Congestion Bottleneck Research Articles

CPT-DF: Congestion Prediction on Toll-Gates Using Deep Learning and Fuzzy Evaluation for Freeway Network in China

Toll-gates are crucial points of management and key congestion bottleneck for the freeway. In order to avoid traffic deterioration and alleviate traffic congestion in advance, it is necessary to predict and evaluate the congestion in toll-gates scattering in large-scale region of freeway network. In this paper, traffic volume and operational delay time are selected from various traffic indicators to evaluate congestion considering the particular characteristics of the traffic flow within the toll-gate area. The congestion prediction method is designed including two modules: a deep learning (DL) prediction and a fuzzy evaluation. We propose a modified deep learning method based on graph convolutional network (GCN) structure in the fusion of dilated causal mechanism and optimize the method for spatial feature extraction by constructing a new adjacency matrix. This new AI network could process spatiotemporal information of traffic volume and operational delay time, that extracted from large-scaled toll-gates spontaneously, and predict key indicators in 15/30/60 min future time. The evaluation module is proposed based on these predicted results. Then, fuzzy C-means algorithm (FCM) is further modified by determining coupling weight for these two key indicators to detect congestion state. Original traffic data are obtained from the current 186 toll-gates served on the freeway network in Shaanxi Province, China. Experimental tests are carried out based on historical data of four months after preprogressing. The comparative tests show the proposed CPT-DF (congestion prediction on toll-gates using deep learning and fuzzy evaluation) outperforms the current-used other models by 6-15%. The successful prediction could extend to the real-time prediction and early warning of traffic congestion in the toll system to improve the intelligent level of traffic emergency management and guidance on the key road of disasters to some extent.

Data-driven trajectory-based analysis and optimization of airport surface movement

Driven by the recent deployment of equipment and the availability of data, aircraft trajectories on the surface can be tracked continuously, and the detailed analysis of surface operations is enabled to measure airport operational performance and prepare for collaborative decision making. This paper proposes a hybrid approach combining traffic analysis and optimization to address the airport surface movement problem. Important metrics of surface management such as runway utilization, taxi routes, taxi times are extracted and characterized based on Automatic Dependent Surveillance-Broadcast (ADS-B) messages. After recognizing the congestion bottleneck, a trajectory-based optimization model is proposed, and an adapted simulated annealing heuristic is applied to solve the related problem. Two concepts for optimizing surface operations: arrival taxi reroute and departure controlled pushback are evaluated. The proposed approach is illustrated for Beijing Capital International airport. Computational experiments show that a mean taxi-in time reduction of 5.1 min is achieved for the rerouting arrivals, and the mean taxi-out time is reduced by 3.7 min with gate holding strategy. This data-driven approach allows advanced characterization of complex surface operations and enables collaborative decision assistance tools for managing surface movement efficiently.

Commuter behavior under travel time uncertainty

We investigate the impact of random deviations in planned travel times using an extension of Vickrey’s celebrated bottleneck model. The model is motivated by the fact that in real life, users can neither exactly plan the time at which they depart from home, nor the delay they experience before they join a particular congestion bottleneck under investigation. We show that the strategy advocated by the Nash equilibrium in Vickrey’s model is not a user equilibrium in the model with travel time uncertainty. We then investigate the existence of a user equilibrium for the latter and show that in general such an equilibrium can neither be a pure Nash equilibrium, nor a mixed equilibrium with a continuous density. Our results imply that when random distortions influence user decisions, the dynamics of deterministic bottleneck models are inadequate to describe this more complex situation. We illustrate with numerical analysis how the mechanics of a bottleneck with delayed arrivals are unstable for any continuous strategy, thus shedding more light on the non-existence result. Alternatively, we formulate a logit model for the day-to-day dynamic adjustment in response to previous travel experiences. We further investigate the conditions for convergence of the dynamic choice model and numerically illustrate the resulting equilibrium strategy.

A congestion bottleneck analysis model for tourist block street network

The development of the tourism industry has led to increased pressure of people flow in tourist blocks. Therefore, it is critical to ease the traffic pressure in these blocks. This paper aims to identify the bottleneck links of street networks in tourist blocks to achieve the effective prevention of congestion accidents. A logit stochastic user equilibrium model combined with spatial syntax is presented to study the travelers’ route choice behavior. The nonlinear Bureau of Public Roads function is applied to calculate the time impedance of each street. A case analysis of the Chongqing Ciqikou tourist block shows that the bottleneck link has the features of high integration and a large degree of negative time impedance evolution. The research’s results are more consistent with practical circumstances because the influence of the road network topological structure on pedestrian path selection has been considered.

Market Clearing Mechanism Considering Congestion under Deregulated Power System

Deregulation in power industry involves restructuring of rules and regulations, set up by the government. The new scenario is brought into practice mainly as a result of privatization, together with the encouragement of innovation of new technology. These factors are expected to reduce the cost of electrical energy. Deregulation creates a platform called as power market where electricity is traded as a commodity. The power market can be single buyer, bilateral or poolco model. Under single buyer model, the market is cleared in a pool by conducting single sided auction among suppliers, for each time slot, for a predicted demand. Bilateral model allows suppliers and buyers to enter into contract for an amount of power during a time period. Under poolco model, the market is cleared by performing double sided auction that involves both suppliers and buyers. Once the market is cleared, market clearing price, in-merit suppliers and buyers with their allotted power, power flow through transmission line between areas are determined. However, the scheduling of power is not coordinated with transmission planning. This results in transmission congestion. One of the methods to negate congestion is to split the market at the predicted congestion bottleneck. This paper presents a real time market clearing hardware model that accepts auction data and clears the market considering transmission congestion. The proposed model uses raspberry pi board that performs market clearing based on offers and bids accepted from a created webpage. The transmission congestion is managed by splitting the market at the predicted congestion bottleneck. The effectiveness of the proposed model is tested on three area deregulated power system.

Study on the SDN‐IP–based solution of well‐known bottleneck problems in private sector of national R&E network for big data transfer

SummaryLast‐mile bottleneck, fire‐wall bottleneck, and internal network congestion bottleneck are well‐known performance bottleneck problems of private sector in national R&E network since Internet is adopted for R&E network. The emergency of big data science makes these problems more severe because a famous scientist under such a circumstance is severely restricted in his/her big data R&D. Therefore, government cannot leave these stubs in the realm of a private organization any longer. We, KREONET (Korea Research Environment Open Network), tried to solve these problems by SDN‐IP (software‐defined network—Internet protocol) based on ONOS (Open Network Operation System); SDN‐IP is the leading technology for the softwarization of network, and it is developed by On Lab. KREONET began to deploy SDN‐IP as a tool for the softwarization of Korea R&E network even though others studied to business its' feature for the enhancement of resource management of data center first. The initial goal during the construction of SDN‐IP for KREONET is to solve well‐known problems in R&E network, which are related with last‐mile, fire‐wall, and internal network congestion. These 3 problems are not solved for a long time because it costs too much when we try to solve them by the way of the existed hardware networking. In this paper, we introduce our experience that can catch two rabbits at the same time. These experiences are about the provision of the initial popular service of SDN‐IP with the solution of those well‐known bottleneck problems and about the economic way of SDN‐IP construction by incremental hybrid networking of legacy Internet and SDN‐IP.

Editorial: recent advances in communication networks and multimedia technologies

To successfully support multimedia applications, it is necessary to improve the state-of-theart multimedia technologies and multimedia transmissions over communication networks in order to provide quality-of-service (QoS) guarantees between end-users and to achieve a high quality-of-experience for end-users. The accepted papers in this special issue deal with a wide range of important aspects and challenging issues of communication networks and multimedia technologies, and the contents are built on analytical modelling, experimental and simulation studies. The contributions of these papers are outlined below. With the development of wireless access and multimedia compression technologies, much attention has been devoted to wireless video communications. Wan, Xiong and Yang propose an integrated routing metric to evaluate path quality taking the hop count, congestion bottleneck and other parameters of a path into account. They design the traffic assignment and traffic adjustment modules to make the routing scheme flexible to the changing environments, and present an enhanced link layer scheduling algorithm. Simulation results show that compared to commonly used schemes, the proposed network layer scheme always provides better performance with various traffic modes; combined with link layer scheduling algorithm, the performance could be further improved. High speed and quality multimedia and data transmission requirements exist widely in wireless networks. Wang et al. investigate the performance of opportunistic scheduling in wireless multimedia and data networks based on popular stochastic network calculus (SNC). The authors firstly bound the traffic arrival process using the exponentially bounded burstiness traffic model of SNC and establish a stochastic arrival curve, and then propose a new wireless opportunistic scheduling, i.e., modified proportional fairness (MPF) scheduling algorithm, which can provide better fairness for input traffic to guarantee QoS performance, and obtain a stochastic service curve. The numerical results show that the proposed stochastic model and MPF scheduling algorithm can provide better QoS performance. Multimed Tools Appl (2015) 74:1–3 DOI 10.1007/s11042-013-1647-x

Cross-layer video transmission over IEEE 802.11e multihop networks

With the development of electronics and computer industry, powerful wireless terminals came to facilitate human life. In the last decade, both customers and researchers paid attention to wireless video applications because they contain abundant and visual information. To ensure wireless video transmission, many cross-layer schemes which combined routing and scheduling mechanisms have been proposed. However, characteristics of wireless networks such as limited resource and fluctuated link quality degrade video transmission quality. In this paper, several mechanisms are designed to improve wireless video transmission performance: (1) an integrated routing metric is proposed to evaluate path quality, taking hop count, congestion bottleneck and other parameters of a path into account; (2) traffic assignment and traffic adjustment modules are designed to make routing scheme be flexible to the changing environments; (3) an enhanced link layer scheduling algorithm based on our previous work is proposed. Simulation results show that compared to commonly used schemes, the proposed network layer scheme always provides better performance with various traffic modes. Combined with link layer scheduling algorithm, the performance could be further improved.

Topological-based bottleneck analysis and improvement strategies for traffic networks

A method is proposed to find key components of traffic networks with homogenous and heterogeneous topologies, in which heavier traffic flow is transported. One component, called the skeleton, is the minimum spanning tree (MST) based on the zero flow cost (ZCMST). The other component is the infinite incipient percolation cluster (IIC) which represents the spine of the traffic network. Then, a new method to analysis the property of the bottleneck in a large scale traffic network is given from a macroscopic and statistical viewpoint. Moreover, three effective strategies are proposed to alleviate traffic congestion. The significance of the findings is that one can significantly improve the global transport by enhancing the capacity in the ZCMST with a few links, while for improving the local traffic property, improving a tiny fraction of the traffic network in the IIC is effective. The result can be used to help traffic managers prevent and alleviate traffic congestion in time, guard against the formation of congestion bottleneck, and make appropriate policies for traffic demand management. Meanwhile, the method has very important theoretical significance and practical worthiness in optimizing traffic organization, traffic control, and disposal of emergency.

Urban traffic congestion propagation and bottleneck identification

Bottlenecks in urban traffic network are sticking points in restricting network collectivity traffic efficiency. To identify network bottlenecks effectively is a foundational work for improving network traffic condition and preventing traffic congestion. In this paper, a congestion propagation model of urban network traffic is proposed based on the cell transmission model (CTM). The proposed model includes a link model, which describes flow propagation on links, and a node model, which represents link-to-link flow propagation. A new method of estimating average journey velocity (AJV) of both link and network is developed to identify network congestion bottlenecks. A numerical example is studied in Sioux Falls urban traffic network. The proposed model is employed in simulating network traffic propagation and congestion bottleneck identification under different traffic demands. The simulation results show that continual increase of traffic demand is an immediate factor in network congestion bottleneck emergence and increase as well as reducing network collectivity capability. Whether a particular link will become a bottleneck is mainly determined by its position in network, its traffic flow (attributed to different OD pairs) component, and network traffic demand.

Mitigating the gateway bottleneck via transparent cooperative caching in wireless mesh networks

Wireless mesh networks (WMNs) have been proposed to provide cheap, easily deployable and robust Internet access. The dominant Internet-access traffic from clients causes a congestion bottleneck around the gateway, which can significantly limit the throughput of the WMN clients in accessing the Internet. In this paper, we present MeshCache, a transparent caching system for WMNs that exploits the locality in client Internet-access traffic to mitigate the bottleneck effect at the gateway, thereby improving client-perceived performance. MeshCache leverages the fact that a WMN typically spans a small geographic area and hence mesh routers are easily over-provisioned with CPU, memory, and disk storage, and extends the individual wireless mesh routers in a WMN with built-in content caching functionality. It then performs cooperative caching among the wireless mesh routers. We explore two architecture designs for MeshCache: (1) caching at every client access mesh router upon file download, and (2) caching at each mesh router along the route the Internet-access traffic travels, which requires breaking a single end-to-end transport connection into multiple single-hop transport connections along the route. We also leverage the abundant research results from cooperative web caching in the Internet in designing cache selection protocols for efficiently locating caches containing data objects for these two architectures. We further compare these two MeshCache designs with caching at the gateway router only. Through extensive simulations and evaluations using a prototype implementation on a testbed, we find that MeshCache can significantly improve the performance of client nodes in WMNs. In particular, our experiments with a Squid-based MeshCache implementation deployed on the MAP mesh network testbed with 15 routers show that compared to caching at the gateway only, the MeshCache architecture with hop-by-hop caching reduces the load at the gateway by 38%, improves the average client throughput by 170%, and increases the number of transfers that achieve a throughput greater than 1 Mbps by a factor of 3.

VLSI Design of a Fully-Parallel High-Throughput Decoder for Turbo Gallager Codes

The most powerful channel coding schemes, namely those based on turbo codes and low-density parity-check (LDPC) Gallager codes, have in common the principle of iterative decoding. However, the relative coding structures and decoding algorithms are substantially different. This paper presents a 2048-bit, rate-1/2 soft decision decoder for a new class of codes known as Turbo Gallager Codes. These codes are turbo codes with properly chosen component convolutional codes such that they can be successfully decoded by means of the decoding algorithm used for LDPC codes, i.e., the belief propagation algorithm working on the code Tanner graph. These coding schemes are important in practical terms for two reasons: (i) they can be encoded as classical turbo codes, giving a solution to the encoding problem of LDPC codes; (ii) they can also be decoded in a fully parallel manner, partially overcoming the routing congestion bottleneck of parallel decoder VLSI implementations thanks to the locality of the interconnections. The implemented decoder can support up to 1 Gbit/s data rate and performs up to 48 decoding iterations ensuring both high throughput and good coding gain. In order to evaluate the performance and the gate complexity of the decoder VLSI architecture, it has been synthesized in a 0.18 μm standard-cell CMOS technology.