Sensor-enabled Calibration of VR-Integrated Co-Simulation Platforms for Enhanced Accuracy in Multi-modal Mobility Models

Traffic simulations provide valuable insights into traffic control measures, infrastructure design, vehicle-to-vehicle communication, route selection behavior, emissions modeling, and more. SUMO (Simulation of Urban MObility), an open-source, microscopic, and multimodal traffic flow simulation platform, facilitates the creation of realistic traffic flow simulations by incorporating road networks, vehicles, pedestrians, and interactions with other applications such as virtual reality platforms and driver simulators. Calibration aims to bridge the gap between simulation outcomes and real-world observations; however, the effectiveness of calibration relies heavily on the realism of interactive behavior models for various agents, such as cars, drivers, bicyclists, pedestrians (including those with accessibility needs), and workers. Integrating multiple interactive simulations into a coherent representation of real-world transportation settings poses significant challenges due to the complexities of microsimulation, the diverse range of metrics, and varying traffic control systems. Calibration metrics, data sources, temporal scales, and spatial versus perceptual accuracies vary among platforms, leading to complexities when synchronizing and calibrating them collectively. To address these challenges, this research endeavors to meticulously calibrate a virtual ecosystem comprising diverse agents, including cars, pedestrians, workers, and agents with disabilities. By integrating data from multiple sensing sources like camera streams, drone data, connected vehicle information, and worker behaviors captured in virtual reality, the research seeks to tackle the intricacies of multi-simulation calibration. The proposed research project aims to embark on an innovative endeavor focused on the calibration of interactive and multi-simulation environments. Starting with SUMO and virtual reality environments, the project intends to create a vocabulary of calibration metrics for simulators, devise methods to expand data sources, and identify challenges on the way to establish a flexible framework for integrated calibration. Additionally, an existing testbed (e.g., Flatbush Avenue testbed) will be expanded to cover a disadvantaged community defined by U.S. DOT Climate and Economic Justice Screening Tool in order to identify the challenges on the way to the proposed calibration platform and defining ways to measure its efficiency, accuracy, and robustness. This research strives to contribute significantly to the field of multi-modal mobility solutions, ultimately enhancing congestion reduction strategies and advancing the capabilities of simulation-driven planning.


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Subject/Index Terms

Filing Info

  • Accession Number: 01897934
  • Record Type: Research project
  • Source Agency: Connected Communities for Smart Mobility Towards Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER)
  • Contract Numbers: 69A3551747124
  • Files: UTC, RIP
  • Created Date: Oct 30 2023 11:06PM