Estimated Time Of Arrival Research Articles

Integrating arrival time estimation in truck scheduling: an explorative study in grocery retailing

ABSTRACT This paper studies a new managerial approach that integrates predictive trucks’ estimated time of arrival (ETA) into truck scheduling to deal with the truck arrival time uncertainty. The approach exploits ETA to reschedule trucks by the up-to-date information and aims to minimize the trucks’ waiting time. The approach was applied in a real case. Results quantified the impact of adopting ETA for truck scheduling, also showing how the ETA accuracy and peak truck arrival rate affect the total trucks’ waiting time. The paper enriches both truck scheduling literature, investigating a new managerial approach to deal with truck arrival time uncertainty, which greatly threatens the synchronization of logistics flows, and Industry 4.0 literature, showing how real-time data availability can change traditional decision-making processes. Insights for the approach application are discussed, together with the economic, environmental, and social benefits by supply chain actors (i.e. carriers, warehouse managers, and drivers).

Cross-Pacific Vessel Estimated Time of Arrival and Next Destination Prediction with Automatic Identification System Data

With the increase in global trade uncertainty and supply chain disruptions, accurately predicting the estimated time of arrival (ETA) of container vessels can effectively help carriers, terminals, and freight forwarders improve operational efficiency. The Asia-North America route has been recently under stress because of strikes and trade wars between the U.S. and China. Voyages are subject to multiple external factors leading to uncertainty in arrival times. This is especially true for cross-Pacific voyages, where long distances without intermediate port visits allow for a large feasible set of trajectories and vessel speed profiles. Large errors in ETA prediction not only hinder the effective planning and execution of other stakeholders but also lead to significant fluctuations in the types and quantities of goods arriving at the port, thereby hindering port competitiveness and efficient multimodal transportation. Existing literature focuses on estimating ETA and next positions for dense, compact areas at the vicinity of ports. We propose and evaluate model framework based on artificial neural networks (ANN) fed by automatic identification system (AIS) historical data to predict the next destination and ETA for cross-Pacific routes for cases where ETA from the captain is missing in the AIS data. Results show our model can effectively predict next destination and ETA of vessels, achieving a mean absolute error value of 4 h when the vessel is 1,500 nmi away from the port. For comparison, the ANN submodules are replaced with gradient boosted trees, providing similar results. We terminate by highlighting the challenges found to improve the model.

Raising data availability and quality for improved disruption and carbon footprint management through a novel approach to primary data sharing: Virtual Watch Tower / VWT

Abstract The global supply chain and logistics industry is a self-organizing ecosystem consisting of numerous actors that work together to move goods from end to end. The different stakeholders involved are usually interdependent organizations, like freight forwarders, carriers, terminals, and homeland security agencies and information exchange between them is required to coordinate the activities along the individual transport chains. However, the exchange of information has often been analog, flawed, late, and incomplete. New circumstances, like unprecedented supply chain disruptions, new regulatory requirements around greenhouse gas (GHG) emissions, and generally growing shipper expectations create an urgent need for improved data sharing between actors. New technologies, like digital platforms, networks, and architectures as well as social media, mobile, analytics, cloud, and internet of things (SMACT) have brought some improvements, but not the required holistic digital perspective required or expected. Averages and approximations are usually insufficient to close data gaps, e.g., only primary data allows for accurate GHG emission calculations. Primary data sharing is widely seen as the missing piece of the puzzle. Primary data is data from the source providing an accurate state and picture of a situation. Primary data sharing at scale requires a new form of digital collaboration. We propose a rethink of digital collaboration as a means for broader primary private data sharing for complete end-to-end datasets and data quality, particularly focusing on the sharing of data associated with both transport plans and progress made in the respective movements of goods for more better disruption and carbon footprint management through more accurate calculations of estimated times of arrival (ETA) and GHG emissions. We introduce an example for digital collaboration in end-to-end supply chains that is focusing specifically on primary data sharing. The new thinking around digital collaboration manifests itself in the Virtual Watch Tower / VWT initiative (www.virtualwatchtower.org). In 2022, RISE and Singapore Maritime Institute signed a collaboration agreement focused on innovation in shipping. The VWT initiative is the first collaborative project under the umbrella of this partnership. The VWT is led by RISE, A*STAR, and VTT. VWT is a community-driven, digitally empowered initiative, a cargo owner-driven, and terminal-centric approach for improved supply chain management. It is the users themselves who co-create and co-evolve the solution that they need. The initiative aims to create a community that shapes the digital tool (VWTnet) they need to reach the required higher levels of visibility. This, through primary data sharing across the supply chain ecosystem and between actors (VWT Users) participating in individual end-to-end transports (VWT Shipments). The VWT serves as an object of research to hone the new thinking and understand the implications of its implementation.

The offload study: A prospective observational study of the time to offload a patient from an ambulance or helicopter into an emergency department or percutaneous coronary intervention centre

Introduction Providing an accurate estimated time for patient handover in the emergency department (ED) can be difficult from the scene. The time taken to offload a patient from a conveying ambulance and transfer into the resuscitation room is often not considered within the estimated time of arrival (ETA). The aim of this study was to assess offload times for pre-alerted patients conveyed by a UK Helicopter Emergency Medical Service (HEMS) by air and road ambulance. Methods A prospective observational study was conducted at The Air Ambulance Service (TAAS) from August 2022 to February 2023. Consecutive emergency patients who were pre-alerted to hospital and arrived at an ED or Percutaneous Coronary Intervention (PCI) centre with an escorting air ambulance crew member were included. The offload time (time between hospital arrival and handover of the patient) was measured using a stopwatch. Results Data was available for 228 patients arriving at 18 hospitals and 6 helipads. The median offload time by road ambulance was 2 min 34 s. The range of helicopter offload times was from 4 min 20 s to 21 min 37 s, which mainly depended on the location of the helipad. Conclusion An additional 2–3 min should be added to the ETA by road ambulance. There is large variation in helicopter offload times depending on the helipad location (secondary/offsite helipad taking longer than a primary rooftop helipad or primary same-level helipads). HEMS services should review their own offload times at local sites to be able to provide a more accurate ETA.

Spatial digital twin framework for overheight vehicle warning and re-routing system

Overhead road obstacles present a significant logistical hazard to the heavy vehicle industry. Traditional overheight vehicle warning systems such as passive warning systems (PWS) and active warning systems (AWS) have not adequately reduced the frequency and impact of overheight incidents, encouraging transportation agencies to employ intelligent transport system (ITS) strategies using state-of-the-art advanced technologies. This research takes an innovative approach in developing an immersive user-focused experience, harnessing multi-disciplinary methods and tools to engineer a spatial digital twin prototype for a novel Internet-of-Things (IoT)-based active warning alert and re-routing system (AWARS). LiDAR and 3D GIS were used to model the complex road environment, tailored to the strict fiscal objectives sought by economically mindful organisations. Tree crowns were extracted from near-Infrared aerial imagery and digital elevation models, supplying the dimensions necessary for 3D tree modelling. IoT connectivity was configured using a real-time analytics approach to deliver alerts and re-routing options. The World Traffic Service with live and predictive traffic data was used for the routing application programming interface (API). A standard-configuration common rigid truck (CRT) was inserted into the 3D road environment model to simulate overheight collisions and to ascertain the effect of re-routing on estimated time of arrival (ETA). Longer ETA durations were observed for routes computed by the digital twin. Theoretically, enhanced situational awareness and subsequent reduction of risk likelihood suggests an optimised response to industry demands, despite extended travel times, cultivating a favorable impact on the supply chain through enhanced safety management.

Bus Priority Procedure for Signalized Intersections Based on Bus Occupancy and Delay

This study proposes a bus prioritisation strategy at signalised intersections to enhance public transport reliability and attractiveness. Nowadays, bus prioritisation at intersections is conducted according to a first-come, first-served principle, lacking compatibility with future vehicle-to-infrastructure communication. A framework for prioritising buses based on their delay and occupancy was developed and tested in a SUMO microscopic traffic simulation subnetwork of the city of Ingolstadt. Buses are prioritised using a 25-level hierarchy. Four degrees of prioritisation interventions are implemented based on bus priority levels with signal cycles adjusted to advance preferred green phases. The timing of the prioritisation is based on an Estimated Time of Arrival (ETA) prediction that considers past speed and travel time data as well as bus stops that are on the way to the intersection. The prioritisation logic was tested in simulation scenarios with on- and off-peak conditions and with several buses requesting priority and varying degrees of priority. The results show that the developed prioritisation concept works, and prioritised buses benefit from a strong reduction in their travel times (up to 87 %) and number of stops. Buses with lower priority levels may experience deterioration in their travel time (up to 126 %) when arriving at the same time as a high-priority bus, but considering the fewer affected passengers and smaller delay, this seems acceptable.

SmallMap: Low-cost Community Road Map Sensing with Uncertain Delivery Behavior

Accurate road networks play a crucial role in modern mobile applications such as navigation and last-mile delivery. Most existing studies primarily focus on generating road networks in open areas like main roads and avenues, but little attention has been given to the generation of community road networks in closed areas such as residential areas, which becomes more and more significant due to the growing demand for door-to-door services such as food delivery. This lack of research is primarily attributed to challenges related to sensing data availability and quality. In this paper, we design a novel framework called SmallMap that leverages ubiquitous multi-modal sensing data from last-mile delivery to automatically generate community road networks with low costs. Our SmallMap consists of two key modules: (1) a Trajectory of Interest Detection module enhanced by exploiting multi-modal sensing data collected from the delivery process; and (2) a Dual Spatio-temporal Generative Adversarial Network module that incorporates Trajectory of Interest by unsupervised road network adaptation to generate road networks automatically. To evaluate the effectiveness of SmallMap, we utilize a two-month dataset from one of the largest logistics companies in China. The extensive evaluation results demonstrate that our framework significantly outperforms state-of-the-art baselines, achieving a precision of 90.5%, a recall of 87.5%, and an F1-score of 88.9%, respectively. Moreover, we conduct three case studies in Beijing City for courier workload estimation, Estimated Time of Arrival (ETA) in last-mile delivery, and fine-grained order assignment.

Adaptive Mobile Recharge Scheduling With Rapid Data Sharing in Wireless Rechargeable Networks

The recent breakthrough in Wireless Power Transfer (WPT) provides a promising way to prolong network lifetime by employing a charging vehicle to replenish energy. Data transmissions from nodes typically happen in response to physical sensory events, leading to time-varying energy consumption. To improve charging efficiency, the existing schemes collect energy information by employing a data-gathering vehicle or data collection protocol. However, in duty cycle networks, these schemes either incur extra vehicles or high data collection delay. To solve this problem, we propose an mobile adaptive charging scheme with rapid data sharing (rShare), which establishes multi-layer collection trees and collects overall energy data to the vehicle. A spatial predicted active sending (SPAS) algorithm is proposed for distant nodes to actively estimate the future position and transmit their data to cover potential positions of the charging vehicle, which significantly reduces data collection delay. We also propose an estimated time of arrival (ETA)-aware scheme based on the TSP Nearest Neighbor algorithm that updates the charging path based on the collected data. Extensive simulation results demonstrate that our scheme outperforms the state-of-the-arts in terms of dead node avoidance with less communication overhead.

An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival

Sustainable concepts for on-demand transportation, such as ridesharing or ridehailing, require advanced technologies and novel dynamic planning and prediction methods. In this paper, we consider the prediction of taxi trip durations, focusing on the problem of the estimated time of arrival (ETA). ETA can be used to compute and compare alternative taxi schedules and to provide information to drivers and passengers. To solve the underlying hard computational problem with high precision, machine learning (ML) models for ETA are the state of the art. However, these models are mostly black box neural networks. Hence, the resulting predictions are difficult to explain to users. To address this problem, the contributions of this paper are threefold. First, we propose a novel stacked two-level ensemble model combining multiple ETA models; we show that the stacked model outperforms state-of-the-art ML models. However, the complex ensemble architecture makes the resulting predictions less transparent. To alleviate this, we investigate explainable artificial intelligence (XAI) methods for explaining the first- and second-level models of the ensemble. Third, we consider and compare different ways of combining first-level and second-level explanations. This novel concept enables us to explain stacked ensembles for regression tasks. The experimental evaluation indicates that the considered ETA models correctly learn the importance of those input features driving the prediction.

Automatic Identification System-Based Prediction of Tanker and Cargo Estimated Time of Arrival in Narrow Waterways

In maritime logistics, accurately predicting the Estimated Time of Arrival (ETA) of vessels is pivotal for optimizing port operations and the global supply chain. This study proposes a machine learning method for predicting ETA, drawing on historical Automatic Identification System (AIS) data spanning 2018 to 2020. The proposed framework includes a preprocessing module for extracting, transforming, and applying feature engineering to raw AIS data, alongside a modeling module that employs an XGBoost model to accurately estimate vessel travel times. The framework’s efficacy was validated using AIS data from the Port of Houston, and the results indicate that the model can estimate travel times with a Mean Absolute Percentage Error (MAPE) of just 5%. Moreover, the model retains consistent accuracy in a simplified form, pointing towards the potential for reduced complexity and increased generalizability in maritime ETA predictions.

Trajectory Mining and Routing: A Cross-Sectoral Approach

Trajectory data holds pivotal importance in the shipping industry and transcend their significance in various domains, including transportation, health care, tourism, surveillance, and security. In the maritime domain, improved predictions for estimated time of arrival (ETA) and optimal recommendations for alternate routes when the weather conditions deem it necessary can lead to lower costs, reduced emissions, and an increase in the overall efficiency of the industry. To this end, a methodology that yields optimal route recommendations for vessels is presented and evaluated in comparison with real-world vessel trajectories. The proposed approach utilizes historical vessel tracking data to extract maritime traffic patterns and implements an A* search algorithm on top of these patterns. The experimental results demonstrate that the proposed approach can lead to shorter vessel routes compared to another state-of-the-art routing methodology, resulting in cost savings for the maritime industry. This research not only enhances maritime routing but also demonstrates the broader applicability of trajectory mining, offering insights and solutions for diverse industries reliant on trajectory data.

A time-series approach for estimated time of arrival prediction in autonomous vehicles

Autonomous Vehicles are expected to play a pivotal role in the future of transportation. While huge accomplishments have been conducted in this field during the recent years, the main field of relevant research is primarly private transportation. Public transport (PT), especially in urban areas, although equally important, is lagging behind when the technological breakthroughs are considered. One of the main PT desiderata is punctual arrival. Therefore, this paper deals with the Estimated Time of Arrival (ETA) issue, tackled as a time-series problem, using contemporary Gradient Boosting (GB) methods, in order to benefit both commuters and stakeholders. The GB methods used are eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machines (LightGBM), and Categorical Boosting (CatBoost). This study proposes a competitive ETA methodology for Autonomous Shuttles, providing encouraging results both in the field of Research & Innovation for Autonomous Vehicles and Public Transport, being able to be expanded for commercial purposes.

넙치 질병 증상 분류를 위한 객체 탐지 딥러닝 모델 성능 평가

Paralichthys olivaceus accounts for a large proportion, accounting for more than half of Korea's aquaculture industry. However, about 25-30% of the total breeding volume throughout the year occurs due to diseases, which has a very bad impact on the economic feasibility of fish farms. For the economic growth of Paralichthys olivaceus farms, it is necessary to quickly and accurately diagnose disease symptoms by automating the diagnosis of Paralichthys olivaceus diseases. In this study, we create training data using innovative data collection methods, refining data algorithms, and techniques for partitioning dataset, and compare the Paralichthys olivaceus disease symptom detection performance of four object detection deep learning models(such as YOLOv8, Swin, Vitdet, MvitV2). The experimental findings indicate that the YOLOv8 model demonstrates superiority in terms of average detection rate (mAP) and Estimated Time of Arrival (ETA). If the performance of the AI model proposed in this study is verified, Paralichthys olivaceus farms can diagnose disease symptoms in real time, and it is expected that the productivity of the farm will be greatly improved by rapid preventive measures according to the diagnosis results

Predictive Dispatch of Volunteer First Responders: Algorithm Development and Validation.

Smartphone-based emergency response apps are increasingly being used to identify and dispatch volunteer first responders (VFRs) to medical emergencies to provide faster first aid, which is associated with better prognoses. Volunteers' availability and willingness to respond are uncertain, leading in recent studies to response rates of 17% to 47%. Dispatch algorithms that select volunteers based on their estimated time of arrival (ETA) without considering the likelihood of response may be suboptimal due to a large percentage of alerts wasted on VFRs with shorter ETA but a low likelihood of response, resulting in delays until a volunteer who will actually respond can be dispatched. This study aims to improve the decision-making process of human emergency medical services dispatchers and autonomous dispatch algorithms by presenting a novel approach for predicting whether a VFR will respond to or ignore a given alert. We developed and compared 4 analytical models to predict VFRs' response behaviors based on emergency event characteristics, volunteers' demographic data and previous experience, and condition-specific parameters. We tested these 4 models using 4 different algorithms applied on actual demographic and response data from a 12-month study of 112 VFRs who received 993 alerts to respond to 188 opioid overdose emergencies. Model 4 used an additional dynamically updated synthetic dichotomous variable, frequent responder, which reflects the responder's previous behavior. The highest accuracy (260/329, 79.1%) of prediction that a VFR will ignore an alert was achieved by 2 models that used events data, VFRs' demographic data, and their previous response experience, with slightly better overall accuracy (248/329, 75.4%) for model 4, which used the frequent responder indicator. Another model that used events data and VFRs' previous experience but did not use demographic data provided a high-accuracy prediction (277/329, 84.2%) of ignored alerts but a low-accuracy prediction (153/329, 46.5%) of responded alerts. The accuracy of the model that used events data only was unacceptably low. The J48 decision tree algorithm provided the best accuracy. VFR dispatch has evolved in the last decades, thanks to technological advances and a better understanding of VFR management. The dispatch of substitute responders is a common approach in VFR systems. Predicting the response behavior of candidate responders in advance of dispatch can allow any VFR system to choose the best possible response candidates based not only on ETA but also on the probability of actual response. The integration of the probability to respond into the dispatch algorithm constitutes a new generation of individual dispatch, making this one of the first studies to harness the power of predictive analytics for VFR dispatch. Our findings can help VFR network administrators in their continual efforts to improve the response times of their networks and to save lives.

Probabilistic Forecasting of Bus Travel Time with a Bayesian Gaussian Mixture Model

Accurate forecasting of bus travel time and its uncertainty is critical to service quality and operation of transit systems: it can help passengers make informed decisions on departure time, route choice, and even transport mode choice, and it also support transit operators on tasks such as crew/vehicle scheduling and timetabling. However, most existing approaches in bus travel time forecasting are based on deterministic models that provide only point estimation. To this end, we develop in this paper a Bayesian probabilistic model for forecasting bus travel time and estimated time of arrival (ETA). To characterize the strong dependencies/interactions between consecutive buses, we concatenate the link travel time vectors and the headway vector from a pair of two adjacent buses as a new augmented variable and model it with a mixture of constrained multivariate Gaussian distributions. This approach can naturally capture the interactions between adjacent buses (e.g., correlated speed and smooth variation of headway), handle missing values in data, and depict the multimodality in bus travel time distributions. Next, we assume different periods in a day share the same set of Gaussian components, and we use time-varying mixing coefficients to characterize the systematic temporal variations in bus operation. For model inference, we develop an efficient Markov chain Monte Carlo (MCMC) algorithm to obtain the posterior distributions of model parameters and make probabilistic forecasting. We test the proposed model using the data from two bus lines in Guangzhou, China. Results show that our approach significantly outperforms baseline models that overlook bus-to-bus interactions, in terms of both predictive means and distributions. Besides forecasting, the parameters of the proposed model contain rich information for understanding/improving the bus service, for example, analyzing link travel time and headway correlation using covariance matrices and understanding time-varying patterns of bus fleet operation from the mixing coefficients. Funding: This research is supported in part by the Fonds de Recherche du Quebec-Societe et Culture (FRQSC) under the NSFC-FRQSC Research Program on Smart Cities and Big Data, the Canadian Statistical Sciences Institute (CANSSI) Collaborative Research Teams grants, and the Natural Sciences and Engineering Research Council (NSERC) of Canada. X. Chen acknowledges funding support from the China Scholarship Council (CSC). Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0214 .

Estimating package arrival time via heterogeneous hypergraph neural network

Estimated Time of Arrival (ETA) for packages plays an essential role in intelligent logistics. As a classic ETA method, Origin–Destination-based (OD-based) ETA predicts the delivery time only based on the attributes (i.e., sender address, receiver address, seller, and payment time) of packages under the condition that the delivery route is unavailable. However, existing OD-based methods only exploit attributes associated with an individual order, which fails to model the higher-order interactions within orders and attributes, and fail to sufficiently exploit the graph-structure knowledge (i.e., relation of orders and attributes) and feature-based knowledge (i.e., statistical properties) of orders simultaneously, resulting in inaccurate predictions. In this paper, we propose a novel Heterogeneous HyperGraph Neural Network (H2GNN) for estimating package arrival time. Specifically, to better capture the high-order interactions within orders and attributes, we construct an order heterogeneous hypergraph that utilizes hyperedges to represent orders and nodes to represent order attributes. Besides, we extend the hypergraph learning for large-scale e-commerce data by Hyper-GraphSAGE. Overall, H2GNN can provide informatively representations of packages while preserving both structure-based knowledge learned by hypergraph and feature-based knowledge captured by Transformer. Experimental results on large-scale Alibaba logistics data demonstrate the superior performance of H2GNN compared to the baselines.

ETA INVESTIGATIONS ON SUT SMART TRANSIT IN RED ROUTE USING ITS AND ARTIFICIAL INTELLIGENT TECHNOLOGY

Large urban community requires convenient public transportation system. In-coming of Intelligent Transportation System (ITS) and newest communication technology produce a fast development of conventional transportation system. Focus on mass transportation system, vehicle equips with commuication devices and GPS modules which transmit latitute, longtitute, ground speed to network or cloud. Estimated Time of Arrival (ETA) were computed by received data on cloud. As result, ETA show to passensgers via mobile application or LCD TV in bus stops. This work presents investigation of ETA error using V2N/ITS technology. Moreover, AI technology was selected to vehicle classification in red route internal bus service period. Finally, ETA error of service period indicates 5-10 Minutes due to traffic especially rush hour in the red routes.

A composite indicator of liveability based on sociodemographic and Uber quality service dimensions: A data-driven approach

This article aims is to create a liveability index that combines traditional population-based datasets and non-traditional data sources from Uber services, potentially supporting when pursuing new perceptions of a city. For that, a comprehensive study was performed focusing on Natal, a Brazilian urban center that has important socioeconomic and public/private transport services inequalities. In order to assess intra-municipal disparities, the units of analysis were neighborhoods and intra-neighborhoods (the Human Development Unit - HDU), which represents small homogeneous areas in socioeconomic terms. A data-driven workflow was conducted to process more than 4 million trips and to retrieve the Estimated Time of Arrival (ETA), considered in this work as a proxy for the Uber quality service. In order to evaluate variations, associations and spatial autocorrelation between ETA and sociodemographic features, different methods were applied, notably the Exploratory Data Analysis (EDA), the Exploratory Spatial Data Analysis (ESDA), and the Regression Analysis. From the results obtained by these methods and the application of a Principal Component Analysis (PCA), a Composite Indicator of Liveability (CIL) could be created and validated as a measure fitted to sociodemographic characteristics, as it describes high values in wealthier regions and lower values in the poorest areas of a city.

Effective Mimicry of Bélády’s MIN Policy

This paper introduces the Mockingjay cache replacement policy, which advances the state-of-the-art by producing single-core performance that approaches that of Bélády’s MIN policy, and which does so with a surprisingly simple solution. The basic idea is to predict a line’s reuse distance so that when a line is inserted into the LLC, its ETA (Estimated Time of Arrival) can be computed. Lines are then evicted based on their ETA ordering. This paper describes the Mockingjay policy and explains the key ideas that make it successful.

A Ship Route Planning Method under the Sailing Time Constraint

This paper realizes the simultaneous optimization of a vessel’s course and speed for a whole voyage within the estimated time of arrival (ETA), which can ensure the voyage is safe and energy-saving through proper planning of the route and speed. Firstly, a dynamic sea area model with meteorological and oceanographic data sets is established to delineate the navigable and prohibited areas; secondly, some data are extracted from the records of previous voyages, to train two artificial neural network models to predict fuel consumption rate and revolutions per minute (RPM), which are the keys to route optimization. After that, speed configuration is introduced to the optimization process, and a simultaneous optimization model for the ship’s course and speed is proposed. Then, based on a customized version of the A* algorithm, the optimization is solved in simulation. Two simulations of a ship crossing the North Pacific show that the proposed methods can make navigation decisions in advance that ensure the voyage’s safety, and compared with a naive route, the optimized navigation program can reduce fuel consumption while retaining an approximately constant time to destination and adapting to variations in oceanic conditions.