面向车载容迟网络的连通性建模与仿真研究

针对车载容迟网络连通性建模进行了研究。首先假设车辆驶入道路的过程服从泊松分布,以及车辆在道路上的行驶速度服从正态分布。继而对基于泊松过程的车间时距分布进行推导,并以此导出行驶车辆在道路上的连通概率。为了验证所提假设和连通模型的正确性和有效性,以欧洲城市卢森堡在7:30 a.m.~8:30 a.m.时间段内的交通数据为实验场景,在城市交通仿真平台（simulation of urban mobility,SUMO）对车辆速度的概率分布、车辆到达率、道路中的平均车辆数及网络连通概率进行了理论计算和仿真实验分析。实验结果表明理论模型的计算值和仿真结果是一致的,所提出的假设和连通模型具有合理性和正确性。     

Sustainability SI: Optimal Prices of Electricity at Public Charging Stations for Plug-in Electric Vehicles

With an increasing deployment of plug-in electric vehicles, evaluating and mitigating the impacts of additional electrical loads created by these vehicles on power distribution grids become more important. This paper explores the use of prices of electricity at public charging stations as an instrument, in couple of road pricing, to better manage both power distribution and urban transportation networks. More specifically, a multi-class combined distribution and assignment model is formulated to capture the spatial distribution of plug-in electric vehicles across the transportation network and estimate the electrical loads they impose on the power distribution network. Power flow equations are subsequently solved to estimate real power losses. Prices of electricity at public charging stations and road tolls are then optimized to minimize both real power losses in the distribution grid and total travel time in the urban transportation network. The pricing model is formulated as a mathematical program with complementarity constraints and solved by a manifold suboptimization algorithm and a pattern search method. Numerical examples are presented to demonstrate the proposed model and solution algorithms.     

基于实时路况的电动汽车充电调度算法

随着电动汽车行业在我国蓬勃发展, 电动汽车的"充电难"问题逐渐显现, 已经逐步成为限制电动汽车行业发展的瓶颈. 电动汽车充电时间长, 城市充电桩数目不足且时空分布不均等问题是导致"充电难"的直接原因. 本文提出一种基于路况的电动汽车充电调度算法, 通过综合考虑路网内的实际情况, 对充电车辆进行统一调度. 文章建立了车辆调度模型并进行了仿真模拟, 结果表明: 该算法能够有效降低电动汽车的充电时间、平衡区域内充电站的负载、提升全局的充电效率.     

ALCA: agent learning–based clustering algorithm in vehicular ad hoc networks

Vehicular ad hoc network (VANET) is an emerging technology which can be used in various applications such as intelligent transport technology, safety applications, etc. But one of the major issues in VANETs is how to cluster the vehicles on the road for efficient operations such as routing, mobility management and generating safety alarms. Clustering of vehicles has been widely used for routing and data dissemination in VANETs. But due to the high mobility of the vehicles/nodes on the road, it is quite difficult to find the exact route in VANETs. Keeping in view of the above issue, in this paper, we propose a new agent learning–based clustering and routing in VANETs. Agents learn from the environment in which they are deployed, and accordingly, their action performed is rewarded or penalized with certain values. Each agent performs its task in collaboration with the other agents, i.e. agents communicate with each other in collaborative manner for information sharing. The deployed agents estimate the mobility of the vehicles, and based upon their learning, clustering of vehicles is performed. An Agent Learning–based Algorithm for Clustering is proposed. The performance of the proposed scheme is evaluated using extensive simulation with respect to the various metrics such as message transmission ratio, percentage of connectivity, node participation, cluster head duration, and connectivity preservation ratio. The results obtained show that the proposed scheme is effective in performing fast clustering and converges quickly to the final solution.     

Multiagent driven dynamic clustering of vehicles in VANETs

Cluster formation in vehicular ad hoc networks (VANETs) is a challenging problem due to rapidly changing network topology and frequent network disconnections of vehicles. Dynamic clustering is a technique to form grouping of vehicles on the fly. We propose a multiagent driven dynamic clustering scheme for VANETS on a lane between two intersections by considering vehicle speed, direction, connectivity degree to other vehicles and mobility pattern. The scheme comprises of heavy-weight static and light-weight mobile agents. Initially, cluster members are identified based on vehicle's relative speed and direction for dynamic clustering. Cluster head is selected among the cluster members based on stability metric derived from connectivity degree, average speed and time to leave the road intersection. Cluster head predicts future association of cluster members based on mobility patterns. The announcement of cluster mobility pattern to all cluster members is made by cluster head. The cluster members with similar mobility pattern can reconnect with cluster head after passing an intersection of the lane. We have evaluated the performance and effectiveness of proposed scheme by comparing with an existing clustering scheme. It is observed that proposed scheme performs better than existing stable clustering scheme in terms of cluster formation time, cluster member selection time, cluster head selection time and control overheads.     

混合动力电动汽车的跟车控制与能量管理

混合动力电动汽车(Hybrid electric vehicles,HEVs)的能量管理问题至关重要,而混合动力电动汽车的跟车控制不仅涉及跟车效果与安全性,也影响着能量的高效利用.将HEVs的跟车控制与能量管理相结合,提出一种基于安全距离的HEVs车辆跟踪与能量管理控制方法.首先,考虑坡度、载荷变动建立了HEVs车辆跟...     

Evaluating traffic effects of a route guidance system by dynamic simulation

This paper will present how to evaluate the global traffic effect of a telematic route guidance system by using a developed simulation tool, DYNEMO. A mesoscopic model, that gathers the merits of both macroscopic and microscopic traffic models, has been applied in this tool for dynamic traffic simulation. The communication between vehicles and the telematic infrastructure inside a road network is simulated by decision points that able to give last traffic messages, as vehicles pass by them. The evaluation will be made from the traffic aspect, safety aspect and consumption as well as emission. We will illustrate the approach by using the city road network of Braunschweig in Germany as an example of implementing a kind of route guidance system like EURO-SCOUT.     

一种基于概率的路径预测与查询算法

研究了路网空间内的路径预测与查询技术,设计了基于统计信息和概率论的最优路径预测算法。实际应用中,路网错综复杂。提出可能路径集合的概念,并设计算法来提取当前路径预测涉及到的路网子网,减小路网规模和路径预测的复杂度。在空间网络环境下,现有移动对象位置预测技术主要针对短期预测,不能预测下一路口的交通情况。为了弥补这一缺陷,降低用户端的位置更新率,设计了路网移动模型来简洁描述提取自大量历史移动路径的移动统计特征,捕捉路口处转向模式。基于移动模型,提出了具有较高精度的交通预测模型来预测对象的运动路径。     

VisLab and the Evolution of Vision-Based UGVs

The technological issues and legal considerations associated with fully automated vehicles have prompted the automotive industry to focus more on supervised systems and advanced driver assistance systems (ADAS). Transportation departments worldwide are concerned with social, economic, or environmental objectives aimed at enhancing fuel and road network efficiency and quality of life. Recently, the automotive industry's success with ADAS has induced the military to reconsider its ground-fleet-automation goal. Researchers are considering unmanned-vehicle technology for many other applications. However, most common and attracting the most industry interest is the automation of road vehicles. Unmanned ground vehicles shape our future by providing enhanced safety and improved mobility     

车载自组织网络中基于停车骨干网络的数据传输

车载自组织网络(vehicular ad hoc networks,简称VANETs)具有网络间歇连通、节点高速移动及动态的网络拓扑结构等特性,如何有效地实现车辆间的数据传输,成为VANETs的重大挑战.现有研究工作基于历史交通流量或历史延迟预测路段当前交通状况的方法并不可靠.此外,要实现高效的数据路由传输,配置大量路边基础设施节点(deploying roadside unit,简称RSU)是一种可行方案,但通常需要额外开销.基于城市区域长时间拥有大量地上停放车辆这一事实,提出了基于停车骨干网络的数据传输策略PBBD(parking backbone based data delivery),不需要配置任何地面基础设施,而是把地面的停放车辆组成一个虚拟的停车覆盖网络,通过该停车覆盖网实现数据的传输.为此,首先,对于每一条道路,把路边和非路边停放车辆组成一个尽可能长的停车簇,并基于这些停车簇组织城市停车骨干网络.其次,设计基于停车覆盖网络的全新数据传输算法来实现车辆间的有效数据传输.基于真实城市地图和交通数据的模拟实验结果表明,与现有的几种数据传输算法相比,PBBD能够以较低的网络传输开销和较小的传输延迟获得较高的数据传输成功率.     

考虑差异化需求的电动汽车充电调度策略

电动汽车行驶里程短、充电时间长是影响驾驶体验的关键.通过对电网、充电设备进行大规模升级的方法减少充电时间,成本昂贵,因此充分利用现有路网、电网资源,制定智能充电调度策略成为提高驾驶体验的重要手段.考虑到驾驶者对充电时间敏感度的异质性,提出具有差异化的调度策略以满足不同优先级驾驶者的需求.首先,为均衡不同优先级驾驶者的利益,提出一种基于动态截断机制的两优先级队列模型;其次,定义充电站的准入原则,保证高优先级驾驶者对预留桩的使用权及对空闲桩的优先抢占权;然后,提出基于截断机制的双层优化模型CCPQ(charging with cut-off priority queue),在顶层高优先级车辆与充电桩最优匹配的基础上,设计底层低优先级车辆的分配策略优化模型,将最小化低优先级驾驶者的总等待时间构建为凸优化问题;最后,通过仿真验证策略的有效性及优越性.     

VeSense: High-performance and energy-efficient vehicular sensing platform

Although vehicular sensing where mobile users in vehicles continuously gather, process, and share location-sensitive and context-sensitive sensor data (e.g., street images, road condition, traffic flow) is emerging, little effort has been investigated in a model-based energy-efficient network paradigm of sensor information sharing in vehicular environments. Upon these optimization frameworks, a suite of optimization subproblems: a program partitioning and network resource allocation problem, we propose a distributed vehicular sensing platform, called VeSense where mobile users in vehicles publish/access sensor data via a cloud computing-based distributed P2P overlay network. The key objective is to satisfy the vehicular sensing application’s quality of service requirements by modeling each subsystem: mobile clients, wireless network medium, and distributed cloud services. By simulations based on experimental data, we present the proposed system can achieve up to 37 times more energy-efficient and 73 times faster compared to a standalone mobile application, in various vehicular sensing scenarios applying a realistic mobility model.     

适用于VANET仿真的车辆合成运动模型

车载自组网(VANET)是移动Ad Hoc网络技术在交通领域的应用,已成为智能交通系统一项非常有前景的技术.准确反映节点运动方式的运动模型对于提高仿真实验准确性至关重要.首次利用了实际的卫星地图信息,在考虑实际道路环境特点基础上实现了新的节点运动模型--车辆合成运动模型,将该模型与随机路点模型和曼哈顿模型进行了对比,并在根据西安城区卫星地图构建的仿真场景中分别采用3种节点运动模型,利用QualNet仿真平台评估了不同节点运动模型对路由协议性能的影响.结果表明,节点运动模型对协议性能影响很大,分析认为合成运动模型更适合于VANET仿真研究.     

车载社交网中基于传递概率的路由算法

在车载社交网(Vehicular Social Network,VSN)中,车辆移动速度快且行驶方向难以预测,导致网络拓扑结构不断变化,通信链路时常中断,因此在进行消息传输时丢失率和传输延迟都居高不下。为了解决上述问题,针对VSN提出了一种基于传递概率的路由算法(ProSim),利用节点间的机会式相遇来进行消息的传输,根据车辆间的社交关系设计VSN路由算法以弥补通信链路中断带来的高丢失率和高延迟;选取了车辆节点的相遇概率和社会相似度这两种社交关系,对其进行量化并计算传递概率。使用真实的道路数据进行仿真,实验结果表明,ProSim与直接传输算法(Direct Delivery,DD)、Epidemic算法以及PRoPHET算法这3种经典路由算法相比,可以在控制传输开销和传输延迟的前提下,有效提高消息的传输率。     

Type-2 fuzzy logic based urban traffic management

This paper presents a multi-agent system based on type-2 fuzzy decision module for traffic signal control in a complex urban road network. The distributed agent architecture using type-2 fuzzy set based controller was designed for optimizing green time in a traffic signal to reduce the total delay experienced by vehicles. A section of the Central Business District of Singapore simulated using PARAMICS software was used as a test bed for validating the proposed agent architecture for the signal control. The performance of the proposed multi-agent controller was compared with a hybrid neural network based hierarchical multi-agent system (HMS) controller and real-time adaptive traffic controller (GLIDE) currently used in Singapore. The performance metrics used for evaluation were total mean delay experienced by the vehicles to travel from source to destination and the current mean speed of vehicles inside the road network. The proposed multi-agent signal control was found to produce a significant improvement in the traffic conditions of the road network reducing the total travel time experienced by vehicles simulated under dual and multiple peak traffic scenarios.     

基于路口相位方案优化设计的城市道路交通信号迭代学习控制方法研究

城市道路交叉口信号控制是交管工作持续关注的课题,关于协调好有限的道路资源与日益增长的交通需求之间的矛盾,有着至关重要的作用。由于道路自身条件约束,交通流的组成特点复杂,路网交通路呈现非线性动态特征,无法进行精准的数学建模控制。本文提出的迭代学习控制方法,根据交通流的组成和变化特点调整信号控制周期及有效绿灯时长,实现交通信号动态优化控制,保证车辆在路网中能够高效、平稳地通行,是针对非线性动态交通流的一种动态寻优控制算法,能够有效减少路口车辆等待时间、提高通行效率。考虑对不同相位设计方案的适应性,在传统配时优化模型的基础上,构建综合相位设计元素的交通信号迭代学习控制模型,并通过Vissim仿真软件和Python编程语言搭建仿真测试环境,验证了提出模型的有效性。     

多个MFD子区边界协调控制方法

为了改善交通网络运行状况,根据车流密度的差异对宏观路网进行子区划分,提出了面向多个宏观基本图（Macroscopic fundamental diagram,MFD）子区的边界协调控制方法.根据划分的多个子区间邻接关系和流入流出交通流率,建立了路网车流平衡方程.通过与最佳累积车辆数进行比较,确定了拥挤度高的子区边界交叉口最佳流入与流出的交通流量;进而建立了以整个路网旅行完成流率最大、平均行程时间和平均延误最小的多目标边界协调优化模型,并通过自适应遗传算法对多目标函数进行求解.以存在4个MFD子区的实际路网为分析对象,对比仿真结果表明所提方法可有效提高路网运行效率、缓解拥堵状况.     

A Dynamic Road Incident Information Delivery Strategy to Reduce Urban Traffic Congestion

Advanced information and communication technologies can be used to facilitate traffic incident management. If an incident is detected and blocks a road link, in order to reduce the incident-induced traffic congestion, a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this work. Time-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information. A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well modeled. Simulation results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles, operation time interval of the proposed strategy, traffic density in the traffic network, and the scope of the area where traffic incident information is delivered. The results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.      

智能网联车环境下基于路段评分的数据转发模型

智能网联车之间的拓扑快速变化导致车间链路质量不稳定,从而使得数据转发的效率降低。对此,本文面向城市路网提出一种基于路段实时评分的智能网联车数据转发模型(Road section scoring-based data forwarding model for intelligent connected vehicles,RSSM)。首先,根据车辆密度将路段分为两部分,并分别对两部分路段上节点间的连通性进行建模,之后得到整条路段上节点间的连通性作为该路段的得分。然后,计算整个路网中所有路段上节点间的连通性并将其作为上述路段的得分,依据整个路网对路段的评分实现源节点到目的节点的动态路径规划,保障所规划的数据转发在整体上最优。最后,在结合实验平台NS3与SUMO上进行仿真对比,实验结果表明：与同类算法相比,本文提出的数据转发模型RSSM在数据投递成功率和时延方面均优于同类数据转发方法。     

面向套牌甄别的流式计算系统

套牌车的甄别具有时效性约束。针对现有计算检测方法中所出现的精度低、响应慢等局限,提出了一种基于实时车牌识别（ANPR）数据流的套牌车流式并行检测方法,设计了基于路段阈值表和时间滑动窗口的套牌计算模型,能够实时地甄别出交通数据流中的套牌嫌疑车。在Storm环境下,利用某市真实交通数据集模拟成实时交通流数据进行实验和评估,实验结果表明计算的准确率达到98.7%,并且一条车牌识别数据的处理时间为毫秒级。最后,在该计算模型基础上实现了套牌车稽查防控系统,能实时甄别并展现出当前时刻城市交通网中出现的所有套牌嫌疑车。