Unexpected Congestion Research Articles

Resilience of Delay-Sensitive Services With Transport-Layer Monitoring in SD-WAN

Today, more and more enterprises are embarking on a digital transformation where most of their applications are hosted in the Cloud. As a result, a reliable Wide Area Network (WAN) has become a primary need to interconnect their distributed branch offices and data centers that accommodate those applications. Software-Defined Wide Area Network (SD-WAN) represents the most promising technology solution for next-generation enterprise networks, being able to increase network agility and reduce costs. In this paper, we present an experimental SD-WAN solution capable of running and optimizing delay-sensitive high-priority services, such as real-time video streaming, while minimizing downtime caused by network failures. This solution comprises a monitoring and a traffic engineering system for SD-WAN. The first consists of a Transport-layer Passive Monitoring (TPM) system based on extended Berkeley Packet Filter (eBPF) technology with the goal of monitoring TCP flows; the second consists of an application, running inside the SD-WAN controller, with the goal of orchestrating the network traffic in consideration of the monitoring measurements by ensuring rapid recovery and resilience in case of unexpected congestion events. We validate our solution over two SD-WAN testbeds: the first is hosted in our laboratory at Politecnico di Milano, while the second is deployed in a municipal network of an Italian city. Results show that our SD-WAN solution can increase the overall service availability while meeting the stringent QoS requirements of delay-sensitive services.s

Enhancing Model-Based Anticipatory Traffic Signal Control with Metamodeling and Adaptive Optimization

Traffic signal control is one effective way to alleviate traffic congestion. Anticipatory traffic signal control determines signal settings from a network planning perspective, which takes into account the influence of travelers’ route choice response and triggers better equilibrium flow patterns for better network performance. For the route choice response, it is usually predicted by a response function known as traffic assignment model. However, the response behavior can never be precisely modeled, leading to a mismatch between the modeled and real traffic flow patterns. This model-reality mismatch generally contributes to suboptimal control performance and hence brings unexpected congestion in real-life traffic operations. This study aims to address the model-reality mismatch and proposes an effective anticipatory traffic control for real operations. A metamodel is introduced that serves as a surrogate of the unknown structural model bias. Then an iterative optimizing control scheme is applied to correct the model bias by learning from observations. By integrating the model-based control design with data-driven learning techniques, the metamodeling framework is able to enhance the control performance. Moreover, the analytical model bias formulation allows theoretical investigation of the model approximation error. To further improve the control performance, a joint traffic model parameter estimation is developed, hence achieving a better model calibration jointly with the model bias correction. The proposed control method is examined on a test network. Numerical examples confirm the effectiveness of the proposed method in improving control performance despite the model-reality mismatch. Comparison results show that the proposed method outperforms the traditional model-based control method and an improvement of 14.8% in total travel time is achieved in the example network.

The Best Way to Find an Evacuation Route for Fire Emergency

Evacuation route planning for urban area is important in a natural or man-made disaster. Fires in urban area have been one of the most dangerous problems in developing countries such as Myanmar. It is needed to develop effective planning to response and rescue in time locally everywhere because damage properties of fires have increased in present day. This paper presents an offline evacuation route planning method to provide for rescue schemes as fast as possible without the internet. The proposed algorithm calculates the shortest route between the nearest fire station such as rescue place and any fire incident as accident place by adding information of parent nodes to the A* algorithm to reduce searching time. It has two parts for generating efficient evacuation route. The first is producing the shortest route for fire engines to go to emergency places. Improving the A* algorithm in this paper speeds up the computation time of the route. Comparison results show that the proposed algorithm has a faster average response time than other algorithms. The second is that handling unexpected congestion on the identified shortest route by two ways not to delay emergency response condition. The simulation experiments were executed by the proposed method. The result of this system helps to find the most likely routes to get to the emergency area quickly.

A novel approach of creating sustainable urban planning solutions that optimise the local air quality and environmental equity in Helsinki, Finland: The CouSCOUS study protocol.

Air pollution is one of the major environmental challenges cities worldwide face today. Planning healthy environments for all future populations, whilst considering the ongoing demand for urbanisation and provisions needed to combat climate change, remains a difficult task. To combine artificial intelligence (AI), atmospheric and social sciences to provide urban planning solutions that optimise local air quality by applying novel methods and taking into consideration population structures and traffic flows. We will use high-resolution spatial data and linked electronic population cohort for Helsinki Metropolitan Area (Finland) to model (a) population dynamics and urban inequality related to air pollution; (b) detailed aerosol dynamics, aerosol and gas-phase chemistry together with detailed flow characteristics; (c) high-resolution traffic flow addressing dynamical changes at the city environment, such as accidents, construction work and unexpected congestion. Finally, we will fuse the information resulting from these models into an optimal city planning model balancing air quality, comfort, accessibility and travelling efficiency.

Congestion avoidance based on artificial bee colony algorithm for routing and wavelength assignment in optical satellite networks

Massive data traffic demand for satellite networks has been increased with the emergence of global broadband services. Due to limited onboard processing capacity and imbalanced distribution of global traffic, severe link congestion may occur in hot spots, deteriorating the performance of the whole network. A link cost modification applied in load balancing algorithm based on artificial bee colony is proposed for routing and wavelength assignment problem to distribute traffic in advance. Moreover, congestion avoidance based on wavelength utilization is designed for unexpected congestion to minimize the adverse impact of congestion on network performance. Experiment results show that the method we proposed realizes high performance in communication success rate and average transmission delay and also achieves the maximum utilization of wavelength utilization under the condition of low blocking rate and the equalization of traffic load.

Spatial Anti-Jamming Scheme for Internet of Satellites Based on the Deep Reinforcement Learning and Stackelberg Game

The anti-jamming communication of the heterogeneous Internet of Satellites (IoS) has drawn increasing attentions due to the smart jamming and high dynamics. This paper investigates a spatial anti-jamming scheme for IoS, with the aim of minimizing anti-jamming routing cost via Stackelberg game and reinforcement learning. Firstly, we formulate the routing anti-jamming problem as a hierarchical anti-jamming Stackelberg game. It has been proved that there is a Stackelberg equilibrium (SE) in the proposed game. Secondly, the spatial anti-jamming scheme for IoS consists of two stages: the available routing selection and the fast anti-jamming decision. To tackle the high dynamics caused by the unknown interrupts and the unexpected congestion, we propose a deep reinforcement learning based routing algorithm (DRLR) to obtain an available routing subset; Furthermore, to make a fast anti-jamming decision, we propose a fast response anti-jamming algorithm (FRA) based on the available routing subset. The user utilizes DRLR and FRA algorithms to empirically analyze the jammer's strategies and adaptively make an anti-jamming decision according to the dynamic and unknown jamming environment. Finally, the simulations have shown that the proposed algorithm has lower routing cost and better anti-jamming performance than existing approaches, and the anti-jamming policies converge to the SE.

A Simple, Fast, and Safe Mediator for Congestion Management

Congestion is a severe problem in cities. A large population with little information about each other's preferences hardly reaches equilibrium and causes unexpected congestion. Controlling such congestion requires us to collect information dispersed in the market and to coordinate actions among agents. We aim to design a mediator that a) induces a game with high social welfare in equilibrium, b) computes an equilibrium efficiently, c) works without common prior, and d) performs well even when only some of the agents in the market use the mediator. We propose a mediator based on a version of best response dynamics (BRD). We prove that, in a simple setting with two resources, “good behavior” (reporting truthfully and following the recommendation) forms an (approximate) ex-post Nash equilibrium in the mediated game; in the equilibrium, the welfare is close to the first-best when preferences diverge enough. Furthermore, under a certain behavioral assumption, those who are not using the mediator can always enjoy non-negative payoff gain by joining it even without the full participation of others. Additionally, our experimental results suggest that such results remain valid for more general settings.

The constrained reliable shortest path problem for electric vehicles in the urban transportation network

Electric vehicles (EVs) have recently received increasing attention, because of the environment-friendly features. As one of the major concerns of electric vehicles, the path planning problem in urban transportation networks faces the difficulties of the travel time uncertainty and driving range limitation, due to the unexpected congestion, bad weather and battery capacity. This paper investigates the constrained reliable shortest path (CRSP) problem for electric vehicles in the urban transportation network. A mixed-integer programming model is developed for the CRSP problem, where the reliable travel time is treated as the objective and the energy consumption, instead of the distance, is taken as the driving range constraint. To simply the scale of the problem, some pre-processing and network reduction techniques are introduced. Then a label setting algorithm based on elaborate dominance conditions is proposed to solve this problem. Besides, the A∗ technique is integrated to improve the search performance of the algorithm. The proposed model and algorithms are tested in two real-world urban transportation networks and scalable grid networks. Numerical studies show that the path choice is impacted by the risk attitudes of travelers and the battery capacity of electric vehicles. It implies that the navigation system should customize the optimal path in more detail to satisfy travelers’ heterogeneous requests. The proposed algorithm can obtain the exact solutions within satisfactory computational time, and it can be embedded into the intelligent navigation system to help the government promote the adoption of electric vehicles.

Position Bidding for Algorithm-Assisted Drivers

Problem definition: We propose and analyze a novel bidding mechanism that, based on new 5G-based inter-vehicle communication technologies, brings economic efficiency to ad-hoc and unexpected traffic congestion points, while avoiding negative externalities to anyone involved. Academic/Practical Relevance: Road traffic delays and congestion is one of the most vexing problems of modern times. Many price-based approaches, such as tolls and zone-congestion pricing, have been implemented in practice and studied in the literature. However, such approaches are not applicable to ad-hoc and unexpected congestion points that frequently occur; concretely in this study, forced merging. We show how one can achieve economic efficiency in these situations, by combining the potential of new technologies with dynamic pricing. Methodology: We analyze the performance of a mechanism that bids sequentially from tail to head of a dense platoon of vehicles (T2H), with the condition that the vehicle can advance only if it wins the bid, imposing, thus, no negative externalities. For benchmarking, we also analyze a mechanism that bids sequentially from head to tail (H2T), clearly favoring the merging driver. In both cases, we show that optimal bids and value functions can be computed via simple Dynamic Programming recursions. Results: For uniformly distributed time valuations, we prove that the expected social welfare of T2H is close to a partial-information social optimum, and that the expected social welfare of H2T is lower than that of T2H, as long as the platoon is not too short. We also propose fast Approximate Dynamic Programming algorithms that perform very well in our numerical experiments, making them suitable for implementation. Managerial Implications: This is a novel proposal for the future management of algorithm-assisted, connected road traffic, which has the potential to achieve economic efficiency without negative externalities to anyone involved; the latter being critical for mass adoption.

Vulnerability Identification of Urban Road Network under Unexpected Congestion

An original method is used to analyze the topology structure of a road network and two indexes of road network performance, namely, connectivity and network efficiency, are used to evaluate road network robustness in medium measure and thus quantify the variety of urban road network vulnerability caused by unexpected congestion. Then, a variety of road network impedances is set as the vulnerability identification index and a network traffic flow model with a link queuing capacity constraint is established, considering such constraint on unexpected congestion condition. Finally, a Lagrange dual algorithm is designed to solve the model, whose accuracy is subsequently tested by an example. Results show that the model based on the vulnerability index can accurately recognize road network vulnerability. Invalid links caused by unexpected congestion lead to changes in road network impedance, thereby directly affecting the robustness of the entire road network.

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.

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.

Next Road Rerouting: A Multiagent System for Mitigating Unexpected Urban Traffic Congestion

During peak hours in urban areas, unpredictable traffic congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability.

An iterative learning approach for anticipatory traffic signal control on urban networks

ABSTRACTTraffic signal control on urban road networks offers high opportunity to improve traffic operations. Among others, anticipatory control determines signal timings to optimize network performance, taking into account explicitly the route choice responses and resulting (equilibrium) network flow patterns. However, the optimal control decisions are usually calculated using equilibrium flow models that are in general only a coarse representation of reality. As a result, model–reality mismatch often leads to suboptimal conditions characterized by unexpected congestion effects. This paper presents a novel method to support control decisions for practical applications in real traffic systems that operate repeatedly, for instance from week to week, month to month, and in the presence of flow measurements. The proposed method generates a sequence of control settings to track the real flow response, by observing errors between modeled flows and the measurements. Improvement in the control performance is achieved by learning from this error information. A theoretical analysis on convergence of the control sequence is provided and the impact of different weighting parameters on the convergence performance is discussed. Numerical examples on a test network confirm the effectiveness of the proposed iterative learning approach in tackling modeling error, as well as a main role of model bias correction in tracking reality. The impact of weighting parameters on its convergence is also illustrated.

Autonomic Navigation System Based on Predicted Traffic and VANETs

To avoid an expected traffic jam, drivers make detours based on limited information; however, the majority following the alike routes may result in an unexpected congestion. Conventional navigation approaches are unable to respond to the unexpected congestion because these approaches do not consider the routes taken by other vehicles. Navigation systems that utilize global traffic information can improve gas consumption, CO2 emissions and travel time. Therefore, in this paper, the authors propose an autonomic navigation system (ANS) operating over vehicular ad-hoc networks (VANETs). The proposed ANS adopts a hierarchical algorithm to plan vehicle routes. The proposed ANS imitates the human nervous system when managing the navigation system, in which vehicles monitor traffic via VANETs. Moreover, this paper proposes a time-dependent routing algorithm that uses a novel traffic prediction method based on the routes of vehicles. This paper adopts EstiNet as simulator tool that dominates hundreds or thousands of VANET-based vehicles routing in two maps, Manhattan area, and Taipei city. The results show that the proposed ANS improves the average speed by 60.02 % when compared with the shortest path first (SPF) algorithm and by 15.49 % when compared with the distributed method of a traffic simulation in the Manhattan area. The proposed ANS also improves the average speed by 30.5 % when compared with the SPF algorithm and by 15.8 % when compared with the distributed method of a traffic simulation in the Taipei area. Furthermore, to emulate real environments, there is a scenario in which only a portion of the vehicles complies with the proposed ANS.

Congestion Induced by the Structure of Multiplex Networks

Multiplex networks are representations of multilayer interconnected complex networks where the nodes are the same at every layer. They turn out to be good abstractions of the intricate connectivity of multimodal transportation networks, among other types of complex systems. One of the most important critical phenomena arising in such networks is the emergence of congestion in transportation flows. Here, we prove analytically that the structure of multiplex networks can induce congestion for flows that otherwise would be decongested if the individual layers were not interconnected. We provide explicit equations for the onset of congestion and approximations that allow us to compute this onset from individual descriptors of the individual layers. The observed cooperative phenomenon is reminiscent of Braess' paradox in which adding extra capacity to a network when the moving entities selfishly choose their route can in some cases reduce overall performance. Similarly, in the multiplex structure, the efficiency in transportation can unbalance the transportation loads resulting in unexpected congestion.

Smart Bandwidth Friendly Buffer: Handling Overflow In Wireless Mesh Networks

With breakthrough of technological advancement, the significance of data transmission has been in highly demanding. On the other hand, limited buffering capacity has been great challenge that limits the Quality of Service (QoS) and degrade the performance of the network particularly in Wireless Mesh Networks (WMNs). Thus, it is important to provide an efficient utilization bandwidth and buffer management. Furthermore, the QoS in WMNs depends immensely on intelligent buffer management to avoid unexpected congestion and data loss. Some algorithms have been introduced to improve the buffer capacity and management. However, these suffer from high latency, even the loss of data because of congestion in the buffers, eventually resulting in low throughput. To address these issues we introduce the scheme “Smart Bandwidth Friendly Buffers (SBFB) for Wireless Mesh Networks. The SBFB is inspired by the features of existing schemes like the EZ Flow, WRED (Weighted Random Early Detection, and Back-off mechanism algorithms. The SBFB scheme contributes to prioritize the packets, allowing the down link nodes to perceive the buffer capacity prior to transmission and it also hunts for alternative routes in case of buffer buildup. Our proposed algorithm is validated using Network simulator-3 (NS3). Based on the experimental results, we have determined that SBFB has lesser congestion and packet loss probability when compared to other known contemporary schemes.

A Delay-Oriented Prioritization Policy Based on Cooperative Lossless Buffering in PTN Domains

New kinds of services and data flows, such as cloud computing, server virtualization, or quality of service (QoS)-aware Internet services, have led to higher demands on network resources. Thus, they have also led to increased bandwidth consumption and unexpected congestion, especially in backbones. In this article a novel proposal is studied, with the aim of improving the performance of delay-sensitive flows. The congestion impact on those QoS-aware flows that require a high level of reliability and delay is analyzed. This management proposal has been implemented on multiprotocol label switching transport profile (MPLS-TP), which is a packet transport networks technology that provides QoS by means of flow differentiation in Internet backbones. In order to improve performance in congested networks, a guarantee of service is proposed. It allows local recovery of lost traffic of MPLS-TP privileged flows, and in order to fulfill the requirements for implementation on MPLS-TP, a minimum set of extensions to RSVP-TE has also been proposed to provide self-management capable routes. Finally, in this paper we have carried out a performance improvement measurement by means of an analytical model and simulations.

Diversion during unexpected congestion on toll roads: the role of traffic information displayed on dynamic message signs

Dynamic message signs (DMS) are electronic signs displayed on roadways, providing travel times, traffic congestion, AMBER alerts, and special events. Orlando-Orange County Expressway Authority (OOCEA) in Florida added 29 fixed DMS to their toll road network between 2006 and 2008. The research issues investigated in this study are: 1) whether use of dynamic information on toll roads is associated with route diversions, and 2) are users satisfied with the available dynamic information? The instrument used to analyse travellers' perception of DMS and traffic information was a survey conducted in Central Florida region in May 2008 using Computer Aided Telephone Interview. Respondents answered questions on knowledge of DMS on OOCEA toll roads, satisfaction with travel information provided on the network and with different aspects of messages, route diversion (revealed and stated preferences), and demographics. A multinomial logit model was estimated to quantify the odds of choosing different diversion alternative over the alternative of ‘stay’ that elucidated the differences in diversion behaviour. Results showed that factors associated with greater route diversion are longer travel times, longer delays, information sources, network familiarity, and trip characteristics. Respondents were generally satisfied with DMS. Hazard warnings and accuracy of dynamic information were deemed important for satisfaction.

Optimization of OSPF Link Weights to Counter Network Failure

A key traffic engineering problem in the Open Shortest Path First (OSPF)-based network is the determination of optimal link weights. From the network operators' point of view, there are two approaches to determining a set of link weights: Start-time Optimization (SO) and Run-time Optimization (RO). We previously presented a Preventive Start-time Optimization (PSO) scheme that determines an appropriate set of link weights at start time. It can counter both unexpected network congestion and network instability and thus overcomes the drawbacks of SO and RO, respectively. The previous work adopts a preventive start-time optimization algorithm with limited candidates, named PSO-L (PSO for Limited candidates). Although PSO-L relaxes the worst-case congestion, it does not confirm the optimal worst-case performance. To pursue this optimality, this paper proposes a preventive start-time optimization algorithm with a wide range of candidates, named PSO-W (PSO for Wide-range candidates). PSO-W upgrades the objective function of SO that determines the set of link weights at start time by considering all possible single link failures; its goal is to minimize the worst-case congestion. Numerical results via simulations show that PSO-W effectively relaxes the worst-case network congestion compared to SO, while it avoids the network instability caused by the run-time changes of link weights caused by RO. At the same time, PSO-W yields performance superior to that of PSO-L.