Parallel Closed-Loop Connected Vehicle Simulator for Large-Scale Transportation Network Management: Challenges, Issues, and Solution Approaches
The augmented scale and complexity of urban transportation networks have significantly increased the execution time and resource requirements of vehicular network simulations, exceeding the capabilities of sequential simulators. The need for a parallel and distributed simulation environment is inevi...
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Zusammenfassung: | The augmented scale and complexity of urban transportation networks have
significantly increased the execution time and resource requirements of
vehicular network simulations, exceeding the capabilities of sequential
simulators. The need for a parallel and distributed simulation environment is
inevitable from a smart city perspective, especially when the entire city-wide
information system is expected to be integrated with numerous services and ITS
applications. In this paper, we present a conceptual model of an Integrated
Distributed Connected Vehicle Simulator (IDCVS) that can emulate real-time
traffic in a large metro area by incorporating hardware-in-the-loop simulation
together with the closed-loop coupling of SUMO and OMNET++. We also discuss the
challenges, issues, and solution approaches for implementing such a parallel
closed-loop transportation network simulator by addressing transportation
network partitioning problems, synchronization, and scalability issues. One
unique feature of the envisioned integrated simulation tool is that it utilizes
the vehicle traces collected through multiple roadway sensors-DSRC onboard
unit, magnetometer, loop detector, and video detector. Another major feature of
the proposed model is the incorporation of hybrid parallelism in both
transportation and communication simulation platforms. We identify the
challenges and issues involved in IDCVS to incorporate this multi-level
parallelism. We also discuss the approaches for integrating
hardware-in-the-loop simulation, addressing the steps involved in preprocessing
sensor data, filtering, and extrapolating missing data, managing large
real-time traffic data, and handling different data formats. |
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DOI: | 10.48550/arxiv.1805.02007 |