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Computer Vision for Public Transportation - Phase 1
Overview
Introduction
Business challenges
Solution overview Partner overview Lab architecture overview Use case validation Conclusions and References

Business challenges

Home > Workload Solutions > Computer Vision > White Papers > Computer Vision for Public Transportation - Phase 1 > Business challenges

Business challenges

  • Cities of all sizes can be considered an ecosystem, with many different services and agencies interacting, playing a critical role in how the community is impacted. Any breakdown in public services can have disastrous domino-like effects on many people. City and government officials are continuously challenged with keeping their communities safe, healthy, and operating effectively.

    Public transportation as an industry is also being challenged to become more efficient and responsive to the communities' evolving needs. As municipal transit agencies look to increase ridership and operational efficiency, real-time data collection and analysis leveraging AI can be crucial in reaching these goals.

    Additionally, access to wireless data is critical in providing continuous, quick Internet access to riders. The Internet lets them stream music/videos, search the web, and check email. It also powers critical solutions that help streamline operations such as bus route management, alert on potential delays, asset tracking, and maintenance needs.

    As a result, supporting communication and AI infrastructure needs to go beyond collecting data. There is a need for actionable information, all while keeping the user and community proactively updated and informed.

    Other ongoing challenges include improved safety, reduced operational cost, and tighter security by correlating video and signals from other sensors for making predictions based on trend data, and sending real-time alerts. Therefore, system monitoring around the clock is paramount.

    Another essential element to improve passenger and personnel safety is ensuring the facility and equipment are physically secure, perhaps with more precise inspection of passengers, baggage, and cargo.

    Whether moving people or goods, transportation environments are in constant motion. Railways and public and private transportation are complex use cases for computer vision. They require real-time situational awareness and faster decisions based on analyzing many different data points. Computer vision and AI are needed to help reshape the transportation industry at every level, from improving the passenger experience to preemptive fleet maintenance to fuel optimization.

    In Summary, as the transportation industry continues to evolve, converging technologies such as 5G and the new capabilities of edge computing need to enable the next generation of data-driven, prescriptive, and adaptive outcomes, benefiting passengers, the industry, and ecosystem sustainability.