A Model with Traffic Routers, Dynamically Managing Signal Phases to Address Traffic Congestion in Real Time

On-road Vehicular traffic congestion has detrimental effect on three lifelines: Economy, Productivity and Pollution (EPP). With ever increasing population of vehicles on road, traffic congestion is a major challenge to the economy, productivity and pollution, notwithstanding continuous developments in alternative fuels, alternative sources of energy. The research develops accurate and precise model in real time which computes congestion detection, dynamic signaling algorithm to evenly distribute vehicle densities while ensuring avoidance of starvation and deadlock situation. The model incorporates road segment length and breadth, quality and achievable average speed to compute road capacity. Vehicles installed with GPS enabled devices provide their location, which enables computing road occupancy. Road occupancy is evaluated based on number of vehicles as well as area occupied by vehicles. Ratio of road occupancy and road capacity provides congestion index important to compute signal phases. The algorithm ensures every direction is serviced once during a signaling cycle ensuring no starvation. Secondly, the definition of minimum and maximum signal timings ensures against dead lock situation. A simulator is developed to validate the proposition and proves it can ease congestion by more than 50% which is better than any of the contemporary approaches offering 15% improvement. In case of higher congestion index, alternate routes are suggested based on evaluation of traffic density graphs for shortest route or knowledge database. The algorithm to compute shortest route is optimized drastically, reducing computation cost to 3*√2N vis-à-vis computation cost of N2 by classical algorithms. The proposal brings down the cost of implementation per traffic junction from USD 30,000 to USD 2000.

MULTI-CLASS TRAFFIC QOS ROUTING FOR LEO SATELLITE NETWORKS

Due to the diversified demands of quality of service（QoS） in volume multimedia application, QoS routings for multiservice are becoming a research hotspot in low earth orbit（LEO） satellite networks. A novel QoS satellite routing algorithm for multi-class traffic is proposed. The goal of the routing algorithm is to provide the distinct QoS for different traffic classes and improve the utilization of network resources. Traffic is classified into three classes and allocated priorities based on their QoS requirements, respectively. A priority queuing mechanism guarantees the algorithm to work better for high-priority classes. In order to control the congestion, a blocking probability analysis model is built up based on the Markov process theory. Finally, according to the classification link-cost metrics, routings for different classes are calculated with the distinct QoS requirments and the status of network resource. Simulations verify the performance of the routing algorithm at different time and in different regions, and results demonstrate that the algorithm has great advantages in terms of the average delay and the blocking probability. Meanwhile, the robustness issue is also discussed.

An integrated approach for dynamic traffic routing and ramp metering using sliding mode control

The problem of designing integrated traffic control strategies for highway networks with the use of route guidance, ramp metering is considered. The highway network is simulated using a first order macroscopic model called LWR model which is a mathematical traffic flow model that formulates the relationships among traffic flow characteristics in terms of density, flow, and mean speed of the traffic stream. An integrated control algorithm is designed to solve the proposed problem, based on the inverse control technique and variable structure control（super twisting sliding mode）. Three case studies have been tested in the presence of an on-ramp at each alternate route and where there is a capacity constraint in the network. In the first case study, there is no capacity constraint at either upstream or downstream of the alternate routes and the function of the proposed algorithm is only to balance the traffic flow on the alternate routes. In the second case study, there is capacity constraint at downstream of alternate routes. The proposed algorithm aims to avoid congestion on the main road and balance the traffic flow on the alternate routes. In the last case study, there is capacity constraint at upstream of alternate routes. The objective of proposed algorithm is to avoid congestion on the main road and to balance the traffic flow on the alternate routes. The obtained results show that the proposed algorithms can establish user equilibrium between two alternate routes even when the on-ramps, located at alternate routes, have different traffic demands.

A Discrete-Time Traffic and Topology Adaptive Routing Algorithm for LEO Satellite Networks

“Minimizing path delay” is one of the challenges in low Earth orbit (LEO) satellite network routing algo-rithms. Many authors focus on propagation delays with the distance vector but ignore the status information and processing delays of inter-satellite links. For this purpose, a new discrete-time traffic and topology adap-tive routing (DT-TTAR) algorithm is proposed in this paper. This routing algorithm incorporates both inher-ent dynamics of network topology and variations of traffic load in inter-satellite links. The next hop decision is made by the adaptive link cost metric, depending on arrival rates, time slots and locations of source-destination pairs. Through comprehensive analysis, we derive computation formulas of the main per-formance indexes. Meanwhile, the performances are evaluated through a set of simulations, and compared with other static and adaptive routing mechanisms as a reference. The results show that the proposed DT-TTAR algorithm has better performance of end-to-end delay than other algorithms, especially in high traffic areas.

Multipath Routing Mathematical Model to Solve the Traffic Engineering in Multi-Protocol Label Switching Network

Multipath Protocol Label Switching (MPLS) is a routing mechanism (technology) used in modern telecommunication networks, which is built as a multi-service network with a certain Quality of Service (QoS). To maintain the required QoS in such complicated networks, the Traffic Engineering (TE) should be equipped with optimum traffic management, routing mechanism, and switching operations, which are what this paper tackles, where the TE is identified as the optimal distribution of flows in the existing network. In this paper, an effective multipath routing mathematical model is developed based on several previous mechanisms to ensure the guaranteed desired QoS and loading balance according to the Telecommunication Network Systems (TCS) resources, to achieve the optimum TE, and hence improving the network usage efficiency. Moreover, the obtained mathematical optimization algorithm results are provided through the relative equations. Indeed the developed routing mathematical model in this paper is suitable for the dynamic distribution of information flows, which is the main improvement in the TE achieved in this work.

Fear Factor: Level of Traffic Stress and GPS Assessed Cycling Routes

Background: Cycling currently comprises only 1% of transport trips in the U.S. despite benefits for air pollution, traffic congestion, and improved public health. Methods: Building upon the Level of Traffic Stress (LTS) methodology, we assessed GPS trip data from utilitarian cyclists to understand route preferences and the level of low stress cycling connection between origins and destinations. GPS data were obtained from adult transport cyclists over multiple days. All bikeable road segments in the network were assigned an LTS score. The shortest paths between each origin and destination along bikeable roadways and along low stress (LTS 1 or 2) routes were calculated. Route trajectories were mapped to the LTS network, and the LTS and distances of observed, the shortest and low stress routes were compared. LTS maps and animations were developed to highlight where low stress connections were lacking. Results: There were 1038 unique cycling trips from 87 participants included in the analysis. An exclusively low stress route did not exist for 51% of trips. Low stress routes that were possible were, on average, 74% longer than the shortest possible path and 56% longer than the observed route. Observed routes were longer and lower stress than the shortest possible route. Conclusions: Results indicate that transport cyclists traveled beyond low stress residential areas and that low stress routes with acceptable detour distances were lacking. Cyclists appeared to weigh both route distance and quality and were willing to trade maximum directness for lower stress. GPS data provide additional information to support planning decisions to increase the impact of infrastructure investments on cycling mode share.

A Chain Routing Algorithm Based on Traffic Prediction in Wireless Sensor Networks

As a representative of chain-based protocol in Wireless Sensor Networks (WSNs), EEPB is an elegant solution on energy efficiency. However, in the latter part of the operation of the network, there is still a big problem: reserving energy of the node frequently presents the incapacity of directly communicating with the base station, at the same time capacity of data acquisition and transmission as normal nodes. If these nodes were selected as LEADER nodes, that will accelerate the death process and unevenness of energy consumption distribution among nodes.This paper proposed a chain routing algorithm based ontraffic prediction model (CRTP).The novel algorithmdesigns a threshold judgment method through introducing the traffic prediction model in the process of election of LEADER node. The process can be dynamically adjusted according to the flow forecasting. Therefore, this algorithm lets the energy consumption tend-ing to keep at same level. Simulation results show that CRTP has superior performance over EEPB in terms of balanced network energy consumption and the prolonged network life.

A Scalable Architecture Supporting QoS Guarantees Using Traffic Engineering and Policy Based Routing in the Internet

The study of Quality of Service (QoS) has become of great importance since the Internet is used to support a wide variety of new services and applications with its legacy structure. Current Internet architecture is based on the Best Effort (BE) model, which attempts to deliver all traffic as soon as possible within the limits of its abilities, but without any guarantee about throughput, delay, packet loss, etc. We develop a three-layer policy based architecture which can be deployed to control network resources intelligently and support QoS sensi-tive applications such as real-time voice and video streams along with standard applications in the Internet. In order to achieve selected QoS parameter values (e.g. loss, delay and PDV) within the bounds set through SLAs for high priority voice traffic in the Internet, we used traffic engineering techniques and policy based routing supported by Border Gateway Protocol (BGP). Use of prototype and simulations validates function-ality of our architecture.

An Evaluation of Routing Algorithms in Traffic Engineering and Quality of Service Provision of Network on Chips

Nowadays the number of cores that are integrated into NoC (Network on Chip) systems is steadily increasing, and real application traffic, running in such multi-core environments requires more and more bandwidth. In that sense, NoC architectures should be properly designed so as to provide efficient traffic engineering, as well as QoS support. Routing algorithm choice in conjunction with other parameters, such as network size and topology, traffic features (time and spatial distribution), as well as packet injection rate, packet size, and buffering capability, are all equivalently critical for designing a robust NoC architecture, on the grounds of traffic engineering and QoS provision. In this paper, a thorough numerical investigation is achieved by taking into consideration the criticality of selecting the proper routing algorithm, in conjunction with all the other aforementioned parameters. This is done via implementation of four routing evaluation traffic scenarios varying each parameter either individually, or as a set, thus exhausting all possible combinations, and making compact decisions on proper routing algorithm selection in NoC architectures. It has been shown that the simplicity of a deterministic routing algorithm such as XY, seems to be a reasonable choice, not only for random traffic patterns but also for non-uniform distributed traffic patterns, in terms of delay and throughput for 2D mesh NoC systems.

A NEW SYNCHRONOUS DIGITAL HIERARCHICAL BI-DIRECTIONAL RING CAPACITY DESIGN ALGORITHM BASED ON THE SHORTEST-ROUTE TRAFFIC MATRIX

In this paper, the concept of the Shortest-Route Traffic Matrix(SRTM) was first presented, and the generalized formula for computing ring capacity requirement in use of SRTM is given. Then, a new capacity design algorithm which is based on SRTM was presented for Synchronous Digital Hierarchical(SDH) Bi-directional Self-Healing Ring (BSHR). The algorithm simulation results demonstrate that this algorithm is very efficient for SDH BSHR capacity design and can make less project investment and make high utilization of lines and equipment. By means of the algorithm in this paper, capacity optimization assignment for SDH Hierarchical Self-Healing Ring (HSHR) and for ATM Virtual Path (VP)-based Self-Healing Ring (SHR) is also discussed.

A Multipath Routing Algorithm Based on Traffic Prediction in Wireless Mesh Networks

The technology of QoS routing has become a great challenge in Wireless Mesh Networks (WMNs). There exist a lot of literatures on QoS routing in WMNs, but the current algorithms have some deficiencies, such as high complexity, poor scalability and flexibility. To solve the problems above, a multipath routing algorithm based on traffic prediction (MRATP) is proposed in WMNs. MRATP consists of three modules including an algo-rithm on multipath routing built, a congestion discovery mechanism based on wavelet-neural network and a load balancing algorithm via multipath. Simulation results show that MRATP has some characteristics, such as better scalability, flexibility and robustness. Compared with the current algorithms, MRATP has higher success ratio, lower end to end delay and overhead. So MRATP can guarantee the end to end QoS of WMNs.

软件定义卫星网络中基于业务调度的混合路由算法

为了解决低轨卫星网络高动态拓扑、大数据传输量、多信令开销的问题,满足全球覆盖和海量移动用户日益增长的需求,在研究不同业务性能指标体系的基础上,构建了高低轨卫星协同的软件定义卫星网络架构及虚拟拓扑模型;设计了综合考虑业务到达率和等待时长的优先级赋值函数,并提出一种基于业务感知与调度的联合星间路由算法,该算法包括针对高QoS需求业务的低复杂度轨道内优先转发路由算法和针对低QoS需求业务的链路加权路由算法.仿真结果表明,所提算法在平均端到端时延、丢包率和吞吐量等方面比传统算法均有显著的改善.

基于传递熵的空中交通航路网络拥堵传播特性分析

为深入剖析空中交通拥堵态势演变特性,辅助空中交通管制,确保航路网络的高效运行,本文研究空中交通航路网络拥堵传播相关问题。首先,选取交通流量、交通密度和交通汇聚度作为航段拥堵评估指标,建立FCM(Fuzzy C-Means)航段交通拥堵状态评估模型;其次,提出基于传递熵理论的航段拥堵传播模型,并分析拥堵与拥堵传播之间的关联性;然后,建立基于传播指数与显著性面积指标的关键航段识别方法;最后,采用广州区域管制扇区内实测数据检验本文提出方法的有效性。研究结果表明:综合考虑宏观和微观特征的拥堵识别模型能够有效地划分航段的交通状态;拥堵传播受时段和拥堵程度的影响较大,夜间时段,畅通态在拥堵传播中占据主导地位,信息传播量比日间高30%,且信息有效期更长;在白天,高拥堵航段在拥堵传播中占主导地位,其信息传播量约为低拥堵航段的1.2倍,但信息有效期较短;广州高空航路网络的拥堵传播呈现明显的时空差异性,拥堵传播高峰时段主要位于12:00-14:00和18:00-20:00,略早于拥堵高峰期,沪蓉航路等东西向主干航路是拥堵传播的关键航路。这些规律可为空中交通管制单位实施管制措施和优化航路结构等提供理论支持。

不确定环境下收费与信息对路径选择行为影响的实验研究

在不确定环境下考虑信息发布与拥挤收费两种措施,基于交通网络均衡,推导了两路径简单路网的最优收费模型,并推广至一般路网。基于两路径路网设计逐日路径选择行为实验,分析了两种措施对路径选择行为的影响。结果表明:完美信息与收费的实施均能减少流量波动,单独实施收费的效果最佳,但只有两种措施相结合时,路网流量趋于用户均衡的趋势最稳定;完美信息会增加路径切换行为,而收费可有效抑制大幅的路径切换;无收费时路径切换往往会造成出行成本增加,而收费时则相反;收费与信息结合时的出行时间比两种措施单独实施时小。

空中交通CPS结构特性及韧性评估

针对空中交通体系结构复杂、耦合性强、脆弱性高的特点,为缓解因受扰导致的级联失效现象,对其结构韧性进行研究。建立了空中交通信息物理系统(CPS)模型,提出同层度与层间度、同层介数与层间介数等指标,分析级联失效过程;提出空中交通CPS韧性的概念,并采用定量评估方法对受损能力和恢复能力进行度量;对比不同扰动-恢复策略下的空中交通CPS表现,制定最佳恢复策略,提高受扰时的韧性。以华东地区空中交通CPS为例进行分析,结果表明:空中交通CPS网络度分布服从幂率分布,介数服从指数分布;在基于介数度值扰动下,采用介数恢复策略可以有效提高受扰时空中交通CPS的韧性。

考虑暴雨灾害动态影响的城市应急车辆救援路径优化研究

近年来,极端天气事件发生频次不断增加,强度不断加大,其中,由暴雨引发的城市内涝导致交通应急事件发生概率进一步增大。为提升暴雨灾害下应急救援响应速度,本文开展应急车辆救援路径优化研究。以通行时间最短为目标,考虑路面积水对车辆通行速度的动态影响,构建应急车辆救援路径优化模型,提出动态最短路径优化算法求解模型。选取上海市长宁区东北部作为研究区域,根据SWMM(Storm Water Management Model)模拟得到的50年一遇暴雨条件下城市道路路面的积水情况,设定应急救援场景,求解应急救援路径。通过本文提出算法求解得到的路径与传统静态最短路径算法求解结果对比可知,通行用时同比减少了25.42%。同时,考虑应急物资储备情况分配应急救援任务,扩展了算法的应用场景,形成可靠和高效的应急响应方案,可为提升暴雨灾害下应急响应效率提供参考。

考虑车辆绕行的低碳校车路径优化模型

为了合理规划校车路径以降低碳排放,建立以考虑行驶距离和载重的碳排放最小化为优化目标,及以车辆绕行和容量为约束的低碳校车路径优化模型(GCSBRPTW).针对绕行问题,引入绕行因子并转化为单侧时间窗约束;设计了一种基于Lin-Kernighan heuristic(LKH)算法和莱维飞行算子的改进蚁群算法(LKH-Levy-ACO)对模型进行求解,其中LKH算法和莱维算子分别用于提高算法寻优效率和全局搜索能力.最后利用泰兴市工业园区班车线路规划实例求解,展开绕行因子取值对比、GCSBRPTW与传统校车路径模型对比、LKH-Levy-ACO与传统蚁群算法等4种算法对比实验.结果显示,绕行因子取值越小,最优解越差,GCSBRPTW比传统校车路径模型降低了约0.70%的碳排放,且LKH-Levy-ACO算法比传统蚁群算法降低了6.19%的碳排放,证明了模型的实用性和算法的有效性.

考虑空间需求不均的模块化公交线路运行方案优化研究

传统固定容量公交车辆难以应对公交线路上空间分布不均的需求。为解决这一问题,引入模块化公交车辆,通过编组与解编的方式实现车队在运行中的容量动态变化,以更贴合需求在空间上的变化。基于运行时空图重构的方法,建立描述模块化公交线路运行方案的优化模型,所构建模型为混合整数非线性规划(Mixed Integer-Nonlinear Program,MINLP)模型,模型决策变量包括车队的运行方案以及模块化公交单元的运行方案。为方便求解,采用时间离散化手段,通过一系列方法将所构建的MINLP模型转化为易于求解的混合整数线性规划(Mixed Integer-Linear Program,MILP)模型,并基于成都市真实公交线路与乘客需求数据进行案例分析。实验结果表明,与传统容量固定式公交相比,模块化公交的应用能够使乘客成本降低11.44%,运营成本降低31.35%,综合降低20.32%的系统总成本。敏感性分析实验结果分析了系统供给与需求变化对系统成本产生的影响。

考虑技术站编组去向的车流径路优化研究

在车流径路规划中,相同到站的车流经同一技术站改编后合并。研究旨在提出一种考虑技术站编组去向的车流径路优化模型,以克服现有车流径路模型中车流相遇即合并的限制。构建反映技术站间编组去向的网络G1,G1中的每条边与实际路网G2对应顶点间的最短路绑定。以路网G2上车流的广义走行费用最小为目标,考虑技术站的流量守恒、改编能力以及区间通过能力等约束条件,构建一个整数规划模型。为求解该模型,采用模拟退火算法,使用惩罚函数处理容量约束。根据相同终点车流在G1相遇即合并的特点生成初始解,通过随机改变某股车流的径路生成新的解,且确保终点相同的车流仍满足相遇即合并的条件。通过算例对比研究提出的模型与传统的“树形径路”模型,验证模型的有效性和合理性。

通勤合乘路径优化模型与算法

道路车辆的增多导致城市交通和环境问题日益严重,共享合乘被认为是减少交通拥堵,降低碳排放的有效方法,特别是在新冠疫情持续影响下,通勤者采用互助合乘出行意愿较高.本文考虑到通勤时间的紧迫性,通勤者存在通勤压力和合乘不适感,在没有经济效益驱动的情况下,限制合乘路径的匹配范围,并加入惩罚因子以提高合乘配对成功率.本文提出了一种基于最优时间插值的贪婪启发式算法,添加了3种扰动算子来提高全局搜索能力,采用多组不同规模案例测试扰动效果.结果表明:设计算法可以在短时间内求解出更优结果,在解决大规模问题上,相比于精确算法、粒子群算法和遗传算法更具竞争力.此外,通过选取位置较远且分布均匀的职员作为接送者,可以改善合乘效果.