AI applications in rail multi modal transportationAI in Freight Rail and Multi Modal Transportation
. The AI Opportunity 4 AI could potentially deliver additional global economic activity of around $13 trillion globally by 2030, or about 16 percent higher cumulative GDP compared with today This amounts to about 1.2 percent additional GDP growth per year
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.According to a McKinsey study, the Transport and Logistics industries can capture the among highest value increase from AI /ML based solutions
.Use Case: AI powered visual inspection for rail and multi -modal yard management – Drone provides automatic, accurate, fast, and safe inventory checks – AGV’s with telescoping camera masks – CCTV cameras connected to cloud-based AI image recognition – Gives visibility into • Inventory • Productivity • Safety
.Use case: AI powered visual inspection Edge Camera devices installed along a train track uploads images to Watson where AI image classifiers identify damage. – Cognitive visual recognition – Inspect wear and damage to physical assets – Determine appropriate corrective action – Accuracy improved to over 98%
. Use case: anticipatory logistics Group Name / DOC ID / Month XX, 2018 / © 2018 IBM Corporation 13 Relationship between logistics providers and consumers is changing. AI can help personalize these interactions to increase customer loyalty and retention. – Predict what customers will purchase – Sources Vast and varied data – browsing behavior, purchase history, weather, social media chatter and news reports – Shorten delivery times – move inventory and resources to meet anticipated demand
.Use Case: Cargo Customs & Compliance with Artificial Intelligence Increase of sales and compliance agent responsiveness on handling Booking requests Reducing and optimizing the time spent on repairs of non-compliant Shipments Reducing or eliminating fines due to noncompliance by reduce fine amount as a result of lower false positives Benefits and business value will apply to both Air Cargo Carriers and Freight Forwarders
Use case: sensor fusion for anomaly detection Edge Devices collecting data on wagon and Locomotive Data Using data analytics and machine learning coupled with the vast amount of data collected through sensors Find patterns in alarms AI to recommend next best action
.Accelerating advances in technology and transforming every part of your rail and logistics business Cognitive analytics Cloud computing Pervasive connectivity Product Lifecycle Management Embedded sensors Creating new products and services Improving operations and lowering costs Driving engagement and customer experience Partnered Innovation • Open ecosystem • Device partnerships • Embedded security • Edge Analytics Data Integration • Weather data • Social data • Application data • Platform of platforms Advanced Analytics • Predictive Analytics • Real-time Analytics • Data Mining • Optimization Cognitive Technology • Natural Language Processing • Machine Learning • Textual Analytics • Video/Image Analytics
https://www.pi.events/IPIC2019/sites/default/files/downloads/d1s1-3.-keith-dierkx_ai-in-rail-and-multi-modal-transportation-v3.pdf