电气专业英文文献及翻译附原文基于GPRS的智能交通系统.docx

1、电气专业英文文献及翻译附原文基于GPRS的智能交通系统外文资料翻译 外文出处：Geo-spatial Information Science 10(3):213-217附 件： 1. 外文原文；2. 外文资料翻译译文。外文原文Traffic Assignment Forecast Model Research in ITSIntroductionThe intelligent transportation system (ITS) develops rapidly along with the city sustainable development, the digital city con

2、struction and the development of transportation. One of the main functions of the ITS is to improve transportation environment and alleviate the transportation jam, the most effective method to gain the aim is to forecast the traffic volume of the local network and the important nodes exactly with G

3、IS function of path analysis and correlation mathematic methods, and this will lead a better planning of the traffic network. Traffic assignment forecast is an important phase of traffic volume forecast. It will assign the forecasted traffic to every way in the traffic sector. If the traffic volume

4、of certain road is too big, which would bring on traffic jam, planners must consider the adoption of new roads or improving existing roads to alleviate the traffic congestion situation. This study attempts to present an improved traffic assignment forecast model, MPCC, based on analyzing the advanta

5、ges and disadvantages of classic traffic assignment forecast models, and test the validity of the improved model in practice.1 Analysis of classic models1.1 Shortcut traffic assignment Shortcut traffic assignment is a static traffic assignment method. In this method, the traffic load impact in the v

6、ehicles travel is not considered, and the traffic impedance (travel time) is a constant. The traffic volume of every origination-destination couple will be assigned to the shortcut between the origination and destination, while the traffic volume of other roads in this sector is null. This assignmen

7、t method has the advantage of simple calculation; however, uneven distribution of the traffic volume is its obvious shortcoming. Using this assignment method, the assignment traffic volume will be concentrated on the shortcut, which is obviously not realistic. However, shortcut traffic assignment is

8、 the basis of all the other traffic assignment methods.1.2 Multi-ways probability assignmentIn reality, travelers always want to choose the shortcut to the destination, which is called the shortcut factor; however, as the complexity of the traffic network, the path chosen may not necessarily be the

9、shortcut, which is called the random factor. Although every traveler hopes to follow the shortcut, there are some whose choice is not the shortcut in fact. The shorter the path is, the greater the probability of being chosen is; the longer the path is, the smaller the probability of being chosen is.

10、 Therefore, the multi-ways probability assignment model is guided by the LOGIT model: (1)Where is the probability of the path section i; is the travel time of the path section i; is the transport decision parameter, which is calculated by the follow principle: firstly, calculate the with different (

11、from 0 to 1), then find the which makes the most proximate to the actual.The shortcut factor and the random factor is considered in multi-ways probability assignment, therefore, the assignment result is more reasonable, but the relationship between traffic impedance and traffic load and road capacit

12、y is not considered in this method, which leads to the assignment result is imprecise in more crowded traffic network. We attempt to improve the accuracy through integrating the several elements above in one model-MPCC.2 Multi-ways probability and capacity constraint model2.1 Rational path aggregate

13、In order to make the improved model more reasonable in the application, the concept of rational path aggregate has been proposed. The rational path aggregate, which is the foundation of MPCC model, constrains the calculation scope. Rational path aggregate refers to the aggregate of paths between sta

14、rts and ends of the traffic sector, defined by inner nodes ascertained by the following rules: the distance between the next inner node and the start can not be shorter than the distance between the current one and the start; at the same time, the distance between the next inner node and the end can

15、 not be longer than the distance between the current one and the end. The multi-ways probability assignment model will be only used in the rational path aggregate to assign the forecast traffic volume, and this will greatly enhance the applicability of this model.2.2 Model assumption1) Traffic imped

16、ance is not a constant. It is decided by the vehicle characteristic and the current traffic situation.2) The traffic impedance which travelers estimate is random and imprecise.3) Every traveler chooses the path from respective rational path aggregate. Based on the assumptions above, we can use the M

17、PCC model to assign the traffic volume in the sector of origination-destination couples.2.3 Calculation of path traffic impedanceActually, travelers have different understanding to path traffic impedance, but generally, the travel cost, which is mainly made up of forecast travel time, travel length

18、and forecast travel outlay, is considered the traffic impedance. Eq. (2) displays this relationship. (2)Where is the traffic impedance of the path section a; is the forecast travel time of the path section a; is the travel length of the path section a; is the forecast travel outlay of the path secti

19、on a; , , are the weight value of that three elements which impact the traffic impedance. For a certain path section, there are different , and value for different vehicles. We can get the weighted average of , and of each path section from the statistic percent of each type of vehicle in the path s

20、ection. 2.4 Chosen probability in MPCCActually, travelers always want to follow the best path (broad sense shortcut), but because of the impact of random factor, travelers just can choose the path which is of the smallest traffic impedance they estimate by themselves. It is the key point of MPCC. Ac

21、cording to the random utility theory of economics, if traffic impedance is considered as the negative utility, the chosen probability of origination-destination points couple (r, s) should follow LOGIT model: (3) where is the chosen probability of the path section (r, s);is the traffic impedance of

22、the path sect-ion (r, s); is the traffic impedance of each path section in the forecast traffic sector; b reflects the travelers cognition to the traffic impedance of paths in the traffic sector, which has reverse ratio to its deviation. If b , the deviation of understanding extent of traffic impeda

23、nce approaches to 0. In this case, all the travelers will follow the path which is of the smallest traffic impedance, which equals to the assignment results with Shortcut Traffic Assignment. Contrarily, if b 0, travelers understanding error approaches infinity. In this case, the paths travelers choo

24、se are scattered. There is an objection that b is of dimension in Eq.(3). Because the deviation of b should be known before, it is difficult to determine the value of b. Therefore, Eq.(3) is improved as follows: ，（4）Where is the average of the traffic impedance of all the as-signed paths; b which is

25、 of no dimension, just has relationship to the rational path aggregate, rather than the traffic impedance. According to actual observation, the range of b which is an experience value is generally between 3.00 to 4.00. For the more crowded city internal roads, b is normally between 3.00 and 3.50.2.5

26、 Flow of MPCCMPCC model combines the idea of multi-ways probability assignment and iterative capacity constraint traffic assignment.Firstly, we can get the geometric information of the road network and OD traffic volume from related data. Then we determine the rational path aggregate with the method

27、 which is explained in Section 2.1. Secondly, we can calculate the traffic impedance of each path section with Eq.(2), which is expatiated in Section 2.3. Thirdly, on the foundation of the traffic impedance of each path section, we can calculate the respective forecast traffic volume of every path s

28、ection with improved LOGIT model (Eq.(4) in Section 2.4, which is the key point of MPCC.Fourthly, through the calculation process above, we can get the chosen probability and forecast traffic volume of each path section, but it is not the end. We must recalculate the traffic impedance again in the n

29、ew traffic volume situation. As is shown in Fig.1, because of the consideration of the relationship between traffic impedance and traffic load, the traffic impedance and forecast assignment traffic volume of every path will be continually amended. Using the relationship model between average speed a

30、nd traffic volume, we can calculate the travel time and the traffic impedance of certain path sect-ion under different traffic volume situation. For the roads with different technical levels, the relationship models between average speeds to traffic volume are as follows: 1) Highway: (5)2) Level 1 R

31、oads: (6)3) Level 2 Roads: (7)4) Level 3 Roads: (8)5) Level 4 Roads: (9)Where V is the average speed of the path section; is the traffic volume of the path section. At the end, we can repeat assigning traffic volume of path sections with the method in previous step, which is the idea of iterative ca

32、pacity constraint assignment, until the traffic volume of every path section is stable.译文智能交通交通量分配预测模型介绍随着城市的可持续化发展、数字化城市的建设以及交通运输业的发展，智能交通系统（ITS）的发展越来越快。ITS的主要功能之一就是改善运输环境，缓和交通阻塞。为了达到这个目的，其中最有效的方法就是运用GIS功能中的路径分析法和相关的相数学分析法预测出交通网络的交通量以及重要的交通节点，这将是一个更好的交通网络规划。交通分配预测是交通量预测的一个重要阶段。它将把预测流量分配到每一个交通部门的道路上。如果某些道路交通量太大，会带来交通堵塞。规划者必须考虑修建新道路或者改善现有道路以缓和交通堵塞的状况。本研究试图提出一个改进过的交通分配预测模型，MPCC。这个模型是在分析现有的典型的交通分配预测模型优缺点的基础上提出的，并在实践中测试了改进模型的有效性。1经典模型分析1.1快捷交通分配快捷交通分配是一种静态交通分配方法。在这个方法中，车辆出行的交通负荷的影响是