Large-scale Urban Traffic Networks Research Articles

Trajectory Data Processing and Mobility Performance Evaluation for Urban Traffic Networks

With the rapid development and deployment of vehicle sensing and communication technology, vehicle trajectory data is becoming increasingly available for urban traffic network applications. However, it is difficult to use raw trajectory data points generated from global navigation satellite system (GNSS) coordinates without matching them to traffic networks. Real-world trajectory data is also prone to noise and errors. This paper proposes a trajectory data processing pipeline to serve different urban traffic network applications. The steps of the pipeline include matching the trajectory points to a well defined network representation, splitting them into different movements, and extracting distance information from their GNSS coordinates. Smoothing and filtering algorithms also reduce the influence of noise and errors. Based on the processed trajectory data, this paper also proposes algorithms for calculating different mobility performance indices including vehicle delay, number of stops, space-mean speed, and coordination measurements. These performance indices provide comprehensive evaluations of urban traffic network from different perspectives. Our case study uses real-world trajectory data collected from the Ann Arbor Connected Vehicle Test Environment. Different mobility performance indices are calculated and visualized. The proposed methods and algorithms are efficient, robust, and scalable, and could be applied to large-scale urban traffic networks.

Glocal (Global/Local) Control for Urban Road Traffic Networks

This paper provides a way of hierarchically distributed control for large-scale urban road traffic networks based on the concept of Glocal (Global/Local) Control. The proposed system consists of demand distribution control by dynamic route guidance (global) and traffic signal control (local), and they are collaborated each other through so called glocal adaptor to achieve increase of traffic volume (global objective) and jam alleviation (local objective). After explaining the structure of the glocal adaptor, we provide model predictive control-based algorithms for demand distribution control and distributed traffic signal control, which are completely independent. The effectiveness of the method is verified by numerical experiments.

Decentralized store-and-forward based strategies for the signal control problem in large-scale congested urban road networks

Signal control strategies for congested urban road networks designed in a centralized framework require many communication links, serious processing power, and infrastructure for the centralized coordination. As a result, strategies based on a centralized framework are not scalable. The use of decentralized signal control strategies for large-scale urban traffic networks is a solution to this problem, since it allows for the implementation of such strategies on networks whose centralized solution is not easily scalable. This paper addresses the problem of designing a decentralized traffic-responsive signal control solution, proposing two methods based on different formulations of the store-and-forward model: (i) the Decentralized Traffic-responsive Urban Control (DTUC) method; and (ii) the Decentralized Decoupled Traffic-responsive Urban Control (D2TUC). The decentralized configuration is such that each intersection is associated with one computational unit, with limited computational power and memory, which controls the traffic signals of the incoming links. Sufficient conditions for the controllability of the considered store-and-forward models are also presented. Both methods are validated resorting to numerical simulations of the urban traffic network of Chania, Greece, for two demand scenarios, and their performance is compared with the performance of the Traffic-responsive Urban Control (TUC) centralized strategy. One of the proposed decentralized methods, D2TUC, is shown to match the performance of TUC.

Data Augmented Deep Behavioral Cloning for Urban Traffic Control Operations Under a Parallel Learning Framework

It is indispensable for professional traffic signal engineers to perform manual operations of traffic signal control (TSC) to mitigate traffic congestion, especially with complicated scenarios. However, such a task is time-consuming, and the level of congestion mitigation heavily relies on individual expertise in engineering practice. Therefore, it is cost-effective to learn traffic engineers’ knowledge to enhance the problem-solving skills for a large-scale urban traffic network. In this paper, a data augmented deep behavioral cloning (DADBC) method is proposed to imitate the problem-solving skills of traffic engineers. The method is under a conceptual framework, parallel learning (PL) framework, that incorporates machine learning techniques for solving decision-making problems in complex systems. The DADBC method enhances a hybrid demonstration by exploiting a generative adversarial network (GAN) and then uses the deep behavioral cloning (DBC) model to learn traffic engineers’ control schemes. According to the validation results using the real manipulation data from Hangzhou, China, our method can imitate complex human behaviors in intervening traffic signal control operations to improve traffic efficiency in urban areas.

Resilient multivariable perimeter control of urban road networks under cyberattacks

The Macroscopic Fundamental Diagram (MFD) concept has become a very valuable tool for modeling the aggregated dynamic of large-scale urban traffic networks. This advance in traffic modeling has fueled the development of many urban traffic control (UTC) policies based on the MFD concept. In particular, perimeter control policies with the aim of maintaining a large-scale urban traffic network operating at its maximum capacity by manipulating the transfer flow of vehicles between adjacent homogeneous regions. In addition to achieve a satisfactory performance level, it is desirable that the control system would maintain a reliable operation under unanticipated events such as cyberattacks. It is important to note that modern UTC systems extensively rely on information technology infrastructures making them vulnerable to cyberattacks. Due to the occurrence of real-world cyberattacks affecting controlled physical systems in different fields, the threat of cyberattacks is becoming a serious and increasing concern for the proper operation of UTC. This paper presents a resilient multivariable control framework for large-scale urban traffic networks subject to several types of cyberattacks such as deception attacks and denial of service (DoS) attacks. Additionally, a feedforward–feedback control scheme combining a time-optimal controller with the resilient multivariable controller is also proposed. The aim of this feedforward–feedback controller is to steer the accumulations to their desired values after a large deviation. Finally, the performance of the proposed resilient controller is demonstrated by means of some realistic simulations of urban traffic networks, including stochastic errors in the traffic demands and in the MFD model, in addition to cyberattacks.

Integrated Control for a Large-Scale Mixed Network of Arterials and Freeways

An urban traffic network is typically composed of two subnetworks: freeways and arterials. When the exchange traffic flow between these two subnetworks is over concentrated on an off-ramp or on-ramp, local congestion occurs in the traffic network. We propose an integrated control (IC) method to effectively adjust and coordinate the traffic flow between the two subnetworks of a large-scale urban traffic network. The proposed method depends on a subregion partitioning scheme based on the macroscopic fundamental diagram (MFD) and the road capacity constraints and integrates macroscopic traffic guidance, ramp-coordinated (RC) control and MFD subregion perimeter control to adjust the traffic flows for exchange between the two subnetworks. A solution method for the proposed IC model is designed using a Lagrange-quasi-Newton algorithm. Simulation studies of a congestion scenario are presented, and the results indicate that in comparison with no RC control, RC control only and a combined RC and perimeter control scheme, the proposed control scheme can reduce the delay, trip distance and trip time for the entire network.

Equilibrium Manifolds in 2D Fluid Traffic Models

The main goal of this paper is to analytically find a steady-state in a large-scale urban traffic network with known and constant demand and supply on its boundaries. Traffic dynamics are given by a continuous two-dimensional macroscopic model, where state corresponds to the vehicle density evolving in a 2D plane. Thereby, the flux magnitude is given by the space-dependent fundamental diagram and the flux direction depends on the underlying network topology. In order to find a steady-state, we use the coordinate transformation such that the 2D equation can be rewritten as a parametrized set of 1D equations. This technique allows us to obtain the curves along which the traffic flow evolves, which are essentially the integral curves of the flux field constructed from the network geometry. The results are validated by comparing the obtained steady-state with the one estimated by using a microsimulator.

Integration of Regional Demand Management and Signals Control for Urban Traffic Networks

Many efforts have been focused on the network-wide traffic signal optimization to deal with the congestion problem in big cities. Nevertheless, research evidence illustrates that both improper traffic network managements and excessive traffic demands are the key factors leading to the oversaturated traffic conditions. Current studies encounter the bottleneck in addressing the multi-objective optimization problem. This point calls for designing the hierarchical control framework. In this paper, we concern a two-level hierarchical model-based predictive control scheme to improve mobility in heterogeneous large-scale urban traffic networks, so as to mitigate traffic jams. On the basis of a network partition, a regional demand management approach regulating the input traffic flow from adjacent regions is proposed for multi-subnetworks management taking the advantage of the concept of a macroscopic fundamental diagram of urban traffic networks. This can be viewed as a higher level control layer and can be integrated with other strategies. The lower level control layer utilizes the traffic signals coordination within the subnetworks based on a detailed link-level traffic model to optimize the allocation of vehicles in each subnetwork as homogeneous as possible. The simulation results show that integrating regional demand management with a local traffic responsive control into a hierarchical framework can significantly improve the whole network performance under different traffic scenarios in comparison with other available control strategies.

On the existence of network Macroscopic Safety Diagrams: Theory, simulation and empirical evidence.

Recent studies have proposed using well-defined relationships between network productivity and accumulation—otherwise known as Network or Macroscopic Fundamental Diagrams (network MFDs)—to model the dynamics of large-scale urban traffic networks. Network MFDs have been used to develop a variety of network-wide traffic control policies to improve a network’s operational efficiency. However, the relationship between a network’s MFD and its safety performance has not been well explored. This study proposes the existence of a Macroscopic Safety Diagram (MSD) that relates safety performance (e.g., likelihood of a crash occurring or number of vehicle conflicts observed) with the current network state (i.e., average density) in an urban traffic network. We theoretically posit a relationship between a network’s MSD and its MFD based on the average maneuver envelop of vehicles traveling within the network. Based on this model, we show that the density associated with maximum crash propensity is always expected to be larger than the density associated with maximum network productivity. This finding suggests that congested states are not only inefficient, but they might also be associated with more crashes, which can be both more unsafe and lead to decreased network reliability. These theoretical results are validated using surrogate safety assessment metrics in microsimulation and limited field empirical data from a small arterial network in Riyadh, Kingdom of Saudi Arabia. The existence of such MSDs can be used to develop more comprehensive network-wide control policies that can ensure both safe, efficient and reliable network operations.

Macroscopic traffic model for large scale urban traffic network design

This paper presents a flexible macroscopic traffic model that is applicable for large scale urban traffic network simulations. The proposed approach combines several advantages. First, the discretized partial differential equation of the macroscopic model is solved numerically with low computational effort. The spatial and temporal resolution of the discretization are tuning parameters to balance computational effort with model accuracy. Second, the model allows arbitrary functional forms of the fundamental diagram defined by a small number of parameters. Thereby, moving density gradients (jam fronts) are represented accurately. The model parameters are physically meaningful and can readily be estimated from measurement data. Third, two general types of intersection handling are proposed and can be combined with different merge models. The first intersection approach is a binary traffic light, similar to real traffic lights. Detailed insights concerning queue length, flow across intersections and routing decisions can be investigated. The second approach is a continuous valve-like approach that allows investigation of averaged effects and large scale interaction and feedback effects. Fourth, the proposed model scales linearly with spatial and temporal resolution as well as network size and can be partially solved in parallel to increase computational efficiency. To demonstrate the above mentioned qualities of the presented model, two realistic example situations and a comprehensive study on the scaling properties are provided.

An analytical framework to model uncertainty in urban network dynamics using Macroscopic Fundamental Diagrams

Recent studies have proposed using well-defined relationships between network productivity and accumulation—otherwise known as Network or Macroscopic Fundamental Diagrams (MFDs)—to model the dynamics of large-scale urban traffic networks. This provides a computationally efficient way to study these complex systems and facilitates the design and control of novel large-scale traffic management strategies. However, empirical and simulation evidence suggests that MFDs are rarely well-defined. Instead, they exhibit large amounts of scatter and uncertainty, which suggests a range of network productivities may be observed for any given accumulation. This paper examines the impact of this MFD uncertainty and uncertainty in aggregate-level vehicle demands (i.e., vehicle exit and entry rates) on large-scale network behavior. It is shown that these uncertainties can cause fundamentally different aggregate network behaviors than would be expected if they were ignored, including unexpected congestion or gridlock. An analytically derived Markov Chain framework is proposed that can be used to model aggregate network dynamics while explicitly accounting for these types of uncertainties, which are very likely to arise on realistic urban networks. Comparison between the analytical predictions and numerical simulations suggest that the Markov Chain framework can accurately predict traffic dynamics under uncertainty for both single- and multi-region networks. Since this framework relies on the careful discretization of both time and accumulation within individual regions of a network, guidance is also provided on how to best select these discretization parameters the most accurate results.

Two-Level Hierarchical Model-Based Predictive Control for Large-Scale Urban Traffic Networks

Network-wide control of large-scale urban traffic networks using a hierarchical framework can be more efficient and flexible than centralized strategies for reducing the traffic congestion in big cities, because it can adequately address some problems that occur in controlling such large systems, e.g., computational complexity, multiple control objectives, weak robustness to uncertainties, and so on. In this paper, we propose a two-level hierarchical control framework for large-scale urban traffic networks. At the upper level, based on decomposing a heterogeneous traffic network into several homogeneous subnetworks, a higher level optimization problem using the concept of macroscopic fundamental diagram is formulated to deal with the traffic demand-balance problem. At the lower level, the controller with a more detailed traffic flow model for each subnetwork determines the optimal signal timing within the given region under the guidance of the upper-level controller through communication. For the application of this architecture in real time, the model-based predictive control approach is utilized so as to obtain the best solutions for both levels. Moreover, in order to decrease the computational complexity, a distributed control scheme within each subnetwork is developed at the lower level. The proposed approach is evaluated by simulation under different scenarios on a hypothetical urban traffic network, and the performance is compared with that of other control strategies.

An analytical framework to model uncertainty in urban network dynamics using Macroscopic Fundamental Diagrams

Abstract: Recent studies have proposed using well-defined relationships between network productivity and accumulation—otherwise known as Network or Macroscopic Fundamental Diagrams (MFDs)—to model the dynamics of large-scale urban traffic networks. This provides a computationally efficient way to study these complex systems and facilitates the design and control of novel large-scale traffic management strategies. However, empirical and simulation evidence suggests that MFDs are rarely well-defined. Instead, they exhibit large amounts of scatter and uncertainty, which suggests a range of network productivities may be observed for any given accumulation. This paper examines the impact of this MFD uncertainty and uncertainty in aggregate-level vehicle demands (i.e., vehicle exit and entry rates) on large-scale network behavior. It is shown that these uncertainties can cause fundamentally different aggregate network behaviors than would be expected if they were ignored, including unexpected congestion or gridlock. An analytically derived Markov Chain framework is proposed that can be used to model aggregate network dynamics while explicitly accounting for these types of uncertainties, which are very likely to arise on realistic urban networks. Comparison between the analytical predictions and numerical simulations suggest that the Markov Chain framework can accurately predict traffic dynamics under uncertainty for both single- and multi-region networks. Since this framework relies on the careful discretization of both time and accumulation within individual regions within a network, guidance is also provided on how to best select these discretization parameters for the most accurate results.

Iterative tuning strategy for setting phase splits with anticipation of traffic demand in urban traffic network

Facing large-scale urban traffic network, countless effort has been made toward intelligent and efficient urban traffic control system to better use existing traffic infrastructures. Recently, a novelty pre-timed traffic signal control strategy known as iterative tuning (IT) has been developed by exploiting repetitive characteristic of junction's vehicle throughput on working days, which is sufficiently efficient in under-saturated traffic conditions. This study further improves IT strategy in saturated traffic conditions with consideration of traffic demand including vehicle throughput and residual queued vehicles. Unlike conventional pre-timed strategies, preparation of signal schedules is not required in IT strategy and fine-tuning process executes iteratively and automatically. This study proposes a generalised traffic model to describe urban network dynamics and explicit split tuning algorithm. Without specific control trajectories, rigorous analysis generates sufficient condition for guaranteeing the convergence of IT strategy globally over repetitions. The robustness of IT controller to variations of traffic flow patterns and errors of initial conditions is also analysed in details. Commonwealth Avenue, an area with heavy traffic in Singapore, is demonstrated in simulations and simulation results indicate the effectiveness and robustness of IT strategy.

Temporal Tracking of Congested Partitions in Dynamic Urban Road Networks

With urban population rapidly growing, traffic congestion is becoming a major problem. In this paper, a framework is proposed for identifying the spatial congested partitions in a dynamic urban road network and for monitoring the temporal changes in their locations and structure. To that end, a given road network is transformed into a suitable graph representation, an initial partitioning based on a spectral clustering approach is performed, and then the partitions continue to be updated incrementally on the basis of the newly obtained traffic data at each new time point. The congested partitions are then identified on the basis of traffic measures (e.g., volume and green time utilization) available from the traffic signal control system. Experiments with the proposed method are conducted with real historical traffic data collected from the 493 signalized traffic sites in Melbourne, Victoria, Australia, with a total of 1,444 road segments and 581 intersection points. Experimental results show that large-scale urban traffic networks undergo many rapid but regular and frequent traffic patterns, which often go unnoticed by the traffic network operators. Tracking these kinds of changes in real time by means of the proposed framework can improve the reaction time of the traffic management team and result in less congestion.

A bi-level model of dynamic traffic signal control with continuum approximation

This paper proposes a bi-level model for traffic network signal control, which is formulated as a dynamic Stackelberg game and solved as a mathematical program with equilibrium constraints (MPEC). The lower-level problem is a dynamic user equilibrium (DUE) with embedded dynamic network loading (DNL) sub-problem based on the LWR model (Lighthill and Whitham, 1955; Richards, 1956). The upper-level decision variables are (time-varying) signal green splits with the objective of minimizing network-wide travel cost. Unlike most existing literature which mainly use an on-and-off (binary) representation of the signal controls, we employ a continuum signal model recently proposed and analyzed in Han et al. (2014), which aims at describing and predicting the aggregate behavior that exists at signalized intersections without relying on distinct signal phases. Advantages of this continuum signal model include fewer integer variables, less restrictive constraints on the time steps, and higher decision resolution. It simplifies the modeling representation of large-scale urban traffic networks with the benefit of improved computational efficiency in simulation or optimization. We present, for the LWR-based DNL model that explicitly captures vehicle spillback, an in-depth study on the implementation of the continuum signal model, as its approximation accuracy depends on a number of factors and may deteriorate greatly under certain conditions. The proposed MPEC is solved on two test networks with three metaheuristic methods. Parallel computing is employed to significantly accelerate the solution procedure.

Multi‐agent model‐based predictive control for large‐scale urban traffic networks using a serial scheme

Urban traffic networks are large-scale systems, consisting of many intersections controlled by traffic lights and interacting connected links. For efficiently regulating the traffic flows and mitigating the traffic congestion in cities, a network-wide control strategy should be implemented. Control of large-scale traffic networks is often infeasible by only using a single controller, that is, in a centralised way, because of the high dimension, complicated dynamics and uncertainties of the system. In this study, the authors propose a multi-agent control approach using a congestion-degree-based serial scheme. Each agent employs a model-based predictive control approach and communicates with its neighbours. The congestion-degree-based serial scheme helps the agents to reach an agreement on their decisions regarding traffic control actions as soon as possible. A simulation study is carried out on a hypothetical large-scale urban traffic network based on the presented control strategy. The results illustrate that this approach has a better performance with regard to computation time compared with the centralised control method and a faster convergence speed compared with the classical parallel scheme.

A fast network partition method for large-scale urban traffic networks

In order to control the large-scale urban traffic network through hierarchical or decentralized methods, it is necessary to exploit a network partition method, which should be both effective in extracting subnetworks and fast to compute. In this paper, a new approach to calculate the correlation degree, which determines the desire for interconnection between two adjacent intersections, is first proposed. It is used as a weight of a link in an urban traffic network, which considers both the physical characteristics and the dynamic traffic information of the link. Then, a fast network division approach by optimizing the modularity, which is a criterion to distinguish the quality of the partition results, is applied to identify the subnetworks for large-scale urban traffic networks. Finally, an application to a specified urban traffic network is investigated using the proposed algorithm. The results show that it is an effective and efficient method for partitioning urban traffic networks automatically in real world.

Multi-level Model-based Control for Large Scale Urban Traffic Networks

In this paper, a multi-level model-based control strategy is applied to control large scale urban traffic networks. Structure of the strategy contains two levels, i.e., a coordinator in the upper level and model predictive controllers in the lower level. The coordinator aims to keep the distribution of vehicles in the network homogeneous, and the two-level structure makes the computational burden acceptable. The simulation shows the performance of multilevel model-based control is close to that of centralized model predictive control and better than decentralized control. Meanwhile, the computational complexity is greatly reduced comparing with centralized control.

A Congestion Eliminating Control Method for Large-scale Urban Traffic Networks

This paper investigates an efficiency of signal control methodology, which mainly focuses on dealing with the traffic congestion problem in those key congested links and is applicable to be implemented in a hierarchical control structure in large-scale heterogeneous urban traffic networks. In this methodology, an algorithm for finding the most congested path is presented firstly, and the urban traffic flow is modeled by using a simplified macroscopic modeling framework. Then the problem of network-wide signal control is formulated as a linear programming problem that aims at minimizing the number of vehicles(or densities) in congested links so as to improve the mobility of the network and mitigate the traffic congestion. For the application of this method in real time, the multi-variables optimization problem including constraints is embedded in a model-based dynamic control procedure. Finally, different traffic demand scenarios are designed and four evaluation criteria are applied to measure the performance of the proposed method in a hypothetical road network. Compared with the fixed-time control strategy, the simulation results show that it is an effective and feasible way to regulate the traffic flow and mitigate the congestion in large-scale urban networks.