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Bus rapid transit (BRT) has been proved to be an effective tool to improve mass transit services. However, BRT's adaptive operations like management and scheduling under different scenarios are too complicated to implement using traditional methods. The ACP approach, which is based on holism and complex system theory and consists of artificial systems (A), computational experiments (C) and parallel execution (P), offers an efficient new method to cope with these complex systems, including BRT. In this paper, the parallel transportation management and control system for BRT (PTMS-BRT) is presented, which is designed and implemented using the ACP approach. PTMS-BRT integrates such functions as BRT's monitoring, warning, forecasting, incident management, and real-time scheduling, to provide its operations smoother, safer, more efficient, and reliable. It has been piloted successfully in Guangzhou BRT to demonstrate it as another successful example of parallel transportation systems.

Accurate assessment and effective control of urban passenger transportation hub operations are an area that traditional methods have yet to address satisfactorily. This article presents a framework for parallel management and control based on the artificial societies, computational experiments, and parallel execution (ACP) paradigm. The authors first introduce the components, internal interactions, and support mechanism of artificial transportation hubs. They then describe an agent-based model for the hub including the computational experiments and responsibilities of parallel execution in active hub management and control. Thy also present an implementation of the framework through field tests. The framework can improve operational efficiency and service quality of urban passenger transportation hubs.