# A Microscopic On-Ramp Model Based on Macroscopic Network Flows
###### Abstract  
While macroscopic traffic flow models adopt a fluid dynamic description of traffic, microscopic traffic flow models describe the dynamics of individual vehicles. Capturing macroscopic traffic phenomena accurately remains a challenge for microscopic models, especially in complex road sections. Based on a macroscopic network flow model calibrated to real traffic data and new rules for the acceleration and merging behavior on the on-ramp, we propose a microscopic model for on-ramps. To evaluate the performance of the new flow-based model, we conduct traffic simulations assessing speeds, accelerations, lane change positions, and risky behavior. Our results show that, although the proposed model exhibits some limitations, its performance is superior to the Intelligent Driver Model in the evaluated aspects. While the Intelligent Driver Model simulations are almost free of conflicts, the proposed model evokes a realistic amount and severity of conflicts and therefore can be considered for safety analysis. traffic flow theory; macroscopic traffic models; car-following models; on-ramps; trajectory data; traffic simulation; SUMO +
Footnote †: journal: Applied sciences  
0000-0001-9911-1100-2249-7/11/0000-00001-9911-1000-9911-1000-9911-1000-9911-  
involving, for example, complex geometries [19; 20; 21]. In these models, the dynamics are typically divided into a longitudinal component, which is described by a car-following model, and a lateral component, which is described by a lane change model. Lateral movements within the lane are usually neglected. There have been numerous car-following models with different underlying concepts proposed in the literature. The Optimal Velocity Model [16] states that drivers aim for an optimal velocity depending on the distance to the leading vehicle. The Full Velocity Difference Model (FVDM) [17] additionally includes speed differences between leading and following vehicles. The Intelligent Driver Model (IDM) [18] distinguishes three driving situations (accelerating, following with constant time headway, and decelerating) and smooth transitions between them. Despite being introduced more than 20 years ago, the IDM is still widely used and performs better than other car-following models in practice [22]. Both the macroscopic and the microscopic modeling approach have their limitations. Macroscopic models make simplifying assumptions; for example, they neglect variability with respect to acceleration [23; 24]. Microscopic models describe the dynamics of vehicles in individual detail, but they do not necessarily reproduce empirical macroscopic traffic patterns such as the capacity drop [25]. Under simplifying assumptions, macroscopic traffic models can be derived as an infinite (vehicle) limit of microscopic models; see, e.g., [26]. Consistency of the FVDM model to a macroscopic model in two space dimensions has been mathematically shown and empirically verified [27; 28]. A promising approach to combine the advantages of models of both scales is to infer information from macroscopic models into the microscopic models by converting traffic flow into time headways and traffic density into spacings. The validation of traffic models is usually considered on the macroscopic level and addresses questions such as the following: does the model produce the traffic flow and density expected from real data? Moreover, it is necessary that microscopic models describe driver behavior on the microscopic scale accurately. Different metrics are required to account for the different scales of the models. To use traffic flow models for road safety analysis, the models must be able to predict the number of accidents or conflicts on a given road section or network [2; 29]. While some microscopic models consider driver conflicts, they are mostly built to be accident free, which means vehicles will behave such that accidents cannot happen while simulating traffic [30].