Enterprise Integration, Infrastructure Management and Predictive Analytics
GEOI Solutions brings a wealth of geospatial talent to the transportation industry. We continue to drive innovation and provide solutions that empower our users with advanced solutions to manage transportation assets.
Enterprise system integration is the process of connecting existing systems to share and communicate information. Integrating applications enables data to flow between systems with ease, simplifying IT processes and increasing agility across your business.
Most large companies use at least several kinds of software and data systems that can benefit from enterprise system integration. GEOI deploys an architectural pattern for enterprise system integration called service-oriented architecture (SOA). The concept of SOA has long been used in general software development and integration frameworks. At its core, SOA promotes loose coupling, flexibility and reusability that tightly coupled architecture cannot provide.
Asset management in the transportation industry provides a solid foundation that optimizes the performance and cost-effectiveness of transportation facilities. Transportation Asset Management allows for a fact-based dialogue among system users and other stakeholders, government officials, and managers concerned with day-to-day operations. As such, decisions can be based on detailed input regarding available resources, current system condition and performance, and estimates of future performance. The information underlying asset management - sometimes raw data and at other times data generated from the analytical process - is fundamental to an improved understanding of the economic trade-offs, return on investment, and potential value of the end product.
Machine learning and intelligence are being applied at GEOI in multiple ways to address predictive analytical challenges in multiple fields, including the transportation industry. GEOI technologists and data scientists have been engaged in multiple efforts to predict and forecast the flows of traffic and critical infrastructure decay. The work leverages machine learning to build services that make use of both real-time live streams of sensed information and large amounts of heterogeneous historical data. This allows us to get a sense of the natural transportation rhythm, as well as identify predictive behavior that may lead to road and infrastructure integrity issues or determine events that can trigger unexpected traffic surges. As a result, we are being proactive by identify patterns and trends rather than be reactive. Contact us to learn more.
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