# A Hybrid Control Path Planning Architecture Based on Traffic Equilibrium Assignment for Emergency
## 2 Related Work
### Path Planning Based on Traffic Assignment
However, while some of the above work attempts to give path-planning decisions by judging the evolution of traffic flows, the evolution of network traffic predicted by the model is usually only applicable to deterministic scenarios, since emergencies have considerable uncertainty [15]. Individuals are likely to change their established routes when faced with unforeseen circumstances, which leads to the failure of traffic prediction models backed by normative data. To address this problem, [PERSON] uses a probabilistic approach to directly predict the time evolution of the probability of evacuating users from an area. But individual rerouting behavior is directly affected by the metrics selected for generating the probabilities, and we cannot judge the merits of the metric selection. Moreover, the probabilities are generated in different emergencies in different ways. Therefore, how to consider the impact of individual trips on macroscopic background traffic flow, so that the model can compatibly predict future background traffic in the existence of anomalies, and thus support the traffic assignment work in real-time becomes the focus of our work. To assess the impact that micro individual demand brings to macro traffic flows, [PERSON]. [25] quantified the impact of urban logistics activities on traffic flow dynamics through simple metrics of the macroscopic fundamental diagram (MFD) to investigate the possible benefits of multi-use lane strategy deployment. [PERSON]. [26] proposed a framework for simulating micro freight demand through a probabilistic generation approach, and combined traffic states with stochastic and microscopic freight demand generation models, i.e., the delivery movements and the delivery duration. The intention is to use MFD and its associated metrics to capture and quantify the macro impacts of double-parked vehicles transporting goods on urban arterials. But both of these works are based on MFD. The macroscopic information in MFD is the impact of the relationship between the three main parameters of the transportation system, namely flow, speed, and density [27], rather than the impact on the network-wide traffic flow data. Furthermore, [28] formulated the dynamic OD demand estimation problem as an excess-demand dynamic traffic assignment (DTA) problem defined for an expanded network with dummy paths, and assumed that the network equilibrium is a compromise between minimizing the individual routing cost, traffic count matching error, and the OD demand entropy. Inspired by this work, we introduce travelers' real-time OD data to improve the prediction of dynamic traffic flows. In summary, background traffic is an important guide for the generation of traffic-assignment strategies in emergencies. However, existing models use quantitative indicators, constraints, or probabilistic methods to predict background traffic, which cannot be adapted to equilibrium path planning in emergencies. In addition, existing allocation methods use iterative computation, which is extremely inefficient in large-scale traffic networks and cannot make real-time decisions. Therefore, in this paper, we combine the background traffic flow data and the traveler's real-time OD data to predict the accurate traffic network state under the influence of real-time OD, and further offer hybrid control of the traveler's path planning strategy. Moreover, the neural network mapping traffic assignment method is used instead of the iterative approach to enhance the real-time performance of the algorithm.