MODELLING CORRIDOR FREIGHT TRANSPORT FOR

M ODELLING C ORRIDOR F REIGHT T RANSPORT FOR D EMONSTRATION OF S EAMLESS I NTERNATIONAL R AIL F REIGHT S ERVICES

RESEARCH REPORT MTI-EC-REORIENT-WP4

Maryland Transportation Initiative University of Maryland

July 2007

M ODELLING C ORRIDOR F REIGHT T RANSPORT FOR D EMONSTRATION OF S EAMLESS I NTERNATIONAL R AIL F REIGHT S ERVICES

University of Maryland University of Bologna

Study Supervisors Study Supervisors

Elise Miller-Hooks Alberto Caprara Hani S. Mahmassani

Silvano Martello Paolo Toth

Graduate Research Assistants Graduate Research Assistants and Post-Docs

and Post-Docs

Vishnu Charan Arcot Claudia D’Ambrosio Jing Dong

Enrico Malaguti Aaron Kozuki April Kuo Chung-Cheng Lu Rahul Nair

RESEARCH REPORT MTI-EC-REORIENT-WP4

Maryland Transportation Initiative University of Maryland

June 2007

Technical Report Documentation Page

1. Report No.

2. Government Accession

3. Recipient's Catalog No.

MTI-EC-REORIENT-WP4

No.

4. Title and Subtitle 5. Report Date

Modelling Corridor Freight Transport for Demonstration of Seamless

June 2007

International Rail Freight Services

6. Performing Organization Code

7. Author/s 8. Performing Organization Vishnu C. Arcot, Alberto Caprara, Claudia D'Ambrosio, Jing Dong, Aaron

Report No. Kozuki, April Kuo, Cheng-Chung Lu, Hani S. Mahmassani, Enrico Malaguti, MTI-EC-REORIENT-WP4 Silvano Martello, Elise Miller-Hooks, Rahul Nair, Paolo Toth, Kuilin Zhang

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) Maryland Transportation Initiative University of Maryland

11. Contract or Grant No. 1173 Glenn Martin Hall

College Park, MD 20742-3021 12. Sponsoring Organization Name and Address 13. Type of Report and Period European Commission Sixth Framework Programme

Covered Directorate-General for Energy and Transport Directorate E

14. Sponsoring Agency Code DM 28 00/100

B-1049 Brussels 15. Supplementary Notes 16. Abstract

This document describes a suite of network modeling tools developed for use in the analysis and evaluation of various intermodal rail freight services contemplated in the REORIENT corridor. These tools were devised and applied to assess the performance of the overall system under different service design options and operational scenarios, including barrier mitigation or removal strategies, and to support the development of the REORIENT business cases for intermodal rail-based service in the selected corridor. The strategies analyzed include implementation of interoperability directives, barrier-removing or barrier- reducing improvements in physical, operational or managerial aspects/business practices of the rail system, as well as other policy measures and potential inducements aimed towards achieving EC policy objectives.

17. Key Words 18. Distribution Statement: No restrictions Intermodal rail services; Freight Transport; Logistics Strategies; European Community; International rail services; Freight demand forecasting; Rail service design and scheduling; Network assignment.

19. Security Classification (of this report)

20. Security Classification

21. No. Of Pages 22. Price

(of this page)

REORIENT WP 4

Project acronym: REORIENT Project full title:

REORIENT, “Implementing Change in the European Railway System” Project number:

Contract number: 513567 To:

REORIENT Contractors:

E DEM

ISD Ingeniería de Sistemas para la Defensa de España, S.A.

Demis B.V. NL DLR

D TOI

Deutschen Zentrum für Luft-um-Raumfahrt e.V.

Institute of Transport Economics N NU

Napier University UK UOB

I UMD

University of Bologna

University of Maryland USA

Preface

REORIENT is a Concerted Action funded by the European Commission within the Sixth Framework Programme that addresses Strategic Objective 3.3.1 “Research to Support the European Transport Policy, Research Domain 3.1, Implementation of Change in the European Railway Area". The REORIENT project is examining the effects of the EU’s legislation on rail interoperability, which is transforming the European rail freight industry from closed, monopolistic, nationally-oriented businesses insulated from market realities such that new players and newcomers both from the rail and logistics industry can find new opportunities, and from nationally-fragmented railway subsystems into an internationally integrated and interoperable pan-European system.

From a research perspective, these massive changes pose a host of challenges in monitoring and understanding how common legislation is transposed under diverse national political and economic conditions, industry changes, and social support and opposition to the changes. From a global perspective, these changes are taking place in the midst of a serious transformation of the transport industry as a whole, and where old solutions rapidly are becoming obsolete.

The project is focusing on a trans-European transport corridor through eleven countries (called the REORIENT Corridor) stretching from Scandinavia in the north to Greece in the south, and is working toward three main objectives:

1. Assessing and monitoring the progress toward the development of an integrated freight railway system in the countries located along the REORIENT Corridor, explaining the variation in the status of interoperability across these countries, assessing the degree of political and social support for improving interoperability in these countries, identifying barriers to seamless rail freight transport through these countries, and recommending ways to overcome the barriers.

2. Identifying and assessing the market potential for new international rail freight transport services through these countries.

3. Evaluating the relevant internal and external effects that will result from implementing the new services, including the effects on rail companies and shippers, and the effects that bear on the whole society and the environment.

As shown in the figure below, the technical part of the project is divided into eight work packages, which are grouped into three sets, roughly corresponding to the three main objectives specified above (although much of the work in Work Package 5 is related to the first main objective). This report documents the work performed in Work Package 4 (WP4) – an assessment of the progress in achieving interoperability across the countries in the REORIENT Corridor, which addresses the first of REORIENT’s main objectives.

Some of the results from the WP4 analysis have been fed into the analyses being carried out in WP5, which is examining barriers to achieving seamless international rail freight transport and identifying ways to reduce or circumvent the most important barriers.

Assessment of Status Assessment of Status

Data collection Data collection

REORIENT Business Case REORIENT Business Case

WP1 WP1 Evaluation Evaluation

WP7 WP7 WP2 WP2

WP6 WP6

Policy Policy interoperability status interoperability status

Assessment of Assessment of

WP4 WP4

WP5 WP5

assessment assessment WP3 WP3

Strategies for Strategies for

Corridor Corridor

Barrier Barrier

matching matching

Social support for Social support for

analyses analyses

analyses analyses

supply with supply with

interoperability interoperability

demand demand

WP 8 WP 8 REORIENT Knowledge Base REORIENT Knowledge Base

This report has been prepared by the REORIENT Consortium, which consists of seven partners and sixteen subcontractors, representing research institutes, private companies, rail organizations, and universities in fourteen European countries and the United States. The work documented in the present report was jointly performed by the University of Maryland and the University of Bologna.

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Executive Summary

Analysis of the complex interactions over space and time associated with the movement of freight between origins and destinations over intermodal networks with rail service as the backbone entails use of sophisticated network modeling methodologies. This document describes a suite of network modeling tools developed for use in the analysis and evaluation of various intermodal rail freight services contemplated in the REORIENT corridor. These tools were devised and applied to assess the performance of the overall system under different service design options and operational scenarios, including barrier mitigation or removal strategies, and to support the development of the REORIENT business cases for intermodal rail-based service in the selected corridor. This report provides an overview of the methodological structure and major components of the network analysis tools that provide the capability to evaluate performance measures, costs and benefits derived from implementation of interoperability directives, barrier-removing or barrier-reducing improvements in physical, operational or managerial aspects/business practices of the rail system, as well as other policy measures and potential inducements aimed towards achieving EC policy objectives.

The modeling approach adopted for the REORIENT network modeling platform integrates a mode choice modeling process within a network flow assignment framework. At the core of the network modeling platform is the ability to evaluate the modal shares for freight flowing between given origins and destinations in the REORIENT corridor network region and determine the corresponding temporal and spatial patterns of these flows on the

III III

A route design model specifies detailed routing of trains within the corridor to serve a set of demands, given the travel times and delays. The route design problem is formulated as an integer linear program and an approximate procedure consisting of column generation and fixing techniques is proposed for its solution. Schedules for the service routes are determined using scheduling algorithms, which also use the link and node performance characteristics from the simulation. The scheduling problem is addressed by a Train Slot Generation Model. The Train Slot Generation Model is composed of a binary multicommodity network flow problem formulation that relies on a train slot representation of the track capacity within a time-space network representation and a column generation technique that, by exploiting certain properties of the problem, can quickly generate near optimal solutions. A train slot adjustment method is employed to adjust the train slots to accommodate shipments identified at pick-up and drop-off terminals. Resulting services can be evaluated in terms of market share response and performance characteristics using the simulation-assignment framework. Because the resulting demand and travel times may be different than the ones used in designing the routes and schedules, the process, including optimal route and schedule decisions, is iterated until mutual consistency is achieved between the procedures.

The network modeling framework and associated network modeling tools are employed within the REORIENT Business Case Support System that supports business decisions for new business entrepreneurs considering investment in the REORIENT or similar intermodal freight transport networks.

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List of Tables

Table 2-1. Summary of differences between ETIS and REORIENT demand. ....................... 17 Table 2-2. NST/R Chapters commodity classification. ........................................................... 22 Table 2-3. Percentage growth in tonnes transported between origin and destination regions,

2001-2006, all goods types, all transport modes. .............................................. 24 Table 2-4. Distribution of values by NST/R chapters. ............................................................. 28 Table 4-1. γ values for each NTS/R good category .............................................................. 103

Table 4-2. Parameters for the computation of the functions describing the sensitivity to delay

of different goods categories ........................................................................... 104

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Chapter 1 Introduction to the REORIENT Network

Modeling Framework

1.1 Introduction

Analysis of the complex interactions over space and time associated with the movement of freight between origins and destinations over intermodal networks with rail service as the backbone entails use of sophisticated network modeling methodologies. The goal of this report is to describe the principal network modeling tools used in the analysis and evaluation of various intermodal rail freight services contemplated in the selected REORIENT corridor. These tools were devised and applied to assess the performance of the overall system under different service design options and operational scenarios, including barrier mitigation or removal strategies. This chapter provides an introduction to the role of the network modeling activities conducted to support the development of the REORIENT business cases for intermodal rail-based service in the selected corridor, and provides an overview of the methodological structure and major components of the network analysis tools used in the analysis.

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In the next section, we articulate the purpose of analytical activities that require a network model representation of the underlying freight movement processes; this provides the basis to define the functional requirements for the general network modeling framework and associated tools. We then define the principal elements of the general framework, including representation of the demand-side activities as well as supply-side considerations that are essential to the questions of interest to the REORIENT project. This includes specification of the types of nodes and node activities that must be represented, the types of link activities and associated attributes, the flows, and level of disaggregation. Third, a general intermodal network representation is sketched out. In section 1.4, elements of the network modelling framework are customized to the specific analysis needs of REORIENT. In section 1.5, we highlight a space-time representation of rail activities oriented towards optimization of operational activities, and slot allocation and scheduling as part of a collaborative decision-making framework envisioned for an evolved fully-interoperable REORIENT corridor. Finally, section 1.6 presents the overall structure of the network model system and introduces the remaining chapters of this report.

1.2 Purpose and Role of Network Models in REORIENT

The representation of services offered and freight goods moved by rail and associated modes from origin to destination requires a model of the underlying network structure and processes. In the REORIENT technical annex, activities associated with Work Packages 4, 5,

6 and 7 all require the capability to evaluate performance measures, costs and benefits derived from implementation of interoperability directives, barrier-removing or barrier- reducing improvements in physical, operational or managerial aspects/business practices of

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the rail system, as well as other policy measures and potential inducements aimed towards achieving EC policy objectives.

Prediction of impacts of alternative actions and scenarios should consider the following actors: (1) shippers, (2) service providers, (3) owners and other industry players, and (4) society at large. Spatially, the impact of alternative actions and scenarios should be disaggregated to the region and country levels. Impact information should also have some level of specificity with regard to major commodity groups, especially as pertains the business development objectives of the program.

The network modeling and analysis capabilities undertaken under the REORIENT project have the following objectives:

1. Support business case development (for rail and related intermodal service) by predicting the profit and cost implications of particular service supply strategies under varying regulatory and interoperability scenarios (in turn corresponding to different levels of implementation of EC directives and related interventions).

2. Support EC policy evaluation: evaluate costs, benefits, service levels and other performance measures (especially pertaining to the social and environmental objectives) of changes in the characteristics of the rail network— physical, operational, and managerial, that might take place in terms of the links and nodes of the system.

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3. Support interoperability assessment and deployment, e.g. prioritization of specific actions and measures aimed towards interoperability, by evaluating the costs and benefits of such actions.

4. Translate changes in operating practices, e.g. in scheduling, into operational attributes of links and nodes in the network representation, to enable prediction of anticipated impacts on service levels, system performance and rail competitiveness in attracting shipper demand.

5. Support development of collaborative decision-making framework and protocols for optimal utilization of existing and anticipated infrastructure under varying degrees of interoperability and access management rules, to maximize the ability of enterprises to provide competitive services in the REORIENT corridor.

The above objectives entail different types of network modeling capabilities and place different requirements on the level of spatial and temporal detail in the representation of the rail network, other modes, interfaces and processes. They also entail different algorithmic requirements. Objectives 1 through 3 could be accomplished only to a limited extent with a planning-level static model of the system of interest (discussed further in the next two sections), for shipper mode choice analysis and assignment of freight demand to the intermodal network—focusing on the rail network components and their intermodal interfaces. Given knowledge of zone-to-zone demand for freight transport within the corridor and a set of service routes with corresponding train frequency (i.e. how many trains should be operated along this route) obtained externally, a scheduling algorithm is employed to create

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the train schedule (also referred to as the timetable) for multiple service routes with single track or double one-way track linking several major terminals. However, given the importance of service design issues, which vary with time of day, as well as various operational characteristics of service provision (such as delays at intermodal terminals, border crossings, and priority rules for track utilization by competing services), it was determined that a time-varying analysis methodology is required for the REORIENT corridor scenarios.

Objectives 4 and 5 require a time-varying representation of the system of interest, focusing in this case almost exclusively on the rail network itself, with some coordination with port operational considerations at selected nodes. For objective 4, scheduling models can provide the core analysis capability; specifically, the ability to produce optimal utilization of existing assets and determine the associated train-carrying and freight-carrying capacity of individual network components is called for. Furthermore, it is necessary to determine congestion locations and associated costs to the extent that such bottlenecks might be present under both current and future scenarios. Supporting the objectives of interest also calls for the ability to then integrate the results of the more detailed optimization models of specific rail activity into the broader network evaluation framework developed for the overall analysis. Objective 5 requires an analysis approach in which slots for utilization of specific segments of infrastructure over specific time intervals are defined; these become the basic objects of analysis, and procedures for managing access to these slots will then need to be determined and evaluated as part of the business case assessment.

The next two sections discuss the overall network modeling framework, which provides a common core representation that supports all five of the above objectives, while

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section 1.5 sketches out elements of the time-space models required for more detailed operational analysis.

1.3 General Intermodal Network Framework

The network of interest is represented as a general network G(N, A), where N denotes the set of nodes N and A the set of arcs. However, the intermodal nature of the system under consideration means that we envision an overall network that consists of as many sub- networks as there are modes of interest (in REORIENT, in addition to rail, we envision the need to represent road traffic for truck access to rail, and port/marine links between essential access points on the rail network). To facilitate the representation, it is convenient to think of each mode as existing in a different plane, with transfers between modes taking place through arcs connecting designated nodes in different planes, as shown in Figure 1-1. Transfer arcs are represented as vertical arcs in Figure 1-1.

With each modal arc (on a given plane) are associated various attributes that reflect the supply-side of the system, namely how much service is offered and at what service levels, such as (1) distance, (2) speed, (3) travel time, (4) capacity, (5) frequency of service (for rail and other scheduled mode links), and (6) type of service, cost (incl. taxes and fees). For the REORIENT project, these characteristics were established primarily from secondary data sources gathered under Work Package 1, as well as from local subcontractors where necessary/applicable.

With each transfer arc (between planes) are associated attributes that reflect capacity, costs, available storage, and so on. Intermodal transfers are allowed on a subset of the nodes.

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Figure 1-1. Multimodal network representation. Each plane corresponds to a mode; vertical arcs represent allowable transfers between corresponding modes at the indicated intermodal transfer nodes.

In general, attributes that reflect operational characteristics of the service provided along the arc will vary by time of day, and possibly by time of week, and season. Assuming that time is discretized into intervals over which service attributes remain constant, we will have time-indexed attributes of the networks of interest.

Demand Nodes: Demand is generated in economic regions, represented in the network model as nodes, corresponding to their respective centroids. The demand information was compiled primarily through the effort of Work Package 1 (the adopted level of spatial detail for demand information in this project is at the NUTS-2 level --Nomenclature of Territorial Units for Statistics). We refer to these as demand-generating (or attracting) nodes. In addition to centroids of major economic regions, demand-generating nodes include major

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intermodal hubs such as ports, and possibly major industrial complexes, as well as special generators that serve as aggregators of demand generated beyond the area in the immediate geographic scope of the analysis (equivalent to “external” zones in conventional planning practice).

Problem addressed: Given the above multi-modal network with known service supply attributes, and the total base year and forecast year(s) O-D (origin-destination) demands for the network of interest, disaggregated by commodity class, the network model is called upon to support objectives 1 through 3 by determining (1) the rail share (including rail-dominant intermodal movement) for the various commodity classes in the O-D markets of interest, (2) the resulting flows on the rail network components (for the various time intervals of interest), (3) the associated service levels experienced by system users, and (4) the array of performance measures, costs, benefits, etc. mentioned previously.

Algorithms and analysis procedures: In addition to a mode split model, which was developed as part of Work Package 6, the primary algorithmic components required for the above network analysis consists of a network assignment capability (which in the general case will

be a dynamic intermodal assignment process), whereby O-D demands are assigned to intermodal paths and corresponding elements of the intermodal networks of interest to go from their respective origins to respective destinations. A key methodological component in this process consists of an algorithm to compute dynamic intermodal paths between origins and destinations of demand. Note that intermodal paths must be calculated not only for flow assignment purposes, but also in order to correctly apply the mode choice modeling

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procedures, as level of service information is required for each modal and intermodal choice alternative included in the analysis.

The modeling approach adopted for the REORIENT network modeling platform integrates the mode choice modeling process within the network flow assignment framework.

A mode choice model was developed at the firm (shipper or consignee) level, and is therefore sensitive to the characteristics of both the shipment/commodity and the usual transportation service level attributes. To apply it for forecasting the impacts of the policies of interest, a procedure was developed to aggregate firm level forecasts to the population level (this is known in the discrete choice modeling literature as the aggregation problem). The most commonly used approach for this purpose is microsimulation of shipper decisions, whereby a sample of actual or hypothetical shippers is generated and the model is applied to the members of this sample. The aggregate results serve as estimators for the underlying unknown population choices. For the REORIENT network modeling platform, a microsimulation approach to the demand side decisions was integrated in the flow assignment process.

1.4 Special Representation Adapted to REORIENT Scope

The network modeling platform used in the REORIENT project analyses supports the general multi-level network representation presented in the previous section. However, given the strategic and operational nature of the questions addressed through the analysis, and the heavy resource requirements for input data and calibration, not all modes were represented to the same level of spatial and operational detail. Judicious decisions were made at the network representation level, resulting in a single integrated network model in which:

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1 the rail network is represented in as much detail as feasible given the state of the data collection process;

2 all relevant intermodal access nodes are included in the representation, with detailed attributes, providing all necessary connections to the major modal

alternatives that shippers might use to access the rail network, and that a service provider might coordinate offerings with or target for special services;

3 major non-rail access links to the rail network are included, especially to the extent that their relative desirability may increase or decrease as a result of

policy intervention, private investment, or increased use contributing to congestion; and

4 competing modes, especially highway trucking, are represented through abstract arcs defined between origin and destination nodes, without the need to

include the full extent of the network topology and services offered on that network. The attributes of the competing mode arcs were obtained through a pre-analysis, as these are not assumed to change through the intervention scenarios under investigation in the selected REORIENT corridor.

The proposed representation allows computation of the desired quantities and performance measures necessary to support the business case, interoperability policy, and operational scenario analyses. It could also allow consideration of scenarios in which competing mode networks might be altered by capturing the net effect of such changes through the specified attributes of the virtual O to D arcs. With its focus on intermodality revolving around the rail system, the representation meets the objectives of the REORIENT

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corridor project. This intermodal “light” network model offers considerable advantage in terms of computer memory requirements and execution performance, enabling more responsive analysis of the various scenarios.

1.5 Dynamic Service Network Representation for Operational Interoperability Analysis

The above network modeling framework is intended to support the service and operational scenario analysis needs of the REORIENT project, as discussed previously. In addition, it enables a finer level of modeling of rail operational processes that interfaces with optimization tools for the design and operation of rail services (routes and schedules). Such tools are required for (1) the identification and mitigation of operational bottlenecks to interoperability and to new competitive rail freight services; and (2) the identification, development and evaluation of new business concepts for the seamless operation of the rail system in conjunction with its feeder modes. The time dimension is essential to both of these aspects, which require a time-space representation of rail service.

Specifically, the key object in this analysis is a time-space slot, namely a period of time over which a particular physical portion of the rail network is occupied by a particular service (train). Slots are inherently finite. Optimal use of critical slots directly determines the service capacity that can be delivered under a given scenario. Operational bottlenecks may result from and translate into inefficient use of slots. Rescheduling of services to obtain better use of slots has been an important driver of Operational Research application and success in the rail industry. Harmonized operation of the REORIENT rail corridor will by necessity entail coordinated schedule adjustments across both existing operators and future entrants.

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Furthermore, any business case proposition will entail the ability to identify, create and evaluate feasible paths through the network of time-space slots. Finally, the development of significant new business models entails the development of operational management procedures to ensure safe and efficient access to the desired portions of the rail network. Collaborative decision-making schemes proposed for this purpose are rooted in the ability to rationally and fairly manage existing slots. As such, the space-time representation will support several key questions of critical interest to the project.

The modeling framework developed for the project enables a close and internally consistent linkage between the detailed operational network used at the level of the scheduling and collaborative management algorithms on one hand, and the overall network scenario analysis platform on the other (with the assignment-simulation capability at its core). Several attributes of the main network links depend on the specific operational procedures followed in that part of the network, and the services designed and scheduled to operate on them. The next section provides an overview of the various model components and procedures used in the overall network modeling methodology for the design and evaluation of services and operational strategies in the selected REORIENT corridor.

1.6 Overall Structure of Network Modeling Platform

As noted in the previous sections, at the core of the network modeling platform is the ability to evaluate the modal shares for freight flowing between given origins and destinations in the REORIENT corridor network region (described in detail in the next chapter), and determine the corresponding temporal and spatial patterns of these flows on the

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rail network and associated intermodal service connections. The mode choice is integrated with path assignment in the intermodal dynamic simulation-assignment platform, shown in Figure 1-2. For a given specification of services and operational strategies, this tool provides detailed information on flows by mode and service between the various origins and destinations in the study area. The platform also provides the associated performance measures, in terms of travel times on links and delays at intermodal terminals, classification yards and other nodes.

This information forms the basis for the design of intermodal rail services in the selected REORIENT corridor. Specifically, a route design model specifies detailed routing of trains within the corridor to serve a set of demands, given the travel times and delays. Schedules for the service routes are then determined using scheduling algorithms, which also use the link and node performance characteristics from the simulation. The resulting services can then be evaluated in terms of market share response and performance characteristics using the simulation-assignment framework. These are specified as a possible scenario. Because the resulting demand and travel times may be different than the ones used in designing the routes and schedules, the process is normally iterated until mutual consistency is achieved between the procedures, as shown in Figure 1-2.

A fourth box in that figure separates out the processes followed to operate the rail system when different services may be competing for space-time slots. Collaborative decision-making strategies can be assessed through these procedures to manage collaboratively competing demands for the use of the infrastructure. These demands result

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from the operation of designed services on the network, and as such require interaction between the scheduling algorithms and the simulation component of the platform.

Demand

Network Services

Mode and Path Choice Route

Model Assignment

Simulation

CDM Operational

Intermodal Path Computation

Modal/Market Shares, by Service Travel Times, Terminal Delays

Figure 1-2. Overall Network Modeling Structure. The network modeling platform integrates mode- choice in a simulation-assignment framework, which provides demand and performance information to the Route Design and Scheduling algorithms. The collaborative decision making procedures perform slot management functions, which interact with the scheduled services and affect the operation of the services on the existing infrastructure.

The next chapter describes the multimodal network used in the REORIENT project service and operational strategy scenario analysis. Chapter 3 describes the simulation-

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assignment platform, which incorporates the mode choice component. The following chapter describes the route design model, which is based on an integer programming formulation over a sub-network of the entire study area. Chapter 5 presents the optimization procedures followed to develop schedules for services in the selected REORIENT corridor. The final chapter presents illustrative results and concluding comments.

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Chapter 2 The REORIENT Network

2.1 Introduction

The network modeling platform and associated solution algorithms exploit a network representation of the REORIENT corridor. In this chapter, details of this network representation in terms of both supply characteristics and demand for its services are provided. The network topology, freight demand estimates by commodity, and short discussion of the network attributes are presented in section 2.2, followed by a description of new services and proposed infrastructure improvement scenarios in section 2.3.

2.2 Demand

Based on the European Transport policy Information System (ETIS, 2006) database (developed as part of the Fifth Framework Programme for the European Commission), approximately 3.2 million shipments used the European freight system in 2000 in the REORIENT corridor area every week. The ETIS database provides matrices of intra-zonal freight flows in annual tons for the years: 2000 and 2020. A portion of both 2000 and 2020 matrices are used in the evaluation of the REORIENT corridor. The network modeling

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platform discussed in Chapter 1 and described in detail in Chapter 3 simulates freight movement at the shipment level for a sub-region of the geographical region covered by the ETIS database. Table 2-1 provides an overview of the extraction and transformation of ETIS freight demand matrices into the REORIENT demand data. The remainder of this section details the extraction process and assumptions made in deriving the set of shipments by product categories.

Table 2-1. Summary of differences between ETIS and REORIENT demand.

Base year

Forecast year

2020 REORIENT study area

Geographic Extent

Global

(see below) Zone Definition

Mostly NUTS 2

Mostly NUTS 2

Transport Modes

-rail

-Truck-only

-inland navigation -sea

-Rail-based Intermodal

-others (pipelines etc.)

Freight Flow Units

TEU, Rail Wagons Temporal Horizon

11 NST/R Chapter

11 NST/R chapter groups Classification

groups

6 Manifestations

-Crude Oil -Dry bulk

2 transport unit types Transport Unit Categories -General Cargo

-Unitized

-Liquid Bulk

2.2.1 Extent

The ETIS database documents global freight flows between 293 zones. The database is most detailed within the European Union (EU), where zones are defined by the Nomenclature of Territorial Units for Statistics (NUTS) level 2 for European countries. This

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zoning scheme roughly corresponds to existing administrative boundaries with 800000 to 3 million inhabitants. For areas outside the EU, zones are coarse and are aggregated as shown in Figure 2-1.

Figure 2-1. ETIS zones outside the European Union.

For the purposes of this study, only freight movements that impact the proposed services within the study area must be considered. These relevant flows are extracted from the ETIS database based on a coarse assignment procedure described in detail in the subsection 2.2.3.

The REORIENT study area is comprised of 117 zones that follow the same NUTS 2 zoning scheme as the ETIS database. Romania, Serbia, and Macedonia are represented as single zones. The NUTS classification used for Romania may require updating. No NUTS classification exists for Serbia and Macedonia. The REORIENT study area extends from Scandinavia in the North to Greece in the South and encompasses significant portions of

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Germany that have competitive truck and rail routes. The REORIENT study area is shown in Figure 2-2.

Figure 2-2. The REORIENT study area.

2.2.2 Modes

The ETIS freight flow matrix considers five modes of transport for each of the transport legs (outbound, transshipment, and inbound). The focus of the REORIENT study is to examine rail-based intermodal transport and its competitive mode, truck transport. If flows between two zones have a rail-based leg, these flows are classified as rail-based intermodal. If all three transport legs use only road, these flows are classified as truck-only.

Inland waterways were not considered in the network model, though they do provide

a competitive alternative in certain areas of the network.

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2.2.3 Demand extraction

A coarse network representation of the global freight network is used to perform an assignment to evaluate the flows that move through the study area. The ETIS zones are classified into study area zones and external zones. The study area is further partitioned into (a) Internal zones and (b) Peripheral Zones. Flows originating outside the study area must traverse through at least one peripheral zone before entering the study area. All zones on the boundary of the study area are demarcated as peripheral. In addition, flows from outside the study area can reach the study area through ports. Zones with ports are also classified as peripheral. With this scheme, the assignment procedure, based on shortest distance and time, classifies flows for all ETIS demand into four categories: (a) Internal-to-Internal, (b) External-to-Internal, (c) Internal-to-External, (d) External-to-External.

Internal-to-Internal flows are retained as is. For Internal-to-External and External-to- Internal flows, flows are assumed to terminate at the peripheral zone through which flows exit or enter the study area. This is shown graphically in Figure 2-3. External-to-External flows that do not pass through the study area are not considered. For External-to-External flows that pass through the study area, both origin and destination zones are assumed to be the peripheral zone through which flows enter and exit the study area.

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Figure 2-3. Flow types modeled in the REORIENT corridor as compared to ETIS flows. Zones are classified as Internal (green), Peripheral (blue) and External.

2.2.4 Classification schemes

Freight transport models present the added challenge of heterogeneity. Different commodities essentially ‘behave’ in different ways. Typically, the problem of diversity is addressed by the use of broad classes of goods that are similar. Certain categories of goods require special handling or different delivery routines. Perishable items, for example, require speedy and refrigerated transit. The goods categorization can be detailed with many goods classes or can be broad. A detailed classification makes analysis more complex due to the sheer number of classes. On the other hand, a broad categorization leads to a loss of fidelity

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of the model. An intermediate classification mechanism is employed, as described in the next subsection.

2.2.4.1 NST/R Chapters

This study uses the Standard Goods Classification for Transport Statistics, NST/R chapters (Nomenclature Uniforme des Marchandises pour les Statistiques de Transport) with

10 commodity groupings plus crude oil. Table 2-2 summarizes the commodity classification used.

This classification is consistent with the ETIS base. Characteristics of each commodity group are discussed in the next section.

Table 2-2. NST/R Chapters commodity classification.

Code Description

0 Agricultural products and live animals

1 Foodstuffs and animal fodder

2 Solid mineral fuels

3 Petroleum products

4 Ores and metal waste

5 Metal products Crude and manufactured minerals, building

6 materials

7 Fertilizers

8 Chemicals Machinery,

transport

equipment,

9 manufactured articles and miscellaneous articles

10 Crude oil

2.2.4.2 Unitized vs. Non-Unitized

Handling of freight moving through a freight transport system varies considerably depending on the transport unit used by a shipment. Shipments can be containerized, carried

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in swap-bodies, transported as bulk, etc. Operations at key facilities, such as terminals, are fundamentally different for two broad categories. We assume that containers, swap-bodies, semi-trailers fall under the “Unitized” category. Though there are differences in operation of each transport unit (swap-bodies cannot be stacked, for example), for the purposes of a regional freight network model, these details are ignored. The other category considered is “Non-Unitized,” which includes dry bulk (e.g. coal and other mineral fuels), liquid bulk (e.g. petroleum products) and commodities typically transported in wagon loads (e.g. cereals).

2.2.5 Assumptions

2.2.5.1 Growth Rates

The ETIS data exists for years 2000 and 2020; however, international transport demand has substantially changed between 2000 and 2006. To obtain more relevant 2006 demand estimates, growth rates were linearly extrapolated to estimate freight flow between zones in 2006. The growth rate employed for this extrapolation was developed based on the SCENES model predictions for year 2006 (with baseline SCENES scenario 1995-2025) obtained by the PolCorridor LOGCHAIN Project (2006). Growth by geographical location was analysed by first dividing the countries into four sub groups:

1. North: Finland, Norway, Sweden

2. North-East: Belarus, Estonia, Latvia, Lithuania, Russia

3. South: Albania, Bosnia-Herzegovina, Bulgaria, Greece, Italy, Yugoslavia, Croatia, Macedonia, Moldova, Romania, Slovak Republic, Slovenia, Turkey, Ukraine, Hungary

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4. PolCorridor host countries (P-C host): Poland, Czech Republic, Austria.

Table 2-3 shows the expected growth in tonnage transported between these regions over the five-year period of 2001-2006 as obtained from the SCENES model.

Table 2-3. Percentage growth in tonnes transported between origin and destination regions, 2001-2006, all goods types, all transport modes.

To

North North-East South P-C host

P-C host

2.2.5.2 Temporal Patterns

In addition, seasonal trends were studied from available EUROSTAT data for EU-15 (i.e. 15 of the EU nations) only. Graphs provided in Figures 2-4 and 2-5 summarize distribution of flow over the year. It was assumed in this analysis that these trends extend to the remaining EU nations. Results of the analysis suggest that no clear seasonal pattern exists. Therefore, we assume for planning purposes that demand is uniform throughout the year.

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