An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control

Abstract

With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment.

A Center-Rule-Based Neighborhood Search Algorithm for Roadside Units Deployment in Emergency Scenarios

“…As shown in Figure 2, R 2 1 can't relay the packet to car B, because car B is not in the communication range of R 2 1 . There are no vehicles in C 2 1 , but we hope that the data packet can be transmitted from A before car A runs into C 1 1 ; the data packet needs to be sent to car B during the time t 1 :…”

“…Formula (5) represents the distance limit of the distance between A and B, and we deduced that d AB should be less than or equal to 3R according to the maximum speed of car B which should be greater than or equal to zero. In other words, car B can receive the packet only if it is in the first half of C 1 1 in the driving direction and its speed meets Equation (4). Otherwise, if the speed of car B is the same as car A, the packet will be relayed to R 0 1 when car A runs into C 0 1 .…”

“…• Car B is running in the opposite direction to car A in C 1 1 , whether there is a RSU or not in C 1 1 . As shown in Figure 3, there is no car in the same direction as Car A in the first half of C 1 1 in the driving direction. If car B arrives before time t 1 , it can receive the message from R 2 1 .…”

“…We assume that time t 2 meets (6), so car B needs car C as a data repeater to transmit the packet to car D, as shown in Figure 4. Because there is no car in the same direction as Car A in the first half of C 1 1 in the driving direction, once car B runs into C 2 1 , the packet can't be transmitted from B to D due to the limit on the communication range even if car D stops in the second half of C 1 1 . Thus, we need car C as a data repeater and require that the distance between B and C is less than R. We assume that B and C run at the same speed as shown in Figure 4.…”

“…The shortage of traffic infrastructures with the Internet of Things (IoT) and the growing number of vehicles are important reasons for traffic congestion, which has increased travel time and costs of travelers and even caused crashes [1]. V2X (Vehicle to Everything) refers to a combination of vehicles and IoT technology, which includes Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Pedestrian (V2P), and Vehicle-to-Network (V2N), namely passing information from a vehicle to any entity that may affect the vehicle, and vice versa.…”