CBLab: Scalable Traffic Simulation with Enriched Data SupportingDownload PDF

Published: 01 Feb 2023, Last Modified: 17 Sept 2023Submitted to ICLR 2023Readers: Everyone
Keywords: Infrastructure, Traffic Policy, Traffic Simulation, Large-scale Dataset
TL;DR: We present CBLab, a toolkit for scalable traffic simulation with enriched input data supporting.
Abstract: Traffic simulation provides interactive data for the optimization of traffic policies. However, existing traffic simulators are limited by their lack of scalability and shortage in input data, which prevents them from generating interactive data from traffic simulation in the scenarios of real large-scale city road networks. In this paper, we present \textbf{C}ity \textbf{B}rain \textbf{Lab}, a toolkit for scalable traffic simulation. CBLab is consist of three components: CBEngine, CBData, and CBScenario. CBEngine is a highly efficient simulator supporting large-scale traffic simulation. CBData includes a traffic dataset with road network data of 100 cities all around the world. We also develop a pipeline to conduct a one-click transformation from raw road networks to input data of our traffic simulation. Combining CBEngine and CBData allows researchers to run scalable traffic simulations in the road network of real large-scale cities. Based on that, CBScenario implements an interactive environment and several baseline methods for two scenarios of traffic policies respectively, with which traffic policies adaptable for large-scale urban traffic can be trained and tuned. To the best of our knowledge, CBLab is the first infrastructure supporting traffic policy optimization in large-scale urban scenarios. The code is available on GitHub:~\url{https://github.com/CityBrainLab/CityBrainLab.git}.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
Submission Guidelines: Yes
Please Choose The Closest Area That Your Submission Falls Into: Infrastructure (eg, datasets, competitions, implementations, libraries)
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 6 code implementations](https://www.catalyzex.com/paper/arxiv:2210.00896/code)
16 Replies