基于无线传感器网络技术的运输网络智能引导及控制系统中英文翻译

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1、英文原文An Intelligent Guiding and Controlling System for Transportation NetworkBased on Wireless Sensor Network Technology AbstractThis paper proposes architecture based on Wireless Sensor Network (WSN) technology for Intelligent Transportation System (ITS) of a transportationnetwork. With the help of

2、WSN technology, the traffic info of the network can be accurately measured in real time. Based on this architecture, an optimization algorithm is proposed to minimize the average travel time for the vehicles in the network. Compared to randomly-chosen algorithm, simulation results show that the aver

3、age speed of the road network is significantly improved by our algorithm, and thus improve the efficiency of the road network. Some extended applications of the proposed WSN system are discussed as well.1. IntroductionTransportation plays an important role in our modern society. How to efficiently e

4、xploit the transportation capacity of the existing transportation infrastructure receives a lot of attention from the researchers across the world. The Intelligent Transportation System (ITS) has been proposed by many researchers to solve the problem.ITS comprises of three main sub-systems. They are

5、 surveillance sub-system, analysis and strategy subsystem and execution sub-system. The execution subsystem can be a traffic control sub-system, a vehicle guiding sub-system, or a navigation sub-system. The surveillance sub-system measures the traffic information such as the vehicles location, speed

6、, number of the vehicles on the road, etc., using certain type of sensor, such as inductive loops 1 or ultrasonic sensor 2. A new method based on video analysis is now under development 1;3.The analysis and strategy sub-system optimizes the traffic flows based on the measurements from the surveillan

7、ce sub-system. Various algorithms are proposed for this purpose, some typical examples follow. Papageorgiou et al. summaries some implementations on fixed-time strategies and trafficresponsive strategies for isolated strategies and coordinated strategies in 4; In 5, Shimizu et al. propose a balance

8、control algorithm to optimize the congestion length of the whole transportation network; in 6, Di Febbraro presents a hybrid Petri Net module to address the problem of intersection signal lights coordination.The control sub-system controls the signal lights on the intersection. The guiding sub-syste

9、m provides the real-time traffic information for the drivers to select the best route. The navigation sub-system uses satellite signal such as GPS to locate the specific vehicle, and with the help of electronic map, select the optimal route for the vehicle.One shortage of the systems mentioned above

10、 is that the sensors can only detect the vehicles in a fixed spot. They can not track the vehicles out of the spot. Clearly, if we can monitor and measure the traffic status dynamically in real time, an efficient traffic control will be easier to realize.With the development of microelectronic and c

11、omputer technologies, the low-power-consumption, low-cost and relatively powerful wireless sensor network (WSN) technology has been applied in many areas7-9. However, the application of WSN in the traffic control system is rarely documented. In 10, we proposed a WSN-based system for an efficient tra

12、ffic control in an isolated road intersection. This paper extends our previous work to a transportation network. A WSN-based traffic control, guiding, and navigation system is proposed to optimize the traffic in a transportation network.The rest of this paper is organized as follows: Section 2 descr

13、ibes the structure of the proposed WSN-based traffic control system. Section 3 describes the optimization algorithm for the traffic network. The simulation results and some discussions are presented in Section 4. Finally, Section 5 concludes this paper.2. System Structure2.1. WSN ModuleWSN module is

14、 a basic component in our traffic control system. As illustrated in Fig. 1, a WSN module comprises of 3 main components, i.e., RF (Radio Frequency), MCU (Micro Control Unit) and Power Supply. The RF encodes, modulates and sends the signal. Also it receives, decodes and demodulates the signal as well

15、. MCU integrates processor and memories, where the programs resides and executes. The Power Supply supplies the power to entire module.In the proposed system, WSN modules are widely distributed on vehicles, roadsides and intersections to collect, transfer and analyze the traffic information. See sec

16、tion 2.3 for details.2.2. Urban Traffic NetworkSeveral different facilities are installed in the urban traffic network to perform their specific functions. For example, the Signal Lights are installed in the road intersection to directly control the vehicle through the intersection; the Variable Mes

17、sage Sign (VMS) is set up along the road side to help drivers to select the optimal route; the Navigation system (electronic-map and satellite-based positioning system) is installed in the vehicle for vehicle locating and navigation.The target of an ITS is to optimize the traffic in a transportation

18、 network by controlling the signal lights in the intersections, by providing the accurate traffic information in the VMS, or by selecting the best route in the e-map.To perform the traffic control, below, we shall first have a look at the configuration of the transportation network. Then, some param

19、eters are introduced to describe traffic information in the network. By optimizing these parameters, the proposed optimization algorithm is expected to optimize the traffic in the transportation network.As a example of a real-life traffic network, Fig. 2 illustrates the road net of Fukuyama city 11.

20、 On the figure some parameters such as the link length, lane numbers, and legal speed are marked on it.In this paper, we consider the traffic system that contains 3 types of basic elements, i.e., intersection (N), Link (L) and Vehicle (V). An Intersection can be described by 2 parameters: 1) the pha

21、se type (the type of the vehicles on different lanes passing through the intersection simultaneously); 2) the duration of every phase. A Link can be described by 4 parameters, i.e., the link length, lane numbers (include every turningdirection), mean speed, vehicle number. A Vehicle can be described

22、 by 5 parameters. They are: 1) the location of the vehicle, 2) the vehicle velocity, 3) the origin, 4) the destination, 5) the length of the route, 6) the total time and, 7) the average speed on the route.Among these parameters, 1) some are fixed, such as the lane numbers and link length; 2) some ar

23、e measured by the surveillance sub-system, such as the mean speed, the number of the vehicles on a link; 3) some are set by an optimization algorithm, such as the intersection signal light and the next link selected by a vehicle.The vehicle velocity, direction, and the number of the vehicles are the

24、 basic variables of the whole system. It is the main task of our algorithm to optimize these parameters.2.3. Data Collection and TransferringAs illustrated in Fig. 3, there are 3 types of WSN nodes installed in our system, i.e., the vehicle unit on the individual vehicle; the roadside unit along bot

25、h sides of the road; and the intersection unit on the intersection.The main function of intersection unit is to receive and analyze the information from other units to control the signal light. The main function of roadside unit is to gather the information of the vehicles around, and transfer it to

26、 the intersection unit. (Roadside units are installed on the lamp posts along both sides of the road every 50200m according to the wireless cover range.) The main function of the vehicle unit is to measure the vehicle parameters and transfer them to the roadside units. (Vehicle unit is installed in

27、every vehicle.) The intersection unit, roadside units and vehicle units are denoted as A, B and C in Fig. 2.Roadside units broadcast messages every second. A message includes the ID of the roadside unit and its relative location to the intersection (xB, yB). Normally, vehicle unit is in the listenin

28、g state. When a vehicle comes into the broadcast range of the roadside units and receives the broadcasted message, the vehicle unit switches to the active state. According to the wireless locating method 12;13, if a vehicle unit receives messages from more than three nodes, it can calculate its loca

29、tion (x, y) and velocity v. After that, the vehicle unit sends the information (x, y, v) to the roadside unit nearby.Based on the (x, y, v) from the vehicles, the roadside unit can calculate the mean speed of the vehicles in its scope. The roadside then transfers the calculated information to the in

30、tersection unit.After receiving the messages from the four directions, the intersection unit analyzes the information and makes the decision to control the signal light, or to send navigate information to the vehicle.3. Optimization Algorithm for Traffic Network3.1 Optimization TargetFrom the point

31、view of the whole transportation network, the objective of the proposed ITS is to improve the use efficiency of the network, maximize the mean speed of the whole road network, and reduce the traffic congestions and accidents. From the view of an individual driver or passenger, the objective is to ar

32、rive at the destination safely with a minimum cost. The cost may be route length, fuel used, payment for taxi, or time spent. Clearly, the minimum length from the origination to the destination is a static problem, and is out of our discussion. In this paper, we only consider the minimum-travel-time

33、 algorithm. That is, the purpose of our optimization algorithm is to minimize the travel time that a vehicle drives from the origination to the destination.3.2 Minimum Travel Time Optimization AlgorithmThe travel time of a vehicle comprises the running time on the road and the waiting time for the g

34、reen light at the intersection. For the ease of discussion, the following a few denotations are defined.Node: The intersection. It is denoted as Ni.(i=0,1,2 )Link: the road from an intersection Ni to a successive intersection Nj. Its denoted as Li,j. Link is one-way.Say, LijL,ij.Total Travel Time (T

35、TT): The total time spent while a vehicle travels from the origination to the destination along a specified route.Link Travel Time (LTT): the time spent while a vehicle travels from a node to the other node along the link.Link Average Velocity (LAV): the average velocity of all the running vehicles

36、in the link.Waiting Green-light Time (WGT): The time elapsed when a vehicle or a queue waits the right-to-go phase in the front of an intersection. The parameter of WGT includes node, incoming link, outgoing link, and the time when the vehicle reach the intersection. So it can be denoted as WGT(Node

37、,Lin,Lout,Time).Total Travel Length (TTL): the total route length that a vehicle traveled.The basic idea of the optimization algorithm is that: Before we choose the next link to ride, we firstly predict the time cost of the candidate routes. The route with the minimum cost is then chosen as the best

38、 route. In order to predict the total time cost, we should know the travel time in all links to pass and the waiting time before every intersection.Lets see a simple situation. As shown in Fig.3, the current time is ; a vehicle C is running on link L1,4 with velocity v; and the destination is N8. Th

39、en, there are two routes with the approximate length:The total travel time of (TTT()can be calculated as follow:TTT() can be calculated similarly. After that, the path with the minimum TTT is selected.From above algorithm, we can see that TTT is related to link length, d, v and LAV(+ t2). Link lengt

40、h is fixed; d and v can be detected by the method presented in section 2.3. Now, the question is how can we get LAV(+ t2)?In 11, The author uses legal velocity to estimate the link average velocity. In 14, the author assumes that if the link is not congested, then the velocity is a constant (say, th

41、e legal velocity), otherwise, the velocity is zero.In fact, the average velocity of a link is also related to the number of vehicles running on it, or the congestion grade since the vehicle should keep a safe distance between each other. We can construction a function between the average velocity an

42、d the vehicle number (VN) based on surveillance. Thus, if we know the vehicle number on a link, we can get the LAV of it.Since the system know the target and previous chosen route, it can compute the vehicle number in the special link at time + t2 -1, i.e., VN(L,+ t2 -1).Then , we can get LAV(L, ,+

43、t2). So the TTT of a special route can be calculated.4. Simulation Result and DiscussionsTo demonstrate the effect of the proposed algorithm, some simulations are conducted in the PC using the data of a real urban road network which is reported in 11.The road network is illustrated in Fig. 2. Vehicl

44、es appear in this network in a random origination to a random destination. The incoming vehicles of the entire network are recorded every 15 minutes, which are illustrated in Fig. 5(a).In our algorithm, the mean speed (MS) of the entire road network is calculated, which is defined as follows:where,

45、V is the vehicles that reach the destination in the time period.Fig.5 (b) presents the result, curve A indicates the optimized route. As a contrast, curve B represents the results of a randomly-chosen route among several routes with approximately equal length.The proposed WSN system can also be used

46、 for many other transportation applications to improve their efficiency. For examples: 1) a “green wave” along the route of important emergent cars will be easier to implement; 2) parking management will be smarter; 3) Electronic Toll Collection (ETC) system can be improved from multilane 15 to free

47、 lane, without any tollgate to limit the vehicle stream. Some more complicated functions, such as Asymmetric signal phase control and automatic “Tide wave” control.The WSN system can also be used as a dual communication network. It can be used for the management center to track and schedule the vehi

48、cles such as taxis, buses and freight carriers.5. ConclusionIn This paper, a WSN-based architecture is presented for ITS of a transportation network. With the help of WSN technology, the traffic info of the network can be accurately measured in real time. Based on this architecture, an optimization

49、algorithm is proposed to minimize the average travel time for the vehicles in the network. Compared to the randomlychosen algorithm, simulation results show that the average speed of the road network is significantly improved by our algorithm, and thus improve the efficiency of the road network. Som

50、e extended applications of the proposed WSN system is discussed as well.中文译文基于无线传感器网络技术的运输网络智能引导及控制系统摘要：这篇论文基于运输网络的智能运输系统（ITS）的无线传感器网络 (WSN) 技术提出一种结构。由于WSN技术的支持, 交通网络信息能实时正确地测量出来。基于这一个结构,提出一个最优化算法能将交通网络平均车流量减到最低。与随机选择算法相比, 我们的算法摹拟出来的结果显示交通网络的公路平均速度和效率有了明显地改善。许多关于这个被提出WSN系统的应用也有很好的效果。1介绍交通运输在我们现代的社会扮

51、演着重要角色。该如何有效率地开发现有运输系统各部分的运输容量已经受到许多国际上研究员的关注。而这些研究员都认为这个智能运输系统（ITS）能解决当前的问题。ITS包含三个主要的子系统。他们是侦测子系统，分析和策略子系统和运行子系统。运行子系统可以描述为一个流量控制的子系统，或者是载体的引导子系统 , 或者是一个导航子系统。侦测子系统使用确定的传感器, 运用归纳的回路 1 或超声纳感应器 2的方法测量交通网络流量信息，例如是载体位置，速度，交通系统中的车辆数等等。同时，一种以视频分析为基础的新方法在迅速发展 1;3.分析和策略子系统根据侦测子系统的测量值来优化交通系统。为了这个目的，提出了各种不同

52、的算法和一些典型的例子，例如Papageorgiou。在4中，摘要关于一些固定时间策略和流量回复策略方面的隔离策略和协调策略的工具; 在5中，例如Shimizu，提出了一个平衡的控制算法。该算法用于优化整个交通网络的车龙长度。在6中,Di Febbraro提出一个混合的Petri网络模型来确定十字路口的交通讯号灯调节问题。控制子系统控制十字路口交通讯号灯。导航子系统提供实时车流量信息让司机选择最好的路径。导航子系统使用宇宙站信号，如全球定位，来定位特定的车辆,和藉由电子地图的帮忙, 选择那最佳的行车路线。上面提到的系统的一个不足是传感器只能在地图内定位一辆固定的车辆，但不能追踪地图外的车辆。很

53、清楚地，如果我们能实时动态地检测并测量交通状态，一个有效率的流量控制将会更容易地被人了解。由于微电子和计算机技术的发展，耗电量低，廉价及有效的无线传感器网络(WSN)技术已经在各个领域广泛应用7-9. 然而,WSN的在交通控制系统中的应用却很少被提起。在10中,我们为一个有效的孤立十字路口的交通控制提出了一个以WSN为基础的系统。本论文把我们早先的工作延伸到一个交通运输网络中，提出一个以WSN为基础的交通控制，引导，及导航系统来优化运输交通网络。本论文的其余部分以下列各项来组织：第2节描述这个以WSN为基础的交通控制系统的结构。第3节描述这个交通网络的优化算法。在第4节中，列出摹拟结果和一些值

54、得讨论的问题。最后，第5节总结本论文。2系统结构2.1. WSN模型图1 本论文的一个用于WSN结点的模型结构WSN模型是我们交通控制系统的一个基本的元件。如图1所示,一个WSN模型包含3个主要的元件，包括射频(无线电频率),MCU(微控制单元)和电源。射频编码,调制后发送信号。同时，它也接收信号, 解调后恢复信号。在程序常驻及运行的地方，MCU整合了处理机和存储器。电源提供能量给整个的模型。在这个提出的系统中，WSN模型广泛地分配到车辆，路傍和十字路口上，来收集,传递及分析交通信息。详见第2.3节。2.2. 城市交通网络一些不同的设备安装在那城市的交通网络中去执行他们的特定功能。例如，安装在

55、十字路口的交通灯直接地控制车辆经过这个十字路口；多变的道路消息信号(VMS)沿着马路两旁设置以帮助司机选择最佳的行车路径；导航系统(电子地图和以卫星为基础的定位系统)安装在车辆中为车辆提供定位和导航服务。ITS的目标是通过控制十字路口的交通信号灯，使用VMS提供的准确交通信息，或是在电子地图中选择最佳行车路线来优化运输网络的交通状况。为了实现交通控制，以下，首先我们了解一下运输网络的配置。然后，认识一些参数用于描述网络的交通信息。通过优化这些参数，提到的那个优化算法将实现优化那运输交通网络的功能。正如现实中交通网络的例子,图2，举例说明了Fukuyama城市的道路网络11。在这个道路网络中的道

56、路参数，如车龙长度,小路数目,以及合法的速度在图2上作上记号。图2、在Fukuyama车站的交通网络图(引证于11)在这本论文中，我们考虑的交通系统包含3中类型的基本元件,那就是,十字路口(N), 道路连接(L)和车辆(V)。一个十字路口能用两个参数描述：1） 状态类型(同一时间内通过十字路口的不同方向上车辆数的状态)；2)每一个状态的持续时间。 一条连接可以用4个参数来描述，那是，连接长度，马路数目(包括每一个转角方向)，平均速度, 车辆数目。车辆能用5个参数来描述，他们是: 1)车辆的位置，2)车辆速度，3)起始点，4)目的地，5)行车路程,6)总时间和7)行驶平均速度。在这些参数中，1)

57、一些是固定的,例如马路数和连接长度; 2) 一些是由侦测子系统测量, 例如相对速度,在一个连接上的车辆数; 3)一些是可以用优化算法设定,例如那十字路口的交通灯信号和车辆选择的下一个道路。车辆的速度，方向，和车辆数量全部都是系统的基本变量。我们的算法的主要任务是优化这些叁数。2.3. 数据收集与传递如图3所示，在我们的系统中安装有3种不同类型的WSN结节，那就是，在个别车辆的车辆单元；马路两旁的路边单位；和十字路口上的十字路口单位。图3 十字路口单位(A)，路边单位(B)和车辆单位(C)，以及他们所在公路网络的十字路口十字路口单位的主要功能是接收并分析来自其他单位的信息来控制交通灯。路边单位的

58、主要功能是收集这附近的车辆信息,和把它传送到十字路口单位。(路边单位是在交通灯上沿着马路两旁每50200米安装一个，因为其无线覆盖距离为50200米)。车辆单位的主要功能是测量车辆参数并且把他们传送到路边单位(车辆单位是安装在每一辆车当中的)。那十字路口单位，路傍单位和车辆单位是图3的A，B和C。路边单位每秒发送一次信息。该信息包括路边单位的身份认证和它到十字路口的相对位置(xB,yB)。正常地,车辆单位是在处于接收状态。当一辆汽车进入路边单位的广播范围，同时接收到广播的信息，车辆单位就会进入活跃状态。依照无线定位方法12;13，如果一个车辆单位接收来自超过三个结点的信息，就能计算出它的位置(

59、x,y)和速度v。之后,车辆单位就会发送信息(x，y，v)给附近路边单位。基于这个来自车辆的信息(x，y，v)，路边单位就能计算出在它附近的车辆的预期速度。然后，马路两旁就会传送这个结果到十字路口单位。在接收到来自四个方向的信息之后，十字路口单位就会分析这些信息并且定出交通灯信号控制方案，或者发送导航信息到车辆中。3. 交通网络的优化算法3.1 优化目标整个的交通网络的其中一方面来看，提出的ITS目标是改善网络的使用效率，提高整个的交通网络的行驶速度，而且减少交通阻塞和意外事件。从一位个别的司机或乘客来看，该目标是以最小的代价安全地到达目的地。其代价可认为是车程，使用的燃料，出租汽车的费用，或者是耗时。清晰地，从始发地到目的地的最小车程是一个静态的问题，也超出我们的讨论范围。在本论文中，我们唯一考虑的是最小的行车时间的算法。我们的优化运算法则的目的是一辆汽车从始发地到目的地的旅行时间的最小化。