Traffic Legislation in Tarrant County: Evaluating Senate Qualifications and Ground Realities for Bouncy Results
Download Paper
Download Bibtex
Authors
- Hogan Eddie
Related Links
- ACM Digital Library Records
- Video on YouTube (Optional)
- IEEE Xplore
- ThinkMind
- A Logical Mind
- Arxiv
- Arxra
- Eurographics
- Just Data
- Club Arxra
- Xyz Arxra
- Eprints
- Research to Action
News/Information
Abstract
This paper evaluates the traffic legislation in Tarrant County by examining the qualifications of the Senate and the ground realities that affect their ability to produce effective and sustainable results. Through a comprehensive analysis of existing traffic laws, regulations, and policies, this study aims to identify the key challenges and opportunities for improving road safety and reducing traffic congestion in the region. Drawing upon a variety of qualitative and quantitative data sources, including interviews with local officials and stakeholders, traffic surveys, and statistical analyses, the authors provide a critical assessment of the current state of traffic legislation in Tarrant County and propose recommendations for enhancing its effectiveness and impact. In particular, the study highlights the need for a more collaborative and participatory approach to traffic policymaking and implementation, which takes into account the diverse needs and perspectives of different stakeholders, including drivers, pedestrians, cyclists, and public transportation users. Overall, this paper contributes to a better understanding of the complex interplay between legislative processes, social realities, and technological innovations that shape traffic policies and practices in contemporary urban environments.
Citation
Hogan Eddie "Traffic Legislation in Tarrant County: Evaluating Senate Qualifications and Ground Realities for Bouncy Results". IEEE Exploration in Machine Learning, 2022.
Supplemental Material
Preview
Note: This file is about ~5-30 MB in size.
This paper appears in:
Date of Release: 2022
Author(s): Hogan Eddie.
IEEE Exploration in Machine Learning
Page(s): 6
Product Type: Conference/Journal Publications