MiTAC Group, a prominent technology and IT solutions provider in Taiwan, has played a significant role in supporting the local Ministry of Transportation's initiatives to advance smart cities, transportation systems, and rail networks. At its 50th Anniversary TechDay on November 18, MiTAC Information Technology and MiTAC Digital Technology showcased their latest AIoT (Artificial Intelligence of Things) innovations in smart transportation.
Smart city innovations
Hsieh Kuang-lung, senior manager at MiTAC Information Technology, detailed the company's comprehensive AIoT smart city solutions that encompass smart transportation, manufacturing, and education. The company has placed particular emphasis on transforming rail transportation through intelligent and digital solutions.
By implementing IoT to collect operational data, MiTAC aims to optimize efficiency, improve performance, and enhance value. Hsieh specifically emphasized the role of AI in railways, particularly visual AI, which uses image-based training to identify and extract critical data.
Dual approach to mobility solutions
Jim Hsu, senior manager of product development at MiTAC Digital Technology, outlined that smart mobility encompasses two main areas: in-vehicle and station-based systems. In-vehicle technologies include dashcams, GPS systems, vehicle-to-everything (V2X) communications, and fleet management systems. Station-based systems incorporate ticket machines, gates, turnstiles, and components like pantographs on trains.
With AI's growing prominence, smart mobility faces new challenges, particularly regarding increased power consumption for AI applications. Hsu revealed that MiTAC Digital Technology is tackling these challenges by developing products in various sizes to meet smart mobility requirements.
AI integration and industry trends
Jonas Chen, manager of Partner Solution Architecture at Intel, highlighted how technological advancements in AI and connectivity are driving sensor fusion integration, leading to more prevalent device-to-device connections. He noted that AI and image processing are transitioning from cloud to edge computing, enhancing smart city services. Chen added that in smart railways, AI and deep learning are improving operational efficiency while reducing potential issues.
Chen observed that rail transportation has historically lagged behind other modes due to deployment challenges. However, post-pandemic demand for advanced safety measures, combined with growing urban populations and competitive pressures among railway operators, is accelerating the development of smart railway technologies.
Strategic implementation
Shyue-Koong Chang, director of the Advanced Public Transportation Research Center at National Taiwan University, presented five strategic goals for applying AI and big data in transportation: enhancing public transportation services through digital transformation and AIoT; improving operational efficiency and productivity; making government agencies smarter and more efficient while enhancing governance; stimulating innovative services to improve industry efficiency; and enabling academia to gather and apply better data to support these goals.
