Increased stress, fuel consumption, air pollution, accidents and delays are some of the consequences of traffic congestion usually incurring in tremendous economic impacts, which society aims to remedy in order to leverage a sustainable development. Recently, unconventional means for modeling and controlling such complex traffic systems relying on multi-agent systems have arisen. This paper contributes to the understanding of such complex and highly dynamic systems by proposing an open-source tool-chain to implement multi-agent-based solutions in traffic and transportation. The proposed approach relies on two very popular tools in both domains, with focus on traffic light control. This tool-chain consists in combining JADE (Java Agent DEvelopment Framework), for the implementation of multi-agent systems, with SUMO (Simulation of Urban MObility), for the microscopic simulation of traffic interactions. TraSMAPI (Traffic Simulation Manager Application Programming Interface) is used to combine JADE and SUMO allowing communication between them. A demonstration of the concept is presented to illustrate the main features of this tool-chain, using Q-Learning as the reinforcement learning method for each traffic light agent in a simulated network. Results demonstrate the feasibility of the proposed framework as a practical means to experiment with different agent-based designs of intelligent transportation solutions.
To date, there are not many solutions for traffic that make full use of the intelligent agent concept. However, the multi-agent system approach has become recognized as a convenient approach for modelling and simulating complex systems @cite_19 . Also, it has grown enormously not only applied to traffic but also to transportation in general terms @cite_20 .
@cite_19 @cite_20
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Advances in autonomous vehicles and intelligent transportation systems indicate a rapidly approaching future in which intelligent vehicles will automatically handle the process of driving. However, increasing the efficiency of today's transportation infrastructure will require intelligent traffic control mechanisms that work hand in hand with intelligent vehicles. To this end, Dresner and Stone proposed a new intersection control mechanism called Autonomous Intersection Management (AIM) and showed in simulation that by studying the problem from a multiagent perspective, intersection control can be made more efficient than existing control mechanisms such as traffic signals and stop signs. We extend their study beyond the case of an individual intersection and examine the unique implications and abilities afforded by using AIM-based agents to control a network of interconnected intersections. We examine different navigation policies by which autonomous vehicles can dynamically alter their planned paths, observe an instance of Braess' paradox, and explore the new possibility of dynamically reversing the flow of traffic along lanes in response to minute-by-minute traffic conditions. Studying this multiagent system in simulation, we quantify the substantial improvements in efficiency imparted by these agent-based traffic control methods. Artificial intelligence research is ushering in a new era of sophisticated, mass-market transportation technology. While computers can already fly a passenger jet better than a trained human pilot, people are still faced with the dangerous yet tedious task of driving automobiles. Intelligent Transportation Systems (ITS) is the field that focuses on integrating information technology with vehicles and transportation infrastructure to make transportation safer, cheaper, and more efficient. Recent advances in ITS point to a future in which vehicles themselves handle the vast majority of the driving task. Once autonomous vehicles become popular, autonomous interactions amongst multiple vehicles will be possible. Current methods of vehicle coordination, which are all designed to work with human drivers, will be outdated. The bottleneck for roadway efficiency will no longer be the drivers, but rather the mechanism by which those drivers' actions are coordinated. While open-road driving is a well-studied and more-or-less-solved problem, urban traffic scenarios, especially intersections, are much more challenging. We believe current methods for controlling traffic, specifically at intersections, will not be able to take advantage of the increased sensitivity and precision of autonomous vehicles as compared to human drivers. In this article, we suggest an alternative mechanism for coordinating the movement of autonomous vehicles through intersections. Drivers and intersections in this mechanism are treated as autonomous agents in a multiagent system. In this multiagent system, intersections use a new reservation-based approach built around a detailed communication protocol, which we also present. We demonstrate in simulation that our new mechanism has the potential to significantly outperform current intersection control technology--traffic lights and stop signs. Because our mechanism can emulate a traffic light or stop sign, it subsumes the most popular current methods of intersection control. This article also presents two extensions to the mechanism. The first extension allows the system to control human-driven vehicles in addition to autonomous vehicles. The second gives priority to emergency vehicles without significant cost to civilian vehicles. The mechanism, including both extensions, is implemented and tested in simulation, and we present experimental results that strongly attest to the efficacy of this approach. The operation of large dynamic systems such as urban traffic networks remains a challenge in control engineering to a great extent due to their sheer size, intrinsic complexity, and nonlinear behavior. Recently, control engineers have looked for unconventional means for modeling and control of complex dynamic systems, in particular the technology of multi-agent systems whose appeal stems from their composite nature, flexibility, and scalability. This paper contributes to this evolving technology by proposing a framework for multi-agent control of linear dynamic systems, which decomposes a centralized model predictive control problem into a network of coupled, but small sub-problems that are solved by the distributed agents. Theoretical results ensure convergence of the distributed iterations to a globally optimal solution. The framework is applied to the signaling split control of traffic networks. Experiments conducted with simulation software indicate that the multi-agent framework attains performance comparable to conventional control. The main advantages of the multi-agent framework are its graceful extension and localized reconfiguration, which require adjustments only in the control strategies of the agents in the vicinity. Traffic flow forecasting has been regarded as a key problem of intelligent transport systems. In this work, we propose a hybrid multimodal deep learning method for short-term traffic flow forecasting, which can jointly and adaptively learn the spatial-temporal correlation features and long temporal interdependence of multi-modality traffic data by an attention auxiliary multimodal deep learning architecture. According to the highly nonlinear characteristics of multi-modality traffic data, the base module of our method consists of one-dimensional Convolutional Neural Networks (1D CNN) and Gated Recurrent Units (GRU) with the attention mechanism. The former is to capture the local trend features and the latter is to capture the long temporal dependencies. Then, we design a hybrid multimodal deep learning framework (HMDLF) for fusing share representation features of different modality traffic data by multiple CNN-GRU-Attention modules. The experimental results indicate that the proposed multimodal deep learning model is capable of dealing with complex nonlinear urban traffic flow forecasting with satisfying accuracy and effectiveness. An increasingly large number of cars are being equipped with global positioning system and Wi-Fi devices, enabling vehicle-to-vehicle (V2V) communication with the goal of providing increased passenger and road safety. This technology actuates the need for agents that assist users by intelligently processing the received information. Some of these agents might become self-interested and try to maximize car owners' utility by sending out false information. Given the dire consequences of acting on false information in this context, there is a serious need to establish trust among agents. The main goal of this paper is then to develop a framework that models the trustworthiness of the agents of other vehicles, in order to receive the most effective information. We develop a multifaceted trust modeling approach that incorporates role-, experience-, priority-, and majority-based trust and this is able to restrict the number of reports that are received. We include an algorithm that proposes how to integrate these various dimensions of trust, along with experimentation to validate the benefit of our approach, emphasizing the importance of each of the different facets that are included. The result is an important methodology to enable effective V2V communication via intelligent agents. In this paper we introduce a multi-faceted trust model of use for the application of ad-hoc vehicular networks (VANETs) – scenarios where agents representing drivers exchange information with other drivers regarding road and traffic conditions. We argue that there is a need to model trust in various dimensions and that combining these elements effectively can assist agents in making transportation decisions. We then introduce two new elements to our proposed model: i) distinguishing direct and indirect reports that are shared ii) employing a penalty for misleading reports, to promote honesty. We demonstrate how these two elements together serve to increase the value of the trust model, through a series of experiments of simulated traffic. In brief, we present a framework to facilitate the effective sharing of information in VANET environments between agents representing the vehicles. In many urban areas where traffic congestion does not have the peak pattern, conventional traffic signal timing methods does not result in an efficient control. One alternative is to let traffic signal controllers learn how to adjust the lights based on the traffic situation. However this creates a classical non-stationary environment since each controller is adapting to the changes caused by other controllers. In multi-agent learning this is likely to be inefficient and computationally challenging, i.e., the efficiency decreases with the increase in the number of agents (controllers). In this paper, we model a relatively large traffic network as a multi-agent system and use techniques from multi-agent reinforcement learning. In particular, Q-learning is employed, where the average queue length in approaching links is used to estimate states. A parametric representation of the action space has made the method extendable to different types of intersection. The simulation results demonstrate that the proposed Q-learning outperformed the fixed time method under different traffic demands. Accurate and robust recognition and prediction of traffic situation plays an important role in autonomous driving, which is a prerequisite for risk assessment and effective decision making. Although there exist a lot of works dealing with modeling driver behavior of a single object, it remains a challenge to make predictions for multiple highly interactive agents that react to each other simultaneously. In this work, we propose a generic probabilistic hierarchical recognition and prediction framework which employs a two-layer Hidden Markov Model (TLHMM) to obtain the distribution of potential situations and a learning-based dynamic scene evolution model to sample a group of future trajectories. Instead of predicting motions of a single entity, we propose to get the joint distribution by modeling multiple interactive agents as a whole system. Moreover, due to the decoupling property of the layered structure, our model is suitable for knowledge transfer from simulation to real world applications as well as among different traffic scenarios, which can reduce the computational efforts of training and the demand for a large data amount. A case study of highway ramp merging scenario is demonstrated to verify the effectiveness and accuracy of the proposed framework. In this paper, multi-agent systems minimizing a sum of objective functions, where each component is only known to a particular node, is considered for continuous-time dynamics with time-varying interconnection topologies. Assuming that each node can observe a convex solution set of its optimization component, and the intersection of all such sets is nonempty, the considered optimization problem is converted to an intersection computation problem. By a simple distributed control rule, the considered multi-agent system with continuous-time dynamics achieves not only a consensus, but also an optimal agreement within the optimal solution set of the overall optimization objective. Directed and bidirectional communications are studied, respectively, and connectivity conditions are given to ensure a global optimal consensus. In this way, the corresponding intersection computation problem is solved by the proposed decentralized continuous-time algorithm. We establish several important properties of the distance functions with respect to the global optimal solution set and a class of invariant sets with the help of convex and non-smooth analysis. This paper deals with the integration of agent technology in the emerging field of vehicular networks. The agents are empowered with domain knowledge and they can perform geospatial and temporal reasoning. For the domain knowledge we developed a vehicular network ontology. The geospatial reasoning is performed with AllegroGraph, while event reasoning in RacerPro. The vehicle overtaking scenario is used to exemplify our solution. This paper presents an approach towards intelligent route planning in public transport systems. The approach focuses on formal modelling of the semi-dynamic intelligent route planning and optimisation. For these purposes, it is essential to have a well developed formal model covering real-time and space aspects. The proposed solution allows designers to extend a public transport system with additional routes, which are created dynamically based on the requests from passengers. The model can be applied within a sustainable Smart City both for (fully or partially) autonomous transport systems and for the decision support systems of a smart transport system. One way to cope with the increasing demand in transportation networks is to integrate standard solutions with more intelligent measures. This problem has been approached from different sides such as the study of the assignment of the demand in the network, and the investigation of the effects of control measures. However, given that most of these approaches are complex and deal with different levels of abstraction of the original problem, there has been few attempts to address both demand and control in a single tool. This paper presents an overview of ITSUMO, a microscopic traffic simulator whose implementation uses agent technologies with a bottom-up philosophy in mind. We give an overview of the system and some details of its modules (simulation kernel, data management, control, driver and routing, and visualization), followed by an example of its use. I. I NTRODUCTION AND MOTIVATION The automation of driving tasks is of increasing interest for highway traffic management. The emerging technologies of global positioning and intervehicular wireless communications, combined with in-vehicle computation and sensing capabilities, can potentially provide remarkable improvements in safety and efficiency. We address the problem of designing intelligent in-tersections, where traffic lights and stop signs are removed, and cars negotiate the intersection through an interaction of centralized and distributed decision making. Intelligent intersections are representative of complex hybrid systems that are increasingly of interest, where the challenge is to design tractable distributed algorithms that guarantee safety and provide good performance. Systems of automatically driven vehicles will need an under lying collision avoidance system with provable safety properties to be acceptable. This condition raises several challenges. We need to ensure perpetual collision avoidance so that cars do not get into future problematic positions to avoid an immediate collision. The architecture needs to allow distributed freedom of action to cars yet should guard against worst-case behavior of other cars to guarantee collision avoidance. The algorithms should be tractable both computationally and in information requirements and robust to uncertainties in sensing and communication. To address these challenges, we propose a hybrid architecture with an appropriate interplay between centralized coordination and distributed freedom of action. The approach is built around a core where each car has an infinite horizon contingency plan, which is updated at each sampling instant and distributed by the cars, in a computationally tractable manner. We also define a dynamically changing partial-order relation between cars, which specifies, for each car, a set of cars whose worst-case behaviors it should guard against. The architecture is hybrid, involving a centralized component that coordinates intersection traversals. We prove the safety and liveness of the overall scheme. The mathematical challenge of accurately quantifying performance remains as a difficult challenge; therefore, we conduct a simulation study that shows the benefits over stop signs and traffic lights. It is hoped that our effort can provide methodologies for the design of tractable solutions for complex distributed systems that require safety and liveness guarantees. Ensuring transportation systems are efficient is a priority for modern society. Technological advances have made it possible for transportation systems to collect large volumes of varied data on an unprecedented scale. We propose a traffic signal control system which takes advantage of this new, high quality data, with minimal abstraction compared to other proposed systems. We apply modern deep reinforcement learning methods to build a truly adaptive traffic signal control agent in the traffic microsimulator SUMO. We propose a new state space, the discrete traffic state encoding, which is information dense. The discrete traffic state encoding is used as input to a deep convolutional neural network, trained using Q-learning with experience replay. Our agent was compared against a one hidden layer neural network traffic signal control agent and reduces average cumulative delay by 82 , average queue length by 66 and average travel time by 20 . Recently, several artificial intelligence labs have suggested the use of fully driverless vehicles with the capability of sensing the surrounding environment to enhance the roadway safety. The idea of having fully automated vehicles running in streets was inapplicable for many years until recently when researchers succeeded in releasing fully automated vehicles (without human drivers). Consequently, the paper develops a heuristic optimization algorithm for driverless vehicles at unsignalized intersections using a multi-agent system. The proposed system models the driverless vehicles as autonomous agents controlled by the intersection controller (manager agent). The input information of the system consists of vehicles’ current location, speed and acceleration in addition to the surrounding environment (weather, intersection characteristics, etc.). The intersection controller processes the input information using a built-in simulator: “OSDI” (Optimization Simulator for Driverless vehicles at Intersections). The simulator objective is to optimize the movements of vehicles to reduce the total delay time for the entire intersection and prevent crashes simultaneously. Thereafter, the intersection controller uses the simulator output for controlling the speed profile of the driverless vehicles within the intersection study zone. The proposed system is compared to two different intersection control scenarios: an All-way stop control (AWSC) and an Intersection manager with built-in OSDI. For both scenarios, it is assumed that there are four driverless vehicles (one vehicle per approach) willing to cross a four-legged unsignalized intersection concurrently. The results show using Monte Carlo simulation show that the proposed system reduces the total delay by 35 seconds on average compared to traditional AWSC. This research is considered as a first step in developing an unmanned vehicle technology system. We consider a multi-agent system where, at every time instant, many users choose to use one of multiple resources, whose performance depends on the number of concurrent users. In addition, a central agent has up-to-date knowledge of the congestion across all resources. What signals should the central agent broadcast to the individual users in order to reduce the total congestion? We model users with varying levels of aversion to risk, and a heterogeneous population of users over these levels. We consider signaling schemes that communicate for each resource an interval of possible congestion values, instead of scalar values. We show how to optimise over these intervals with respect to the social cost, under constraints that these intervals be consistent with past observations. Moreover, under mild assumptions, the resulting distribution of users across resources converges. In their goal to effectively manage the use of existing infrastructures, intelligent transportation systems require precise forecasting of near-term traffic volumes to feed real-time analytical models and traffic surveillance tools that alert of network links reaching their capacity. This article proposes a new methodological approach for short-term predictions of time series of volume data at isolated cross sections. The originality in the computational modeling stems from the fit of threshold values used in the stationary wavelet-based denoising process applied on the time series, and from the determination of patterns that characterize the evolution of its samples over a fixed prediction horizon. A self-organizing fuzzy neural network is optimized in its configuration parameters for learning and recognition of these patterns. Four real-world data sets from 3 interstate roads are considered for evaluating the performance of the proposed model. A quantitative comparison made with the results obtained by 4 other relevant prediction models shows a favorable outcome. Having access to the future traffic state information is crucial in maintaining successful intelligent transportation systems (ITS). However, predicting the future traffic state is a challenging research subject involving prediction reliability issues. Predictive performance measures, including the accuracy, efficiency, and stability, are generally considered as the most important priorities in the evaluation of prediction modules. Researchers have developed various K-nearest-neighbors-based searching algorithms that find the future state from the historical traffic patterns. Interestingly, there has not been sufficient effort made for improving the performance. For the emerging big data era, incorporating an efficient search strategy has become increasingly important since the applicability of the prediction module in ITS heavily relies on the efficiency of the searching method used. This paper develops a novel sequential search strategy for traffic state predictions. The proposed sequential strategy is found to be outperforming the conventional single-level search approach in terms of prediction measures, which are prediction accuracy, efficiency, and stability. Compared with the conventional approach, the proposed sequential method yields significantly more accurate results via internal hierarchical improvements across sublevels while maintaining excellent efficiency and stability. The ability to exert real-time, adaptive control of transportation processes is the core of many intelligent transportation systems decision support tools. Reinforcement learning, an artificial intelligence approach undergoing development in the machine- learning community, offers key advantages in this regard. The ability of a control agent to learn relationships between control actions and their effect on the environment while pursuing a goal is a distinct improvement over prespecified models of the environment. Prespecified models are a prerequisite of conventional control methods and their accuracy limits the performance of control agents. This paper contains an introduction to Q-learning, a simple yet powerful reinforcement learning algorithm, and presents a case study involving application to traffic signal control. Encouraging results of the application to an isolated traffic signal, particularly under variable traffic conditions, are presented. A broader research effort is outlined, including extension to linear and networked signal systems and integration with dynamic route guidance. The research objective involves optimal control of heavily congested traffic across a two-dimensional road network—a challenging task for conventional traffic signal control methodologies. Vehicles equipped with intelligent systems designed to prevent accidents, such as collision warning systems (CWSs) or lane-keeping assistance (LKA), are now on the market. The next step in reducing road accidents is to coordinate such vehicles in advance not only to avoid collisions but to improve traffic flow as well. To this end, vehicle-to-infrastructure (V2I) communications are essential to properly manage traffic situations. This paper describes the AUTOPIA approach toward an intelligent traffic management system based on V2I communications. A fuzzy-based control algorithm that takes into account each vehicle's safe and comfortable distance and speed adjustment for collision avoidance and better traffic flow has been developed. The proposed solution was validated by an IEEE-802.11p-based communications study. The entire system showed good performance in testing in real-world scenarios, first by computer simulation and then with real vehicles.