Agent-Based Approach in Roadway Traffic and Transportation Systems: State of the Art

2007 ◽  
Author(s):  
Bo Chen ◽  
Harry H. Cheng ◽  
Joe Palen
Keyword(s):  
Large Scale ◽  
Dynamic Environment ◽  
Congestion Management ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Agent Technology ◽  
Future Research ◽  
Traffic Signal Control ◽  
Multi Agent Systems ◽  
Agent Based

Agent technology is rapidly emerging as one of the powerful technologies for the development of large-scale distributed systems to deal with the uncertainty in a dynamic environment. The domain of traffic and transportation systems is well suited for an agent-based approach because systems are usually geographically distributed in dynamic changing environments. Our literature survey shows that the techniques and methods resulted from the field of agent and multi-agent systems have been applied to many aspects of traffic and transportation systems, including modeling and simulation, dynamic routing and congestion management, intelligent traffic management, and urban traffic signal control. This paper examines agent-based approach and its applications in roadway traffic and transportation systems, and discusses several future research directions towards successful deployment of agent technology in traffic and transportation systems.

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

scholarly journals Trust in multi-agent systems

2004 ◽  
Vol 19 (1) ◽  
pp. 1-25 ◽  
Author(s):  
SARVAPALI D. RAMCHURN ◽  
DONG HUYNH ◽  
NICHOLAS R. JENNINGS
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Comprehensive Treatment ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Agent Systems ◽  
The Core ◽  
Open Distributed Systems ◽  
Multi Agent

Trust is a fundamental concern in large-scale open distributed systems. It lies at the core of all interactions between the entities that have to operate in such uncertain and constantly changing environments. Given this complexity, these components, and the ensuing system, are increasingly being conceptualised, designed, and built using agent-based techniques and, to this end, this paper examines the specific role of trust in multi-agent systems. In particular, we survey the state of the art and provide an account of the main directions along which research efforts are being focused. In so doing, we critically evaluate the relative strengths and weaknesses of the main models that have been proposed and show how, fundamentally, they all seek to minimise the uncertainty in interactions. Finally, we outline the areas that require further research in order to develop a comprehensive treatment of trust in complex computational settings.

scholarly journals SUMO Based Platform for Cooperative Intelligent Automotive Agents

10.29007/sc13 ◽  
2019 ◽  
Author(s):  
Levente Alekszejenkó ◽  
Tadeusz P. Dobrowiecki
Keyword(s):  
Traffic Control ◽  
Urban Traffic ◽  
Future Research ◽  
Complex Solution ◽  
Agent Based ◽  
Traffic Lights ◽  
Lane Changes ◽  
Control Scheme ◽  
Variable Topology ◽  
Intelligent Communication

Starting from the problems of nowadays’ urban traffic (congestions, imperfect timing of traffic lights, high impact of lane changes) we investigate the feasibility of a cooperative intelligent agent based solution as an overall control scheme governing the car flow in congested urban intersections.The proposed complex solution features both the intelligent traffic control and the car platooning. In order to test and verify the merits of the proposed solution in urban intersection of a widely variable topology, but also to support our future research aims, a simulation platform, extending the basic functionalities of SUMO with the options of intelligent communication and cooperative co-acting, was designed and developed.

scholarly journals DEVELOPMENT OF A MULTI-AGENT SYSTEM FOR THE ASSESSMENT OF ECOLOGICAL INFLUENCES OF TRANSPORT MEGAPROJECTS: TAKING INTO ACCOUNT THE INFORMATION ENVIRONMENT FACTOR

2020 ◽  
Vol 3 (1) ◽  
pp. 61-67
Author(s):  
Tatyana N. Yesikova ◽  
Svetlana V. Vakhrusheva
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
A Priori ◽  
Dynamic Environment ◽  
Information Environment ◽  
Information Space ◽  
Bering Strait ◽  
Multi Agent Systems ◽  
Multi Agent ◽  
Business Entities

The paper considers the issues of accounting and reflection in multi-agent systems of the influence of the information environment, information flows on agent behavior and the assessment of consequences, including environmental ones, of decisions made by them at various stages of large-scale infrastructure projects. The information space is a priori a multidimensional dynamic environment that is continuously updated and transformed, sometimes under the primacy of the interests of individual agents or influence groups, and much less frequently from the standpoint of ensuring the viability of the economic system as a whole. A large-scale project for the construction of a transcontinental highway (TKS) through the Bering Strait was chosen as the object of study. The article provides a fairly detailed description of the groups of agents involved in the decision-making process, as well as the elements of the information space that are significant for an agent at certain stages of its activity. To model the influence of the information space on decision-making processes by agents of different hierarchy levels (business entities, managerial entities, etc.), algorithms and special procedures have been developed.

Agent-¬based matching of demands and supplies in business transaction formation

2011 ◽  
pp. 58-77
Author(s):  
Raymund J. Lin ◽  
Seng-Cho T. Chou
Keyword(s):  
Game Theory ◽  
Mechanism Design ◽  
Agent Technology ◽  
Heuristic Methods ◽  
Future Trends ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Agent Systems ◽  
Business Transaction ◽  
Multi Agent

The theme of this chapter includes topics of matching, auction and negotiation. We have shown that the knowledge of game theory is very important when designing an agent-based matching or negotiation system. The problem of bounded rationality in multi¬-agent systems is also discussed; we put forward the mechanism design and heuristic methods as solutions. A real negotiation scenario is presented to demonstrate our proposed solutions. In addition, we discuss the future trends of the agent technology in e¬commerce system.

Analysis of Dynamic Impedance Functions in Urban Traffic Flow Guidance System

2012 ◽  
Vol 238 ◽  
pp. 503-506 ◽  
Author(s):  
Zhi Cheng Li
Keyword(s):  
Traffic Flow ◽  
Network Model ◽  
Large Scale ◽  
Choice Model ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Guidance System ◽  
Traffic Network ◽  
Dynamic Traffic ◽  
Run Time

The successful application of Intelligent Transportation Systems (ITS) depends on the traffic flow at any time with high-precision and large-scale assessments, it is necessary to create a dynamic traffic network model to evaluate and forecast traffic. Dynamic route choice model sections of the run-time function are very important to the dynamic traffic network model. To simplify the dynamic traffic modeling, improve the calculation accuracy and save computation time, the flow on the section of the interrelationship between the exit flow and number of vehicles are analyzed, a run-time functions into the flow using only sections of the said sections are established.

scholarly journals OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 560 ◽  
Author(s):  
Amira Mimouna ◽  
Ihsen Alouani ◽  
Anouar Ben Khalifa ◽  
Yassin El Hillali ◽  
Abdelmalik Taleb-Ahmed ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Weather Conditions ◽  
Autonomous Driving ◽  
Radar Data ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Future Research ◽  
Acoustic Data ◽  
Cluttered Environment ◽  
Environment Perception

A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception. This is the first public, multimodal and synchronized dataset that includes UWB radar data, acoustic data, narrow-band radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, presenting four categories: pedestrian, cyclist, car and tram. The dataset includes various challenges related to dense urban traffic such as cluttered environment and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research.

scholarly journals Large-Scale Traffic Congestion Prediction Based on the Symmetric Extreme Learning Machine Cluster Fast Learning Method

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 730 ◽  
Author(s):  
Yiming Xing ◽  
Xiaojuan Ban ◽  
Xu Liu ◽  
Qing Shen
Keyword(s):  
Extreme Learning Machine ◽  
Traffic Congestion ◽  
Large Scale ◽  
High Efficiency ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Fast Learning ◽  
Congestion Prediction ◽  
Learning Machine ◽  
Scale Data

The prediction of urban traffic congestion has emerged as one of the most pivotal research topics of intelligent transportation systems (ITSs). Currently, different neural networks have been put forward in the field of traffic congestion prediction and have been put to extensive use. Traditional neural network training takes a long time in addition to easily falling into the local optimal and overfitting. Accordingly, this inhibits the large-scale application of traffic prediction. On the basis of the theory of the extreme learning machine (ELM), the current paper puts forward a symmetric-ELM-cluster (S-ELM-Cluster) fast learning methodology. In this suggested methodology, the complex learning issue of large-scale data is transformed into different issues on small- and medium-scale data sets. Additionally, this methodology makes use of the extreme learning machine algorithm for the purpose of training the subprediction model on each different section of road, followed by establishing a congestion prediction model cluster for all the roads in the city. Together, this methodology fully exploits the benefits associated with the ELM algorithm in terms of accuracy over smaller subsets, high training speed, fewer parameters, and easy parallel acceleration for the realization of high-accuracy and high-efficiency large-scale traffic congestion data learning.

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