Identifying the Components of Artificial Intelligence in Improving Safety, Reducing Traffic Crashes and Costs with Content Analysis

Artificial intelligence systems in urban transportation can utilize various tools and methods to enhance safety. The current study was undertaken to investigate the utilization of artificial intelligence in improving urban transportation safety, reducing traffic crashes, and cutting costs, using a qualitative approach and expert opinions in the field of transportation.

Seven experts in the field of urban transportation in Tehran were selected as the sample. The interview responses, structured around 10 questions, were collected and analyzed using Maxqda software and content analysis methodology. In this type of analysis, the content is examined to identify patterns, themes, ideas, and implicit or underlying messages.

From the conducted interviews, four main contents were extracted, including decision support systems (with five sub-contents), data analysis systems (five sub-contents), accident prevention systems (four sub-contents), and alerting systems (four sub-contents).

Given the advanced technologies in the field of artificial intelligence, intelligent systems in urban transportation will be of greater importance. These systems include traffic prediction and management, driver detection and alerting, smart vehicle systems, and data analysis. The role of these systems in improving urban transportation safety, reducing crashes, cutting costs, and enhancing system efficiency is crucial. These systems are capable of identifying warning signs and providing safety solutions. Through the use of these systems, city managers can identify problems and offer appropriate solutions to improve urban transportation, thereby enhancing the living conditions of citizens