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Network-wide traffic control strategies (e.g. perimeter control and route guidance) in urban networks have recently been mainly studied to relieve or postpone congestion based on the theory of macroscopic fundamental diagram (MFD). Nevertheless, these studies are mostly applied to the statically partitioned networks or the dynamic networks that fail to fully consider traffic state prediction, conflicting with strongly spatiotemporal variability of traffic congestion or objective of active traffic management (ATM). This paper proposes a methodology to dynamically identify critical congestion warning areas from heterogeneous urban road networks, which aids to design efficient perimeter control approaches. In the methodology, a dynamic directed weighted network is built on the base of the link connectivity and the real-time traffic loads, and a link travel time prediction method based on Kalman filter is developed to calibrate directed weight values and undirected input values for links. With the undirected link input information, a dynamic congestion warning community detection method which consists of three consecutive steps is developed. Firstly, it could capture emergence of new congestion areas based on the definition of congestion seed intersection. Secondly, expansion and regression of each congested area could be achieved with the objectives of spatial compactness and traffic condition homogeneity. Thirdly, a two-level merging algorithm of adjacent different congestion areas is designed utilizing modularity model in community detection of complex networks. The proposed methodology is validated using ground truth data from downtown network of Jinan City in China. The results show that the proposed algorithms can efficiently track congestion evolutionary processes and effectively detect congestion warning areas from the test real network.

Reasonable traffic network model and flexible traffic control strategy play important roles in improving the urban traffic control efficiency. Introducing granular computing theory into traffic network modeling and traffic control is a useful attempt, since granular computing is closer to the human thinking in solving problems. In this paper, the traffic elements are depicted using S-rough set to achieve the granulation partition of traffic network. Four layers are partitioned in the proposed hierarchical multigranularity traffic network model, such as vehicle layer, platoon layer, segment and intersection layer, and subregion layer. Each traffic granule is represented in rough representation form, and the dynamic characteristics are described using the elementary transfer operations based on S-rough set theory. As an application on the proposed traffic network model, an extended max-pressure traffic control strategy is applied on the platoon and segment and intersection layer. Simulation results illustrate that the proposed traffic network model and traffic control strategy achieve better performance.

The car-following model is an important micro-traffic model for simulating car-following behaviour in traffic engineering and research studies. Conventional car-following models are always presented using mathematical equations reflecting ideal traffic conditions. In the big data era, data-driven models become a popular trend. In this study, a data-driven car-following model based on the rough set theory is proposed to consider information hidden in a field data set. On the basis of field data obtained from measurement devices such as the next generation simulation (NGSIM) trajectory data set, and using the methods of the rough set theory, an optimal decision rule set is established. Redundant attributes and redundant attribute values are removed for simplifying the car-following behaviour decision problem. Attribute significance and weights are computed for selecting matching rules. A car-following behaviour decision algorithm is designed to choose appropriate rules to determine the follower's velocity according to current observations. Simulations illustrate that the proposed data-driven car-following model can simulate the micro-traffic behaviour of followers well.

On the foundation of the original backpressure-based traffic light control algorithm, a distributed cooperative backpressure-based traffic light control method is proposed in this paper. The urban traffic network is modeled as a smart agent-controlled queuing network, in which the intersection agents exchange the queue length information and the selected activating light phase information of neighboring intersections through communications and determine the activating light phase at each time slot according to local traffic information. The improved phase pressure computation method considers the phase state of downstream intersections instead of only the queue length of the local intersections. Light phase switching coordination among adjacent intersections is achieved using the consensus-based bundle algorithm, in which the cooperative light phase switching problem is viewed as a task assignment issue among adjacent intersections. Simulation results illustrated that the proposed cooperative backpressure-based traffic light control method obtained better performance than the original backpressure-based and fixed-time traffic control methods.

Macroscopic fundamental diagram (MFD) characterizes a unimodal and low-scatter relationship between traffic accumulation and trip completion rate for homogenous urban network, providing a new perspective for the development of regional congestion control strategies. This paper contributes to the state-of-the-art by proposing a perimeter traffic control strategy for single urban congested region with MFD model and boundary conditions. For an urban protected region, the MFD is introduced to capture network traffic dynamics and optimal capacity. When regional accumulation exceeds the optimal capacity, the restricted inflow distribution considering boundary condition constraints of real-time traffic flows and queue dynamics is used to formulate a perimeter traffic flow control strategy for adjusting traffic signal timing on the periphery of region. To proactively reduce the possibility of queue spillbacks at border links, the dynamic queue-dependent border gated points are also integrated into the proposed control method. The simulation experiment was performed using part of urban network in Jinan, China. The results demonstrate the proposed perimeter control method alleviates the issue of congestion and improves traffic performance in the protected region and at the boundaries.

In this study, the problem of car-following stability is considered. This study presents a clustered car-following strategy based on inter-vehicle communications (IVCs), which contributes to derive a more accurate feedback control for improving the stability. In order to maintain car-following stability, two conditions are considered, which are based on the control theory and a modified optimal velocity car-following model. In simulations, the results demonstrate that the strategy is effective on the stability of car-following, and the effect becomes more obvious as the market penetration rates of IVC-equipped vehicles increase. Even in low market penetration rate (5%), the proposed strategy has significant influence on the stability and the recovering time.

To detect traffic state using Smart-phone equipped vehicles (for short, equipped vehicles) data is popular in recent years. Calculating the average speed of all equipped vehicles simply is no longer reasonable without considering random distribution of equipped vehicles. In this paper, a new method of detecting traffic state based on equipped vehicle data was presented. Inspired by information entropy theory, the key homogeneous distribution degree (KHDD) can be obtained, which contributes to correct the detection error caused by the random distribution of equipped vehicles. In simulations, the results show the validity of the proposed method to detect traffic state under the different KHDD.