Accounting for Transport and Logistics Hubs in Freight Demand

Models

Tatiana Tuneva and Yana Yaroslavtseva

Ural State University of Railway Transport, Ekaterinburg, Russia

Keywords: Cargo transportation, transport process modelling, transport and logistics hubs, demand, model.

Abstract: The importance of logistics and its impact on transport processes were considered when modeling the demand

for freight transport using new models that include several aspects. However, transport logistics hubs without

a storage function, which are mainly used for cargo transshipment, are not sufficiently accounted for in most

cargo models, although they are of great importance for cargo transportation. Currently, it is not clear which

models actually take into account transport and logistics hubs and how they do it. The purpose of this article

is to review the accounting of transport and logistics hubs in models of demand for cargo transportation.

1 INTRODUCTION

Until a few years ago, logistics elements were

considered incomplete or not considered at all in most

national models. This made it difficult to accurately

map freight transport and logistics as a factor

affecting imports. The following will provide an

overview of developments in freight transport

modeling related to logistics integration. It will

provide a general basis for the subsequent

presentation of various models that consider logistics

and transport logistics hubs in particular.

2 MATERIALS AND METHODS

Early attempts to integrate logistics aspects into

models can be found in the field of disaggregated

modeling, both related to method selection and

logistics selection.

There are various models that take logistics into

account. Although transport demand modeling in

relation to logistics issues has been greatly developed

in recent years, only a few models are currently used

that specifically include logistics aspects (Golubchik,

2020). Some examples can be found in the British

EUNET, in Dutch SMILE or in SLAM implemented

in SCENES the European model. A striking example

in this area is the national transport model system

implemented in Sweden and Norway (SAMGODS

and NEMO) (Mirotin, 2019).

Model applied to a region of Sweden named

SAMGODS, is a model of national resolution and

macroscopic scale analysis. From a certain point of

view, it can be considered as a mixed model, if we

talk about its depth of aggregation. The model is

based on several submodels that take into account the

development of the economy and trade, from which it

deduces the flow generation. The model takes into

account 35 product groups and offers 86 predefined

transport chains for transport processes through a

multimodal network. Permission for cargo size,

suitable routes and means of transportation is made

using the logistics module (Ushko, 2019).

The logistics module consists of three stages and

follows the "aggregated-disaggregated-aggregated"

structure. Flows of goods between places of

production and consumption are primarily provided

at the aggregate level. In order to assign them to

individual firms, they are disaggregated.

Consequently, firm’s logistics decisions (e.g.,

shipment size, use of collection and distribution

centers) can be modeled in this disaggregated part of

the model.

By selecting one of the predefined transport

chains, the logistics module sets the modes of

transport for each section and determines how

transportation is carried out (directly or through the

use of logistics nodes). The model also includes

transport and logistics hubs defined as cargo

transshipment and possible storage locations

(Gubanova, 2009). The logistics module consists of

subprograms that gradually develop solutions.

Tuneva, T. and Yaroslavtseva, Y.

Accounting for Transport and Logistics Hubs in Freight Demand Models.

DOI: 10.5220/0011576600003527

In Proceedings of the 1st International Scientiﬁc and Practical Conference on Transport: Logistics, Construction, Maintenance, Management (TLC2M 2022), pages 16-21

ISBN: 978-989-758-606-4

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Therefore, the available transport chains, including

optimal transfer points between transport sections, are

initially generated in the first subroutine. This is

followed by the second one, which selects transport

chains based on minimizing overall logistics costs,

shown in Figure 1.

NEMO is the national model used in Norway. Due

to its evolution (parallel toSAMGODS), NEMO

views logistics centers in a similar way to

SAMGODS. Thus, the model is an extension of the

Swedish model to the spatial domain of Norway (de

Jong G, 2007).

The Dutch SMILE model, which predicts traffic

flows at the national level, was one of the first models

to take logistics aspects into account.

SMILE models traffic flows by taking into

account economic events and linking the economy,

logistics, and transportation.

Figure 2 shows the combination of logistics

nodes, which in this case are represented by

distribution centers, becomes noticeable when

considering the characteristics of the nodes, as well as

the attributes of goods and their requirements in terms

of inventory, processing and transportation (Gruzdev,

2021).

ure 1: Inte

ration of lo

istics hubs in SAMGODS.

Figure 2: Node in SMILE.

Accounting for Transport and Logistics Hubs in Freight Demand Models

SLAM is integrated into the European SCENES

model. The main ideas and experience of the Dutch

SMILE model were used in the development SMILE

process. SLAM is designed to assess the impact of

changes in logistics and transport systems across

Europe. Therefore, one of the main applications is the

discovery and location of distribution centers in

Europe. The SLAM model takes into account

changes in distribution structures (for example, the

number and location of intermediate warehouses used

for distribution) and includes them in distribution

flows (Avramchikova, 2019).

SLAM obtains production and consumption flows

that integrate alternative distribution chains. In this

context, the distribution chain is defined as a set of

distribution centers and transport links for trade flows

between the producing region and the consumer.

Thus, the main function of the model is to consider

alternative distribution chains (production –

distribution center – consumption). The model is

shown in Figure 3 (Musatov, 2019).

SLAM achieves a more accurate picture of traffic

flows by integrating distribution logistics nodes into

the transport system. SLAM does not go into details

about networks, since flows are strictly economically

rational and pass through an abstract distribution-

consumption network in the most cost-effective way

(de Jong G, 2007).

EUNET is a regional model developed in the UK.

It covers freight traffic in the central part of the UK,

as well as imports and exports from and to the region.

The purpose of the model is to predict the demand for

cargo transportation depending on economic

operations and cargo logistics (Rustamov, 2021).

Thus, there is a set of distribution channels

through a set of possible nodes, shown in Figure 4.

The LAMTA freight forecasting model is a

multimodal transportation demand model shown in

Figure 5.

While the model primarily focuses on road freight

transport (cargo transportation), it also includes a

multi-modal framework to support cargo

transportation solutions and logistics hubs.

GoodTrip – is an urban freight transport model

used in the Netherlands. It is based on consumer

demand: it builds logistics chains, linking the

activities of consumers, supermarkets, hypermarkets,

distribution centers and manufacturers.

The four-component model (spatial organization

of activities, cargo flows, transport, and

infrastructure) calculates the volume of goods by

product group for each spatially defined zone. Thus,

the flow of goods between consumers and producers

is determined. Within this definition, product flows

are affected by both the spatial distribution of

activities, and the market share of each group of

activities (consumers). After that, the product groups

are classified and a matrix is created. At the final

stage, vehicle trips are generated and assigned to the

network (de Jong G, 2007).

Although the model takes into account logistics

aspects, transport logistics hubs are not fully covered

(loading facilities or similar objects). Approaches to

the concept of urban distribution centers can be

considered in various scenarios. One example here is

the scenarios for urban logistics distribution centers

shown in Figure 6.

Figure 3: Node in SLAM.

ure 4: Nodes in EUNET.

TLC2M 2022 - INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE TLC2M TRANSPORT: LOGISTICS,

CONSTRUCTION, MAINTENANCE, MANAGEMENT

However, the influence of logistics hubs in

general and transport logistics hubs in particular on

transport within the study area is ignored from a

large-scale point of view. To summarize the

understanding of various models, it can be stated that

there are various ways to integrate transport and

logistics nodes into modeling.

A fairly simple method is to integrate nodes as

sources and sinks in models. Thus, logistics nodes are

considered in a simple way as so-called special

generators or singular flow generators, the volume of

transport of which is supplied from outside.

Integration of transport logistics nodes using

logistics modules that choose between several

predefined transport chains is a more complex

method. The properties of shipments (for example,

goods, shipment size) have a decisive influence on the

consideration of nodes within logistics modules. Due

to the peculiarities of the nodes, there are often

restrictions in the handling of certain goods. If the

nodes are not suitable for handling certain loads, the

probability of transportation through these nodes will

be reduced. The result of this consideration are

various transport chains processed only through

certain nodes. Thus, the characteristics of nodes in

combination with the characteristics of transportation

determine the use and impact of nodes or transport

demand, respectively. Another aspect of the

integration of logistics nodes into the simulation is the

inclusion of additional characteristics of nodes that

exceed the characteristics (the ability to handle

certain loads). For example, integrating information

about differences in the technologies used in nodes

can be very useful if the technologies used differ

significantly. This method makes it possible to

examine the hubs in more detail if they differ in their

characteristics.

At the same time, it is necessary to understand

what data could help to achieve adequate integration

of transport logistics hubs in the modeling of

transport demand. Such data should contain

information about the characteristics of the node (for

example, total area, capacity, branches of customers),

as well as information about the transport process

Figure 6: Nodes in GoodTrip.

ure 5: Nodes in LAMTA.

Accounting for Transport and Logistics Hubs in Freight Demand Models

itself (for example, vehicles, transport facilities,

volume of traffic). This additional information will

improve the integration of transport and logistics hubs

(Table 1). The data is presented in table 1.

The use of new data containing the above

attributes will help to deduce key parameters in order

to identify correlations and dependencies between

certain node characteristics and the specific role of

nodes in the transport process.

3 RESULTS

The article examines the integration of transport and

logistics hubs in the model of demand for freight

transportation. It is revealed that the growing

relevance of logistics in freight transportation in

recent decades is taken into account in demand

modeling due to the greater integration of logistics

aspects into some models. This development was

important because models are relevant tools for

forecasting transport demand and supporting decision

makers through the evaluation of transport policy

measures. However, the analysis showed that,

although the demand models for freight transport

have undergone significant improvements in recent

decades, important logistical aspects have not yet

been sufficiently taken into account.

4 CONCLUSIONS

Within the framework of cargo transportation

processes, transport and logistics hubs have recently

become increasingly important empirically. This

implies a greater relevance of their consideration in

demand modeling. Nevertheless, the conducted

review showed that the accounting of transport and

logistics hubs when modeling the demand for freight

transportation differs significantly. Most of the

existing freight transport models do not integrate

transport and logistics hubs. Application models in

many cases focus on distribution logistics hubs, but

only a few integrate transport logistics hubs to some

extent.

Thus, the integration of transport and logistics

hubs differs in many ways. Firstly, not all types are

considered models. Secondly, the consideration

differs not only quantitatively, but also qualitatively.

There are also certain restrictions that prevent

node integration. The potential of many models is

limited due to the availability of data. More detailed

data is needed for node integration. The lack of

adequate and detailed data remains a serious problem.

To solve these problems and achieve greater

realism, transport logistics hubs should be

highlighted in the modelling of demand for freight

transportation, as well as in empiricism.

REFERENCES

Golubchik, A. M., 2020. Freight forwarding business:

creation, formation, management. TransLit.

Mirotin, L. B., Gudkov, V. A., Zyryanov, Z. Z., 2019.

Cargo flow management in transport and logistics

systems.

Ushko, A. U., 2019. Innovations of the economy of the

Russian Federation: urban agglomerations, territorial

production clusters, tourist and recreational zones and

transport and logistics hubs.

Gubanova, E. S., Selyakova, S. A., 2009. Questions urban

agglomeration development. Economic and social

changes in the region: facts, trends, forecast.

Gruzdev, A. I., 2021. Multimodal transportation and

transport hubs. History and prospects of transport

development in the North of Russia.

Avramchikova, N. T., Rozhnov, I. P., Zakharova, L. N.,

2019. Multimodal transport hubs: advantages and

prospects of development. Management of Social

economic systems.

Musatov, D. V., Kubareva, O. A., 2019. Transport hubs as

the basis of the country's transport and logistics

infrastructure. Education, science and technology:

problems and prospects: collection of scientific tr.

based on the materials of the II International Scientific

and Practical Conference.

Table 1: Opportunities for improving the integration of transport and logistics hubs.

ortant attributes that characterize trans

ort and lo

istics hubs

Basic node data

(

. area

)

Vehicles

Total area Transport facilities

Processing area Infrastructure connection

Bandwidth Customer branches

Number of tri

s Loadin

units and loadin

and unloadin

facilities

Number of vehicles Volume of traffic

Trans

ortation distance T

e of business

Network structure Empty trips and capacity utilization

TLC2M 2022 - INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE TLC2M TRANSPORT: LOGISTICS,

CONSTRUCTION, MAINTENANCE, MANAGEMENT

Rustamov, A. F., 2021. Transport hubs: development

prospects and digitalization. Transport: Science,

Education, Production.

de Jong, G., Ben-Akiva, M., 2007, Microsimulation model

of cargo size and transport chain selection.

Accounting for Transport and Logistics Hubs in Freight Demand Models