A multi-agent architecture for cooperative inter-jurisdictional traffic congestion management

This paper describes , an innovative multi-agent architecture for the provision of real-time decision support to Traffic Operations Center personnel for coordinated, inter-jurisdictional traffic congestion management on freeway and surface street (arterial) networks. is composed of two interacting knowledge-based systems that perform cooperative reasoning and resolve conflicts, for the analysis of non-recurring congestion and the on-line formulation of integrated control plans. The two agents support incident management operations for a freeway and an adjacent arterial subnetwork and interact with human operators, determining control recommendations in response to the occurrence of incidents. The multi-decision maker approach adopted by reflects the spatial and administrative organization of traffic management agencies in US cities, providing a cooperative solution that exploits the agencies’ willingness to cooperate and unify their problem-solving capabilities, yet preserves the different levels of authority and the inherent distribution of data and expertise. The interaction between the agents is based on the functionally accurate, cooperative paradigm, a distributed problem solving approach aimed at producing consistent solutions without requiring the agents to have shared access to all globally available information. The cornerstone of this approach is the assumption that effective solutions can be efficiently obtained even when complete and up-to-date information is not directly available to the agents, thus reducing the need for complex data communication networks and synchronization time delays. The simulation-based evaluation of the system performance validates this assumption. The paper focuses on the distributed architecture of the agents and on their communication and decision making characteristics.     

Development and evaluation of a knowledge-based system for traffic congestion management and control

This paper describes a real-time knowledge-based system (KBS) for decision support to Traffic Operation Center personnel in the selection of integrated traffic control plans after the occurrence of non-recurring congestion, on freeway and arterial networks. The uniqueness of the system, called TCM, lies in its ability to cooperate with the operator, by handling different sources of input data and inferred knowledge, and providing an explanation of its reasoning process. A data fusion algorithm for the analysis of congestion allows to represent and interpret different types of data, with various levels of reliability and uncertainty, to provide a clear assessment of traffic conditions. An efficient algorithm for the selection of control plans determines alternative traffic control responses. These are proposed to an operator, along with an explanation of the reasoning process that led to their development and an estimation of their expected effect on traffic. The validation of the system, which is one of only few examples of validation of a KBS in transportation, demonstrates the validity of the approach. The evaluation results, in a simulated environment demonstrate the ability of TCM to reduce congestion, through the formulation of traffic diversion and control schemes.     

A methodology for evaluating the performance of model-based traffic prediction systems

Model-based traffic prediction systems (mbTPS) are a central component of the decision support and ICM (integrated corridor management) systems currently used in several large urban traffic management centers. These models are intended to generate real-time predictions of the system’s response to candidate operational interventions. They must therefore be kept calibrated and trustworthy. The methodologies currently available for tracking the validity of a mbTPS have been adapted from approaches originally designed for off-line operational planning models. These approaches are insensitive to the complexity of the network and to the amount and quality of the data available. They also require significant human intervention and are therefore not suitable for real-time monitoring. This paper outlines a set of criteria for designing tests that are appropriate for the mbTPS task. It also proposes a test that meets the criteria. The test compares the predictions of the mbTPS in question to those of a model-less alternative. A t-test is used to determine whether the predictions of the mbTPS are superior to those of the model-less predictor. The approach is applied to two different systems using data from the I-210 freeway in Southern California.     

Multiagent architectures for intelligent traffic management systems

This paper reports our experiences with agent-based architectures for intelligent traffic management systems. We describe and compare integrated TRYS and TRYS autonomous agents, two multiagent systems that perform decision support for real-time traffic management in the urban motorway network around Barcelona. Both systems draw upon traffic management agents that use similar knowledge-based reasoning techniques in order to deal with local traffic problems. Still, the former achieves agent coordination based on a traditional centralized mechanism, while in the latter coordination emerges upon the lateral interaction of autonomous traffic management agents. We evaluate the potentials and drawbacks of both multiagent architectures for the domain, and develop some conclusions respecting the general applicability of multiagent architectures for intelligent traffic management.     

RENAISSANCE – A unified macroscopic model-based approach to real-time freeway network traffic surveillance

The paper presents a unified macroscopic model-based approach to real-time freeway network traffic surveillance as well as a software tool RENAISSANCE that has been recently developed to implement this approach for field applications. RENAISSANCE is designed on the basis of stochastic macroscopic freeway network traffic flow modeling, extended Kalman filtering, and a number of traffic surveillance algorithms. Fed with a limited amount of real-time traffic measurements, RENAISSANCE enables a number of freeway network traffic surveillance tasks, including traffic state estimation and short-term traffic state prediction, travel time estimation and prediction, queue tail/head/length estimation and prediction, and incident alarm. The traffic state estimation and prediction lay the operating foundation of RENAISSANCE since RENAISSANCE bases the other traffic surveillance tasks on its traffic state estimation or prediction results. The paper first introduces the utilized stochastic macroscopic freeway network traffic flow model and a real-time traffic measurement model, upon which the complete dynamic system model of RENAISSANCE is established with special attention to the handling of some important model parameters. The algorithms for the various traffic surveillance tasks addressed are described along with the functional architecture of the tool. A simulation test was conducted via application of RENAISSANCE to a hypothetical freeway network example with a sparse detector configuration, and the testing results are presented in some detail. Final conclusions and future work are outlined.     

A new framework for the integration,analysis and visualisation of urban traffic data within geographic information systems

Current geographical information systems (GIS) are not well adapted to the management of very dynamic geographical phenomena. This is due to the lack of conceptual and physical interoperability with real-time computing facilities. The research described in this paper is oriented towards the identification and experimentation of a new methodological and applied framework for the real-time integration, manipulation and visualisation of urban traffic data. It is based on proactive interaction between the spatio-temporal database and visualisation levels, and between the visualisation and end-user levels. The proposed framework integrates different spatial and temporal levels of granularity during the analysis of urban traffic data. Urban traffic behaviours are analysed either by observation of the movements of several vehicles in space, or by changes in urban network properties (i.e., micro- versus macro-modelling). Visualisation and interaction tools together constitute a flexible interface environment for the visualisation of urban traffic data within GIS. These concepts provide a relevant support for the visual analysis of urban traffic patterns in the thematic, spatial and temporal dimensions. This integrated framework is illustrated by an experimental prototype developed in a large town in the UK.     

Real-time traffic state estimation in urban corridors from heterogeneous data

In recent years, rapid advances in information technology have led to various data collection systems which are enriching the sources of empirical data for use in transport systems. Currently, traffic data are collected through various sensors including loop detectors, probe vehicles, cell-phones, Bluetooth, video cameras, remote sensing and public transport smart cards. It has been argued that combining the complementary information from multiple sources will generally result in better accuracy, increased robustness and reduced ambiguity. Despite the fact that there have been substantial advances in data assimilation techniques to reconstruct and predict the traffic state from multiple data sources, such methods are generally data-driven and do not fully utilize the power of traffic models. Furthermore, the existing methods are still limited to freeway networks and are not yet applicable in the urban context due to the enhanced complexity of the flow behavior. The main traffic phenomena on urban links are generally caused by the boundary conditions at intersections, un-signalized or signalized, at which the switching of the traffic lights and the turning maneuvers of the road users lead to shock-wave phenomena that propagate upstream of the intersections. This paper develops a new model-based methodology to build up a real-time traffic prediction model for arterial corridors using data from multiple sources, particularly from loop detectors and partial observations from Bluetooth and GPS devices.     

Automated detection of lane-blocking freeway incidents using artificial neural networks

A major source of urban freeway delay in the U.S. is non-recurring congestion caused by incidents. The automated detection of incidents is an important function of a freeway traffic management center. A number of incident detection algorithms, using inductive loop data as input, have been developed over the past several decades, and a few of them are being deployed at urban freeway systems in major cities. These algorithms have shown varying degrees of success in their detection performance. In this paper, we present a new incident detection technique based on artificial neural networks (ANNs). Three types of neural network models, namely the multi-layer feedforward (MLF), the self-organizing feature map (SOFM) and adaptive resonance theory 2 (ART2), were developed to classify traffic surveillance data obtained from loop detectors, with the objective of using the classified output to detect lane-blocking freeway incidents. The models were developed with simulation data from a study site and tested with both simulation and field data at the same site. The MLF was found to have the highest potential, among the three ANNs, to achieve a better incident detection performance. The MLF was also tested with limited field data collected from three other freeway locations to explore its transferability. Our results and analyzes with data from the study site as well as the three test sites have shown that the MLF consistently detected most of the lane-blocking incidents and typically gave a false alarm rate lower than the California, McMaster and Minnesota algorithms currently in use.     

Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing

This paper reports on real data testing of a real-time freeway traffic state estimator, with a particular focus on its adaptive capabilities. The pursued general approach to the real-time adaptive estimation of complete traffic state in freeway stretches or networks is based on stochastic macroscopic traffic flow modeling and extended Kalman filtering. One major innovative feature of the traffic state estimator is the online joint estimation of important model parameters (free speed, critical density, and capacity) and traffic flow variables (flows, mean speeds, and densities), which leads to three significant advantages of the estimator: (1) avoidance of prior model calibration; (2) automatic adaptation to changing external conditions (e.g. weather and lighting conditions, traffic composition, control measures); (3) enabling of incident alarms. These three advantages are demonstrated via suitable real data testing. The achieved testing results are satisfactory and promising for subsequent applications.     

Cooperative traffic control of a mixed network with two urban regions and a freeway

Currently most optimization methods for urban transport networks (i) are suited for networks with simplified dynamics that are far from real-sized networks or (ii) apply decentralized control, which is not appropriate for heterogeneously loaded networks or (iii) investigate good-quality solutions through micro-simulation models and scenario analysis, which make the problem intractable in real time. In principle, traffic management decisions for different sub-systems of a transport network (urban, freeway) are controlled by operational rules that are network specific and independent from one traffic authority to another. In this paper, the macroscopic traffic modeling and control of a large-scale mixed transportation network consisting of a freeway and an urban network is tackled. The urban network is partitioned into two regions, each one with a well-defined Macroscopic Fundamental Diagram (MFD), i.e. a unimodal and low-scatter relationship between region density and outflow. The freeway is regarded as one alternative commuting route which has one on-ramp and one off-ramp within each urban region. The urban and freeway flow dynamics are formulated with the tool of MFD and asymmetric cell transmission model, respectively. Perimeter controllers on the border of the urban regions operating to manipulate the perimeter interflow between the two regions, and controllers at the on-ramps for ramp metering are considered to control the flow distribution in the mixed network. The optimal traffic control problem is solved by a Model Predictive Control (MPC) approach in order to minimize total delay in the entire network. Several control policies with different levels of urban-freeway control coordination are introduced and tested to scrutinize the characteristics of the proposed controllers. Numerical results demonstrate how different levels of coordination improve the performance once compared with independent control for freeway and urban network. The approach presented in this paper can be extended to implement efficient real-world control strategies for large-scale mixed traffic networks.     

Real-time traffic network state estimation and prediction with decision support capabilities: Application to integrated corridor management

This paper presents a real-time traffic network state estimation and prediction system with built-in decision support capabilities for traffic network management. The system provides traffic network managers with the capabilities to estimate the current network conditions, predict congestion dynamics, and generate efficient traffic management schemes for recurrent and non-recurrent congestion situations. The system adopts a closed-loop rolling horizon framework in which network state estimation and prediction modules are integrated with a traffic network manager module to generate efficient proactive traffic management schemes. The traffic network manger adopts a meta-heuristic search mechanism to construct the schemes by integrating a wide variety of control strategies. The system is applied in the context of Integrated Corridor Management (ICM), which is envisioned to provide a system approach for managing congested urban corridors. A simulation-based case study is presented for the US-75 corridor in Dallas, Texas. The results show the ability of the system to improve the overall network performance during hypothetical incident scenarios.     

Effective arterial road incident detection: A Bayesian network based algorithm

Timely and accurate incident detection is an essential part of any successful advanced traffic management system. The complex nature of arterial road traffic makes automated incident detection a real challenge. Stable performance and strong transferability remain major issues concerning the existing incident detection algorithms. A new arterial road incident detection algorithm TSC_ar is presented in this paper. In this algorithm, Bayesian networks are used to quantitatively model the causal dependencies between traffic events (e.g. incident) and traffic parameters. Using real time traffic data as evidence, the Bayesian networks update the incident probability at each detection interval through two-way inference. An incident alarm is issued when the estimated incident probability exceeds the predefined decision threshold. The Bayesian networks allow us to subjectively build existing traffic knowledge into their conditional probability tables, which makes the knowledge base for incident detection robust and dynamic. Meanwhile, we incorporate intersection traffic signals into traffic data processing. A total of 40 different types of arterial road incidents are simulated to test the performance of the algorithm. The high detection rate of 88% is obtained while the false alarm rate of the algorithm is maintained as low as 0.62%. Most importantly, it is found that both the detection rate and false alarm rate are not sensitive to the incident decision thresholds. This is the unique feature of the TSC_ar algorithm, which suggests that the Bayesian network approach is advanced in enabling effective arterial road incident detection.     

Stochasticity of freeway operational capacity and chance-constrained ramp metering

The notion of capacity is essential to the planning, design, and operations of freeway systems. However, in the practice freeway capacity is commonly referred as a theoretical/design value without consideration of operational characteristics of freeways. This is evident from the Highway Capacity Manual (HCM) 2000 in that no influence from downstream traffic is considered in the definition of freeway capacity. In contrast to this definition, in this paper, we consider the impact of downstream traffic and define freeway operational capacity as the maximum hourly rate at which vehicles can be expected to traverse a point or a uniform section of a roadway under prevailing traffic flow conditions. Therefore freeway operational capacity is not a single value with theoretical notion. Rather, it changes under different traffic flow conditions. Specifically, this concept addresses the capacity loss during congested traffic conditions. We further study the stochasticity of freeway operational capacity by examining loop detector data at three specifically selected detector stations in the Twin Cities’ area. It is found that values of freeway operational capacity under different traffic flow conditions generally fit normal distributions. In recognition of the stochastic nature of freeway capacity, we propose a new chance-constrained ramp metering strategy, in which, constant capacity value is replaced by a probabilistic one that changes dynamically depending on real-time traffic conditions and acceptable probability of risk determined by traffic engineers. We then improve the Minnesota ZONE metering algorithm by applying the stochastic chance constraints and test the improved algorithm through microscopic traffic simulation. The evaluation results demonstrate varying degrees of system improvement depending on the acceptable level of risk defined.     

Multi-criteria analysis of optimal signal plans using microscopic traffic models

Increasing concerns on environment and natural resources, coupled with increasing demand for transport, put lots of pressure for improved efficiency and performance on transport systems worldwide. New technology nowadays enables fast innovation in transport, but it is the policy for deployment and operation with a systems perspective that often determines success. Smart traffic management has played important roles for continuous development of traffic systems especially in urban areas. There is, however, still lack of effort in current traffic management and planning practice prioritizing policy goals in environment and energy. This paper presents an application of a model-based framework to quantify environmental impacts and fuel efficiency of road traffic, and to evaluate optimal signal plans with respect not only to traffic mobility performance but also other important measures for sustainability. Microscopic traffic simulator is integrated with micro-scale emission model for estimation of emissions and fuel consumption at high resolution. A stochastic optimization engine is implemented to facilitate optimal signal planning for different policy goals, including delay, stop-and-goes, fuel economy etc. In order to enhance the validity of the modeling framework, both traffic and emission models are fine-tuned using data collected in a Chinese city. In addition, two microscopic traffic models are applied, and lead to consistent results for signal optimization. Two control schemes, fixed time and vehicle actuated, are optimized while multiple performance indexes are analyzed and compared for corresponding objectives. Solutions, representing compromise between different policies, are also obtained in the case study by optimizing an integrated performance index.     

Simulation studies of information propagation in a self-organizing distributed traffic information system

This paper investigates the feasibility of a self-organizing, completely distributed traffic information system based upon vehicle-to-vehicle communication technologies. Unlike centralized traffic information systems, the proposed system does not need public infrastructure investment as a prerequisite for implementation. Due to the complexity of the proposed system, simulation is selected as the primary approach in the feasibility studies. A simulation framework is built based on an existing microscopic traffic simulation model for the simulation studies. The critical questions for building the proposed market-driven system are examined both from communication requirements and traffic engineering points of view. Traffic information propagation both in freeway and arterial networks via information exchange among IVC-equipped vehicles is tested within the simulation framework. Results on the probability of successful IVC and traffic information propagation distance obtained from the simulation studies are generated and analyzed under incident-free and incident conditions for various roadway formats and parameter combinations. Comparisons between the speed of the incident information wave and the speed of the corresponding traffic shock wave due to the incident are analyzed for different scenarios as the most crucial aspect of the information propagation as a potential foundation for application in such a decentralized traffic information system.     

Developing freeway lane‐changing support systems using artificial neural networks

Lane‐changing involves many concerns about safety and efficiency which makes it one of the most difficult tasks of driving. It is indeed quite personal since drivers operate vehicles according to their integrated perception of comprehensive circumstances rather than individual rules. A lane‐changing decision support model is developed in this study using artificial neural networks (ANN). The advantages of the ANN approach lie in the learning capability. Due to its nature, an ANN model can consolidate various kinds of information surrounding the vehicle for the drivers and generate reliable results to help control vehicles. It then becomes a useful mechanism to assist drivers in judging current situations and making the right decisions. Several preliminary validations and comparisons are conducted with the field survey data. It is confirmed that the ANN model mimics traffic characteristics more accurately than conventional methods. This product would expedite the implementation of relevant applications in the intelligent transportation systems context. In particular, the ANN model can be adapted to individual driver characteristics. This reveals practical feasibility and significant market potential for customized in‐vehicle equipment.     

Operational impacts of incident quick clearance legislation: a simulation analysis

Many states in the US have enacted quick clearance laws requiring drivers of vehicles involved in minor incidents to move their vehicles from travel lanes prior to the arrival of first responders. Since little is known about the effectiveness of these laws, this research sought to find the benefit–cost ratio of advertising quick clearance legislation to improve driver compliance, and compare it with benefit–cost ratios of other incident management strategies, particularly traffic cameras, freeway service patrols, and traffic sensors. The analysis used traffic simulation that applied application programming interfaces to produce random spatial and temporal occurrence of incidents, including incident start times, durations, and locations, based on normal distributions developed from field data, to test before and after the law scenarios. The results provide decision makers with support for prioritizing funding between these incident management strategies and indicated that investments in the advertisement of this law was beneficial. Copyright © 2011 John Wiley & Sons, Ltd.     

Modeling the effects of traveler information on freeway origin–destination demand prediction

The primary focus of this research is to develop an approach to capture the effect of travel time information on travelers’ route switching behavior in real-time, based on on-line traffic surveillance data. It also presents a freeway Origin–Destination demand prediction algorithm using an adaptive Kalman Filtering technique, where the effect of travel time information on users’ route diversion behavior has been explicitly modeled using a dynamic, aggregate, route diversion model. The inherent dynamic nature of the traffic flow characteristics is captured using a Kalman Filter modeling framework. Changes in drivers’ perceptions, as well as other randomness in the route diversion behavior, have been modeled using an adaptive, aggregate, dynamic linear model where the model parameters are updated on-line using a Bayesian updating approach. The impact of route diversion on freeway Origin–Destination demands has been integrated in the estimation framework. The proposed methodology is evaluated using data obtained from a microscopic traffic simulator, INTEGRATION. Experimental results on a freeway corridor in northwest Indiana establish that significant improvement in Origin–Destination demand prediction can be achieved by explicitly accounting for route diversion behavior.     

Hysteresis phenomena of a Macroscopic Fundamental Diagram in freeway networks

Observations of traffic pairs of flow vs. density or occupancy for individual locations in freeways or arterials are usually scattered about an underlying curve. Recent observations from empirical data in arterial networks showed that in some cases by aggregating the highly scattered plots of flow vs. density from individual loop detectors, the scatter almost disappears and well-defined macroscopic relations exist between space-mean network flow and network density. Despite these findings for the existence of well-defined relations with low scatter, these curves should not be universal. In this paper we investigate if well-defined macroscopic relations exist for freeway network systems, by analyzing real data from Minnesota’s freeways. We show that freeway network systems not only have curves with high scatter, but they also exhibit hysteresis phenomena, where higher network flows are observed for the same average network density in the onset and lower in the offset of congestion. The mechanisms of traffic hysteresis phenomena at the network level are analyzed in this paper and they have dissimilarities to the causes of the hysteresis phenomena at the micro/meso level. The explanation of the phenomenon is dual. The first reason is that there are different spatial and temporal distributions of congestion for the same level of average density. Another reason is the synchronized occurrence of transitions from individual detectors during the offset of the peak period, with points remain beneath the equilibrium curve. Both the hysteresis phenomenon and its causes are consistently observed for different spatial aggregations of the network.     

A modeling framework for impact assessment of urban transport systems

An integrated software tool environment is presented, and a methodology is proposed for the operational support of the local authority, for analysis of the impact of transport measures in terms of network energy consumption and pollutant emissions. It is based on work done by the European Union within the save program (specific actions for vigorous energy efficiency)—Slam project (supporting local authorities methodology). As background, the Slam project is described, with the principal aspects and needs of environmental and traffic network management. The central section defines a methodology able to support technicians in recognizing the traffic asset and decision makers in evaluating interventions on urban transport infrastructures or technological systems. The role of the different models and their interactions with the transport telematics services currently active on the Florence (Italy) network is discussed. Finally, the procedure for calculating the traffic impacts on energy consumption is described with the help of a test case, the evaluation of a dedicated bus corridor in Florence.