Inverse Control Constrained Optimization of Vessel Speed Decisions Under Environmental Risk: Evidence from Arctic Shipping
- URL: http://arxiv.org/abs/2605.27270v1
- Date: Tue, 26 May 2026 16:46:27 GMT
- Title: Inverse Control Constrained Optimization of Vessel Speed Decisions Under Environmental Risk: Evidence from Arctic Shipping
- Abstract summary: Tug Tow and Cargo balance operational speed with environmental and ecological considerations.<n>Several vessel groups, including Fishing, Passenger, and Unspecified vessels, are strongly influenced by ice related risk.<n>The dominant status, under way using engine, displays a clear trade off, whereas other statuses, such as aground and undefined, are strongly shaped by ice related constraints.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Understanding how decision makers balance operational efficiency with environmental and ecological risks is central to vessel navigation. We model vessel speed as a control variable in a constrained optimization framework in which vessel operators balance multiple competing objectives, including transit efficiency, ice related navigational risk, and whale related ecological risk. The underlying risk parameters are estimated using over 14 million Automatic Identification System (AIS) observations from the United States Arctic (2010-2019), together with environmental covariates and spatially explicit whale density estimates. The framework incorporates a nonlinear risk objective, vessel heterogeneity, and regularization to ensure stable and interpretable results.The inferred trade offs reveal distinct decision making patterns across vessel groups and navigational statuses. Vessel types such as Tug Tow and Cargo balance operational speed with environmental and ecological considerations. In contrast, several vessel groups, including Fishing, Passenger, and Unspecified vessels, are strongly influenced by ice related risk, while Pleasure Craft and Tankers exhibit higher sensitivity to whale related risk. Across navigational status categories, similar heterogeneity is observed. The dominant status, under way using engine, displays a clear trade off, whereas other statuses, such as aground and undefined, are strongly shaped by ice related constraints. Statuses including restricted maneuverability and engaged in fishing exhibit higher estimated sensitivity to whale related risk, though with substantial uncertainty.Sensitivity analysis indicates that increasing whale-related risk weighting produces limited changes in model-implied optimal speed, whereas increasing ice-related risk leads to more consistent reductions.
Related papers
- Behavioural Signatures of Risk-Sensitive Decision-Making in Large Language Models [121.70781851874035]
Large language models (LLMs) are increasingly used in decision support.<n>It is important to understand whether their choices under uncertainty exhibit stable and interpretable behavioural regularities.<n>Here we examine this question using a controlled multi-model framework based on no-limit Texas Hold'em.
arXiv Detail & Related papers (2026-07-11T10:33:39Z) - Action-Conditioned Risk Gating for Safety-Critical Control under Partial Observability [79.08785366532287]
We propose a lightweight risk-gated reinforcement learning approximation for risk-sensitive control under partial observability.<n>We evaluate the approach in two safety-critical partially observable domains: automated glucose regulation and safety-constrained navigation.
arXiv Detail & Related papers (2026-05-14T01:23:09Z) - Fusing Driver Perceived and Physical Risk for Safety Critical Scenario Screening in Autonomous Driving [20.663269212781426]
We propose a driver risk fusion based hazardous scenario screening method for autonomous driving.<n>During training, the method combines an improved Driver Risk Field with a dynamic cost model to generate high quality risk supervision signals.<n>During inference it directly predicts scenario level risk scores through fast forward passes, avoiding per frame risk computation.
arXiv Detail & Related papers (2026-03-07T13:24:23Z) - COLREGs Compliant Collision Avoidance and Grounding Prevention for Autonomous Marine Navigation [8.778606523987198]
Maritime Autonomous Surface Ships (MASS) are increasingly regarded as a promising solution to address crew shortages, safety, and operational efficiency in the maritime industry.<n>This paper presents a unified motion planning method for MASS that achieves compliance with International Regulations for Preventing Collisions at Sea (COLREGs) and prevention requirements.
arXiv Detail & Related papers (2026-03-03T00:11:26Z) - A Gradient Boosted Mixed-Model Machine Learning Framework for Vessel Speed in the U.S. Arctic [0.0]
We analyzed Automatic Identification System (AIS) data from 2010-2019 to examine vessel speed over ground (SOG)<n>We applied a two-stage machine learning framework, first modeling the probability of SOG greater than zero and then modeling SOG conditional on being positive.<n>Results: Distance to coast and bathymetric depth were dominant determinants of both the likelihood and magnitude of vessel speed, while changes in course, vessel group, and navigational status introduced secondary variation.
arXiv Detail & Related papers (2026-01-31T14:53:28Z) - A Theoretical Framework for Environmental Similarity and Vessel Mobility as Coupled Predictors of Marine Invasive Species Pathways [0.3277163122167433]
Marine invasive species spread through global shipping and generate substantial ecological and economic impacts.<n>Traditional risk assessments require detailed records of ballast water and traffic patterns, which are often incomplete.<n>This work advances a theoretical framework that quantifies invasion risk by combining environmental similarity across ports with observed and forecasted maritime mobility.
arXiv Detail & Related papers (2025-11-05T14:31:39Z) - Multi-vessel Interaction-Aware Trajectory Prediction and Collision Risk Assessment [0.3277163122167433]
We present a transformer-based framework for multi-vessel trajectory prediction with integrated collision risk analysis.<n>We evaluate the model on large-scale real-world AIS data using joint multi-vessel metrics.
arXiv Detail & Related papers (2025-09-01T23:38:01Z) - HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks [51.95824566163554]
We introduce HACSurv, a survival analysis method that learns Hierarchical Archimedean Copulas structures.<n>By capturing the dependencies between risks and censoring, HACSurv improves the accuracy of survival predictions.
arXiv Detail & Related papers (2024-10-19T18:52:18Z) - Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models [5.109551157014016]
Aquatic non-indigenous species (NIS) pose significant threats to biodiversity, disrupting ecosystems and inflicting substantial economic damages across agriculture, forestry, and fisheries.
This study develops a new physics-informed model to forecast maritime shipping traffic between port regions worldwide.
arXiv Detail & Related papers (2024-01-23T21:22:51Z) - A Counterfactual Safety Margin Perspective on the Scoring of Autonomous
Vehicles' Riskiness [52.27309191283943]
This paper presents a data-driven framework for assessing the risk of different AVs' behaviors.
We propose the notion of counterfactual safety margin, which represents the minimum deviation from nominal behavior that could cause a collision.
arXiv Detail & Related papers (2023-08-02T09:48:08Z) - Cautious Adaptation For Reinforcement Learning in Safety-Critical
Settings [129.80279257258098]
Reinforcement learning (RL) in real-world safety-critical target settings like urban driving is hazardous.
We propose a "safety-critical adaptation" task setting: an agent first trains in non-safety-critical "source" environments.
We propose a solution approach, CARL, that builds on the intuition that prior experience in diverse environments equips an agent to estimate risk.
arXiv Detail & Related papers (2020-08-15T01:40:59Z) - A General Framework for Survival Analysis and Multi-State Modelling [70.31153478610229]
We use neural ordinary differential equations as a flexible and general method for estimating multi-state survival models.
We show that our model exhibits state-of-the-art performance on popular survival data sets and demonstrate its efficacy in a multi-state setting.
arXiv Detail & Related papers (2020-06-08T19:24:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.