meng2023towards
Abstract
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.
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Contact
- Zhuoxiao Meng
- Anibal Siguenza-Torres
- Mingyue Gao
- Margherita Grossi
- Alexander Wieder
- Xiaorui Du
- Stefano Bortoli
- Christoph Sommer
- Alois Knoll
BibTeX reference
@inproceedings{meng2023towards,
author = {Meng, Zhuoxiao and Siguenza-Torres, Anibal and Gao, Mingyue and Grossi, Margherita and Wieder, Alexander and Du, Xiaorui and Bortoli, Stefano and Sommer, Christoph and Knoll, Alois},
title = {{Towards Discrete-Event, Aggregating, and Relational Control Interfaces for Traffic Simulation}},
booktitle = {2023 ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS 2023)},
address = {Orlando, FL},
doi = {10.1145/3573900.3591116},
month = {June},
pages = {12--22},
publisher = {ACM},
year = {2023},
}
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