This article presents an overview of the agent-based modeling and simulation approach and its recent developments in transport fields, with the purpose of discovering the advantages and gaps and encouraging more valuable investigations and applications of agent-based models. We clarify the agent-based model from agents, the background of development, and the basic structure applied in transport systems. Then, the agent-based transport modeling toolkits are discussed. The applications of agent-based models in transport systems are reviewed in three time scale models followed by an additional discussion of hybrid modeling approaches. The extensive modeling of the beliefs, desires, learning, and adaptability of individuals and the optimization problems using agent-based models are explored. Besides, we point out some limitations in terms of calibration and validation procedure, agents’ behavior modeling, and computing efficiency. In conclusion, some recommendations are given and suggest potential and insightful directions such as Big Data and Digital Twin for future research.

This study focuses on the reconfiguration of bus services in an urban area with a newly constructed rail system. A hub-and-spoke network framework is introduced for the multimodal transit system. The rail services are taken as the backbone, and the bus services (main bus lines and feeder bus lines) are reconfigured to better integrate with rail services forming the core of a Mobility as a Service (MaaS). A cluster-based approach is used for selecting hubs from rail stations. Bus stops are taken as non-hub nodes. Main bus lines are designed based on a heuristic line generation approach and feeder bus lines are developed by solving a travelling salesman problem. A bi-level programming model is proposed to determine frequencies of each mode and addressed by the artificial bee colony algorithm. The effectiveness of the proposed methods is illustrated by numerical examples and applied to the Mandl's benchmark compared with existing studies.

Roads congestion pricing has been considered as an effective solution following the successful implementation of such programs by many cities such as Singapore, Stockholm, and London. In multiple cases, congestion pricing projects have not been implemented, and multitudinous industrialized countries’ governments are struggling to find an effective and satisfactory way of introducing congestion pricing schemes that will not be affected by the public’s negative opinion and resistance. The lack of political and public acceptability can, therefore, be blamed for the nonimplementation of many congestion pricing projects in many cities around the world. This paper reviews eight cases where congestion pricing schemes were implemented or rejected, as well as the major influencing factors that enable congestion pricing introduction and acceptability by road users, discusses public and political acceptance of urban road pricing, and provides a valuable guideline for policy and decision-makers.

The bus stop-skipping scheme is designed to reduce the number of stops and increase bus operating speed. But in a mixed traffic environment, buses can hardly maintain a high and stable operating speed due to frequent merging and overtaking activities. In this regard, the exclusive bus lane is used to separate buses from normal traffic. However, the permanent bus lane would inevitably decrease road capacity and result in more congestions along the bus lane, especially during peak hours. This paper proposes a hybrid bus operational scheme combining the bus lane reservation strategy and the stop-skipping control. Considering the uncertainty of passenger demand, this paper proposes a multi-stage stochastic programming model. The evolution of the uncertainty is interpreted by a scenario tree. A progressive hedging-based method is developed to separate the large-scale optimization problem into sub-problems. Two numerical experiments are conducted to verify the proposed model and solution algorithm.