AI-powered port call predictions
Enhance operational efficiency and savings with precise port call predictions.
|Category||Container | Dry Bulk | Marine|
Solving Global Challenges in Maritime Logistics
Uncertainty in the arrival times of a vessel causes many problems; longer waiting times, higher costs, port congestion, etc. There have long been attempts to tackle the challenges posed by data management in maritime logistics. These problems unfortunately still occur globally every day. Studies have identified that better predictability and information sharing is required to boost operational efficiency.
As a response to global challenge in maritime logistics, Awake.AI has launched Prediction API to improve the competitiveness in maritime logistics, boost the efficiency of port operators’ routine activities, support the development of automation, help to anticipate exceptional circumstances, and reduce environmental emissions.
Operational Efficieny and Savings
Awake.AI’s ETA prediction API provides global predictions on vessel voyages. The API can be called for individual vessels to obtain machine learning – optimized predictions on where the vessel is headed currently, arrival times for current and even future port calls, and remaining travel times for vessels not currently under way.
The API can also be called by destination, providing schedule predictions on all vessels currently detected to be headed to the selected port. The prediction service has global coverage, enabling automated monitoring and visibility of future arrivals up to several weeks ahead for oceangoing vessels.
See more use case examples below:
Tangible business benefits
Assisted by Awake.AI’s ETA prediction API you’ll create accurate plans and use predictions to warn you if plans will be disrupted. Operate with accuracy by having resources at the right place at the right time and adjust your plans on-the-fly with the help of the predictions.
With the increasing focus on environmental issues in maritime logistics you’ll be able to reach emission goals with reduced vessel turnaround times.
Achieve concrete savings by utilizing the industry’s best machine learning powered predictions.
AccuracyCustom machine learning models are trained for each customer destination to optimize prediction accuracy. These enable reducing variation in prediction results (less than 5% prediction error) caused for example by location-specific changes in vessel speed.
CoverageGlobal coverage is provided for predicting current vessel destinations and arrival times, and remaining travel times for vessels not currently under way. Predictions can be queried per vessel or per port.
DocumentationFull swagger style API documentations are available and kept up-to-date with each release. Our support will assist you with API integration and accurate service uptime status and planned maintenance information is always available at our status site.
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The ETA API provides continuously updated predictions on vessel arrival times to relevant waypoints at ports, such as pilot boarding places and berth areas. Predictions can be queried per vessel or per port, providing a collection of predictions for all vessels currently approaching the selected port. The predictions are available globally and their accuracies are optimized using machine learning models. The prediction service setup includes configuration of target prediction locations and optimization of prediction models as needed. After the configuration phase, you will receive an API key to access the prediction service.
We are continuously improving the predictions by integrating additional data sources relevant for vessel traffic, for example global public port call planning data, vessel data, or data on port areas and facilities. If you would like to collaborate on this, contact awake.ai.
We provide high-availability REST APIs with continuously updated technical documentation and status monitoring available online.
Swagger documentation: https://api-docs.awake.ai/voyages-api/index.html
System status page: https://status.awake.ai/
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