恶劣天气因素下最优车辆路线调度路径选择

考虑恶劣天气因素下的最优车辆路线调度关系到灾害环境下的货物运输效率。由于在较为恶劣的天气环境下,车辆路径的属性特征会发现不可预估的变化。上述属性变化无法通过设定权值进行程度的描述。利用传统算法进行车辆路线调度,没有充分考虑天气因素给车辆路径选择带来的影响。往往通过经验设定固定的影响权值,没有考虑对不同路径选择属性数据影响的差异性,调度过程缺陷明显。提出采用模拟退火遗传算法的最优车辆路线调度方法。依据相关理论构建车辆调度优化模型,结合在恶劣天气环境下,车辆行驶路径所需时间、交叉路口密度、通行能力等因素综合变化,根据模拟退火算法模拟差异化的天气影响因素,利用遗传算法求取模型最优解,实现考虑恶劣天气因素的最优车辆路线调度的路径选择。实验结果表明,利用改进算法进行车辆路线调度,能够有效的获取车辆当前的最佳路线,提高了车辆调度的效率。     

一种车载自组织网络中交通拥塞自适应的出行计划方案

交通堵塞现象越来越威胁正常的城市交通,针对选择最短路径的出行方案往往不能取得最短的出行时间的现象,提出了一种交通拥塞自适应的出行计划方案.该方案克服了现有方案在规划出行路线时未能考虑行车过程中实际交通状况的缺点,根据车辆在各路段行驶的平均通过时间来判断路段当前的拥塞状况,并动态优化行车路线,从而提高交通效率.仿真实验表明该方案能够自适应路段的交通拥塞,根据当前拥塞状况重新优化行车路线,从而缩短平均行车时间.     

基于双层规划的自动卡车运输专用网络设计优化

为保障交通系统安全性和卡车货运自动化的发展,有必要对自动卡车货物运输专用网络进行科学规划与布局.考虑到自动卡车专用道会减少普通车辆的路权,对普通车辆的出行路径选择行为造成影响,首先从路网整体出发,以系统出行时间最小为目标,充分考虑路网普通车辆的出行路径选择行为,构建一种新的自动卡车专用运输网络设计的双层规划模型;然后提出一种基于实数编码的改进差分进化算法求解建立的双层规划模型,不仅保证解的可行性,还可避免复杂的不可行解修复过程.通过Sioux\ Falls基准网络实例和大量随机算例对比测试验证了所提出模型和算法的有效性.     

战时物流配送车辆路径优化问题

采用一种新的信息素更新方式对传统蚁群算法进行改进,有效解决了带硬时间窗的车辆路径优化问题;建立考虑战场环境中敌方火力打击影响的物流配送车辆路径优化模型,采用所提算法得出优化路线;进一步考虑不同作战单元对物资需求的可能变化,将排队策略用于算法求解过程,得出适应需求变化的后续配送路线,并通过仿真实验结果验证了相应配送路线的合理性.     

易爆品运输车辆中的路径选择模型仿真

易爆品运输车辆必须选择最优路线模型,以保证运输安全.易爆品运输过程中,不但需要考虑运输成本和运输的线路,还需要考虑运输风险的因素.传统的路线选择模型仅仅以成本分析为基础,没有加入易爆品运输的风险因素,路线选择存在较大缺失.提出采用粒子群离散变换算法的易爆品运输车辆路径选择方法.根据最小二乘法相关原理,计算运输路径危险环境函数,获取危险程度拟合曲线,根据该函数得到运输路径中的危险程度.根据最小危害路径选择的目标函数,对所有的待优化变量进行二进制编码,并针对编码结果进行离散化变换,实现易爆品运输车辆路径的选择.实验结果表明,利用改进算法进行易爆品运输车辆路径选择,降低了路径协同误差和路径选择误差.     

启发式RRT算法的AGV路径规划

在实际应用中，B-RRT*算法规划的路径存在着转折次数多、路线不平滑、路线贴合障碍物和最大转角过大等不符合车辆运动学的问题。为了获得适用于自动导引小车（Automatic Guided Vehicle，AGV）的优化路径，通过使用Reeds-Shepp曲线进行预处理以解决车辆在目标点朝向的问题。此外，提出启发式滑动窗口采样减少B-RRT*算法随机采样所带来的误差，并将车辆运动学约束加入到重选父节点和重布随机树的过程，使用贝塞尔曲线对所规划的路径进行平滑处理。实验结果表明：在规划相同路径上，改进B-RRT*算法规划的路径能够有效地解决上述算法存在的最大转角不合理、路径靠近障碍物、路径不平滑和不符合车辆运动学等问题。     

网联共享车路协同智能交通系统综述

网联车辆、交通大数据、共享出行等技术给智能交通系统的发展与应用革新带来了机遇和挑战.在全面总 结共享出行系统、网联车辆协同优化控制、交通大数据分析等领域最新研究成果的基础上,系统论述智能交通技术的研究进展,特别对智能交通系统中的交通流及出行需求预测、共享出行系统车辆调度、交通网及电网联合优化、网联车辆协调控制及车-路协同控制等方面进行全面综述.分析智能交通系统存在的问题及挑战,并对其未来发展方向进行展望.     

基于Spark平台城市出租车乘客出行特征分析

从海量出租车GPS轨迹数据中挖掘和分析城市出租车乘客的出行特征,可以为城市交通管理者和出租车行业管理者在城市交通规划与管理、城市交通流均衡与车辆调度等方面提供决策依据.基于Spark大数据处理分析平台,选择YARN作为资源管理调度系统,采用HDFS分布式存储系统,对出租车GPS轨迹数据进行挖掘.给出了基于Spark平台的出租车乘客出行特征的挖掘方法,包括出租车乘客出行距离分布、出租车使用时间分布及出租车出行需求.实验结果表明,基于Spark平台分析方法能够快速且准确的分析出出租车乘客出行特征.     

考虑非标准车辆特殊行为的混合交通建模

在中国大多数的城市交通中,由机动车辆和非机动车辆组成了混合交通流。除了标准型车辆如各种出租车、私车、公交车和卡车外,还有非标准车辆如自行车、摩托车及其他改装车等。对于其中的非标准车来说,由于交通法规、道路状况和技术规范等对其约束较轻,出行的目的和路线更为灵活,不仅车辆的特性各有很大的不同,而且驾驶者的行为同标准车相比,更有着显著的差异。目前适合于分析包括非标准车在内的交通流的模型几乎没有,已有的方法将面临重大的模型改造。论文分析了混合交通流中非标准车辆的特点和驾驶者的行为特点,以及这些特殊行为对混合交通建模的影响因素。文章中讨论了采用一种模拟交通工具定位的方法并基于模糊逻辑规则控制的决策过程,并以此建立了用于混合交通的模型。该模型利用中国西安部分地区的数据来进行测试和评价。     

城市路网多路口路径动态实时选择方法

为了缓解城市交通拥堵问题,如何充分利用现有的道路资源进行有效的路线导航,一直是学者们关心的热点问题.现有的研究方法包括：优化交通灯信号周期以增大交通流量;对个别车辆的行驶路线进行优化;利用历史交通数据或者通过路网中心和车辆之间的主从式博弈进行路径导航等.然而,这些研究并没有考虑到微观行驶车辆的个性化交通需求以及多车辆彼此之间的路线选择冲突,对于城市路网中交通状况的动态不确定性也没有充分考虑.基于以上问题,提出了城市交通路网动态实时多路口路径选择模型DR²SM（dynamic and real-time route selection model in urban traffic networks）,结合车辆对前方可选路线的偏好以及可选路线的实时交通状况,并利用自适应学习算法SALA（self-adaptive learning algorithm）进行博弈,以使得各行驶车辆的动态路线选择策略达到Nash均衡.     

基于云网格集成调度的防拥堵车辆路径规划算法

在道路交通路网中,车辆拥堵问题是流量与路网结构之间相互作用的一个复杂动态过程,通过车辆路径规划,实现对路网网格集成调度,从而提高路网通行吞吐量。传统方法采用并行微观交通动态负载平衡预测算法实现车辆拥堵调度和车辆路径规划,不能准确判断路面上的车辆密度,路径规划效益不好。提出一种基于云网格集成调度的防拥堵车辆路径规划算法,即构建基于Small-World模型的云网格路网模型,采用RFID标签信息进行路况信息采集,实现交通网络拥堵评估信息特征的提取,采用固有模态函数加权平均求得各车道的车辆拥塞状态函数,对所有车道内车辆密度取统计平均可获得簇内的车辆密度。设计交通路网拥堵检测算法来对当前个体道路信息进行一维邻域搜索,从而实现车辆路径规划控制目标函数最佳寻优。通过动态博弈的方式求得车辆防拥堵路径的近似最优轨迹,实现路径规划算法的改进。仿真结果表明,该算法能准确规划车辆路径,实现最优路径控制,从而提高严重拥堵路段的车流速度和路网吞吐性能,性能优越。     

基于路由机制的变权网络路径快速生成算法

在大规模交通流仿真中,车辆个体路径生成环节存在着大量重复计算。为避免重复计算及提高车辆个体路径生成速度,将计算机网络中的路由机制引入到交通流仿真中,提出一种基于路由机制的变权网络路径快速生成算法,即把每个道路路口节点作为路由器,分解并存储原本与车关联的路径作为指路信息。仿真车辆通过访问该指路信息获取下一步行车方向,并且当路网权值发生变化时,能及时响应路网的动态变化,从而给出求实时路况下仿真车辆行驶路径的一种方法。     

Long-term traffic flow prediction using multivariate SSA forecasting in SDN based networks

Accurate and real-time traffic flow forecasting plays an important role in optimizing traffic routing enabling adaptive and sophisticated applications on the network. Managing and routing enormous traffic flow with dynamic behavior is a highly challenging task. However, arriving at a precise model for traffic forecasting in a short interval of time is not trivial because of the dynamic nature of traffic flow. A novel multivariate time series framework is designed to analyze and forecast the dynamic traffic flow in SDN based networks. The proposed framework adapts the Multivariate Singular Spectrum Analysis (MSSA) forecasting model and incorporates the Randomized Singular Value Decomposition (RSVD) to improve the accuracy of flow prediction. Simulations are conducted to evaluate the effectiveness of the proposed MSSA method. The proposed method predicts the long-term traffic fluctuation from the observed traffic traces. The SDN controller is trained using the traffic traces and future traffic flows are forecasted. The performance evaluation of the proposed method predicts real-time traffic trends accurately with 2.2% MAPE, 9.44 MAE and 13.803 RMSE. The results show that the learning ability of MSSA helps to forecast future network traffic with low prediction errors.     

Routing using reinforcement learning in vehicular ad hoc networks

In vehicular ad hoc networks (VANETs), the frequent change in vehicle mobility creates dynamic changes in communication link and topology of the network. Hence, the key challenge is to address and resolve longer transmission delays and reduced transmission stability. During the establishment of routing path, the focus of entire research is on traffic detection and road selection with high traffic density for increased packet transmission. This reduces the transmission delays and avoids carry-and-forward scenarios; however, these techniques fail in obtaining accurate traffic density in real-time scenario due to rapid change in traffic density. Thus, it is necessary to create a model that efficiently monitors the traffic density and assist VANETs in route selection in an automated way with increased accuracy. In this article, a novel machine learning architecture using deep reinforcement learning (DRL) model is proposed to monitor and estimate the data essential for the routing protocol. In this model, the roadside unit maintains the traffic information on roads using DRL. The DRL predicts the movement of the vehicle and makes a suitable routing path for transmitting the packets with improved transmission capacity. It further uses predicted transmission delays and the destination location to choose the forwarding directions between two road safety units (RSUs). The application of DRL over VANETs yields increased network performance, which provides on-demand routing information. The simulation results show that the DRL-based routing is effective in routing the data packets between the source and destination vehicles than other existing method.     

An object-oriented evaluation framework for dynamic vehicle routing problems under real-time information

The dynamic vehicle routing problems (DVRP) is an extension of vehicle routing problems (VRP) in order to consider possible variations of travel times in the network. In this research, a two-stage framework for solving dynamic vehicle routing problem is proposed. In the first stage, the sweep method is adopted in vehicle assignment. In the second stage, a tabu search algorithm is implemented to improve routes under real-time information. The framework is implemented in an object-oriented approach and possible benefit from real-time information is illustrated through numerical simulation. The simulation-assignment model, DynaTAIWAN is applied in numerical simulation to evaluate real-time routing strategies in a traffic network. Numerical experiments are conducted in a 50 Nodes Network and a Taichung City. The results show that positive benefits could be achieved through utilization of real-time information with careful design.     

Vehicle routing under time-dependent travel times: The impact of congestion avoidance

Daily traffic congestion forms a major problem for businesses such as logistic service providers and distribution firms. It causes late arrivals at customers and additional costs for hiring the truck drivers. Such costs caused by traffic congestion can be reduced by taking into account and avoiding predictable traffic congestion within vehicle route plans. In the literature, various strategies are proposed to avoid traffic congestion, such as selecting alternative routes, changing the customer visit sequences, and changing the vehicle-customer assignments. We investigate the impact of these and other strategies in off-line vehicle routing on the performance of vehicle route plans in reality. For this purpose, we develop a set of vehicle routing problem instances on real road networks, and a speed model that reflects the key elements of peak hour traffic congestion. The instances are solved for different levels of congestion avoidance using a modified Dijkstra algorithm and a restricted dynamic programming heuristic. Computational experiments show that 99% of late arrivals at customers can be eliminated if traffic congestion is accounted for off-line. On top of that, about 87% of the extra duty time caused by traffic congestion can be eliminated by clever congestion avoidance strategies.     

基于人工免疫的工业认知无线网络路由机制

工业互联网（industrial Internet）已成为第四次工业革命的代表技术.根据工业网络数据传输服务的需求,以及针对工业无线网络拓扑相对稳定、流量规律变化等特点,提出了一种基于人工免疫系统（artificial immune system,简称AIS）的工业认知无线网络路由机制,包含基于链路质量的域内静态路由算法和基于多路径的域间动态路由算法,以实现工业网络的可靠路由.根据人工免疫系统特点,将工业网络的拓扑结构进行区域划分：提出了基于链路质量的域内静态路由算法,采用软硬件结合的方式监视网络链路,并根据移动窗口指数加权平均法计算链路丢包率;提出了基于多路径的域间动态路由算法,根据模式距离对节点的流量周期进行预测,防止节点因流量过大而导致丢包.基于OMNET++仿真平台进行仿真实验,结果表明,所提出的路由机制在应对突发流量时与组合定向地理路由算法相比,丢包率及网络开销分别降低1倍;应对链路失效的情况时与图路由算法相比丢包率降低4倍.     

Augmenting predictive with oblivious routing for wireless mesh networks under traffic uncertainty

Traffic routing is central to the utility and scalability of wireless mesh networks. Many recent routing studies have examined this issue, but generally they have assumed that the demand is constant and given in advance. On the contrary, wireless traffic studies have shown that demand is highly variable and difficult to predict, even when aggregated at access points.There are several approaches for handling volatile traffic. On one hand, traffic may be modeled in real-time with a dynamic routing based upon forecasted traffic demand. On the other hand, routing can be made with the focus towards maximally unbalanced demand, such that the worst-case performance is contained (known as oblivious routing). The first approach can perform competitively when traffic can be forecasted with accuracy, but may result in unbounded worst-case performance when forecasts go wrong. It is an open question how these two approaches would compare with each other in real networks and if possible at all, whether a benchmark could be defined to guide the selection of the appropriate routing strategy.To answer the above open question, this paper conducts a systematic comparison study of the two approaches based on the extensive simulation study over a variety of network scenarios with real-world traffic trace. It identifies the key factors of the network topology and traffic profile that affect the performance of each routing strategy. A series of metrics are examined with varying powers of forecasting whether predictive routing or oblivious routing will perform better. Following the guidelines defined by these metrics, we present an adaptive strategy which augments the performance of the predictive routing with the worst-case bound provided by the oblivious routing through adaptive selection of routing strategies based on the degree of traffic uncertainty.     

动态车辆路径问题中实时信息生成算法

实时信息的产生对动态车辆路径问题仿真器的研究起着非常重要的作用。为此，提出了实时信息的生成算法，包括随机公路网络的生成，实时交通信息的生成和随机客户需求的生成等。实验结果表明，算法所产生的实时信息和现实中的实时信息比较接近，能够满足动态车辆路径问题仿真器的要求。     

一种基于三角模糊数的VANETs机会路由算法*

分析了现有车用自组织网络（VANETs）的路由算法,提出一种新的基于三角模糊数的机会路由算法。新算法将转发结点距离目标结点的距离、到达目标结点的方向、行驶速度向量、重传次数、车流状况等因素作为目标函数进行分析计算,采用熵权系数法确定各因素权重。路由过程中,贪婪选择向量值最大的节点转发数据包;遇到网络不连续时,将采用“存储－携带－转发”的机会路由策略。仿真结果表明,该算法能够较好的适用于VANETs各种场景。