How Artificial Intelligence is Postively Changing Public Transportation

Artificial Intelligence can be used for transportation services like city transit on our daily commute to work every day. Machine learning provides unprecedented opportunities for public transportation engineering. But it is imperative for software engineers to comprehend the innovative technology's restricitons. "For civil engineers, this means learning about AI systems and using the extensive data collected from road sensors, bridges, vehicles, cameras, public transportation networks, and commuters to train such systems on how to operate in a practical, safe, and ethical manner (Pennetti and Porter, 2024). "The integration of AI in public transportation, such as predictive maintenance for buses and trains, can improve reliability and enhance passenger satisfaction"(Mckinsley & Company, 2021). Artificial Intelligene has the possibilities of using autonomous public transportation taxis and buses. If implemented correctly this can greatly reduce accidents and driving under the influence crimes on a global scale.

"Autonomous vehicles, powered by AI, will not only transform personel transportation but also revolutionize logistics and supply chains" (Tesla, 2023). Artificial Intellgence has unlimited possibilitie in improving the public transportation industry from self driving vehicles to predictiing vehicle repairs and upkeep, making public transportation safer, more efficient, and stable. As a structural technology in the background on creating architectures with capabilities of utilizing data patterns to implement various tasks. The machine learning training protocol includes giving the architecture with instances and modifications measures to mitigate errors and optimize positive outcomes.

To earn enhanced results, requires the use of Artificial Intelligence software that frequently uses balancing architecture that is complex when reacting to the dataset size and then current information system resources. "For civil engineering applications, GPS systems can analyze datasets such as community surveys, public meeting transcripts, transportation performance data, and project records"(Pennetti & Porter, 2024). The utilization of unsupervised and supervised learning, the architecture can reveal complicated relations between elements like public thoughts on an issue, transportation settings, and project results. Civil engineers and software developeers have been constantly dealing with a variety of discriptive technologies, like computer aided design, geopgraphic data infrastructures, building data architectures, and virtual design and development, each contributes significant modificatins to project strategies and implementation. Artificial Intelligence being introduced to public transportation is a tectonic change that is very similar to the advent of personal computers or the internet.

This blog covered machine learning technology and how it can be applied to public transportation and our daily communte to work. This technological innovation is constantly changing and should incorporate cybersecurity software in order to reduce cyberattacks or information breaches that can lead to accidents. I appreciate and thank anyone who has taken time out there busy schedule to read my post as this blog website. Your feedback is needed. Please do not feel obligated to respond to my post, but I would appreciate everyones feedback, and if they could let me know what they liked about my post. I would like to consider this a learning experienve for everyone and sharing your thoughts and ideas by responding by interacting to this blog can assist in facilitating the learning provess. It is an exciting time to be alive to witness safer transportation solutions with the use of machine learning.

References:

IBM. (2024). The Future of AI in Transportation. IBM. https://www.ibm.com/industries/travel-transportation

Marr, B. (2023). Artificial Intelligence in Practice. Wiley.9

McKinsey & Company. (2021). The Impact of AI on Transportation. McKinsey & Company. https://www.mckinsey.com/~/media/mckinsey/industries/automotive%20and%20assembly/our%20insights/the%20road%20to%20artificial%20intelligence%20in%20mobility%20smart%20moves%20required/smart-moves-required-the-road-towards-artificial-intelligence-in-mobility.pdf
Pennetti, C. A., Ph.D., P.E., & Porter, M. D., Ph.D. (2024, November 1). How artificial intelligence can speed up transportation engineering. Civil Engineering. Retrieved from https://www.asce.org/publications-and-news/civil-engineering-source/civil-engineering-magazine/issues/magazine-issue/article/2024/11/how-artificial-intelligence-can-speed-up-transportation-engineering
Tesla. (2023). Tesla Autonomy. Tesla. https://www.tesla.com/autopilot

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