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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. Some extended applic
