Development of agent-based network transmission model for inter-network traffic flow control in urban area

Abstract

The field of research that has recently come to the fore is the perimeter control, which aims to manage the transfer flow between urban road networks. However, there is a lack of description on the behavior of transfer flow between networks in the relation to macroscopic fundamental diagram (MFD). Hence, a conceptual description on the transfer flow is suggested, and such suggestion is investigated with microscopic simulation experiments. Throughout the investigation, it is found that the network outbound demand pattern strongly influences the maximum outbound flow regardless of the spatial inhomogeneity. It is found that the boundary capacity influences the network outbound flow as well. The restriction effect on outbound flow of a network due to the limited supply level of a neighboring network is also examined. Furthermore, it is found that a network’s inflow or outbound flow in each direction (north, south, east, and west) is proportional to the demand level in each direction. The suggested conceptual description on transfer flow has been confirmed through the investigations, and this can be useful for various analyses on inter-network traffic dynamics. The focus of the perimeter control is to control traffic demand for large urban area prior to controlling internal flow inside the area. Such control concept needs to be tested by simulations, hence, it is necessary to develop a model that can appropriately estimate or predict the network-wide flow dynamics. In this research, agent-based network transmission model (ANTM) is proposed for describing the aggregated flow dynamics over the urban area of multiple large-scale networks. The proposed model is the combination of the CTM, MFD, and agent concept. The CTM-based simulation is adopted for the simplicity considering the computation requirements for real-time feasibility. The MFD concept is applied for representing the network properties, and a new approach is taken particularly for estimating network outbound flow that gets affected by both demand patterns and boundary capacity. The agent concept is applied for representing drivers’ travel direction choice behaviors. By using the choice behaviors, the split fractions of the network flow in each direction can be dynamically changed based on the traffic condition. The model is compared with microscopic simulations and shows an appropriate level of prediction accuracy for large areas. In addition, various direction choice behaviors are applicable to this model. Then, several practices have been carried out to see the effect of adjusting the capacity values at the boundaries between network pairs. The first example of the application is the feedback control strategy and the second example is the model predictive control, and a new distributed model predictive control (DMPC) strategy is particularly provided and tested. With these case study examples, the applicability of the newly developed simulation model is presented. Since various perimeter control strategies are applicable, the proposed model can be a useful tool for developing new control approaches in terms of reducing the traffic congestion in urban areas.