Unlocking the Power of Oryx MLLM: Transforming Urban Mobility Through Spatial-Temporal Insights

EasyChair Preprint 15045

Abstract

The rapid growth of urban populations has placed unprecedented pressure on cities to develop more efficient and sustainable mobility solutions. Harnessing big data through innovative machine learning platforms like Oryx MLLM (Machine Learning Language Model) offers a revolutionary approach to urban mobility by providing deep insights into spatial and temporal dynamics. Oryx MLLM processes vast datasets, revealing hidden patterns in traffic flows, transportation networks, and commuter behavior. These insights enable urban planners and policymakers to optimize infrastructure, reduce congestion, and improve the overall mobility experience. This article explores how Oryx MLLM unlocks the potential of big data to transform urban mobility, paving the way for smarter cities and sustainable transport systems that meet the demands of modern urban life.

Keyphrases: Big Data, Oryx MLLM, Smart Cities, Spatial-Temporal Insights, sustainable transport, traffic optimization, urban mobility

@booklet{EasyChair:15045,
  author    = {John Owen},
  title     = {Unlocking the Power of Oryx MLLM: Transforming Urban Mobility Through Spatial-Temporal Insights},
  howpublished = {EasyChair Preprint 15045},
  year      = {EasyChair, 2024}}