The global application container market was valued at approximately USD 5.83 billion in 2024 and is expected to reach around USD 104.76 billion by 2034

ABSTRACT This paper describes a study of the dispatch planning/scheduling process for inbound containers handled with a reach stacker. Client container pickup is scheduled at least one day in advance for one of six two-hour time windows (six five-container-high stacks per time window) on a given day. A buffer area is available for the containers to be moved in when clients are being served. The aim of this study was to determine the conditions required to ensure that all the containers are dispatched within the scheduled time window and so meet the clients’ requirements. To this end, the performance indicators were identified and compared using simulations as an analytical tool. The results indicate that the shortest-processing-time (SPT) queueing discipline is preferable to the first-come-first-served (FCFS) discipline and that client arrivals can usefully be restricted to periods shorter than two hours in order to meet container-dispatch and service-quality objectives.

A linear mixed effects model was fit to the data using house as a random effect (n = 80 containers).

Container Side

## Overview

Container Side is a dataset for object detection tasks - it contains Container Side annotations for 360 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Container Labelling

## Overview

Container Labelling is a dataset for object detection tasks - it contains Containers annotations for 483 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

1) Data Introduction • This dataset provides images and information on industrial waste, especially Metal Containers.

2) Data Utilization (1) Metal Container Data has characteristics that: • The dataset contains images of various types of metal containers, making it useful for identifying and analyzing the characteristics of each container. • Provides information to understand the various types, uses, and characteristics of metal containers. (2) Metal Container Data can be used to: • Recycling and waste management: Developing systems to sort and manage metal containers for recycling purposes is useful for increasing waste management efficiency. • Logistics and Inventory Management: Helps create a system for tracking and managing metal container inventory in warehouses and logistics chains. • Durability and material analysis: Analyzes the durability and material of metal containers to help with quality control and material selection. •Develop recognition and classification models: AI models can be trained to recognize and classify different types of metal containers. For example, in an industrial application, when a user scans a container, the model can identify the type and purpose of that container.

Container Security for Insurers Market Outlook

According to our latest research, the container security for insurers market size reached USD 1.87 billion globally in 2024, driven by the rapid adoption of containerized applications across the insurance sector. The market is witnessing robust expansion, with a compound annual growth rate (CAGR) of 22.1% forecasted during the period 2025 to 2033. By 2033, the global container security for insurers market is expected to achieve a value of USD 13.97 billion. This growth is primarily fueled by escalating cyber threats, stringent regulatory requirements, and the digital transformation initiatives being aggressively pursued by insurance companies worldwide.

A significant growth factor for the container security for insurers market is the increasing reliance of insurance companies on microservices and containerized environments to modernize their IT infrastructure. Insurers are leveraging containers to enhance agility, scalability, and deployment speed of critical applications such as policy administration, claims management, and risk assessment. However, this shift also introduces new security vulnerabilities, particularly around container orchestration, image integrity, and runtime protection. As a result, insurers are prioritizing investment in advanced container security solutions that provide real-time threat detection, automated compliance monitoring, and seamless integration with DevSecOps pipelines. This trend is expected to accelerate as insurers expand their digital capabilities to meet evolving customer expectations and competitive pressures.

Another major driver of market growth is the stringent regulatory landscape governing the insurance industry. Regulatory bodies across North America, Europe, and Asia Pacific are mandating robust cybersecurity frameworks to safeguard sensitive policyholder data and ensure business continuity. Compliance requirements such as GDPR, HIPAA, and NAIC’s Insurance Data Security Model Law are compelling insurers to adopt comprehensive container security platforms that offer continuous vulnerability scanning, audit trails, and automated remediation capabilities. The growing frequency and sophistication of ransomware attacks targeting the insurance sector further underscore the need for proactive security measures, thereby propelling demand for container security solutions tailored specifically for insurers.

The proliferation of cloud-native technologies and hybrid deployment models is also catalyzing market expansion. As insurers migrate core workloads to public, private, and hybrid clouds, the attack surface becomes more complex and dynamic. Container security platforms that deliver unified visibility, policy enforcement, and threat intelligence across multi-cloud environments are gaining traction. Furthermore, the integration of artificial intelligence and machine learning into security solutions is enabling insurers to detect anomalous behavior, predict emerging threats, and automate incident response. These technological advancements, coupled with the rising adoption of containers in both greenfield and legacy modernization projects, are expected to sustain the upward trajectory of the container security for insurers market over the next decade.

From a regional perspective, North America currently dominates the container security for insurers market, accounting for the largest share in 2024. This leadership is attributed to the presence of major insurers, advanced IT infrastructure, and early adoption of containerization technologies. Europe and Asia Pacific are also witnessing rapid growth, driven by digital transformation initiatives, regulatory mandates, and increasing awareness of cybersecurity risks. Latin America and the Middle East & Africa, while smaller in market share, are expected to demonstrate significant growth potential as insurers in these regions accelerate their digital journeys and invest in modern security solutions.

Component Analysis

The changes in pressure, temperature, and filling rate inside LNG tank container are related to non-destructive storage time. In order to solve these problems, it was necessary to understand the distribution of temperature, pressure and evaporation in the LNG storage tank during transportation. The study on the relationship between the initial filling rate and safe storage time is still rare. Based on the principle of mass conservation and energy conservation, the pressure calculation model of the horizontal low-temperature LNG tank was established and verified by an experiment. A study was conducted on the LNG tank container in Golmud-Lhasa, and experimental data such as pressure changes in LNG tank containers during transportation were obtained. The storage calculation results obtained by the model were compared with the experimental data. The research results show that the simulated calculated values agree well with the experimental values, there was an “optimal initial filling rate” in the LNG tank. When the initial filling rate was less than the optimal initial filling rate, the safe storage time of the tank increases with the increase of the initial filling rate. When the initial filling rate was greater than the optimal initial filling rate, the safe storage time of the tank decreases with the increase of the initial filling rate.

This database includes the technical efficiency scores derived from a stochastic frontier model for 63 container ports in Latin America and the Caribbean.

Truck Container

## Overview

Truck Container is a dataset for object detection tasks - it contains Containers annotations for 4,703 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This repository contains additional data to be used with the implementation of the real-to-simulation framework of the paper Towards safe human-to-robot handovers of unknown containers. The data include pre-trained models and annotations of the 3D hand poses for selected recordings from the public training and testing sets of CORSMAL Container Manipulation (CCM) dataset. The pre-trained models are used for classifying the filling type and filling level of a container. 3D hand poses are annotated as 21 keypoints based on the OpenPose format.

Der globale Markt für Anwendungscontainer wurde im Jahr 2024 auf rund 5.83 Milliarden US-Dollar geschätzt und soll bis 2034 rund 104.76 Milliarden US-Dollar erreichen.

This dataset was used in the paper “A Standards-Based Approach to BIM-GIS Integration: Extending the Multi-Model Container Schema” to demonstrate Geo Link Extensions for the Multi-Model Container.

Le marché mondial des conteneurs d'applications était évalué à environ 5.83 milliards USD en 2024 et devrait atteindre environ 104.76 milliards USD d'ici 2034.

Container Freight Station Automation Software Market Outlook

According to our latest research, the global container freight station automation software market size reached USD 1.74 billion in 2024. The market is projected to grow at a robust CAGR of 13.2% from 2025 to 2033, with the forecasted market size expected to reach USD 5.13 billion by 2033. This impressive growth trajectory is fueled by the increasing digitization of supply chain operations and the rising demand for real-time visibility and operational efficiency in container freight stations worldwide.

One of the primary growth drivers for the container freight station automation software market is the rapid surge in global trade volumes, which has placed immense pressure on logistics operators to enhance throughput and reduce turnaround times. As ports and freight stations handle escalating container traffic, traditional manual processes have proven to be insufficient, often leading to bottlenecks and operational inefficiencies. Automation software is being rapidly adopted to streamline yard management, inventory control, and gate operations, thereby enabling operators to optimize resource allocation, minimize human errors, and improve overall productivity. The integration of advanced technologies such as IoT, AI, and machine learning within these software solutions is further propelling their effectiveness, providing actionable insights and predictive analytics that drive informed decision-making in real time.

Another significant factor contributing to the market’s expansion is the growing emphasis on regulatory compliance and security across the logistics ecosystem. Governments and international trade bodies are continually tightening regulations around customs clearance, cargo tracking, and documentation to combat smuggling, theft, and other security threats. Container freight station automation software addresses these challenges by automating documentation workflows, enhancing cargo visibility, and facilitating seamless data exchange among stakeholders, including customs authorities, shipping lines, and freight forwarders. This not only accelerates clearance processes but also reduces the risk of compliance-related penalties, making automation software an indispensable tool for modern freight stations.

The shift towards cloud-based deployment models has also emerged as a transformative trend in the container freight station automation software market. Cloud solutions offer unparalleled scalability, cost-efficiency, and accessibility, enabling logistics operators of all sizes to deploy sophisticated automation tools without heavy upfront investments in IT infrastructure. The cloud model supports remote access, real-time data synchronization, and seamless integration with third-party logistics platforms and IoT devices, further enhancing operational agility. As digital transformation initiatives gain momentum across the logistics sector, the adoption of cloud-based automation software is expected to accelerate, driving market growth over the forecast period.

Regionally, Asia Pacific continues to dominate the container freight station automation software market, driven by the presence of major transshipment hubs, rapid infrastructure development, and the proliferation of e-commerce. North America and Europe are also witnessing substantial growth, fueled by technological advancements and the increasing need for efficient supply chain management. Meanwhile, emerging markets in Latin America and the Middle East & Africa are gradually embracing automation to enhance their competitiveness in the global trade landscape. The regional outlook remains highly positive, with each geography contributing uniquely to the overall expansion of the market.

Component Analysis

The component segment of the container freight station automation software market is bifurcated into software and services. The software sub-segment encompasses a wide array of solutions tailored to address the specific needs of container freight stations, including inventory management, yard management, gate operations, and cargo tracking. These software solutions are increasingly incorporating advanced features such as predictive analytics, real-time alerts, and seamless integration with enterprise resource planning (ERP) systems. The demand for comprehensive software suites is rising as freight operators seek to automate end-to-end workflows, reduce operational complexities, and gain actionable insig

BackgroundAedes aegypti carries several viruses of public health importance, including the dengue virus. Dengue is the most rapidly spreading mosquito-borne viral disease in the world. Prevention and control of dengue mainly rely on mosquito control as there is no antiviral treatment or a WHO-approved vaccine. To reduce the Ae. aegypti population, studying the characteristics of their habitats is necessary. Aedes aegypti prefer breeding in artificial water holding containers in peridomestic or domestic settings. Their juveniles (1st - 4th instar larvae and pupae) have a tendency to cluster in certain types of containers. To inform control strategies, it is important to assess whether the pupae subgroup has a distinct distribution by container type as compared to the overall group of juveniles. The objective of this study was to assess for distinct predictors (location, season, and function) of Ae. aegypti juveniles and pupae numbers in water holding containers by applying hurdle model analyses.MethodologyThe field component of this study was carried out from November 2013 to July 2015 in Southern Taiwan where annual autochthonous dengue has been reported for decades. Water holding containers with stagnant water were identified in a predefined urban area in Kaohsiung City (KH) and a rural area in Pingtung County. Given that mosquito survey data often include many containers with zero Ae. aegypti, a negative binomial hurdle model was applied to model the association between location, seasonal and functional characteristics of the water holding containers and the number of Ae. aegypti in each container.ResultsThe results showed that Ae. aegypti were almost exclusively present in the urban area. In this area, the negative binomial hurdle model predicted significantly more juveniles as well as pupae Ae. aegypti in water holding containers during the wet season when compared to the dry season. Notably, the model predicted more juveniles in containers located on private property compared to those on government property, irrespective of season. As for pupae, the model predicted higher amounts in indoor containers used for water storage compared to outdoor water storage containers, irrespective of season. However, for the specific category ‘other water receptacle’, higher amounts of pupae were predicted in outdoor compared to indoor in water receptacles, such as flower pot saucers and water catchment buckets.ConclusionsThe difference in predictors for juveniles and the pupae subgroup was identified and it may be of importance to the control strategies of the authorities in KH. At present the authorities focus control activities on all water holding containers found on government property. To improve the ongoing control efforts in KH, the focus of control activities maintained by the KH authorities should be expanded to indoor water storage containers and outdoor water receptacles on both private and government properties to adequately address habitats harboring greater numbers of pupae. In addition, it is proposed to increase community engagement in managing water in all types of water holding containers located on privately owned properties (indoor and outdoor), especially during wet season.

This dataset contains comprehensive port call statistics from UNCTAD, tracking vessel traffic patterns, ship characteristics, and port efficiency metrics across global economies. The data covers key performance indicators including vessel age profiles, port turnaround times, and cargo capacity metrics.

Data Sources:

Collected from UNCTAD's Data Center (unctadstat.unctad.org)

Contains 4 original datasets concatenated into one unified view

Coverage:

803 port call records

Global coverage (Economy-level granularity)

Includes commercial market classifications

Key Metrics:

Vessel Characteristics: - Average age (years) - Size (Gross Tonnage - GT) - Cargo capacity (Deadweight Tonnage - DWT) - Container capacity (TEU)

Port Performance: - Median time in port (days) - Maximum Capacity Indicators: - Maximum vessel size (GT) - Peak cargo/container capacities

Potential Use Cases: - Benchmark port efficiency across regions - Analyze trends in vessel sizes and ages - Model port congestion using turnaround times - Study capacity evolution in container shipping

Data Notes: - Missing values occur where metrics are inapplicable (e.g., container capacity for non-container ships) - "period" field indicates reference year/quarter - CommercialMarket_Label identifies operator types (e.g., "Mainliner", "Feeder")

Container Tracking AI Market Outlook

According to our latest research, the global container tracking AI market size reached USD 1.8 billion in 2024, reflecting robust adoption of artificial intelligence across logistics and shipping sectors. The market is poised for accelerated growth, with a projected compound annual growth rate (CAGR) of 22.5% from 2025 to 2033, culminating in a forecasted value of USD 13.3 billion by 2033. The rapid expansion is primarily driven by the increasing demand for real-time visibility, operational efficiency, and security within the global supply chain ecosystem.

A major growth factor propelling the container tracking AI market is the intensifying need for real-time container visibility across complex, multi-modal supply chains. As global trade volumes surge and shipping networks become more intricate, businesses are seeking advanced AI-enabled solutions to monitor container locations, conditions, and estimated arrival times. This real-time intelligence enables companies to optimize fleet utilization, reduce demurrage costs, and enhance customer satisfaction by providing accurate delivery predictions. Additionally, the integration of AI with IoT devices and sensors has significantly improved the granularity and accuracy of container tracking, further fueling market expansion.

Another significant driver is the increasing emphasis on security and regulatory compliance within the shipping and logistics industry. The proliferation of high-value and sensitive cargo, coupled with stringent regulations on cargo safety and customs, has necessitated the adoption of AI-based container tracking systems. These solutions leverage machine learning and computer vision technologies to detect anomalies, unauthorized access, and potential security breaches in real time. By automating compliance checks and providing actionable insights, AI-driven container tracking systems help stakeholders mitigate risks, avoid penalties, and ensure adherence to international standards.

The market is also benefiting from the digital transformation initiatives undertaken by shipping companies, port operators, and logistics service providers. The shift towards smart ports and automated terminals has accelerated the deployment of AI-powered container tracking platforms, which streamline operations, reduce manual interventions, and lower operational costs. Furthermore, the growing adoption of cloud-based deployment models has made advanced container tracking solutions accessible to small and medium-sized enterprises (SMEs), democratizing the benefits of AI across the logistics value chain. As a result, the market is witnessing widespread adoption across various end-user segments, from large shipping conglomerates to niche e-commerce logistics providers.

From a regional perspective, Asia Pacific continues to dominate the global container tracking AI market, supported by the region’s massive port infrastructure, rising trade activities, and rapid technological adoption. North America and Europe follow closely, driven by mature logistics networks and stringent regulatory frameworks. Meanwhile, emerging markets in Latin America and the Middle East & Africa are witnessing increased investments in port modernization and digitalization, presenting lucrative opportunities for AI solution providers. The interplay of these regional dynamics is shaping the competitive landscape and driving innovation across the global container tracking AI ecosystem.

Component Analysis

The container tracking AI market is segmented by component into software, hardware, and services, each playing a pivotal role in the overall ecosystem. The software segment is witnessing the fastest growth, as AI-powered platforms become the backbone of container tracking operations. These platforms incorporate advanced algorithms for real-time data processing, anomaly detection, and predictive analytics, enabling users to derive actionable insights from vast amounts of tracking data. The increasing adoption of cloud-based and SaaS models has further democratized access to sophisticated software solutions, allowing even smaller logistics firms to leverage AI for container tracking.

On the hardware front, the market is characterized by the proliferation of IoT-enabled devices, sensors, GPS trackers, and RFID tags. These hardware components serve as the primary data acquisition layer, capturi

In this study, to investigate the factors affecting container semitrailer rollovers while driving on interchange loop ramps, a simulation model was established in TruckSim on the basis of data collected from an in-depth investigation of the vehicle, road, and driving behaviors in a real accident. The established model was validated by reproducing the accident in a simulation. The effects of vehicle speed, the height of the cargo’s center of gravity, tractor-semitrailer interactions, and the radius of the circular curve on container semitrailer rollover were investigated using the established model. The results reveal that if the height of the container semitrailer is within the limits in Chinese standards and the container semitrailer is moving at a speed of less than 45.3 km/h, it can safely travel through a loop ramp with a circular curve radius of 60 m. The height of the cargo’s center of gravity and the lateral load transfer ratio have a negative relationship, and a higher center of gravity reduces the safe speed for a container semitrailer traveling through a loop ramp. During rollover, the rear axle of the semitrailer first begins to roll over and then drives the tractor to overturn.