A vehicular traffic congestion predictor system using Mamdani fuzzy inference

The process of traffic control systems significantly relies on the immediate detection of breakdown states. As a result of their crisp (non-fuzzy) based calculation procedures, conventional traffic estimators and predictors cannot effectively model traffic states. In fact, these methods are characterized by exact features, while traffic is defined by uncertain variables with vague properties. Furthermore, typical numerical methodologies have constraints on evaluating the overall system status in heterogeneous and convoluted networks mainly due to the absence of reliable and real-time data. This study develops a fuzzy inference system that uses data from the Hungarian freeway networks for predicting the severity of congestion in this complex network. Congestion severity is considered the output variable, and traffic flow along with the length and the number of lanes of each section are assigned as input variables. Seventy-five fuzzy production rules were generated using accessible datasets, percentile distribution, and experts' consensus. The MATLAB fuzzy logic toolbox simulates the designed model and analysis steps. According to available resources, the results demonstrate linkages among input variables. Analyses are also used to construct intelligent traffic modeling systems and further service-related planning.

Cordon-Based Pricing Schemes for Mixed Urban-Freeway Networks using Macroscopic Fundamental Diagrams

The development of traffic models based on macroscopic fundamental diagrams (MFD) enables many real-time control strategies for urban networks, including cordon-based pricing schemes. However, most existing MFD-based pricing strategies are designed only to optimize the traffic-related performance, without considering the revenue collected by operators. In this study, we investigate cordon-based pricing schemes for mixed networks with urban networks and freeways. In this system, heterogeneous commuters choose their routes based on the user equilibrium principle. There are two types of operational objective for operating urban networks: (1) to optimize the urban network’s performance, that is, to maximize the outflux; and (2) to maximize the revenue for operators. To compare those two objectives, we first apply feedback control to design pricing schemes to optimize the urban network’s performance. Then, we formulate an optimal control problem to obtain the revenue-maximization pricing scheme. With numerical examples, we illustrate the difference between those pricing schemes.