Analyzing transportation mode interactions using agent-based models

Traffic in urban areas contributes significantly to congestion and air pollution, which contributes to climate change issues and causes economic losses and fuel wastage. Agent-based models have significant advantages for analyzing urban transportation and its sustainability. The main objective of this paper is to provide a critical review of research on agent-based models for traffic simulation in urban areas. This article reviews the literature on the subject and examines earlier case studies that dealt with agent-based models for micro-mobility and traffic simulation considering six criteria. The study analyzes multiple publications obtained from databases such as Google Scholar, Scopus, and Web of Science. These publications span from 2014 to 2022 and are scrutinized to fulfill the stated objectives. Furthermore, a thorough critical evaluation is performed on a chosen set of 16 publications. The research also proposes traffic simulation tools based on insights gathered from case studies. Further, it discusses how to choose a decent data set through a balanced and objective summary of study findings on the topic and recommends future work in this topic. This paper discusses computational models that can be used to understand and control traffic.This paper discusses how various studies have explored using the mentioned models to manage urban traffic using scooters, bikes etc.This paper reviews how computer simulations are useful for dealing with different kinds of traffic