Attracting sets in network regulatory theory

We study the problem of providing real-time service to hard and soft real-time messages in wavelength division multiplexing (WDM) optical networks. We propose and evaluate a set of scheduling algorithms which schedule message transmissions in single-hop WDM passive star networks based on specific time constraints. We compare the performances of our algorithms with that of the typical WDM scheduling algorithm which does not consider the time constraint of the transmitted messages by discrete-event simulations and an analytical model. We find that the improvement on real-time performance can be attributed to our scheduling algorithms where the time constraint of messages has been taken into consideration. This study suggests that when scheduling real-time messages in WDM networks, one has to consider not only the problem of resources allocation in the network but also the problem of sequencing messages based on their time constraints.

To utilize the wavelength resources of Internet protocol (IP) over wavelength division multiplexing (WDM) optical networks, a multi-constrained multicast traffic grooming algorithm is proposed in this paper. This algorithm can achieve multicast traffic grooming in IP over WDM optical networks under multi-constraints, including the number of optical transceivers, wavelength conversion capability and wavelength splitting capability. We simulated the traffic grooming algorithm using the topology of GEANT. Emulation results show that our algorithm has a lower blocking proportion compared with the multi-hop traffic grooming algorithm, which can achieve multicast traffic grooming more effectively.

In this paper we provide analytical insights for dimensioning a multi-layer network and in particular the virtual optical transport network topology exported to the packet client layer according to cost and power consumption. A new approach is described, approximating optimum routing, that can be effectively used to provide an estimation of network cost and reference architectures in network implementations. Results using this estimation approach are compared with those obtained using other estimation approaches regarding bandwidth allocation and network costs. We found that the approximated routing calculations, applied on a virtual network topology, do not significantly penalize the accuracy of the results. This approach can be extended to analyze the impact of new a optical networking technique, flexible-grid wavelength division multiplexing (WDM), providing some insights regarding scenarios in which migration from fixed-grid WDM systems to flexible-grid WDM systems would be advantageous in terms of capital cost and power consumption.

This study introduces a method for classifying traffic flow segments on expressways to estimate impact zones in merging/diverging sections and accident-prone sites. I propose a spatiotemporal dynamic segmentation approach that enables real-time identification of traffic hazard sections, reflecting changes in traffic flow, as opposed to traditional traffic analysis based on predefined segments in a node–link network. This methodology uses high-resolution vehicle trajectory data to precisely identify unstable and low-speed traffic sections. Using the geohash algorithm, the area is hierarchically segmented based on the standard deviation of speed in general traffic flow, facilitating the identification of unstable traffic flow patterns. For eight expressway routes, traffic flow was categorized into stable or minimum-size spaces. From a total of 1207 segments, 943 unstable flow segments were identified. The impact zones of the merging and diverging sections on Expressway 50 were analyzed using the results of spatial segmentation. Furthermore, by comparing traffic data before and after accidents, I assessed the short- and long-term effects of accidents on traffic flow. The proposed methodology provides precise data essential for reducing the likelihood of traffic accidents and for predicting post-accident congestion and duration. The patterns of such accident impact zones can contribute to preventing secondary accidents by providing advance information to following vehicles through various communication methods, including those involving autonomous vehicles. This enables effective traffic management strategies and rapid responses to accidents.

In a many-to-many session, a participant distributes and receives traffic flows to/from all others in the same session. It is desirable to achieve both high resource utilization and low power consumption when such sessions are provisioned. In wavelength division multiplexing (WDM) optical networks, traffic grooming has been widely applied as the key technology for aggregating sessions to improve resource utilization. However, the current many-to-many grooming approaches, the lightpath circle and hubbed light-tree, do not make an effort to reduce the power consumption of many-to-many sessions. In this paper, we formulate the green routing and aggregation of many-to-many sessions problem and prove its NP-completeness. For problem solving in WDM optical networks, we apply a rational combined method of lightpaths. Both an integer linear programming-based approach and a novel graph-based heuristic are devised. We further provide an illustrative comparison between our heuristic and the existing benchmarks in terms of power consumption. Extensive simulation results demonstrate that, compared with the benchmarks, our heuristic improves the average power efficiency in the range of 13-19%.

Understanding traffic flow in urban areas has great importance and implications from an economic, social and environmental point of view. For this reason, numerous disciplines are working on this topic. Although complex network theory made their appearance in transportation research through empirical measures, the relationships between dynamic traffic patterns and the underlying transportation network structures have scarcely been investigated so far. In this work, a novel Networks in Networks (NiN) approach is presented to study changes in traffic flows, caused by topological changes in the transportation network. The NiN structure is a special type of multi-layer network in which vertices are networks themselves. This embedded network structure makes it possible to encode multiple pieces of information such as topology, paths, and origin-destination information, within one consistent graph structure. Since each vertex is an independent network in itself, it is possible to implement multiple diffusion processes with different physical meanings. In this way, it is possible to estimate how the travellers’ paths will change and to determine the cascading effect in the network. Using the Sioux Falls benchmark network and a real-world road network in Switzerland, it is shown that NiN models capture both topological and spatial-temporal patterns in a simple representation, resulting in a better traffic flow approximation than single-layer network models.

Traffic Flow Model for Staggered Intersection without Signal Lamp

Traffic engineering (TE) has become a challenging mechanism for network management and resources optimization due to uncertain and difficult to predict traffic patterns. Recent works have proposed robust optimization techniques to cope with uncertain traffic, computing a stable routing configuration that is immune to demand variations within certain uncertainty set. However, using a single routing configuration for longtime periods can be highly inefficient. Even more, the presence of abnormal and malicious traffic has magnified the network operation problem, claiming for solutions which not only deal with traffic uncertainty but also allow to detect and identify faulty traffic to take the appropriate countermeasures. In this paper, we introduce the Reactive Robust Routing (RRR) for TE, an approach that combines both proactive and reactive techniques to tackle the problem. Based on expected traffic patterns, we adapt the uncertainty set and build a multi-hour yet robust routing scheme that outperforms the stable robust approach. For the case of anomalous and unexpected traffic, we propose a fast anomaly detection/isolation algorithm to detect and localize abrupt changes in traffic flows and decide routing changes. This algorithm is optimal in the sense that it minimizes the decision delay for a given mean false alarm rate and false isolation probability. We validate these proposals using real data from two different backbone networks and we show how the RRR can handle uncertain and highly dynamic traffic in an automatic fashion, simplifying network operation.

In optical wavelength-division multiplexing (WDM) networks, traffic can be very “bursty” at a fine time scale, even though it may seem to be smooth at coarser scales (e.g., Poisson or Poisson-related traffic). This paper analyzes the instantaneous characterization of Poisson traffic at a fine time scale. The analysis shows that the irregular oscillation of the instantaneous traffic load and the occurrence of blockings in a light-loaded network are highly correlated. Specifically, most blockings occur concentratively at the peaks of the instantaneous load. In some other time, network resources may not be sufficiently utilized. To make better utilization of network resources, a novel wavelength-buffering (WB) scheme is proposed for the first time in this paper. By reserving a portion of resources in a “wavelength buffer” under light loading and releasing them when the load goes up, a number of blockings brought by the oscillation of the traffic load can be avoided. Simulation results show that compared with other schemes such as adaptive routing, wavelength conversion (WC), and rerouting, the novel wavelength-buffering scheme achieves significantly better performance with respect to the network utilization and overall blocking probability.

Traffic flow prediction is an important part of intelligent transportation system (ITS). Accurate traffic flow prediction information can provide reliable reference for traffic management decision-makers, effectively reduce traffic congestion, reduce vehicle exhaust pollution, save energy and facilitate the travel of the people. Most of the existing traffic flow prediction models simplify the traffic flow change process into a linear and stable process and ignore the influence of weather conditions on traffic flow, which will lead to large prediction deviation. In order to improve the accuracy of traffic flow prediction, this paper proposes an ensemble learning method based on ET and AdaBoost for fusing traffic and meteorological data under the non-stationary environment to predict the changes of traffic flow. The experiment is based on real traffic and meteorological data, and the results show that the proposed ensemble method is superior to the baseline methods.

Addresses the problem of efficient communication in unreliable multi-hop optical networks supported by wavelength division multiplexing (WDM). We first define a new cost model for routing in (optical) WDM networks that is more general and realistic than the existing models. Our model takes into consideration not only the cost of wavelength access and conversion but also the cost for wavelength switching when several optical channels of the same wavelength arrive at the same node. We then propose a set of efficient algorithms in both reliable and unreliable WDM networks on the new cost model respectively for each of three important communication patterns-multiple point-to-point routing, multicast and multiple multicast.

We address the issue of inapproximability of the wavelength assignment problem in wavelength division multiplexing (WDM) optical networks. We prove that in an n-node WDM optical network with m lightpaths and maximum load L, if NP ≠ ZPP, for any constant δ>0, no polynomial time algorithm can achieve approximation ratio n1/2−δ or m1−δ, where NP is the class of problems which can be solved by nondeterministic polynomial time algorithms, and ZPP is the class of problems that can be solved by polynomial randomized algorithms with zero probability of error. Furthermore, the above result still holds even when L=2. We also prove that no algorithm can guarantee the number of wavelengths to be less than $$(\sqrt{n}/2)L$$ or (m/2)L. This is the first time inapproximability results are established for the wavelength assignment problem in WDM optical networks. We also notice the following fact, namely, there is a polynomial time algorithm for wavelength assignment which achieves approximation ratio of O(m(log log m)2/(log m)3). Therefore, the above lower bound of m1−δ is nearly tight.

Identifying the correlation between rainfall, traffic flow performance and air pollution concentration in Seoul using a path analysis

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

With the wide application of ETC system in China, how to effectively manage and apply its massive data information has gradually become a key research direction in the field of transportation. Firstly, the layout mode and function of ETC gantry system is introduced. ETC gantry system can collect a large amount of basic data, transaction data and traffic data, including ETC transaction flow, ETC passage record, image flow record, CPC cartoon line record, ETC. Then, the actual data of expressway sections collected by ETC gantry system are used to replace the simulation parameters of the traffic flow model in the traditional method for calculation, and three key performance indexes such as travel time, service flow and waiting time are considered. Combined with the capacity constraints, the freeway on-ramp traffic control model based on ETC gantry data is established. And then to determine the highway road traffic condition, and according to the actual operation conditions of the sections, formulate corresponding traffic control strategy, help the highway operator according to the actual situation of the highway road traffic flow changes, realize timely controlling driving state and traffic flow control, in order to better serve the highway road users and administrators.