The use of IoT and AI/ML to extract insights for Data-Driven Decision-Making (DDDM) in Intelligent Traffic Systems (ITS) is becoming increasingly popular. While simulation is a cost-effective and safe way to evaluate such approaches, existing simulators are often impractical due to inefficient control interfaces. In this work, we propose a Discrete-Event, Aggregating, and Relational Control Interfaces (DAR-CI) framework for achieving efficient traffic management simulations through a coupled approach. It enables a non-blocking interaction mode based on a discrete-event synchronization architecture. The overhead caused by data exchange is substantially reduced by supporting the direct retrieval of temporal metrics, data batch processing and customized in-situ aggregation. Combined with flexible, extendable, easy-to-understand, and implementation-friendly semantic specifications, we propose DAR-CI to serve as a universal tool for the traffic simulation community, taking the use and control of traffic simulation to a new level. A proof-of-concept study on the simulation of an adaptive traffic light control system demonstrates a 9.53X speedup compared to TraCI, a widely used protocol for controlling traffic simulators.     «

The use of IoT and AI/ML to extract insights for Data-Driven Decision-Making (DDDM) in Intelligent Traffic Systems (ITS) is becoming increasingly popular. While simulation is a cost-effective and safe way to evaluate such approaches, existing simulators are often impractical due to inefficient control interfaces. In this work, we propose a Discrete-Event, Aggregating, and Relational Control Interfaces (DAR-CI) framework for achieving efficient traffic management simulations through a coupled app...     »