Year: 2025
Type: preprint
Abstract: Urban planning plays a very important role in development modern cities. It effects the economic growth, quality of life, and environmental sustainability. Modern cities face challenges in managing traffic congestion. These challenges arise to due to rapid urbanization. In this study we will explore how AI can be used to understand the traffic and mobility related issues and its effects on the resident’s sentiment. The approach combines real-time traffic data with geo-located sentiment analysis, offering a comprehensive and dynamic approach to urban mobility planning. AI models and exploratory data analysis was used to predict traffic congestion patterns, analyze commuter behaviors, and identify congestion hotspots and dissatisfaction zones. The findings offer actionable recommendations for optimizing traffic flow, enhancing commuter experiences, and addressing city-specific mobility challenges in the Middle East and beyond.
Source: arXiv (Cornell University)
Author: Kanwal Aalijah
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893067670
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