Traffic and Mobility Optimization Using AI: Comparative Study between Dubai and Riyadh

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.

Authors

Publication Details

Journal
ArXiv.org
Published
2025-03-06
DOI
https://doi.org/10.5281/zenodo.15038456
Primary Topic
Organizational and Employee Performance
Type
article
Field-Weighted Citation Impact
0.00
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article

Traffic and Mobility Optimization Using AI: Comparative Study between Dubai and Riyadh

Kanwal Aalijah
ArXiv.org
Organizational and Employee Performance
article

Traffic and Mobility Optimization Using AI: Comparative Study between Dubai and Riyadh

Kanwal Aalijah
article en

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.

ArXiv.org
Openalex Percentile: Top 85%
Organizational and Employee Performance
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