Active Traffic Signal Decisions Using Vector‐Matrix Multiplication. (18th January 2023) - Jang, Jingon; Jeon, Takgyeong; Wang, Gunuk
Abstract : 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. Abstract : A novel methodology of vector‐matrix multiplication (VMM) architecture is suggested for intelligently determining traffic signal changes in real time to enhance the flow of urban traffic. The VMM system actively optimizes the signal configuration in an irregular shape according to the traffic density distribution, resulting in a reduction in the time cost by ≈10%.