Optimization design and application of artificial intelligence in intelligent transportation system
Year: 2025
Type: conference-paper
Abstract: The Intelligent Transportation System (ITS) is the effective and comprehensive application of advanced science and technology (information technology, computer technology, data communication technology, sensor technology, electronic control technology, automatic control theory, operations research, artificial intelligence, etc.) in transportation, service control, and vehicle manufacturing to strengthen the connection between vehicles, roads, and users. The result is an integrated transport system that ensures safety, enhances efficiency, improves the environment and saves energy. It is an effective means to solve the current urban traffic problems and an important indicator for the construction of a "smart city". Through the construction of an urban traffic collection system, the system solves the existing traffic problems, further integrates all kinds of road traffic resources, builds the urban "traffic brain", mines the huge amount of traffic data, deconstructs the operation law and characteristics of urban traffic, realizes the accurate urban traffic management, scientific management and normal management, and makes people's traffic travel safer. It makes people's traffic travel safer, more convenient and more efficient. This study introduces innovative computational models and algorithms specifically designed for traffic optimisation, as well as data-driven machine learning techniques to improve the operational efficiency of intelligent transport systems. An extensive analysis of case studies on the integration of AI into traffic management systems provides empirical evidence of their impact, focusing on the performance evaluation of these AI applications as well as potential scalability issues that may arise in different urban environments.
Author: Bibo Qiu
Institution: Wuhan Donghu University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4413812586