毕业设计(论文)外文资
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外文原文
Traffic Assignment Forecast Model Research in ITS
Introduction
The intelligent transportation system (ITS) develops rapidly along with the city sustainable development, the digital city construction 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 GIS 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 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 advantages and disadvantages of classic traffic assignment forecast models, and test the validity of the improved model in practice.
1 Analysis of classic models
1.1 Shortcut traffic assignment
Shortcut traffic assignment is a static traffic assignment method. In this method, the traffic load impact in the vehicles’ 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
assignment 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 the basis of all the other traffic assignment methods. 1.2 Multi-ways probability assignment
In 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 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. Therefore, the multi-ways probability assignment model is guided by the LOGIT model:pi?exp(??Fi)?exp(??F)ij?1n (1)
Where pi is the probability of the path section i; Fi is the travel time of the path section i; θ is the transport decision parameter, which is calculated by the follow principle: firstly, calculate the pi with different θ (from 0 to 1), then find the θ which makes pi the most proximate to the actualpi.
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 capacity 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 model
2.1 Rational path aggregate
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 starts 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 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 assumption
1) Traffic impedance 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 MPCC model to assign the traffic volume in the sector of origination-destination couples. 2.3 Calculation of path traffic impedance
Actually, travelers have different understanding to path traffic impedance, but generally, the travel cost, which is mainly made up of forecast travel time, travel length and forecast travel outlay, is considered the traffic impedance. Eq. (2) displays this relationship. Ca??Ta??La??Fa (2)
Where Cais the traffic impedance of the path section a; Tais the forecast travel time of the path section a; Lais the travel length of the path section a; Fais the forecast travel outlay of the path section a; α, β, γ are the weight value of that three elements which impact the traffic impedance. For a certain path section, there are different α, β and γ value for different vehicles. We can get the weighted average of α, β and γ of each path section from the statistic percent of each type of vehicle in the path section.
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