Traffic Rerouting Research Articles

The benefits of cooperative policies for transportation network protection from sea level rise: A case study of the San Francisco Bay Area

This research investigates the influence of decision-maker behavior on policies that are likely to be adopted for the protection of highway infrastructure against inundations resulting from sea level rise. We analyze two different types of games to represent decision-maker behavior, and use the San Francisco Bay Area shoreline with a scenario of a 0.5 m sea level rise as a case study, which is expected in 2054. In our model, the objective of the decision-makers (the counties bordering the SF Bay Area) is to minimize the traffic delay caused by inundations in the transportation network that lies in the geographical boundaries of their counties.Our model considers hydrodynamic interactions, traffic flow patterns changes as a result of inundations, and budget constraints on the protection costs. The hydrodynamics in the Bay Area are affected by the shoreline protection strategy: protection of the shoreline of a county may lead to increased inundations in another, unprotected, county. Furthermore, closure of a highway link in one county affects traffic delays in other counties due to traffic re-routing. Thus, protection decisions made by a county have potential impacts on several other counties, and therefore counties must take into account other counties' actions.Both competitive (Nash) and cooperative games are analyzed. A Nash equilibrium is achieved among the counties when each acts to minimize its objective without the possibility of collaborating with other counties. We compare the results of Nash games with possible cooperation strategies for a range of funding scenarios. It is shown, through several examples, that cooperation among counties increases benefits (reduction of Vehicle Hours Traveled) for all participants in most cases. In some cases, cooperation also reduces protection costs. We have identified that there are counties that are not beneficial to protect, in terms of traffic delay minimization, such as Napa, while other counties for which protection is critical, such as Marin. However, these results depend on the formation of the collaborations and the available budget, and therefore our conclusions cannot be generalized.

Chaotic Encryption Algorithm With Key Controlled Neural Networks for Intelligent Transportation Systems

The security of sensitive information is vital in many aspects of multimedia applications such as Intelligent Transportation Systems (ITSs), where traffic data collection, analysis and manipulations is essential. In ITS, the images captured by roadside units form the basis of many traffic rerouting and management techniques, and hence, we should take all precautions necessary to deter unwanted traffic actions caused by malicious adversaries. Moreover, the collected traffic images might reveal critical private information. Consequently, this paper presents a new image encryption algorithm, denoted as ChaosNet, using chaotic key controlled neural networks for integration with the roadside units of ITSs. The encryption algorithm is based on the Lorenz chaotic system and the novel key controlled finite field neural network. The obtained cryptanalysis show that the proposed encryption scheme has substantial mixing properties, and thus cryptographic strength with up to 5% increase in information entropy compared to other algorithms. Moreover, it offers consistent resistance to common attacks demonstrated by nearly ideal number of changing pixel rate (NPCR), unified averaged changed intensity (UACI), pixel correlation coefficient values, and robustness to cropped attacks. Furthermore, it has less than 0.002% difference in the NPCR and 0.3% in the UACI metrics for different test images.

Dynamic traffic re-routing as a method of reducing the congestion level of road network elements

Dynamic traffic re-routing as a method of reducing the congestion level of road network elements

Rule Minimization for Traffic Evolution in Software-Defined Networks

This letter addresses the problem of traffic evolution in software defined networks (SDNs). SDNs achieve optimal flow distribution by using flow optimization models, but as traffic evolves, the underlying optimization model changes. Applying the online model frequently can result in a flood of control messages to switches for deleting, modifying, or installing new rules, and the resulting traffic re-routing can cause transient loops and synchronization issues. In this letter, we present minimum rule application (MIRA), a mixed integer linear programming-based model, which re-calculates flow distribution dynamically while minimizing the number of rule installations. Since the proposed model is NP-hard, we also propose an efficient heuristic-based greedy algorithm. In addition, we also propose a rule-aggregation (RA) optimization for minimizing rule installation (MIRA-RA). Finally, we propose a multi-objective optimization model, which jointly minimizes the conflicting objectives of rule installation and link utilization (PARETO) and use the $\epsilon $ -constraint method to achieve pareto-optimality. We implement MIRA, MIRA-RA, Greedy, PARETO, and an existing solution in the area and present numerical results.

Secure Vehicular Traffic Re-routing System using SCMS in Connected Cars

Secure Vehicular Traffic Re-routing System using SCMS in Connected Cars

DIVERT: A Distributed Vehicular Traffic Re-Routing System for Congestion Avoidance

Centralized solutions for vehicular traffic re-routing to alleviate congestion suffer from two intrinsic problems: scalability, as the central server has to perform intensive computation and communication with the vehicles in real-time; and privacy, as the drivers have to share their location as well as the origins and destinations of their trips with the server. This article proposes DIVERT, a distributed vehicular re-routing system for congestion avoidance. DIVERT offloads a large part of the re-routing computation at the vehicles, and thus, the re-routing process becomes practical in real-time. To take collaborative re-routing decisions, the vehicles exchange messages over vehicular ad hoc networks. DIVERT is a hybrid system because it still uses a server and Internet communication to determine an accurate global view of the traffic. In addition, DIVERT balances the user privacy with the re-routing effectiveness. The simulation results demonstrate that, compared with a centralized system, the proposed hybrid system increases the user privacy by 92 percent on average. In terms of average travel time, DIVERT's performance is slightly less than that of the centralized system, but it still achieves substantial gains compared to the no re-routing case. In addition, DIVERT reduces the CPU and network load on the server by 99.99 and 95 percent, respectively.

Experimental End-to-End Demonstration of Shared N:M Dual-Homed Protection in SDN-Controlled Long-Reach PON and Pan-European Core

Seeking reduction of capital and operational costs on next-generation fiber networks is the holy grail of network planning and deployment for all operators worldwide. In particular, efficient deployment strategies of next-generation fiber access networks is of paramount importance to enable wide-scale ubiquitous deployment of high-speed broadband services. Exploiting the large capacity of fiber networks to support heterogeneous services from residential and business users is a promising strategy toward this goal. Long-reach passive optical networks (LR-PON) is one such strategy that also adopts greater sharing of active and passive components, together with consolidation of central offices, to reduce capital and operational expenditures. However, due to its long reach and large split ratio, protection mechanisms become a major consideration when designing an LR-PON, as a single feeder cable cut could disrupt services for several thousand users. In this paper, we demonstrate fast restoration of LR-PON services using a dual-homed shared optical line terminal (OLT) protection mechanism. Our end-to-end testbed connects our optical access broadband laboratory operating on custom built LR-PON ONU and OLT field programmable gate array prototypes with a Europe-wide testbed core network. We use a software-defined network control plane to manage the dual-homed N:M protection switching and traffic reroute in the core and achieve access protection and end-to-end services restoration times of 40 and 80 ms, respectively.

A Fast Reroute Algorithm for Infotainment Service in Internet of Vehicles

Failure recovery in Internet of Vehicles (IoVs) is critical to high quality service provisioning. The main challenge is how to achieve fast rerouting without introducing high complexity and resource usage due to the dynamic topology and the constraints on bandwidth. In this paper, we propose a traffic prediction-based fast reroute algorithm for use among the vehicles in IoVs. The proposed algorithm uses the Wavelet Neural Network (WNN) model to predict a vehicle's network traffic. When the predicted value is greater than the predefined network traffic threshold, both Adaptive Retransmission Trigger (ART) that contributes to switch to a better alternate path in advance and trigger efficient retransmission behaviors are enabled successively. Performance comparison of our proposed algorithm with Ant-based Delay-Sensitive Vehicular Routing (AntVehiNet) shows that WNNPFR can: (a) maximize the service data delivery rate by load balancing, (b) provide high quality of service delivery for multimedia streams by switching to a better path towards a target node in advance, (c) reduce useless data retransmissions when various network failures occur, and (d) maintain lower routing overhead.

Quality of Service Provisioning and Energy Minimized Scheduling in Software Defined Flexible Optical Networks

The over-provisioning of capacities in optical networks is not a sustainable approach in the long run. In this paper, we propose a software defined networking scheme for quality of service provisioning through energy efficient assignment of optical transponders, employing bandwidth variable distance adaptive modulation and coding. Our scheme enables avoiding over-provisioning of transponder capacity as well as short-term major changes in equipment allocation for networks with dynamic traffic. We make use of the seasonal auto-regressive integrated moving average model to forecast the statistics of network traffic for an arbitrary time span based on the requirements and the constraints of the service provider. The quality of service measure is defined as the probability of congestion at the core router ports. A stochastic linear programming approach is used to provide a solution for energy efficient assignment of optical transponders and electronic switching capacity while ensuring a certain level of quality of service to core routers. The scheduling of optical lightpath capacities is performed for the entire duration of time under consideration, whereas the scheduling of electronic switching capacities is performed based on the short-term dynamics of the traffic. Numerical results show up to 48% improvement in the energy efficiency of optical networks and 45% reduction in the number of optical lightpaths through the implementation of the proposed technique, compared to a design based on employing conventional fixed optical transponders and no traffic rerouting, where both schemes satisfy the congestion probability requirements.

Experimental Demonstration of Segment Routing

Segment routing (SR) technology has been recently proposed to enforce effective routing strategies without relying on signaling protocols. So far, the SR technology has received limited attention within the scientific community. In this paper, two SR implementations are presented and successfully demonstrated in two different network testbeds. The first implementation focuses on a software defined networking (SDN) scenario where nodes consist of OpenFlow switches and the SR controller is a specifically designed enhanced version of an OpenFlow controller. The second implementation includes a novel path computation element (PCE) scenario where nodes consist of commercially available IP/MPLS routers and the SR controller is a new extended version of a PCE solution. Both implementations have been successfully applied to demonstrate dynamic traffic rerouting. In particular, by enforcing different segment list configurations at the ingress node, rerouting is effectively achieved with no packet loss and without requiring the use of signaling protocols. Effective scalability performance is achieved in both proposed implementations, under different segment list conditions.

A novel resource reservation based on cross layer design over mobile internet protocol version 6

Quality of Service (QoS) is an important issue that should be addressed to provide acceptable level of service to the mobile nodes. The assumption is that mobile nodes require the same quality of services as fixed nodes. However, due to the mobility feature within a mobile network, it is difficult to achieve this goal. Besides, routing methods in a mobile IPv6 network deliver packets from a correspondent node to the mobile node and vice versa via specific tunnel. In the tunnel, packets are encapsulated and routers do not have any access to the original packets. This results in the situation where resource reservation cannot be handled at the routers. For this purpose, a new cross layer design called Resource Reservation over Mobile Application (ROMA) is proposed. In this design, modified mobility signaling collects QoS parameters through the network. Then, these parameters are used to make the desired level of QoS for real-time applications. Traffic engineering is also proposed based on effective envelope algorithm to improve network resource utilization. It allows a fraction of traffic to exceed QoS requirements. Simulation is used to evaluate the proposed approach by considering parameters such as the number of reservation request messages, link throughput, and end-to-end delay. Simulation results demonstrate that the proposed approach enhances the link utilization, while maximizing the link throughput in comparison with recent works. The proposed approach also reduces the traffic reroute time when router or link failure occurs in network.

Proactive Vehicular Traffic Rerouting for Lower Travel Time

Traffic congestion causes driver frustration and costs billions of dollars annually in lost time and fuel consumption. This paper presents five traffic rerouting strategies designed to be incorporated in a cost-effective and easily deployable vehicular traffic guidance system that reduces travel time. The proposed strategies proactively compute individually tailored rerouting guidance to be pushed to vehicles when signs of congestion are observed on their route. The five proposed strategies are the dynamic shortest path (DSP), the A* shortest path with repulsion (AR*), the random k shortest path (RkSP), the entropy-balanced kSP (EBkSP), and the flow-balanced kSP (FBkSP). Extensive simulation results show that the proposed strategies are capable of reducing the travel time as much as a state-of-the-art dynamic traffic assignment (DTA) algorithm while avoiding the issues that make DTA impractical, such as the lack of scalability and robustness, and high computation time. Furthermore, the variety of proposed strategies allows tuning the system to different levels of tradeoffs between rerouting effectiveness and computational efficiency. In addition, the proposed traffic guidance system can significantly improve the traffic even if many drivers ignore the guidance or if the system adoption rate is relatively low.

Parameter tuning for a multi-fidelity dynamical model of the magnetosphere

Geomagnetic storms play a critical role in space weather physics with the potential for far reaching economic impacts including power grid outages, air traffic rerouting, satellite damage and GPS disruption. The LFM–MIX is a state-of-the-art coupled magnetospheric–ionospheric model capable of simulating geomagnetic storms. Imbedded in this model are physical equations for turning the magnetohydrodynamic state parameters into energy and flux of electrons entering the ionosphere, involving a set of input parameters. The exact values of these input parameters in the model are unknown, and we seek to quantify the uncertainty about these parameters when model output is compared to observations. The model is available at different fidelities: a lower fidelity which is faster to run, and a higher fidelity but more computationally intense version. Model output and observational data are large spatiotemporal systems; the traditional design and analysis of computer experiments is unable to cope with such large data sets that involve multiple fidelities of model output. We develop an approach to this inverse problem for large spatiotemporal data sets that incorporates two different versions of the physical model. After an initial design, we propose a sequential design based on expected improvement. For the LFM–MIX, the additional run suggested by expected improvement diminishes posterior uncertainty by ruling out a posterior mode and shrinking the width of the posterior distribution. We also illustrate our approach using the Lorenz ‘96 system of equations for a simplified atmosphere, using known input parameters. For the Lorenz ‘96 system, after performing sequential runs based on expected improvement, the posterior mode converges to the true value and the posterior variability is reduced.

Protection based on backup radios and backup fibers for survivable Fiber-Wireless (FiWi) access network

In Fiber-Wireless (FiWi) access network, the optical back-end is vulnerable to the network component failure due to its tree topology. Any failure at the optical back-end may cause huge data loss. Thus, the survivability in FiWi is an important issue, especially the protection for the optical back-end. Some works propose to protect the optical back-end in FiWi by means of the wireless rerouting in the wireless front-end. However, these works cannot guarantee that there are always the available wireless paths for the traffic rerouting. In this paper, we divide the failures at the optical back-end into ONU-level failure and OLT-level failure according to the failure severity. To tolerate the ONU-level failure, we allocate each ONU a partner ONU and establish the wireless-backup-path between them by deploying backup radios on the traversed wireless routers. To tolerate the OLT-level failure, we cluster all segments in the network and place the backup fibers among the segments in each cluster to establish the protection ring. Thus, each pair of segments in the same cluster can backup for each other along the optical-backup-paths on the protection ring. We propose the heuristic algorithms to minimize the cost of backup radios and the cost of backup fibers. The simulation results demonstrate that the proposed algorithms are effective in enhancing the survivability of FiWi, while requiring less cost than the previous works.

Left-Turn Prohibition and Partial Grade Separation for Signalized Intersections: Planning-Level Assessment

AbstractRecurring congestion at signalized intersections is caused by the inability of traffic signal controls to serve demand even in cities where advanced traffic signal timing and management systems are in operation. Left-turn prohibition (LTP) and partial grade separation with low-clearance underpass (LCUP) are possible congestion mitigation actions for urban complex and congested signalized intersections that have exhausted other options for capacity enhancement, such as lane addition and signal timing optimization. LTP and LCUP improve traffic signal efficiency by eliminating signal phases, increasing green splits, or adding lanes. They also improve intersection safety by eliminating certain conflicts. At the same time, they necessitate traffic rerouting, may have negative impacts at downstream locations, and may have perceived or actual impacts to accessibility and neighborhood character. A planning-level assessment method to quantify and compare the benefits and costs LTP and LCUP was developed to...

Dynamic energy-saving algorithm in green hybrid wireless-optical broadband access network

Since the access network plays the dominant role of energy consumption in the whole network, it is necessary to study the energy-efficient design on the access technique. This paper aims to develop the green Hybrid Wireless-Optical Broadband Access Network (HWOBAN) by sleeping low-loaded Optical Network Units (ONUs) as much as possible to save the energy. Correspondingly, a heuristic approach, Energy-saving Algorithm based on Sleeping Low-loaded OUNs (EASLO), is proposed in this paper. In EASLO, most of traffic has higher probability to be carried by the currently active ONUs through dynamic energy-awareness mechanism, so that the higher resource utilization of ONUs can be achieved. Meanwhile, in order to improve the energy efficiency, we can sleep low-loaded ONUs, each of which can satisfy the conditions of traffic rerouting, and then transfer the residual traffic on these sleeping ONUs to the other active ones. The simulation results show that, our proposed EASLO method has better performance of energy savings than traditional method.

Traffic Rerouting Strategy against Jamming Attacks in WSNs for Microgrid

In a microgrid as an energy infrastructure, the vulnerability against jamming attacks is fatal. Thus, the ability to deal with jamming attacks and maintain an acceptable level of service degradation in presence of the attacks is needed. To solve the problem, we propose a traffic rerouting scheme in wireless communication infrastructure for islanded microgrid. We determine disjoint multiple paths as candidates of a detour path and then select the detour path among the candidates in order to reduce the effect of jamming attack and distribute traffic flows on different detour paths. Through performance comparison, we show that our scheme outperforms a conventional scheme in terms of packet delivery ratio and end-to-end delay.

Probability of Collapse Expression for Bridges Subject to Barge Collision

Accounting for waterway vessel collision is required in the structural design of bridges spanning navigable waterways. During collision events, massive waterway vessel groups such as barge flotillas are capable of dynamically transmitting horizontal forces to impacted bridge components. Furthermore, collision-induced forces can be sufficient to collapse piers or roadway spans in the vicinity of the impact location. If collapse takes place, economic loss is suffered because of subsequent traffic rerouting and bridge replacement costs. Additionally, fatalities may occur if the roadway is occupied during or shortly after collapse. This paper focuses on the development of a probability of collapse expression for bridge piers subject to barge impact loading, where the relationship can be readily integrated into existing bridge design methodologies. The expression is developed by employing probabilistic descriptions for a multitude of random variables related to barge traffic characteristics and bridge structures in conjunction with nonlinear dynamic finite-element analyses of barge-bridge collisions. Highly efficient, advanced probabilistic simulation techniques are necessarily incorporated into the barge-bridge collision analysis framework to allow feasible estimation of structural reliability parameters. These parameters facilitate the formation of an expression that, in turn, bridge designers can use to estimate probabilities of structural collapse attributable to barge collision, without performing probabilistic analyses.

Network routing control with G-networks

The aim of this paper is to detail a control scheme for packet computer networks whose purpose is to minimise a quality-of-service oriented performance metric by re-routing the traffic. The model is based on G-networks with triggered customer movement to represent traffic re-routing, and on a gradient descent based optimisation algorithm. The model and the algorithm are presented and we show that the gradient descent algorithm is of computational complexity O ( N 3 ) where N is the number of nodes in the packet network. Via the use of multiple classes of normal traffic and multiple classes of triggers, our approach allows one not only to evaluate the effect of the control, but also to incorporate the overhead that the control traffic will induce, and the consequences of the delays or possible losses of the control traffic. Similarly, these effects will naturally be incorporated when one considers both the impact of the control traffic on the cost function, and the details of this control traffic in the control algorithm itself.

Lightpath Rerouting Strategies in WDM All-Optical Networks Under Scheduled and Random Traffic

In WDM all-optical networks without wavelength conversion, traffic rerouting is motivated either by an optimization of resource utilization or by network survivability. In this paper, we use rerouting to optimize network resources allocation in order to set up an incoming lightpath demand to be blocked for lack of resources. Rerouting aims at reassigning the wavelength and/or the path of one or several established connections in order to free enough wavelengths to satisfy the incoming demand. Rerouting refers implicitly to dynamic traffic. In most previous studies related to rerouting, only random (dynamic) traffic is considered. In this paper, we propose a new lightpath rerouting scheme considering three types of traffic demands, referred to as permanent lightpath demands (PLDs), scheduled lightpath demands (SLDs), and random lightpath demands (RLDs). PLDs are static, whereas SLDs and RLDs are dynamic. SLDs are preplanned, whereas RLDs are stochastic. PLDs may be seen as a particular case of SLDs. PLDs and SLDs correspond to guaranteed services, whereas RLDs correspond to best-effort services. Thus, PLDs and SLDs cannot be rerouted. We here describe two new routing and wavelength assignment (RWA) strategies applying rerouting. Both strategies assume that PLDs are routed offline during the network planning phase. The first strategy computes the RWA for SLDs and RLDs on the fly. The second strategy proceeds in two separate phases. It first computes offline the RWA for SLDs before considering RLDs on the fly on the remaining network resources. Our rerouting schemes aim at minimizing the number of RLDs or the number of optical channels to be rerouted. Through numerical examples and experimental simulations, we outline that routing SLDs offline and RLDs online instead of routing SLDs and RLDs online enables lower rejection ratios. We also compare our proposed rerouting algorithms with other approaches from the literature in terms of complexity.