Simulation Environment for Traffic Control Systems Targeting Mixed Autonomy Traffic Scenarios - Link, Christopher; Malena, Kevin; Gausemeier, Sandra; Trächtler, Ansgar (Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems)
The development of autonomous vehicles and their introduction in urban traffic offer many opportunities for traffic improvements. In this paper, an approach for a future traffic control system for mixed autonomy traffic environments is presented. Furthermore, a simulation framework based on the city of Paderborn is introduced to enable the development and examination of such a system. This encompasses multiple elements including the road network itself, traffic lights, sensors as well as methods to analyse the topology of the network. Furthermore, a procedure for traffic demand generation and routing is presented based on statistical data of the city and traffic data obtained by measurements. The resulting model can receive and apply the generated control inputs and in turn generates simulated sensor data for the control system based on the current system state.
Keywords: Traffic Simulation; Traffic Control; Car2X; Mixed Autonomy; Autonomous Vehicles; SUMO; Sensor Simulation; Traffic Demand Generation; Routing; Traffic Lights; Graph Analysis; Traffic Observer

Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments - Malena, Kevin; Link, Christopher; Bußemas, Leon; Gausemeier, Sandra; Trächtler, Ansgar (Communications in Computer and Information Science)
Modern traffic control systems are key to cope with current and future traffic challenges. In this paper information obtained from a microscopic traffic estimation using various data sources is used to feed a new developed traffic control approach. The presented method can control a traffic area with multiple traffic light systems (TLS) reacting to individual road users and pedestrians. In contrast to widespread green time extension techniques, this control selects the best phase sequence by analyzing the current traffic state reconstructed in SUMO and its predicted progress. To achieve this, the key aspect of the control strategy is to use Model Predictive Control (MPC). In order to maintain realism for real world applications, among other things, the traffic phase transitions are modelled in detail and integrated within the prediction. For the efficiency, the approach incorporates a fuzzy logic preselection of all phases reducing the computational effort. The evaluation itself is able to be easily adjusted to focus on various objectives like low occupancies, reducing waiting times and emissions, few number of phase transitions etc. determining the best switching times for the selected phases. Exemplary traffic simulations demonstrate the functionality of the MPC-based control and, in addition, some aspects under development like the real-world communication network are also discussed.
Keywords: Traffic control; Traffic estimation; Real-time; MPC; Fuzzy; Isolated intersection; Networked intersection; Sensor fusion