Revolutionizing Road Traffic Management and Enforcement: Harnessing AI, ML, and Geospatial Techniques

Title

Revolutionizing Road Traffic Management and Enforcement: Harnessing AI, ML, and Geospatial Techniques

Creator

Salunke S.J.; Bang S.S.

Description

This study investigates the synergistic application of Artificial Intelligence (AI), Machine Learning (ML), and Geospatial Technologies in optimizing traffic management systems. Through a mixed-methods research design, it evaluates the potential of these technologies to enhance urban traffic flow and reduce congestion. The research emphasizes the critical importance of data quality, ethical considerations, and the selection of appropriate technological solutions based on specific urban traffic scenarios. Findings highlight the significant role of integrated AI and geospatial analyses in improving traffic predictions and operational efficiency. Future work will focus on developing more sophisticated models that ensure privacy, equity, and adaptability to new transportation trends. 2024 IEEE.

Source

TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Artificial Intelligence; Data Quality; Ethical Considerations; Geospatial Technologies; Machine Learning; Traffic Management; Urban Mobility

Coverage

Salunke S.J., Christ University, Bengaluru, India; Bang S.S., Christ University, Bengaluru, India

Rights

Restricted Access

Relation

ISBN: 979-835038427-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/TQCEBT59414.2024.10545190

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204469248&doi=10.1109%2fTQCEBT59414.2024.10545190&partnerID=40&md5=2bfb579c77393094b0bb961a571affc4