A dynamic and automatic traffic light control expert system for solving the road congestion problem

Traffic congestion is a severe problem in many modern cities around the world. To solve the problem, we have proposed a framework for a dynamic and automatic traffic light control expert system combined with a simulation model, which is composed of six submodels coded in Arena to help analyze the traffic problem. The model adopts interarrival time and interdeparture time to simulate the arrival and leaving number of cars on roads. In the experiment, each submodel represents a road that has three intersections. The simulation results physically prove the efficiency of the traffic system in an urban area, because the average waiting time of cars at every intersection is sharply dropped when the red light duration is 65 s and the green light time duration is 125 s. Meanwhile, further analysis also shows if we keep the interarrival time of roads A, B, and C, and change that of roads D, E, and F from 1.7 to 3.4 s and the interdeparture times at the three intersections on roads A, B, and C are equal to 0.6 s, the total performance of the simulation model is the best. Finally, according to the data collected from RFID readers and the best, second and third best traffic light durations generated from the simulation model, the automatic and dynamic traffic light control expert system can control how long traffic signals should be for traffic improvement.     

Approximating the traffic grooming problem in tree and star networks

We consider the problem of grooming paths in all-optical networks with tree topology so as to minimize the switching cost, measured by the total number of used ADMs. We first present efficient approximation algorithms with approximation factor of 2ln(δ⋅g)+o(ln(δ⋅g))$2ln(\delta \cdot g)+o(ln(\delta \cdot g))$ for any fixed node degree bound δ$\delta$ and grooming factor g$g$, and 2lng+o(lng)$2lng+o(lng)$ in unbounded degree directed trees, respectively. In the attempt to extend our results to general undirected trees, we completely characterize the complexity of the problem in star networks by providing polynomial time optimal algorithms for g≤2$g\le 2$ and proving the intractability of the problem for any fixed g>2$g>2$. While for general topologies, the problem was known to be NP-hard g$g$ not constant, the complexity for fixed values of g$g$ was still an open question.     

Estimating traffic capacity for weaving segments using neural networks technique

The impact of weaving vehicles on the capacity of freeway segments is uncertain due to the complexity in operation. The Highway Capacity Manual (HCM) 2000 provides values for capacity on various weaving segments (Exhibit 24-8) based on sets of conditions (configuration, speed, length, volume ratio, and number of lanes). However, to find capacity for a given set of conditions, an iterative process should be carried out using a properly programmed spreadsheet. This paper suggests alternative and convenient procedure for estimating capacity on weaving segments. Two capacity prediction models are developed using regression and neural networks (NNT). Although, linear regression (LR) technique showed satisfactory results, neural network technique outscored linear regression in the prediction performance, and generalization ability. The trained neural network architecture represented by weight and bias values for each layer is simply used to predict capacity for weaving segments under new conditions.     

A global approach for designing reliable WDM networks and grooming the traffic

In this paper, we address the global problem of designing reliable wavelength division multiplexing (WDM) networks including the traffic grooming. This global problem consists in finding the number of optical fibers between each pair of optical nodes, finding the configuration of each node with respect to transponders, finding the virtual topology (i.e., the set of lightpaths), routing the lightpaths, grooming the traffic (i.e, grouping the connections and routing them over the lightpaths) and, finally, assigning wavelengths to the lightpaths. Instead of partitioning the problem into subproblems and solving them successively, we propose a mathematical programming model that addresses it as a whole. Numerical results are presented and analyzed.     

优化无线传感器网络寿命的动态路由算法

针对多跳无线传感器网络能量受限的特点,以提高网络寿命为目标,建立基于最大最小节点寿命的线性规划网络模型。当传感器节点失效时,根据网络的拓扑结构动态更新节点的路由。仿真分析结果表明：基于网络拓扑结构变化动态更新节点路由的方法能够拓展网络寿命,大幅度地增加基站接收信息的数量和提高节点能量的使用效率。     

A fast network partition method for large-scale urban traffic networks

In order to control the large-scale urban traffic network through hierarchical or decentralized methods, it is necessary to exploit a network partition method, which should be both effective in extracting subnetworks and fast to compute. In this paper, a new approach to calculate the correlation degree, which determines the desire for interconnection between two adjacent intersections, is first proposed. It is used as a weight of a link in an urban traffic network, which considers both the physical characteristics and the dynamic traffic information of the link. Then, a fast network division approach by optimizing the modularity, which is a criterion to distinguish the quality of the partition results, is applied to identify the subnetworks for large-scale urban traffic networks. Finally, an application to a specified urban traffic network is investigated using the proposed algorithm. The results show that it is an effective and efficient method for partitioning urban traffic networks automatically in real world.     

Automatic control of flow gathering networks: A mixed-integer receding horizon control applied to an onshore oilfield

This paper presents a real-world application of a mixed-integer receding horizon control in an onshore oil gathering network. The objective is to stabilize the operation of the gathering network by coordinating the automatic switching of pumps to avoid abrupt flow variations at the central station, minimizing pump switching and, at the same time, maintaining the level of fluid in the satellite stations under control. The applied technological solution easily integrates different systems, optimization tools and heuristic rules, allowing in-house development of complex applications.     

Modeling and optimizing Random Walk content discovery protocol over mobile ad-hoc networks

Content discovery is one of the challenges over mobile ad-hoc networks. Peer to peer content discovery techniques including structured and unstructured can be employed in MANETs by considering its special characteristics and limitations. The most important characteristics of MANETs are the mobility of the nodes, power consumption limitations and transitive links which create a dynamic topology. Unstructured techniques present higher performance compared to the structured ones over MANETs and among unstructured peer to peer protocols, Random Walk delivers less energy consumption and satisfactory hit rate awhile. This paper proposes an adaptive method to optimize the random walk unstructured content discovery protocol. First, it models this protocol using the G-network which is a queuing system with two types of customer, negative and positive. Then, it optimizes this protocol by the gradient descend technique based on a cost function which consists of three parameters. Two of these parameters are hit rate and response time which are derived from the content discovery protocol performance metric. The other parameter is energy consumption which is one of the most important performance metrics in MANET.     

Multiple link failures survivability of optical networks with traffic grooming capability

This paper investigates the problem of survivable traffic grooming (STG) in shared mesh optical networks and proposes different frameworks for improving the survivability of low speed demands against multiple near simultaneous failures. Spare capacity reprovisioning has recently been considered for improving the overall network restorability in the event of dual failures; here, after the recovery form the first failure, some connections in the network may become unprotected and exposed to new failures. Capacity reprovisioning then allocates protection resources to unprotected and vulnerable connections so that the network can withstand a future failure. In this paper, we propose two different reprovisioning schemes (lightpath level reprovisioning, LLR, and connection level reprovisioning, CLR); they differ in the granularity at which protection resources are reprovisioned. Further, each of these schemes is suitable for a different survivable grooming policy. While LLR provides collective reprovisioning of connections at the lightpath level, CLR reprovisions spare bandwidth for lower speed connections instead. We use simulation methods to study the performance of these schemes under two grooming policies (PAL and PAC), and we show that while CLR reprovisions substantially many more connections than LLR (i.e., potentially more management overhead) CLR yields a much better network robustness to simultaneous failures due to its superior flexibility in using network resources.     

On the complexity and algorithm of grooming regular traffic in WDM optical networks

In SONET/WDM networks, a high-speed wavelength channel is usually shared by multiple low-rate traffic demands to make efficient use of the wavelength capacity. The multiplexing is known as traffic grooming and performed by SONET Add-Drop Multiplexers (SADM). The maximum number of low-rate traffic demands that can be multiplexed into one wavelength channel is called grooming factor. Since SADMs are expensive, a key optimization goal of traffic grooming is to minimize the total number of SADMs in order to satisfy a given set of traffic demands. As an important communication traffic pattern, all-to-all traffic has been widely studied for the traffic grooming problem. In this paper, we study the regular traffic pattern, which is considered as a generalization of the all-to-all traffic pattern. We focus on the Unidirectional Path-Switched Ring (UPSR) networks. We prove that the traffic grooming problem is NP-hard for the regular traffic pattern in UPSR networks, and show that the problem does not admit a Fully Polynomial Time Approximation Scheme (FPTAS). We further prove that the problem remains NP-hard even if the grooming factor is any fixed value chosen from a subset of integers. We also propose a performance guaranteed algorithm to minimize the total number of required SADMs, and show that the algorithm achieves a better upper bound than previous algorithms. Extensive simulations are conducted, and the empirical results validate that our algorithm outperforms the previous ones in most cases. In addition, our algorithm always uses the minimum number of wavelengths, which are precious resources as well in optical networks.     

Modeling and optimizing large-scale data flows

Modern scientific collaborations require large-scale integration of various processes. Higher-level dataflow languages are used on top of parallel and distributed dataflow systems to enable faster data-intensive workflow programs development, their easier optimization, and more maintainable code. In this paper, we present the rationales, design, and application of the needed advanced support for modeling and optimizing data flows for data mining and integration processes. The optimization research and development is based on dataflow pre-execution modeling and extending the registry of process activities by advanced annotations. Additionally, the overall process from a dynamic model to a static model as input for the optimization algorithms is described. This novel approach is implemented within an advanced graphical user interface, called the Process Designer, in order to support semi-automatic optimization as well as within a dataflow execution platform, called the Gateway. It can be adapted to any dataflow language implementation. The Process Designer architecture based on modern (meta-)modeling concepts naturally supports validated transformations between external textual and internal graphical representations of the targeted dataflow language, and in this way significantly increases the productivity and robustness of the implementation processes.     

基于有向含权复杂网络的城市交通路网关键路段识别

针对目前基于复杂网络识别城市交通路网关键路段缺乏考虑现实影响因素和路段方向性问题,提出了一种基于有向含权复杂网络的关键路段识别方法。第一阶段利用复杂网络理论将城市交通路网构建成有向含权复杂网络模型;第二阶段利用LinkRank算法对复杂网络中边进行重要度排序,以此识别关键边,即城市交通路网关键路段;第三阶段利用变异的易感—感染（susceptible-infective,SI）模型对关键路段进行影响评估。通过对浙江省海宁市城区的城市交通路网分析,验证了方法的实用性和有效性。     

Camera location optimisation for traffic surveillance in urban road networks with multiple user classes

New sensor technologies (e.g. surveillance cameras, loop detectors) enable the synthesis of disaggregated vehicle information from multiple locations. This article studies the camera location problem for traffic surveillance in urban road networks with multiple user classes. All users are differentiated by their own acceptance degree of camera monitoring and make their route choices in a logit-based stochastic user equilibrium manner. A bi-level programming model is proposed to formulate the problem and solved by the sensitivity analysis based branch and bound method. Numerical examples are presented to illustrate the model application and show the effectiveness of the solution method.     

On-line distributed prediction of traffic flow in a large-scale road network

For on-line traffic control at traffic control centers there is a need for fast computations of predictions of traffic flow over a short prediction horizon, say 30 min, to evaluate the impact of different scenarios for the purpose of on-line scenario selection. A novel approach is presented to predict the traffic flow in a large-scale traffic network in an asynchronous, parallel, and distributed way at two or more subnetworks combined with a consistency check at the network level within a reasonable-small computation time.     

Towards the fast and robust optimal design of wireless body area networks

Wireless body area networks are wireless sensor networks whose adoption has recently emerged and spread in important healthcare applications, such as the remote monitoring of health conditions of patients. A major issue associated with the deployment of such networks is represented by energy consumption: in general, the batteries of the sensors cannot be easily replaced and recharged, so containing the usage of energy by a rational design of the network and of the routing is crucial. Another issue is represented by traffic uncertainty: body sensors may produce data at a variable rate that is not exactly known in advance, for example because the generation of data is event-driven. Neglecting traffic uncertainty may lead to wrong design and routing decisions, which may compromise the functionality of the network and have very bad effects on the health of the patients. In order to address these issues, in this work we propose the first robust optimization model for jointly optimizing the topology and the routing in body area networks under traffic uncertainty. Since the problem may result challenging even for a state-of-the-art optimization solver, we propose an original optimization algorithm that exploits suitable linear relaxations to guide a randomized fixing of the variables, supported by an exact large variable neighborhood search. Experiments on realistic instances indicate that our algorithm performs better than a state-of-the-art solver, fast producing solutions associated with improved optimality gaps.     

Genetic algorithms and neural networks: optimizing connections and connectivity

Genetic algorithms are a robust adaptive optimization method based on biological principles. A population of strings representing possible problem solutions is maintained. Search proceeds by recombining strings in the population. The theoretical foundations of genetic algorithms are based on the notion that selective reproduction and recombination of binary strings changes the sampling rate of hyperplanes in the search space so as to reflect the average fitness of strings that reside in any particular hyperplane. Thus, genetic algorithms need not search along the contours of the function being optimized and tend not to become trapped in local minima. This paper is an overview of several different experiments applying genetic algorithms to neural network problems. These problems include

1. (1) optimizing the weighted connections in feed-forward neural networks using both binary and real-valued representations, and

2. (2) using a genetic algorithm to discover novel architectures in the form of connectivity patterns for neural networks that learn using error propagation.

Future applications in neural network optimization in which genetic algorithm can perhaps play a significant role are also presented.     

Fuzzy logic based smart traffic light simulator design and hardware implementation

The objective of this study is to develop fuzzy logic based traffic junction light simulator system for design and smart traffic junction light controller purposes and also to observe its performance. Traffic junction simulator hardware is developed to overcome difficulties of working in a real environment and to easily test the performance of the controller. By using the traffic light simulator developed in this study, results of constant duration (conventional) traffic light controller and fuzzy logic based traffic light controller are compared where the vehicle inputs are supplied by the simulator. Statistical experimental results obtained from the implemented simulator show that the fuzzy logic traffic light controller dramatically reduced the waiting time at red lights since the controller adapts itself according to traffic density. It is obvious that the intelligent light controller is going to provide important advantages in terms of economics and environment.     

Optimizing regenerator cost in traffic grooming

In optical networks, regenerators have to be placed on lightpaths every d consecutive nodes in order to regenerate the signal. In addition, grooming enables the use of the same regenerator by several lightpaths. Up to g (the grooming factor) lightpaths can use the same regenerator. In this work we consider the problem of minimizing the number of regenerators used in traffic grooming in optical networks. Starting from the 4-approximation algorithm of Flammini et al. (2010) [10] for d=1 and a path topology, we provide an approximation algorithm with the same approximation ratio for d=1and the ring and tree topologies. We present also a technique based on matching that leads to the same approximation ratio in tree topology and can be used to obtain approximation algorithms in other topologies. We provide an approximation algorithm for general topology that uses this technique. Finally, all the results are extended to the case of general d.