基于蚁群算法的动态用户均衡配流

在路网中,为了使用户的出行时间降到最低,提出一个适用于多OD对的路网的动态用户均衡离散模型,并应用蚁群算法求解动态用户均衡问题.通过设计一个算例,利用仿真得出路网中的流量分配数据,并和二次规划Frank-Wolfe算法求解的流量分配数据进行比较,最后得出蚁群算法在求解动态交通用户均衡问题时具有一定的优势.     

Comparison of Methods for Path Flow Reassignment for Dynamic User Equilibrium

Models to describe or predict of time-varying traffic flows and travel times on road networks are usually referred to as dynamic traffic assignment (DTA) models or dynamic user equilibrium (DUE) models. The most common form of algorithms for DUE consists of iterating between two components namely dynamic network loading (DNL) and path inflow reassignment or route choice. The DNL components in these algorithms have been investigated in many papers but in comparison the path inflow reassignment component has been relatively neglected. In view of that, we investigate various methods for path inflow reassignment that have been used in the literature. We compare them numerically by embedding them in a DUE algorithm and applying the algorithm to solve DUE problems for various simple network scenarios. We find that the choice of inflow reassignment method makes a huge difference to the speed of convergence of the algorithms and, in particular, find that ??travel time responsive?? reassignment methods converge much faster than the other methods. We also investigate how speed of convergence is affected by the extent of congestion on the network, by higher demand or lower capacity. There appears to be much scope for further improving path inflow reassignment methods.     

A self-adapting connection admission control solution for mobile WiMAX: Enabling dynamic switching of admission control algorithms based on predominant network usage profiles

WiMAX is a connection-oriented wireless network that provides QoS in metropolitan broadband communications. One important component in WiMAX QoS provisioning and management is the Connection Admission Control (CAC), which must be aware of the network conditions (e.g., user traffic demands and physical aspects). In our research, we define the association between a particular user traffic demand and a specific physical condition as a network usage profile. State-of-the-art proposals focus on optimizing CAC algorithms considering a single network usage profile; the adaptation of CAC algorithms when the predominant network usage profile changes is partially or fully neglected. In this article, we introduce a self-adapting CAC solution that, using a library of CAC algorithms, is able to switch the running algorithm according to the current network usage profile. The evaluation results, obtained through simulations, demonstrate that our self-adapting CAC solution is able to detect the changes on the predominant network usage profile. In addition, the results show how much different profiles can impact on the efficiency of CAC algorithms, thus confirming the need of switching the running CAC algorithm so that QoS can be guaranteed for the ongoing connections.     

Distributed dynamic lane reversal and rerouting for traffic delay reduction

Traffic congestion is a major source of delays in modern road networks. Motivated by this, we propose two distributed algorithms to reduce delays: a dynamic lane reversal algorithm and a rerouting algorithm. When there is a density imbalance on a road, time can be saved by reallocating lanes from the less dense side to the more dense side, which motivates dynamic lane reversal. When a road has greater density than nearby roads, time can be saved by redirecting flow into the least congested roads, this motivates dynamic rerouting. Given a communication system between infrastructure and vehicles on the road, the local state of the network can be approximated and utilised by the algorithms to minimise travel time. In order to provide a better fundamental understanding of the system dynamics, we analyse equilibrium conditions for the system and prove convergence of the lane reversal algorithm to a critical point. Overall performance is also examined in simulation.     

Road traffic density estimation using microscopic and macroscopic parameters

In this paper we present a comparative study of two approaches for road traffic density estimation. The first approach uses the microscopic parameters which are extracted using both motion detection and tracking methods from a video sequence, and the second approach uses the macroscopic parameters which are directly estimated by analyzing the global motion in the video scene. The extracted parameters are applied to three classifiers, the K Nearest Neighbor (KNN) classifier, the LVQ classifier and the SVM classifier, in order to classify the road traffic in three categories: light, medium and heavy. The methods are compared based on their robustness to the classification of different road traffic states. The goal of this study is to propose an algorithm for road traffic density estimation with a high precision.     

交通出行诱导的离散Stackelberg动态博弈模型及其求解算法

针对交通出行诱导的实际需要,分析了道路交通管理者和道路使用者的博弈策略和博弈特征.通过道路使用者信息的模糊模型.建立了交通出行诱导的离散动态Stackelberg博弈模型.分析了交通出行诱导的两阶段博弈特征,提出了基于逆向归纳法的博弈模型求解算法.通过对模型无约束转化,运用Monte-Carlo法对示例路网进行了计算机求解,求解结果表明产生的交通出行诱导方案能够实现系统最优下的用户最优.     

Data Preparation for Mining World Wide Web Browsing Patterns

The World Wide Web (WWW) continues to grow at an astounding rate in both the sheer volume of traffic and the size and complexity of Web sites. The complexity of tasks such as Web site design, Web server design, and of simply navigating through a Web site have increased along with this growth. An important input to these design tasks is the analysis of how a Web site is being used. Usage analysis includes straightforward statistics, such as page access frequency, as well as more sophisticated forms of analysis, such as finding the common traversal paths through a Web site. Web Usage Mining is the application of data mining techniques to usage logs of large Web data repositories in order to produce results that can be used in the design tasks mentioned above. However, there are several preprocessing tasks that must be performed prior to applying data mining algorithms to the data collected from server logs. This paper presents several data preparation techniques in order to identify unique users and user sessions. Also, a method to divide user sessions into semantically meaningful transactions is defined and successfully tested against two other methods. Transactions identified by the proposed methods are used to discover association rules from real world data using the WEBMINER system [15].     

封闭型小区交通开放对城市交通的影响

随着交通需求的增加,城市交通问题日益严重,能否通过开放现有封闭型小区的道路以缓解城市交通拥堵是一个值得研究的问题.本文针对这一问题,建立了基于最短路的交通分流线性规划网络模型,提出了交通量改善因子、路段饱和度均值以及路段饱和度标准差等三个评价指标,以评价和对比封闭性小区开放前后的交通量与道路占用程度,从而反映了封闭性小区的开放对城市交通的具体影响.本文对两种不同的交通网络及交通流量数据进行了仿真与模型求解,结果表明：尽管小区开放后路网密度提高,有可能使得总交通负荷降低,但并非任何小区开放均能缓解城市交通拥堵.小区面积、位置、外部及内部道路状况等诸多因素都能影响路网的交通状态,所以,是否开放封闭型小区,需要用针对具体的小区与周边道路状况进行评估后再进行决策.     

The CONTRAM Dynamic Traffic Assignment Model

CONTRAM is a computer model of time-varying traffic in road networks, which takes as input the network definition and time-varying demand for travel between a set of origin and destination zones, and outputs the resulting network flows, routes and travel times. It combines a macroscopic time-sliced traffic model with disaggregate dynamic assignment of traffic, so is intermediate between macroscopic equilibrium and microscopic models. The paper details the methods used, including time-dependent queuing which plays a central role, and the treatment of network definition, user classes, road capacities, signals and coordination, vehicle emissions, Intelligent Transport Systems and research lines.     

Data mining with various optimization methods

Road traffic represents the main source of noise in urban environments that is proven to significantly affect human mental and physical health and labour productivity. Thus, in order to control noise sound level in urban areas, it is very important to develop methods for modelling the road traffic noise. As observed in the literature, the models that deal with this issue are mainly based on regression analysis, while other approaches are very rare. In this paper a novel approach for modelling traffic noise that is based on optimization is presented. Four optimization techniques were used in simulation in this work: genetic algorithms, Hooke and Jeeves algorithm, simulated annealing and particle swarm optimization. Two different scenarios are presented in this paper. In the first scenario the optimization methods use the whole measurement dataset to find the most suitable parameters, whereas in the second scenario optimized parameters were found using only some of the measurement data, while the rest of the data was used to evaluate the predictive capabilities of the model. The goodness of the model is evaluated by the coefficient of determination and other statistical parameters, and results show agreement of high extent between measured data and calculated values in both scenarios. In addition, the model was compared with classical statistical model, and superior capabilities of proposed model were demonstrated. The simulations were done using the originally developed user friendly software package.     

SALA: A Self-Adaptive Learning Algorithm—Towards Efficient Dynamic Route Guidance in Urban Traffic Networks

In order to alleviate traffic congestion for vehicles in urban networks, most of current researches mainly focused on signal optimization models and traffic assignment models, or tried to recognize the interaction between signal control and traffic assignment. However, these methods may not be able to provide fast and accurate route guidance due to the lack of individual traffic demands, real-time traffic data and dynamic cooperation between vehicles. To solve these problems, this paper proposes a dynamic and real-time route selection model in urban traffic networks (DR2SM), which can supply a more accurate and personalized strategy for vehicles in urban traffic networks. Combining the preference for alternative routes with real-time traffic conditions, each vehicle in urban traffic networks updates its route selection before going through each intersection. Based on its historical experiences and estimation about route choices of the other vehicles, each vehicle uses a self-adaptive learning algorithm to play congestion game with each other to reach Nash equilibrium. In the route selection process, each vehicle selects the user-optimal route, which can maximize the utility of each driving vehicle. The results of the experiments on both synthetic and real-world road networks show that compared with non-cooperative route selection algorithms and three state-of-the-art equilibrium algorithms, DR2SM can effectively reduce the average traveling time in the dynamic and uncertain urban traffic networks.     

Influence of meta-heuristic optimization on the performance of adaptive interval type2-fuzzy traffic signal controllers

Intelligent traffic control systems optimized using meta-heuristic algorithms can greatly alleviate traffic congestions in urban areas. Meta-heuristics are broadly used as efficient approaches for complex optimization problems. Comparing the performance of optimization methods on different applications is a way to evaluate their effectiveness. The current literature lacks studies on how performance of traffic signal controllers is affected by utilized optimization algorithms. This paper evaluates the performance of three meta-heuristic optimization methods on an advanced interval type-2 adaptive neuro-fuzzy inference system (IT2ANFIS)-based controller for complex road networks. Simulated annealing (SA), genetic algorithm (GA), and the cuckoo search (CS) are applied for optimal tuning of IT2ANFIS controller. Optimizations methods adjust the parameters in a way to reduce the total travel time of vehicles in the road network. Paramics is used to design and simulate urban traffic network models and implement proposed timing controllers. Comprehensive simulation and performance evaluation are done for both single and multi-intersection traffic networks. Obtained results reveal significant superiority of IT2ANFIS trained using CS method over other controllers. The average performance of the CS-IT2ANFIS is about 31% better than the benchmark fixed-time controllers. This is 17% and only 3% for GA-IT2ANFIS and SA-IT2ANFIS controllers respectively.     

最优动态配流问题的离散模型及其解

对一般路网建立了具有良好结构形式的离散动态配流模型，该模型在多起点单讫点路网中的最优解满足交通流守恒约束和动态平衡原则。在提出了完全不同路段概念的基础上，对最优解的奇异性进行了分析，得到稳态最优解为非奇异解的结论。给出的算例验证了结论的正确性。     

A GIS based component-oriented integrated system for estimation, visualization and analysis of road traffic air pollution

Road traffic is a dominant source of air pollution. Therefore it is necessary to provide decision-makers with up-to-date emission information in an easily understandable form. To achieve this goal we have integrated existing emission calculation software with a graphical user interface, which includes a GIS (geographical information system) component. The paper first gives a summary of the basic road traffic emission model and then focuses on the design and implementation of the computer application with the emphasis on the used component and GIS technology. The integrated emission evaluation system offers entirely new ways of using the emission model and gives additional visualization and analysis possibilities.     

基于博弈论的多服务器多用户视频分析任务卸载算法

在过去的近10年中,人工智能相关的服务和应用大规模出现,它们要求高算力、高带宽和低时延.边缘计算目前被认为是这些应用最适合的计算模式,尤其是视频分析相关应用.研究多服务器多用户异构视频分析任务卸载问题,其中用户选择合适的边缘服务器,并将他们的原始视频数据上传至服务器进行视频分析.为了有效处理众多用户对有限网络资源的竞争和共享,并且能够获得稳定的网络资源分配局面,即每个用户不会单方面地改变自己的任务卸载决策,该多服务器多用户异构视频分析任务卸载问题被建模为一个多玩家的博弈问题.基于最小化整体时延的优化目标,先后研究非分布式视频分析场景和分布式视频分析场景两种情形,分别提出基于博弈论的潜在最优服务器选择算法和视频单元分配算法.通过严格的数学证明,两种情形下提出的算法均可以达到纳什均衡,同时保证较低的整体时延.最后,基于真实数据集的大量实验表明,所提方法比其他现有算法降低了平均26.3%的整体时延.     

Solving the Dynamic User Optimal Assignment Problem Considering Queue Spillback

This paper studies the dynamic user optimal (DUO) traffic assignment problem considering simultaneous route and departure time choice. The DUO problem is formulated as a discrete variational inequality (DVI), with an embeded LWR-consistent mesoscopic dynamic network loading (DNL) model to encapsulate traffic dynamics. The presented DNL model is capable of capturing realistic traffic phenomena such as queue spillback. Various VI solution algorithms, particularly those based on feasible directions and a line search, are applied to solve the formulated DUO problem. Two examples are constructed to check equilibrium solutions obtained from numerical algorithms, to compare the performance of the algorithms, and to study the impacts of traffic interacts across multiple links on equilibrium solutions.     

Performance analysis of a threshold based distributed channel allocation algorithm for cellular networks

Rapid advances of the handheld devices and the emergence of the demanding wireless applications require the cellular networks to support the demanding user needs more effectively. The cellular networks are expected to provide these services under a limited bandwidth. Efficient management of the wireless channels by effective channel allocation algorithms is crucial for the performance of any cellular system. To provide a better channel usage performance, dynamic channel allocation schemes have been proposed. Among these schemes, distributed dynamic channel allocation approaches showed good performance results. The two important issues that must be carefully addressed in such algorithms are the efficient co-channel interference avoidance and messaging overhead reduction. In this paper, we focus on our new distributed channel allocation algorithm and evaluate its performance through extensive simulation studies. The performance evaluation results obtained under different traffic load and user mobility conditions, show that the proposed algorithm outperforms other algorithms recently proposed in the literature.     

城市交通噪声环境承载力分析模型及算法

以城市噪声环境容量为约束条件计算城市区域路网最大交通承载力。分析模型是一个双层优化问题,其中上层是噪声环境容量约束下的最大路网交通流量模型;下层是道路网上的用户均衡分配模型。应用遗传算法进行求解,仿真示例表明该模型和算法是可行的、有效的,可以为城市交通可持续发展的规划和需求管理提供依据。