THE OPTIMIZATION OF ROAD NETWORK IN LOGISTICS

HUB BASED ON LOW-CARBON ASPECT

Yisong Li and Mengna Lai

Beijing Jiaotong University, Haidian District, Beijing, China

Keywords: Low carbon, Logistics Hub, Road network.

Abstract: Carbon emission is becoming a more and more popular issue recently. It is important to design a reasonable

road network within logistics hub to reduce the whole carbon emissions. Previous studies have targeted as

maximum economics benefits or least time consumed, which ignored the environmental effects. This paper

uses the model of minimum cost maximum flow to optimize the road network, in the model, “cost” is

defined as carbon emissions, so we can finally achieve the goal, then classify the type of road according to

the capacity, the decreasing order is trunk road, secondary road and slip road. The above classification can

help with arranging transportation and guiding the traffic flow. The paper lists the specific optimal plan to

the logistics hub in Luan County. However the factors concluded in the model is not adequate, we should

consider the factor of road length, and it is also a factor affected carbon emission.

1 INTRODUCTION

With the development of the logistics industry in

China, the domestic Logistics Hus are expanded, the

number is increasing to about 600 recently.

According to related data, the CO

emission during

transportation is accounted for 40% of the whole

logistics process. Apparently the main reason which

leads into carbon pollution in Logistics Hub is

transportation. Some studies show that CO

emissions is related to traffic volume, road

congestion, and therefore a reasonable layout of

Logistics Hub are helpful to reduce carbon

emissions, such as controlling the traffic volume in

trunk road and improving the road condition.

The design of the Logistics Hub network include

choosing the form of road network, planning red

line(the direction and location of trunk road and

secondary roads and the functional division of

them),green belts and road node planning. Previous

studies are aimed at the minimum of the total cost of

the transportation or the shortest transit time, but

environmental issues is not taking into

consideration, for instance, total carbon emissions.

This paper is to improve road network then increase

the efficiency of trunk road and realize the low

carbon transportation in the Logistics Hub, and

finally reduce total carbon emissions.

The procedure of road network in Logistics Hub

are as follows: 1, Forecasting the main logistics

volume and the road capacity, then translate the

logistic capacity into traffic volume. 2, Select the

entrance of the Logistics Hub from the railway

entrance, highway entrance, link entrance and other

locations around the Logistics Hub which

accumulate cargo flow. Then optimize road network

within them. 3, Using EcoTransIT tool to calculate

the volume of carbon emissions of each unit distance

and weight, with the minimum cost maximum flow

model to optimize the road network, the specific

process are as follows:

Figure 1: The steps of optimizing road network.

2 THE LOGISTICS HUB

NETWORK OPTIMIZATION

FACTOR ANALYSIS AND

MODEL SELECTION

Logistics Hub road network is composed of different

666

Li Y. and Lai M..

THE OPTIMIZATION OF ROAD NETWORK IN LOGISTICS HUB BASED ON LOW-CARBON ASPECT.

DOI: 10.5220/0003594406660670

In Proceedings of the 13th International Conference on Enterprise Information Systems (MMLM-2011), pages 666-670

ISBN: 978-989-8425-56-0

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

levels of city roads and railways. The road network

planning should determine the form of road network;

then determine the nature of roads, width, the form

of road cross section, the location of intersection and

the parking lots and the receipt of road network

maps. The current form of the existing road system

can be summarized into four main types: grid-type,

ring radial, freestyle, and hybrid. Then use model to

plan direction and capacity of roads which make the

overall road network release less emissions.

Optimization methods of road network are

consisted of four-stage method, the total control

method and graph theory method. They are mostly

targeted as maximum economic benefits and least

time consumed, they distributes the predicted traffic

flow to the road after considering these factors,

which are the layout of road network, traffic flow in

the direction and distribution of goods. Further,

according to the amount of traffic flow to determine

technical level, the direction of the selected roads,

extension angle and optimal points to the line.

However, the above methodologies ignore the

environmental effects. Carbon emissions are related

to traffic volume and road resistance coefficient,

while considering carbon emission, there are

four-stage method and graph theory method can be

selected.

Four-stage method is based on investigation, and

then predicts the future distribution at the base of

traffic condition. The basic steps are traffic

generation, traffic distribution, traffic model

selected, traffic assignment. We can gain some

parameters about cargo flow through investigation.

But it is difficult to implement, it will cost a lot of

manpower, material and financial resources, so the

minimum cost maximum flow is practical to

optimize the road network. The so-called minimum

cost maximum flow problem is to find a maximum

flow f; simultaneously the total cost is minimum.

Studying this problem is trying to find out: In order

to achieve minimum cost, how to choose the path

and assign traffic flow from A to B. The meaning of

cost can be defined as carbon emissions in this

model.

3 THE ESTABLISHMENT OF

THE OPTIMIZE MODEL OF

ROAD NETWORK IN

LOGISTICS HUB

3.1 The Establishment Mind

While the initial road network planning is finished,

then we can use EcoTransIT tool to obtain carbon

emissions’ weight, as we get the parameters about

the minimum cost maximum flow, the road network

can be optimized. Network planning is to reach the

minimum carbon emissions based on meeting

logistics requirements and rational distribution

network. At the condition of knowing the total

demand in a cycle, the permitted maximum capacity

of each road, and carbon emissions of unit weight on

different road type, we can determine which can be

used as the trunk roads, secondary roads and slip

roads for two random entrances of Logistics Hub.

3.2 Mathematical Model

If f whose flow is v(f) is the minimum cost flow of

all feasible flow, and u is the smallest-cost

augmented chain of all the augmented chain

contained f, then adjust f along u, we will get f’,

which is the minimum cost flow of all the feasible

flow. Thus, when f’ is the maximum flow, it is what

required minimum cost maximum flow.

As b



≥0, so f=0 must be the minimum cost

flow whose flow is 0. This always starts from f=0.

Generally, it also sets f is the minimum cost flow,

and its flow is v (f), the remaining problem is how to

find the augmented chain of minimum cost about f.

So we can construct a weighted diagraph w(f), its

vertices are vertices of the original network D, and

change arc included in D into two arcs in opposite

directions (vi,vj) and (vj,vi). We define the

weights of arcs in the w (f) as:

Wij=







<



+∞,



=



(1)



=

−b







+∞,





(2)

So seek the minimum cost flow in network D is

equivalent to find the shortest paths in the weighted

diagraph. Therefore, the following algorithm is:

Firstly, it can setf

(

)

=0, if at the (k-1) step we

get the minimum cost flowf(k − 1), then construct a

THE OPTIMIZATION OF ROAD NETWORK IN LOGISTICS HUB BASED ON LOW-CARBON ASPECT

667

weighted diagraph W(f(k − 1), and seek the shortest

paths in W(f(k − 1)). If the shortest paths are not

existed, and then f(k − 1) is the minimum cost

maximum flow; if existed, we can get corresponding

augmented chain u, and adjust f(k − 1) on u, the

adjusted volume is:

() ()

= min min min

ij ij ij

cf f

+−

−−

⎧⎫

⎛⎞⎛⎞

⎪⎪

θ−⏐

⎨⎬

⎜⎟⎜⎟

⎪⎪

⎝⎠⎝⎠

⎩⎭



(



)













(



)

v



v



 ∈u





(



)

−

v



v



 ∈u



(



)

v



v



 ∉ u

(3)

We get new feasible flow, and then repeat the

above steps.

Then use EcoTransIT (Ecological Transport

Information) tools to predict the relevant index, and

calculate carbon emission of the unit mile and

weight in different types of road. The tool is

developed by Heidelberg’s energy and

environmental agencies and Cink Company, if we

input related data in the software, we can obtain the

amount of total energy consumption which include

production and transportation, and gas emissions

including CO

, NO

and SO

NMHC, and PM10

and so on.

The tool mentioned the road resistance

coefficient of different road levels; it can be

recognized as the carbon emissions standards.

Table 1: Related index about model.

The trunk

road

Secondary

road

Slip road Rail

way

road

resistance

coefficient

3 4 5 2

Traffic

volume per

day

6000~10000 4500~8000 2500~5500 10000

Unit carbon

emission

index

3 4 5 2

Capacity

index

11 5 2 25

Therefore, the specific model is as follows:

Object: making the maximum road network flow

and minimum carbon emissions on transportation.

Constraints:

The capacity of each arc is greater than the total

amount of logistics.

The traffic volume on each arc is less than the

road capacity.

Related variables:

Total logistics cycle Q,

Capacity and flow of each road are C



and f



Carbon emissions of unit mile and weight are b



The functions are:

MinZ=bij ∗ ij

(4)

Constraints： (1)

∑

ij≥Q;

(2) ij≤Cij .

Figure 2: The model of minimum cost maximum flow.

2 CASE STUDY

We take example about Luan county’s Logistics

Hub planning, then show the procedure of

optimizing road network. Luan County is 260 km

west from Beijing, 84 km east from Qinhuangdao,

adjacent to northeast old industrial base in the north,

access to Cao Feidian National industrial Park. From

the point of location, Luan city is in the development

area of Beijing, Tianjin, it has close relationship

with the main cities, and also inseparable from

Tangshan industrial equipment manufacturing base,

it is the only way which connected Northeast

Economic Zone and North Economic Zone. It is

Datong-Qinhuangdao railway that makes it through

the western region, Luan county becomes an

important transit node.

Luan county has a superior transportation, there

are totally about 6 railways travel through it, and

locates about 11 rail way stations .102 State Road,

State Road 205, Provincial Road S 252 and

Beijing-Shenyang Expressway runs through the

county (Figure 3). Luan county connects with

Tianjin port and Qinhuangdao port and Jing Tang

port and other port by railway or highways, so

shipping is very convenient. While choosing Luan

County as a logistics transit point, it can display the

advantage of transportation, reduce regional logistics

costs and improve the efficiency of regional logistics

and promote regional development of related

industries.

ICEIS 2011 - 13th International Conference on Enterprise Information Systems

668

Figure 3: Traffic map in Luan County.

Figure 4: Initial road network in Logistics Hub.

According to the traffic condition of Luan

County, We set four logistics nodes. They are node 1,

node 2, node 3 and 4 from top to bottom and left to

right. Generally, the nodes locate in the point of

large amount of traffic flow, so the nodes are in the

junction of railway and state road or the junction of

state road and provincial road.

Node 1 and Node 4 are regarded as the entrance;

node 2 and node 3 are regarded as exports. We set

node 1 and node 2 as OD points, there are following

path, the weights and capacity index are given in

table 1. The initial paths are:

Figure 5: The initial paths.

The flow after adjusted are:

Figure 6: The traffic flow in different kind of road after

adjusted.

As the figure shows, the path V

—>V

has the largest amount of traffic flow,

so we set it as the trunk road.

The path V

—>V

has the

traffic volume. So it can be used as secondary roads.

The smallest traffic volume is V

—>V

, so it is the slip road.

Then we can choose every two nodes in the

Logistics Hub to plan the grade of road, finally

optimize the whole network, the planning chart is:

Figure 7: Schematic diagram of optimized network.

The two thickest arrows are represented the trunk

road, the three dotted arrows are represented

secondary road; the thin arrow in the middle are

represented slip road. And the affiliated hollow

arrows are the direction of them.

5 CONCLUSIONS

With the intensification of the greenhouse effect,

carbon dioxide emissions reduction is increasingly

G102

G205

S252

京沈高速公路

滦县

THE OPTIMIZATION OF ROAD NETWORK IN LOGISTICS HUB BASED ON LOW-CARBON ASPECT

669

becoming the focus of activities related to

warehouse, transportation, distribution, information

process and other process of logistics. Each process

will have carbon emissions, but transportation is

main factor, so the most important thing is how to

optimize the road network to achieve minimum

carbon emissions, this paper use minimum cost

maximum flow model to optimize the road network,

then define road grade and guide traffic. This paper

takes Luan County for example, using simulated

data which is deduced by EcoTransIT tool to

optimize the existing network, in order to make the

Logistics Hub’s carbon emissions reduced. But the

model is just take the factor of carbon emissions and

traffic volume into consideration, carbon emission is

also related with the length of road, how to combine

them is to be solved.

REFERENCES

Mo Yi Kui, Yan Kefei, Chengfeng. The study of

optimization urban mixed network based on genetic

algorithm [J]. Computer Engineering and Applications,

2007, 43(14):240~243.

XuDunchu. City road and traffic planning [M]. Beijing:

China Building Industry Press, 2007.

Fan Decheng, WangTingjian. The analysis of current

situation and problems in construction of Logistics

Hub [J]. Technology of Logistics, 2004, 28(113).

Lianye, ChenDapeng, Wangzhiyun. The road network

planning of circular economy industrial park based on

logistics optimization [J]. Chemical Industry, 2009,

(11).

Li Hongbing, Sun Zhiyuan, GeXijun. Traffic impact

analysis of urban Logistics Hub based on S-Paramics

micro-simulation technology [J]. Technology of

logistics, 2009, 28(4).

Gaofei, Zhangbaoyu, Luoligao.The application of DEA in

the development of road network [J]. Shanxi

Architecture, 2010, 36(13).

Liu Shiduo, Wu Qunqi. The availably study of road

network which based on the transport demand [J].

Learned journal of Chang’an University, 2010, 12(1).

Dai Yue, Lu jian.The study of prediction of traffic flow in

Logistics Hub [J]. Shanxi Architecture, 2008, 4(36).

ICEIS 2011 - 13th International Conference on Enterprise Information Systems

670