS, EfficientDet, YOLOX, RT-DETR, SSD, and Faster R-CNN—establishing comprehensive performance baselines; YOLOv8 and YOLOv7 achieve the best accuracy–efficiency trade-off. Our transfer learning analysis shows that domain-specific pretraining substantially often outperforms pretraining on general-purpose datasets, yielding up to more than 50% improvement in low-data regimes versus training from scratch, with markedly lower seed-to-seed variance than scratch training. Sequential pretraining on COCO followed by UWOD achieves the strongest results on our most challenging dataset. Due to upstream licensing constraints, we release trained model weights and an automated annotation pipeline that encapsulate the learned underwater-domain knowledge, enabling immediate application to new imagery while respecting intellectual property.