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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:∑---=n j i

i i F F p 1)exp()

exp(θθ (1)

Where i p is the probability of the path section i; i F 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 i p with different θ (from 0 to 1), then find the θ

which makes i p the most proximate to the actual i p .

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. a a a a F L T C γβα++= (2)

Where a C is the traffic impedance of the path section a; a T is the forecast travel

time of the path section a; a L is the travel length of the path section a; a F is 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.

2.4 Chosen probability in MPCC

Actually, 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. According to the random utility theory of economics, if traffic impedance is considered as the negative

utility, the chosen probability rs p of origination-destination

points couple (r, s) should follow LOGIT model:∑---=n j j

rs rs bC bC p 1)exp()

exp( (3) where rs p is the chosen probability of the path

section (r, s);rs C is the traffic impedance of the path sect-ion (r, s); j C 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

impedance 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 choose 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:

∑---=n j OD j OD rs

rs C bC C bC p 1

)exp()exp(，∑-=n j j OD C n C 11（4） Where OD C is the average of the traffic impedance of all the as-signed paths; b

which is 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 Flow of MPCC

MPCC model combines the idea of multi-ways probability assignment and

Fig.1 Flowchart of MPCC 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 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 section 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 new 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 and

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: 1082.049.179A

N V = (5) 2) Level 1 Roads: 11433.084.155A

N V = (6) 3) Level 2 Roads: 66.091.057.112A

N V = (7) 4) Level 3 Roads: 3.132.01.99A

N V = (8) 5) Level 4 Roads: 0988.05.70A N V =

(9)

Where V is the average speed of the path section;

N is the traffic volume of the

A

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 capacity 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不考虑的，并且交通阻抗（行程时间）是一个常数。每一对从始发站到终点站间的交通量将会被平均地分配到始发站和终点站的快捷通道上，而此时其它道路上的交通量则为零。这种分配方法的优点是计算简单，而其明显的缺点是交通量的不平均分配。使用这种分配方法将会导致所有的交通分配量都集中在快捷通道上，而这显然是不现实的。然而，快捷交通分配法是所有其他交通分配方法的基础。

1.2多途径概率分配

事实上，旅行者总是想选择一条捷径到达目的地，这就是所谓的捷径因素。然而，由于交通网络的复杂性，所选择的道路不一定是一条捷径，这就是随机因素。虽然每个旅行者希望选择一条捷径，但是有些选择的路其实不是捷径。道路越短，被选择的可能性就越大；道路越长，被选择的可能性越小。因此，多途径概率分配模型是由LOGIT模型所指

导的：∑---=n j i

i i F F p 1)exp()

exp(θθ (1)

其中，i p 是i 段道路被选择的可能性，i F 是i 段道路的行程时间；θ是运输决策参数，它是由以下因数计算而来：首先，用不同的θ值（从0到1）计算i p ，然后找出使i p 最接近真实i p 的θ值。

在多途径概率分配中考虑捷径因素和随机因素会使分配结果更加合理。但是，在这个方法中没有考虑交通阻抗和交通负荷的关系以及道路通行能力，从而导致了分配结果是在拥挤的交通网络不精确。我们试图在MPCC 模型中能过整合各种要素来提高精确性。 2多途径概率和容量约束模型

2.1合理路径集合

为了使改进过的模型在应用中更加合理，人们提出了合理路径集合的概念。合理路径集合是MPCC 模型的基础，它约束了计算范围。合理路径集合指的是起点和终点之间路径的集合，其根据以下规则由内部结点定义：开始结点和下一个结点的距离不能短于当前结点和起点间的距离；在同一时间，下一结点和终点间的距离不长于当前结点和终点间的距离。多路径概率分配模型将只用于在合理路径集合中分配预测交通量。这大大提高了该模型的适用性。

2.2模型假设

交通阻抗不是一个常数。它取决于车辆的特点和当前的交通状况。

旅客所估计的交通阻抗是随机的并且是不精确的。

旅行者从各自的合理路径集合中选取道路。

基于以上假设在一对始发站这一部分中，我们可以用MPCC 模型来分配交通量。

2.3道路交通阻抗的计算

事实上，不同旅客对对道路交通阻抗有着不同的理解。但一般来说，交通损耗一般由预计行程时间、行程长度以及预计交通支出构成，这就是交通阻抗。等式2表明了这种关系。a a a a F L T C γβα++= （2）

a C 是a 道路的交通阻抗；a T 是a 道路的预计行程时间；a L 是a 道路的行程长度；a F 是

a 道路上的预计交通支出；α，β，γ是影响交通阻抗的这三个要素的权重。对一个确定的道路来说，对不同的车辆α，β，γ取不同的值。我们可以从道路上不同车辆的统计百分数上得到α，β，γ的平均值。

2.4MPCC 中概率的选择

事实上，旅客总是遵循最佳路径（广义上的快捷路径），但由于随机因素的影响旅客只能选择他们自己所估计的交通阻抗最小的路径。这就是MPCC 的关键。根据经济学的随机效应理论，如果交通阻抗被认为是负效用，在一对起点到终点的选择概率rs p 应该遵循LOGIT 模型：∑---=n j j

rs rs bC bC p 1)exp()

exp( （3）rs p 是路径（r ,s ）部分的选择概率；rs C 是路径 （r ,s ）

部分的交通阻抗；j C 是所预测的交通部分中每个路径部分的交通阻抗；b 反应了旅客对道路交通阻抗的认识力，它们具有反向相关性。如果b → ∞，旅客对交通阻抗的理解误差趋近于0。在这种情况下，所有的旅客会选择交通阻抗最小的路径，这就相当于捷径交通分配法的结果。反之，如果b → 0，旅客的理解误差趋近无穷大。在这种情况下，旅客选择的路径是分散的。有人反对等式（3）中的b 是一个尺寸。因为b 的偏差应该提前就知道，但b 的值是难以确定的。下面是改进过的等式（3）：

∑---=n j OD j OD rs

rs C bC C bC p 1

)exp()exp( ∑-=n j j OD C n C 11 （4） OD C 是所有预测路径交通阻抗的平均值；b 不是一个尺寸，只是表示合理路径集合的关系，而不是交通阻抗。根据实际观察，b 的范围是一个经验值，一般在3.00~4.00之间。对于较为拥挤的城市中心道路，b 一般在3.00~3.50之间。

2.4 MPCC 流

MPCC 模型相结合的多概率分配方式和迭代容量限制交通分配的思想。

第一步，我们可以得到的道路网络的几何信息，并且从相关的数据得到道路OD 的交通量。然后，我们2.1中介绍的方法确定合理路径集。

第二步，我们可以用等式（2）计算每条道路的交通阻抗，方法已经在2.3部分中作了详细的说明。

第三步，在每条道路交通阻抗的基础上，我们可以用2.4部分中提到的改进过的LOGIT 模型（等式（4））计算出每条道路的预测交通量，这是MPCC 中的关键。

第四步，通过上面的计算过程，我们可以得到每条道路的选择概率和预测交通量，但

这并不是最后。我们必须在新的交通量情况下重新计算交通阻抗。如图1所示，由于交通阻抗和交通负荷之间的关系，交通阻抗和每条道路上的预测交通分配量将不断地得到改善。运用平均速度和流量的关系模型，我们可以计算出行程时间和在不同交通量下的确定道路的交通阻抗。对于具有不同技术水平的道路，从交通量到平均速度的关系模型如下：

1）公路：1082.049.179A N V =

（5）

2）1级路：11433.084.155A

N V = （6） 3）2级路：66.091.057.112A

N V = （7） 4）3级路：3.132.01.99A

N V = （8） 5）4级路：0988.05.70A N V =

（9） V 是道路上速度的平均值；A N 是道路流

量。

最后，我们可以用前面的方法重复地在各

道路上分配交通量，这就是容量的迭代约束分

配理念。直到每条道路上的流量都是稳定的。