CBLAB: SCALABLE TRAFFIC SIMULATION WITH EN-RICHED DATA SUPPORTING

Abstract

Traffic simulation provides interactive data for the optimization of traffic control 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 City Brain Lab, a toolkit for scalable traffic simulation. CBLab consists 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, CB-Scenario implements an interactive environment and several baseline methods for two scenarios of traffic control policies respectively, with which traffic control 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: https://github.com/CityBrainLab/CityBrainLab.git.



. These policies depend on data generated by interaction with the traffic environment where they explore to make good decisions under different consequences. However, real-world urban traffic cannot provide enough interactive data to train these policies, because the exploration of the policy may have a toxic impact on the urban traffic e.g. provoke severe congestion. Traffic simulators are therefore born as alternatives to provide traffic environments for traffic control policies to interact with. These simulators Lopez et al. ( 2018 For each time step, they describe the traffic state, obtain a traffic action from the decision of traffic control policies and make it happen in the simulation. traffic control policies can then learn from how the traffic evolves under certain actions and improve decision making.



Figure 1: An overview of CBLab.

); Zhang et al. (2019); Chen et al. (2020b) simulate the microscopic evolution of the urban traffic.

