The combination of Artificial Intelligence (AI) and Internet-of-Things (IoT), which is denoted as AI-powered Internet-of-Things (AIoT), is capable of processing huge amount of data generated from a large number of devices and handling complex problems in social infrastructures. As AI and IoT technologies are becoming mature, in this paper, we propose to apply AIoT technologies for traffic light control, which is an essential component for intelligent transportation system, to improve the efficiency of smart city's road system. Specifically, various sensors such as surveillance cameras provide real-time information for intelligent traffic light control system to observe the states of both motorized traffic and non-motorized traffic. In this paper, we propose an intelligent traffic light control solution by using distributed multi-agent Q learning, considering the traffic information at the neighboring intersections as well as local motorized and non-motorized traffic, to improve the overall performance of the entire control system. By using the proposed multi-agent Q learning algorithm, our solution is targeting to optimize both the motorized and non-motorized traffic. In addition, we considered many constraints/rules for traffic light control in the real world, and integrate these constraints in the learning algorithm, which can facilitate the proposed solution to be deployed in real operational scenarios. We conducted numerical simulations for a real-world map with real-world traffic data. The simulation results show that our proposed solution outperforms existing solutions in terms of vehicle and pedestrian queue lengths, waiting time at intersections, and many other key performance metrics.
Based on game theory and dynamic Level-k model, this paper establishes an intelligent traffic control method for intersections, studies the influence of multi-agent vehicle joint decision-making and group behavior disturbance on system state. The simulation results show that this method has a good performance when there are more vehicles or emergency vehicles have higher priority.
Open radio access network (ORAN) Alliance offers a disaggregated RAN functionality built using open interface specifications between blocks. To efficiently support various competing services, \textit{namely} enhanced mobile broadband (eMBB) and ultra-reliable and low-latency (uRLLC), the ORAN Alliance has introduced a standard approach toward more virtualized, open and intelligent networks. To realize benefits of ORAN in optimizing resource utilization, this paper studies an intelligent traffic steering (TS) scheme within the proposed disaggregated ORAN architecture. For this purpose, we propo…
This paper implements a traffic signal control system by using real-time traffic flow feedback. This system is designed to deal with two-lane intersections. We construct an experiment field similar to the roads and drivers in Taiwan using an autonomous simulation software called Virtual Test Drive (VTD) released by MSC Software. We erect four cameras on the side of the roads to get the image of the intersection, then transfer the image information into traffic flow information. Analyze the traffic information in each lane by using Greenshields traffic flow model. Control the traffic signals by…
Intelligent transport systems have efficiently and effectively proved themselves in settling up the problem of traffic congestion around the world. The multi-agent based transportation system is one of the most important intelligent transport systems, which represents an interaction among the neighbouring vehicles, drivers, roads, infrastructure and vehicles. In this paper, two traffic management models have been created to mitigate congestion and to ensure that emergency vehicles arrive as quickly as possible. A tool-chain SUMO-JADE is employed to create a microscopic simulation symbolizing t…
In this paper, methods have been explored to effectively optimise traffic signal control to minimise waiting times and queue lengths, thereby increasing traffic flow. The traffic intersection was first defined as a Markov Decision Process, and a state representation, actions and rewards were chosen. Simulation of Urban MObility (SUMO) was used to simulate an intersection and then compare a Round Robin Scheduler, a Feedback Control mechanism and two Reinforcement Learning techniques - Deep Q Network (DQN) and Advantage Actor-Critic (A2C), as the policy for the traffic signal in the simulation u…
In this paper, a unified multi-vehicle formation control framework for Intelligent and Connected Vehicles (ICVs) that can apply to multiple traffic scenarios is proposed. In the one-dimensional scenario, different formation geometries are analyzed and the interlaced structure is mathematically modelized to improve driving safety while making full use of the lane capacity. The assignment problem for vehicles and target positions is solved using Hungarian Algorithm to improve the flexibility of the method in multiple scenarios. In the two-dimensional scenario, an improved virtual platoon method …
Intelligent traffic signal controllers, applying DQN algorithms to traffic light policy optimization, efficiently reduce traffic congestion by adjusting traffic signals to real-time traffic. Most propositions in the literature however consider that all vehicles at the intersection are detected, an unrealistic scenario. Recently, new wireless communication technologies have enabled cost-efficient detection of connected vehicles by infrastructures. With only a small fraction of the total fleet currently equipped, methods able to perform under low detection rates are desirable. In this paper, we …
SEIDM: A Safe and Efficient Intelligent Driver Model for Autonomous Driving Behavior
Department of Information and Communication Engineering, Tongji University（信息与通信工程系，同济大学）；Department of Electrical and Computer Engineering, Duke University（电气与计算机工程系，杜克大学）
In this paper, we design a stochastic Model Predictive Control (MPC) traffic signal control method for an urban traffic network when the uncertainties in the estimation of the exogenous (in/out)-flows and the turning ratios of downstream traffic flows are taken into account. Assuming that the traffic model parameters are random variables with known expectations and variance, the traffic signal control and coordination problem is formulated as a quadratic program with linear and second-order cone constraints. In order to reduce computational complexity, we suggest a way to decompose the optimiz…
Connected and automated vehicles (CAVs) have the potential to improve traffic throughput and achieve a more efficient utilization of the available roadway infrastructure. They also have the potential to reduce energy consumption through traffic motion harmonization, even when operating in mixed traffic with other human-driven vehicles. The key to realizing these potentials are coordinated control schemes that can be implemented in a distributed manner with the CAVs. In this paper, we propose a distributed predictive control framework that features a two-dimensional maneuver planner incorporati…
Effective traffic optimization strategies can improve the performance of transportation networks significantly. Most exiting works develop traffic optimization strategies depending on the local traffic states of congested road segments, where the congestion propagation is neglected. This paper proposes a novel distributed traffic optimization method for urban freeways considering the potential congested road segments, which are called potential-homogeneous-area. The proposed approach is based on the intuition that the evolution of congestion may affect the neighbor segments due to the mobility…
Distributed Traffic Signal Control for Maximum Network Throughput
Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院研究联盟）；Nanyang Technological University（南洋理工大学）；Massachusetts Institute of Technology（麻省理工学院）
Motivated by the fact that intelligent traffic control systems have become inevitable demand to cope with the risk of traffic congestion in urban areas, this paper develops a distributed control strategy for urban traffic networks. Since these networks contain a large number of roads having different directions, each of them can be described as a multi-agent system. Thus, a coordination among traffic flows is required to optimize the operation of the overall network. In order to determine control decisions, we describe the objective of improving traffic conditions as a constrained optimization…
This paper presents a distributed synchronization strategy for connected and automated vehicles in traffic networks. The strategy considers vehicles traveling from one intersection to the next as waves. The phase angle and frequency of each wave map to its position and velocity, respectively. The goal is to synchronize traffic such that intersecting traffic waves are out of phase at every intersection. This ensures the safe collective navigation of intersections. Vehicles share their phase angles through the V2X infrastructure, and synchronize these angles using the Kuramoto equation. This is …
Throughput Optimal Distributed Traffic Signal Control
Ministry of Science and Technology, Thailand（泰国科技部）；Singapore-MIT Alliance for Research and Technology, Singapore（新加坡-麻省理工学院研究联盟）；Massachusetts Institute of Technology, Cambridge, MA, USA（麻省理工学院）；Nanyang Technological University, Singapore（南洋理工大学）
Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework
VinUniversity（VinUniversity（文大学））；University of Luxembourg（卢森堡大学）；University of Oulu（奥卢大学）；PHENIKAA University（PHENIKAA大学（菲尼卡大学））；Purdue University（普渡大学）；University of Technology Sydney（悉尼科技大学）
In this paper, a novel distributed scheduling algorithm is proposed, which aims to efficiently schedule both the uplink and downlink backhaul traffic in the relay-assisted mmWave backhaul network with a tree topology. The handshaking of control messages, calculation of local schedules, and the determination of final valid schedule are all discussed. Simulation results show that the performance of the distributed algorithm can reach very close to the maximum traffic demand of the backhaul network, and it can also adapt to the dynamic traffic with sharp traffic demand change of small-cell BSs qu…
Global Center for Automotive Performance Simulation（全球汽车性能模拟中心）；Honda Research Institute, USA Inc.（美国本田研究院）；Virginia Tech Transportation Institute（弗吉尼亚理工交通研究所）
Department of Civil, Architectural, and Environmental Engineering, University of Texas at Austin（德克萨斯大学奥斯汀分校土木、建筑与环境工程系）；Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology（巴布里·纳希里万理工大学电气与计算机工程学院）；Transportation Research Institute (IMOB), UHasselt-Hasselt University（乌赫斯泰特大学交通研究所）；Faculty of Mechanical Engineering, Babol Noshirvani University of Technology（巴布里·纳希里万理工大学机械工程学院）；Department of Computer Engineering, Amirkabir University of Technology（阿姆irkabir技术大学计算机工程系）
