基于道路风险评估的城市路网实时路径选择

为了缓解城市交通拥堵、避免交通事故的发生,城市路网的路径选择一直以来是一个热门的研究课题.随着边缘计算和车辆智能终端技术的发展,城市路网中的行驶车辆从自组织网络朝着车联网(Internet of vehicles,IoV)范式过渡,这使得车辆路径选择问题从基于静态历史交通数据的计算向实时交通信息计算转变.在城市路网路径选择问题上,众多学者的研究主要聚焦如何提高出行效率,减少出行时间等.然而这些研究并没有考虑所选路径是否存在风险等问题.基于以上问题,首次构造了一个基于边缘计算技术的道路风险实时评估模型(real-time road risk assessment model based on edge computing, R³A-EC),并提出基于该模型的城市路网实时路径选择方法(real-time route selection method based on risk assessment, R²S-RA). R3A-EC模型利用边缘计算技术的低延迟,高可靠性等特点对城市道路进行实时风险评估,并利用最小风险贝叶斯决策验证道路是否存在风险问...     

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.     

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.     

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.     

时间递推耦合神经网络的交通路径动态诱导技术

通过"人—机—环境"耦合关系,对路况与时间变化关系进行研究。综合车辆运行过程中不同时段的路况差异和人因作用、突发道路事故随机性,以神经网络有师学习作为经验累积方法,提出时间递推预测方法确定路径最短时间,从而实现对交通路径的动态诱导。递推预测以知识库累积经验与实时路况信息作比较,为驾驶者提供实时有效的路况信息支撑。结果表明,该诱导技术可辅助驾驶者及时对路况作出正确判断,减少因经验不足和突发事件造成的时间损失,适用于安装有GPS导航的车辆。实例分析表明,所构建模型与实际数据结合收到良好效果。     

SMashQ: spatial mashup framework for k-NN queries in time-dependent road networks

The k-nearest-neighbor (k-NN) query is one of the most popular spatial query types for location-based services (LBS). In this paper, we focus on k-NN queries in time-dependent road networks, where the travel time between two locations may vary significantly at different time of the day. In practice, it is costly for a LBS provider to collect real-time traffic data from vehicles or roadside sensors to compute the best route from a user to a spatial object of interest in terms of the travel time. Thus, we design SMashQ, a server-side spatial mashup framework that enables a database server to efficiently evaluate k-NN queries using the route information and travel time accessed from an external Web mapping service, e.g., Microsoft Bing Maps. Due to the expensive cost and limitations of retrieving such external information, we propose three shared execution optimizations for SMashQ, namely, object grouping, direction sharing, and user grouping, to reduce the number of external Web mapping requests and provide highly accurate query answers. We evaluate SMashQ using Microsoft Bing Maps, a real road network, real data sets, and a synthetic data set. Experimental results show that SMashQ is efficient and capable of producing highly accurate query answers.     

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。     

Ontology-based curriculum content sequencing system with semantic rules

Curriculum content sequencing involves managing a learning route to help users achieve learning goals. A conventional learning route consists of ordered content and is primarily based on a single course material. In an e-learning system, amount of similar course contents are available. These contents are expected to mutually substitute for one another in creating flexible learning routes. Owing to inconsistency in materials editing and cataloging, composing contents based on multiple sources leads to sequencing complexity. Most significantly, most e-learning systems lack a sequencing mechanism for dominating content composition. This study utilizes a knowledge-intensive approach to create a general sequencing knowledge base. This approach includes two components: (1) ontology is used to represent abstract views of content sequencing and course materials and (2) added semantic rules are used to represent relationships between individuals. Following knowledge base creation, both practical curriculum sequences and course materials can be inserted as factual knowledge. A reliable knowledge base can be established using inference power. An example involving mathematics course in elementary school education is designed using Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL). Experimental lessons demonstrate that semantic rules in conjunction with ontologies not only solve sequencing problems but also achieve a durable knowledge base and a reliable system.     

Road scene analysis for determination of road traffic density

Road traffic density has always been a concern in large cities around the world, and many approaches were developed to assist in solving congestions related to slow traffic flow. This work proposes a congestion rate estimation approach that relies on real-time video scenes of road traffic, and was implemented and evaluated on eight different hotspots covering 33 different urban roads. The approach relies on road scene morphology for estimation of vehicles average speed along with measuring the overall video scenes randomness acting as a frame texture analysis indicator. Experimental results shows the feasibility of the proposed approach in reliably estimating traffic density and in providing an early warning to drivers on road conditions, thereby mitigating the negative effect of slow traffic flow on their daily lives.     

RFID，GPS和GIS技术集成在交通智能监管系统中的应用研究*

为实现在城市复杂路网情况下对交通车辆的实时监控,并且能通过一定数量的车辆运行状态来判断道路交通的拥挤状况,采用射频识别技术(RFID)对道路上运行的车辆进行动态识别和数据信息交换;依靠全球定位系统(GPS)技术实时获得目标车辆的位置信息,并通过地理信息系统(GIS)将车辆的运行状况以及路网的交通状况以电子地图形式实时地展现给用户。将GPS、GIS与RFID技术综合应用于城市道路交通管理系统中,在此基础上设计出道路交通车辆的全程监控模型和系统框架。对交通监管的信息化建设具有一定的借鉴意义。     

神经网络和遗传算法在动态路径诱导中的应用*

针对智能交通路径诱导目前存在的实时性差和求解效率低的问题,提出了将神经网络与遗传算法结合的动态路径诱导方法,研究了基于神经网络的交通信息实时预测方法,构造了具有时变性的路阻矩阵,解决了传统静态路阻存在时变性差等的局限性问题;探讨了基于遗传算法的最优路径求解问题,提出了适用于路径优化的编码方式、适应度函数和遗传操作算子,解决了求解效率和求解质量的平衡问题。仿真实验表明,该方法满足路径诱导的准确性、实时性和快速性要求。     

Red Swarm: Reducing travel times in smart cities by using bio-inspired algorithms

This article presents an innovative approach to solve one of the most relevant problems related to smart mobility: the reduction of vehicles’ travel time. Our original approach, called Red Swarm, suggests a potentially customized route to each vehicle by using several spots located at traffic lights in order to avoid traffic jams by using V2I communications. That is quite different from other existing proposals, as it deals with real maps and actual streets, as well as several road traffic distributions. We propose an evolutionary algorithm (later efficiently parallelized) to optimize our case studies which have been imported from OpenStreetMap into SUMO as they belong to a real city. We have also developed a Rerouting Algorithm which accesses the configuration of the Red Swarm and communicates the route chosen to vehicles, using the spots (via WiFi link). Moreover, we have developed three competing algorithms in order to compare their results to those of Red Swarm and have observed that Red Swarm not only achieved the best results, but also outperformed the experts’ solutions in a total of 60 scenarios tested, with up to 19% shorter travel times.     

Longitudinal control algorithm for automated vehicle merging

This paper considers longitudinal control of automated vehicle merging in a mathematical approach for automated highway systems. Merging manoeuvre is defined as one vehicle in the merging lane to be inserted in the middle between two vehicles in the main lane at fixed merging point which is the intersection of those two lanes. The main lane vehicles can change speed. To achieve this, the merging vehicle must properly adjust its speed and acceleration such that it reaches the merging point at the right time with the same speed and acceleration as the main lane vehicles. This problem is a little similar to but different from the well-known missile interception problem. The longitudinal control problem is proposed for different road layouts, based on which a unified mathematical model is established. Then a new concept, virtual platooning, is introduced, which effectively avoids a two-point boundary value problem . Based on this concept, an analytic solution with mathematical proof is provided. It is also discretized as a recursive algorithm for real-time use. A dynamic real-time simulation is published at PATH website. This algorithm has been successfully implemented with automated cars.     

Control of connected and automated vehicles: State of the art and future challenges

Autonomous driving technology pledges safety, convenience, and energy efficiency. Its challenges include the unknown intentions of other road users: communication between vehicles and with the road infrastructure is a possible approach to enhance awareness and enable cooperation. Connected and automated vehicles (CAVs) have the potential to disrupt mobility, extending what is possible with driving automation and connectivity alone. Applications include real-time control and planning with increased awareness, routing with micro-scale traffic information, coordinated platooning using traffic signals information, and eco-mobility on demand with guaranteed parking. This paper introduces a control and planning architecture for CAVs, and surveys the state of the art on each functional block therein; the main focus is on techniques to improve energy efficiency. We provide an overview of existing algorithms and their mutual interactions, we present promising optimization-based approaches to CAVs control and identify future challenges.     

基于移动终端的道路边缘检测方法

面向自动驾驶技术,研究一种实时的道路边缘检测方法—MERD(Mobile Equipment based Road-edgeDetection).该方法基于智能移动终端的Android系统平台,将针对HSV图像的数学形态学方法和针对灰度图的Hough变换检测方法相结合,针对不同路段环境进行道路边缘检测.实验验证了该方法在处理傍晚路段、阴影较多路段等不同情况的道路环境时,能够提供较高准确度的实时道路边缘检测,为车辆的自动驾驶提供安全保障.     

A telematics service discovery platform using the distributed peer‐to‐peer networking system

In this paper, a distributed telematics peer‐to‐peer networking system is proposed to provide an efficient and feasible service discovery mechanism for mobile users. When mobile users travel on the road, they may request for some services from service providers to meet their demands. Mobile users in vehicles are assumed to go through a lot of regions, in which each region is associated with a region server. Related information of all service providers in a region is stored in its region server, and each region server is in charge of the service discovery requests from all vehicles located in its region. This paper focuses on the issues of developing a distributed peer‐to‐peer telematics service discovery networking system over the vehicular network environment for mobile users. To provide the communication mechanism for telematics service discovery among region server, mobile users, and service providers, the Telematics Service Markup Language is proposed. According to clients' demands, three types of query scenarios are proposed, which are (1) reference point based query, (2) continual query, and (3) route‐based query. Finally, we present usage examples and system implementation of distributed telematics peer‐to‐peer. Copyright © 2012 John Wiley & Sons, Ltd.     

基于接口自动机的混合Web服务组合方法

在Web服务描述语言(WSDL)与网络服务的本体语言(OWL-S)共存的环境中难以实现Web服务混合组合,为此提出一种以接口自动机为基础的混合Web服务组合方法。分析和描述WSDL与OWL-S间的关系后,在服务组合过程中用接口自动机实现WSDL与OWL-S自动识别和组合;并将服务组合结果与预先设定好的服务质量进行优化对比,选择服务质量最优的来实现不同的业务逻辑。最后通过一组旅游消费服务实验分析验证表明,该方法可行、有效,服务组合效率提高了5%~10%。     

Optimal feedback control for route tracking with a bounded-curvature vehicle

The problem of driving a vehicle along a given path is considered. The vehicle is supposed to move forward only, with a given velocity profile, and to have a bounded turning radius. Such a model, also known as 'Dubins' vehicle', is relevant to the kinematics of road vehicles as well as aircraft cruising at constant altitude, or sea vessels. We consider the optimal control problem consisting of minimizing the length travelled by the vehicle starting from a generic configuration to connect to a specified route. A feedback law is proposed, such that straight routes can be approached optimally, while the system is asymptotically stabilized. Experimental results are reported showing real-time feasibility of the proposed approach.     

基于分布式实时信息的车载网络路由协议

提出了一种基于分布式实时信息的车载自组织网络路由协议(distributed real-time information based routing protocol,简称DRIP).网络中,车辆基于提出的分布式实时路段延时估计机制(distributed real-time delay evaluation scheme,简称DRES)获取各路段网络状态的实时信息,车辆根据对各路段网络延时的实时估计,采用DRIP协议,保证了数据的有效传递.与现有协议相比,DRIP在最低限度消耗资源的情况下,具有实时性和高效性的特点.通过仿真实验对比现有协议,验证了DRIP在性能上的优越性.     

Energy Control of Plug-In Hybrid Electric Vehicles Using Model Predictive Control With Route Preview

The paper proposes an adoption of slope, elevation, speed and route distance preview to achieve optimal energy management of plug-in hybrid electric vehicles (PHEVs). The approach is to identify route features from historical and real-time traffic data, in which information fusion model and traffic prediction model are used to improve the information accuracy. Then, dynamic programming combined with equivalent consumption minimization strategy is used to compute an optimal solution for real-time energy management. The solution is the reference for PHEV energy management control along the route. To improve the system's ability of handling changing situation, the study further explores predictive control model in the real-time control of the energy. A simulation is performed to model PHEV under above energy control strategy with route preview. The results show that the average fuel consumption of PHEV along the previewed route with model predictive control (MPC) strategy can be reduced compared with optimal strategy and base control strategy.