A novel methodology in the manner of vector-matrix multiplication (VMM) architecture is suggested for intelligently determining traffic signal changes to enhance the flow of urban traffic. Unlike the conventional prediction-based traffic model, a real-time decision model considering the traffic density at each transport section is established, which simplifies the traffic signal decision process as a convolutional transformation. Compared with a periodically repetitive signal changing system, the suggested VMM system actively optimizes the signal configuration in an irregular shape according to the traffic density distribution, resulting in reduction in the time cost with highly improved decision efficiency. With this system based on particle dynamics, the travel time is reduced by ≈10% at the same pass ratio for different road structures (one-way, bidirectional, and intersectional transport). The pass ratio and resulting flow dynamics can be controllable using the different transformation matrix selections according to the traffic conditions. In addition, the analog conductance of the memristor device to the transformation matrix elements is applied, maintaining its reduction rate with a deviation tolerance of the VMM process up to ≈50%. It is believed that VMM-based signal decision platform can lead to great progress for fast and efficient transport in complex urban traffic networks.
Bibliographical noteFunding Information:
The authors acknowledge financial support from the KU-KIST research fund, a Korea University Grant, the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2022M3H4A1A01009526 and NRF-2022R1A2B5B02001455) and the Basic Science Research Program through the NRF funded by the Ministry of Education (2020R1I1A1A01073059 and 2022R1I1A1A01073911).
© 2023 The Authors. Advanced Intelligent Systems published by Wiley-VCH GmbH.
- signal decisions
- traffic control vector-matrix multiplications
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
- Mechanical Engineering
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Materials Science (miscellaneous)