Department for Transport Contract PRO 04/03/14data.dft.gov.uk/byfm/byfm-report.pdf · 7.4 Maritime...

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Base Year Freight Matrices: Final Report Contract PPRO 04/03/14 Department for Transport

Contract PRO 04/03/14

July 2011

QM

Issue/revision Issue 1 Revision 1 Revision 2 Revision 3 Revision 4 Revision 5

Remarks Incomplete

Draft

Full Draft Revised Revised Revised Revised

Date 19/10/09 23/11/09 August 2010 Nov 2010 April 2011 July 2011

Prepared by Gordon Deane,

Diana Kabeizi,

Ian Williams,

Yan Zhu

Gordon

Deane, Ian

Williams

John Pharoah,

Ian Williams,

Gordon Deane

Ian Williams Gordon

Deane

Ian Williams

Signature Gordon

Deane

Gordon

Deane, Ian

Williams

Ian

Williams

Gordon

Deane

Ian

Williams

Checked by Gordon Deane Ian Williams Gordon

Deane,

Gordon

Deane,

Ian Williams Kaveh

Jahanshahi

Signature Gordon

Deane

Ian

Williams

Gordon

Deane

Gordon

Deane

Ian

Williams

Kaveh

Jahanshahi

Authorised by Ian Williams Ian Williams Ian Williams Ian Williams Ian Williams Ian Williams

Signature Ian Williams Ian

Williams

Ian Williams Ian

Williams

Ian

Williams

Ian

Williams

Project number 1164 - 1226 1164 –

1226

1164 – 1226

File reference Final BYFM

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Final BYFM

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BYFM

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Final BYFM

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Final BYFM

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Final BYFM

report

WSP Development and Transportation 66-68 Hills Road CambridgeCB2 1LA

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Contents

Executive Summary 1

1 Introduction 9

1.1 The objectives of the study 9

1.2 Structure of the Report 9

Part 1 Model Structure and Design 12

2 Introduction to Freight Modelling 13

2.2 Background studies 13

2.3 Freight and logistics concepts and terminology 13

3 The Spatial Input-Output and Economic Model 16

3.1 Introduction to the SIO model 16

3.2 Making effective use of the SIO model 17

4 BYFM Model Structure 19

4.1 Why it needs a model to create the matrices 19

4.2 Model overview 20

4.3 Freight generation and attraction 21

4.4 The spatial pattern of distribution legs 23

4.5 Port choice 26

4.6 Main mode split 26

4.7 Conversion from units of value to tonnes 27

4.8 Road vehicle type choice 28

4.9 Matching to observed matrix data 29

4.10 Path-building / assignment 30

4.11 Synthesis and summary of the model structure 30

5 Segmentation of Products and Flow Types 33

Part 2 Data Inputs to the Model 37

6 Zoning System 38

7 Networks and Supply Characteristics 44

7.1 Introduction 44

7.2 Road network 44

7.3 Rail network 47

Base Year Freight Matrices: Final Report 3

7.4 Maritime and air networks 50

7.5 Definition of vehicle types 51

7.6 Operating costs and tariffs 52

8 Methodology: Zonal Freight Generation and Attraction 60

8.2 Approaches to assembling consistent input data 60

8.3 Bottom up / direct estimation of zonal volumes 61

8.4 Top down estimation of zonal volumes 61

9 Data Sources: Freight Movements and Logistics 63

9.1 Introduction 63

9.2 Goods movements on HGVs 63

9.3 Goods movements in vans 66

9.4 Network based traffic statistics 67

9.5 Goods movements on rail 68

9.6 Port and air movements 69

9.7 Trade statistics 70

9.8 Distribution legs 71

10 LGV Data and Modelling 74

10.1 Introduction 74

10.2 The scale and trends of LGV movements of goods 75

10.3 Modelling LGV movements of goods 78

Part 3 Model Implementation, Calibration and Validation 79

11 Implementation and Calibration of Freight Demand 80


11.2 Consistency in aggregate demand totals 81

11.3 Implementation and calibration methodology 85

11.4 Matching port statistics by commodity type 87

11.5 LGV implementation 88

11.6 Road / rail: main mode and trip length calibration 89

11.7 Volume-to-value ratio and handling factor calibration 92

11.8 Results and validation 96

11.9 Production of O-D and P-C commodity matrices 103

12 Calibration of Vehicle Size and Vehicle-km 107

12.1 Overview 107

12.2 Vehicle size calibration 107

12.3 Loading factors 108

12.4 Generation of empty vehicle movements 110

12.5 Results for vehicle size 110

13 Network Assignment and Validation 117


13.2 Screenlines 117

13.3 Vehicle-km by road type 119

14 Model Implementation and Software Details 123

Part 4 Conclusions and Recommendations 125

15 Findings and Recommendations 126


15.2 Data collection 126

15.3 Use of link count data to improve road matrices 128

15.4 Applicability of Results 131

15.5 Providing forecasting capability 131

16 Conclusions 134

17 References 137

18 Key Technical Terms and Definitions 141

Appendices [in a separate document]

A Data Sources: Zonal Freight Generation and Attraction

B Processing VOA Data

C Data Sources: Freight Movements and Port Statistics

D The Road Network

E The Rail Network

F The Maritime Network and Air Network

G Vehicle Operating Costs and Rail Costs

H Volume to Value Ratios, Handling and Loading Factors

I Mathematical Structure of the SIO Model

J Input/Output Model Implementation Details

K Logistics Chains and Factor/Trade Codes

L Additional Validation Tables and Model Results

M Glossary and Abbreviations

List of Figures Figure 2.1: One P-C movement and its associated two O-D legs .............. 14 Figure 3.1: The I-O relationships between industries and to households . 17 Figure 4.1: Producers, consumers and logistic suppliers of freight:

summary ..................................................................................................... 21 Figure 4.2: The production-consumption, distribution chain and

distribution channels for raw materials and parts used in production 23 Figure 4.3: Model components and data flows of the BYFM model .......... 32 Figure 6.1: Area based zones plus point zones for the largest ports,

airports........................................................................................................ 39 Figure 6.2: Geographic location of point zones for distribution centres .. 40 Figure 6.4: European external zones, ports and rail terminals................... 42 Figure 7.1: Large rail freight terminals on the BYFM rail network ............. 49 Figure 9.1: GV Surveys compared to Road Traffic Estimates .................... 67 Figure 10.1: LGV trip categories Venn diagram ........................................... 75 Figure 10.2: Growth in vehicle kms (AADF) by vehicle type across all road

types in GB ................................................................................................. 77 Figure 11.1: Example of distribution legs within a distribution chain ....... 93 Figure 11.2: Tonne-km by commodity group and main mode, model

versus observed ........................................................................................ 97 Figure 11.3: Validation of total tonnes lifted by region/nation and main

mode, 2006 ............................................................................................... 101 Figure 12.1: Comparison of modelled and observed vehicle-km loaded

and empty by detailed vehicle size........................................................ 112 Figure 12.2: Road freight vehicle-km by region/nation, model versus Road

Traffic Estimate, 2006.............................................................................. 113 Figure 13.1: Vehicles both ways across screenlines, model versus

observed AADF ........................................................................................ 118 Figure 13.2: HGV Vehicle-km by road classification, model versus Road

Traffic Estimate ........................................................................................ 120

List of Tables Table 4.1: Summary of the main model components........................................ 20 Table 5.1: Product groups used in the I-O model and associated flow types by

Table 7.3: Assumed free-flow vehicle speeds (mph) used to calculate speed

Table 7.7: Vehicle sizes in the BYFM model, with COBA/WebTAG equivalents

Table 7.9: Relative fuel consumption by road type: WebTAG versus sample

distribution stage.......................................................................................... 34 Table 5.2: Flow types used in transport model and their distribution stages .... 35 Table 6.1: BYFM model zoning system for Great Britain .................................. 43 Table 7.1: Summary of road link type codes and characteristics...................... 45 Table 7.2: Official speed limits by road type for goods vehicles ....................... 46

scaling factors by road and goods vehicle type........................................... 47 Table 7.4: Rail link type codes and characteristics ........................................... 48 Table 7.5: Vehicle types used ........................................................................... 52 Table 7.6: Fuel VOC formulae parameter values (litres per km)....................... 53

and fuel efficiency ranges ............................................................................ 53 Table 7.8: Vehicle size adjustment coefficients................................................. 55

observed vehicle.......................................................................................... 57 Table 7.10: Adjustment factors ktype for vehicles over 7.5 tonnes ..................... 58

Table 7.11: Fuel costs and duty excluding VAT in 2006 ................................... 58

Table 10.2: GB road traffic: by type of vehicle and class of road: 2006 (Billion

Table 11.3: BYFM calibration target: total aggregate demand by main mode and

Table 11.6: Tonnes lifted and moved by aggregate commodity group, model

Table 11.7: Tonnes lifted and moved by commodity group and distribution

Table 11.8: Validation of total tonnes lifted by region/nation and main mode,

Table 11.9: Handling factors and tonnes lifted by groups of commodities, for

Table 11.10: Total tonnes lifted in P-C matrix, by type of commodity and P-C

Table 12.1: Lorry loading factors (average tonnes per trip) by commodity type

Table 12.2: Comparison of modelled and observed vehicle-km loaded and

Table 12.3: Road freight vehicle-km by region/nation, model versus Road Traffic

Table 13.1: Vehicles both ways across screenlines, model versus observed

Table 13.2: HGV vehicle-km by region/nation, vehicle size and road type: model

Table 15.2: Questionnaire-based data sources on goods vehicle movements

Table 18.1: Relationship between product group and flow group - mode,

Table 7.12: RPI Inflation index used to adjust currency measurements. .......... 58 Table 9.1: Commodity type classification adopted in CSRGT and SFVA......... 65 Table 9.2: Coverage of data about road freight that enters/exits ports ............. 70 Table 9.3: Data sources used to estimate distribution legs............................... 71 Table 10.1: Representation of types of trips within transport models ............... 75

vehicle kms per annum)............................................................................... 77 Table 11.1: HGV vehicle kilometre statistics and targets for 2006.................... 82 Table 11.2: HGV activity by CSRGT commodity group from CSRGT 2006

(published versus scaled to match RTE)..................................................... 84

vehicle size .................................................................................................. 84 Table 11.4: Summary of main implementation and calibration tasks................ 86 Table 11.5: Exogenous demand matrices......................................................... 95

versus observed .......................................................................................... 98

stage, model estimates, 2006.................................................................... 100

2006 ........................................................................................................... 102

endogenously modelled demand............................................................... 103

relation, 2006 ............................................................................................. 105

and distribution stage by vehicle size ........................................................ 108

empty by detailed vehicle size ................................................................... 111

Estimates, 2006 ......................................................................................... 114

AADF ......................................................................................................... 119

versus Road Traffic Estimate (RTE).......................................................... 121 Table 14.1: BYFM times for a non-equilibrium model run ............................... 124 Table 15.1: Draft classification of land-use activities for use in CSRGT......... 128

................................................................................................................... 129

commodity and distribution stage classification ........................................ 143

Executive Summary

THE OBJECTIVES OF THE STUDY

The main objective of the study is to develop and validate a set of base year freight matrices for 2006 which include the representation of logistics within freight supply chains. Matrices are created by origin-destination (O-D) zone for each of the distribution stages along the supply chain that leads from where goods are initially produced through distribution centres and warehouses to where the goods are ultimately consumed. The matrices are required in two forms: in units of tonnes segmented by commodity type and distribution leg; and in units of vehicles, segmented by main mode (road or rail) and by vehicle type within each mode.

These matrices represent all domestic freight moved within Great Britain by heavy goods vehicles (HGVs), light goods vehicles (LGVs - vans) and by rail. The matrices do not include domestic transport by water, air or pipeline. Most such movements are not in significant competition with road freight movements (especially primary petroleum and chemical products) and were not a priority for base year modelling. They also do not include LGVs used for purposes other than movement of goods.

The matrices cover all road and rail freight movements within Great Britain, whether by UK or by foreign registered vehicles, bearing in mind the rapid growth in the latter over recent years. The representation of movements to or from overseas is primarily from the point of view of ensuring that the spatial pattern of the movements within Great Britain is correct. This requires that major airfreight terminals, the Channel Tunnel, ferry and short-sea container ship movements be explicitly represented in order to take account of their impact through terminal choice on the pattern of traffic on the domestic leg of overseas movements. The creation of overseas legs of matrices of freight movements is not otherwise an aim of this study.

No single data source covers all of the component elements of these base year matrices in sufficient detail for our needs. Accordingly, a modelling methodology has been developed that enables the key information from various datasets to be extracted and merged in a consistent fashion so as to create the base matrices in a form that provides maximum detail and accuracy.

The main outputs are matrices of zone-to-zone movements of freight in the base year of 2006. These are produced in three forms:

Production-Consumption matrices of tonnes, segmented by

o commodity type o by type of producer (domestic factory/farm/quarry or import) and of

consumer (domestic factory/farm/quarry or household or export);

Origin-Destination (O-D) matrices of tonnes, segmented by

o commodity type o main mode (road/rail) o distribution leg;

O-D matrices of vehicle movements, segmented by

11641226 BYFM Final Report 1

o main mode (road/rail) o for road: segmented by vehicle size.

Although, the primary aim of this study was to create matrices for 2006, the subsidiary aim was where feasible to develop the system to produce these matrices in a form that could subsequently be used as the foundation for a flexible freight forecasting model. A London-focused variant of this model has been created for TfL which is run in forecasting mode through to 2031.

The tasks of this study are divided into two phases:

Phase 1, Scoping and Design: Specified the methods to be adopted in building and validating the base year matrices, together with the data to be used in their construction.

Phase 2, Implementation and Validation: Implement the methodology to create validated zone pair matrices covering the whole of Great Britain. These should be in a suitable form for use as an input to the creation of spatially detailed matrices in local studies.

THE MAIN DATA SOURCES

The key to producing an accurate and complete description of the base pattern of movements of goods across GB lies in making effective and consistent use of all of the main data sources available. Accordingly we have examined all of these sources in turn and then have designed methods to use these data to improve the estimated base matrices of movement.

We started by examining whether there is adequate data to estimate zonal generation and attraction rates for goods based on the employment or floorspace in the zone, in a manner analogous to the residents based trip generation rates used successfully in passenger modelling. For many of the bulk goods there are potential data sources that when combined can identify the major locations from which the goods emanate. For the consumption end of bulk movements and for higher value manufactured goods there are fewer direct data sources available, so that indirect estimates are required.

We examined data on the spatial location of the entities (firms, households, buildings, distribution centres, etc.) at which goods are picked up or delivered. In general, this data source is adequate for the purpose of providing indirect estimates of freight trip ends.

We examined the data sources for observed transport movements for each transport mode in turn (road, rail and maritime), and identified the main sources of data that can be utilised to measure the movements of goods and of vehicles between locations.

We then examined the data sources that are available to separate movements between the distribution stages of the supply chain. This is one topic for which there is a shortage of comprehensive national data, particularly for movements on HGVs.

Transport networks are used to provide the cost and travel time information needed to create the modal supply characteristics that are used as inputs to the model that generates synthetic matrices of movements by mode. The road

BYFM Final Report 2

network is based on that in use for passenger modelling in the Department's NTMv3. The EUNET2.0 Pilot Model study (WSP, 2005) freight rail network is used which has had appended to it data on the gauge of each link.

MODELLING METHODS

This merging of the many data sources is achieved through creating a freight transport model that takes the various sources of observed data as its inputs and uses them to create the required set of base matrices as its output. The MEPLAN software package, which has already been used to create a similar style of model in the EUNET2.0 study, is also used here. The tasks carried out in setting up and running the model include:

minimising the effects of sampling errors in the input data;

identifying and addressing inconsistencies between data sources used as input data;

infilling missing data observations;

increasing the dimensions of the cross-categorisation of the segmentation in the matrices, in particular to distinguish the distribution leg of the movement within the overall distribution chain.

ZONING SYSTEM

The zoning system adopted for the matrices starts at the local authority district level. This strikes a balance between providing sufficient spatial detail to enable freight transport movements to be represented realistically, but not requiring input data at a level of spatial detail that is difficult to obtain comprehensively at the national level.

The internal zoning system is based primarily on the full set of 408 local authority districts, unitary authorities and London boroughs covering all of Great Britain but excluding Northern Ireland. The number of zones is augmented in a number of places that are specific major generators or receivers of goods, in which the surrounding district-level zone is further sub-divided, so as to separate out the specific locations where these major goods movements are concentrated. These point zones comprise the following types of freight generators and attractors.

The 88 largest ports used by deep-sea, short-sea and dry bulk movements;

The 5 main freight airports, - Heathrow, Gatwick, Stansted, East Midlands and Manchester;

56 major concentrations of distribution centres (DCs) across the country, particularly those adjacent to major motorway junctions; goods are stored in these DCs in the course of the distribution chain that delivers them from producers to consumers.

The definition of the external zones is reasonably detailed within Europe which is represented by 44 zones, mainly at the level of individual countries. The rest of the world is represented using just 7 zones.


MODELLING METHODOLOGY TO ESTIMATE THE MATRICES

The main components of the model used to create the base year matrices are summarised in Table 1. The Table also lists the main output from each stage of the model. Each stage that involves choice among alternatives, as indicated in column 1, operates as some form of logit discrete choice model within a larger multi-level choice hierarchy. In cases where suitable observed data on these choices is available at an aggregate level, it is used to constrain the set of modelled choices.

Table 1: Summary of the main model components

Model stage Space Main outputs

The total volume of freight by commodity type that is produced, distributed or consumed in a zone

Uses an SIO model to build up P-C relationships through a fixed proportional split of distribution legs appropriate to each commodity type, including those legs to, from or between distribution centres, together with the spatial allocation of each leg across competing zones to generate O-D matrices of goods movements

For unitised movements between the UK and the rest of Europe, uses an SIO model to subdivide between the UK ports for imports and exports

Subdivides the transported total for each O-D movement into main modes, including their associated feeder legs to and from intermodal terminals

Convert the units of O-D movement from value (£) to volume units (tonnes)

Subdivides the total road tonnes for each O-D into road vehicle types (artic, rigids [4], LGV) Includes: conversion from volume (tonnes) to vehicles

The total modal volume between a pair of zones as estimated synthetically, is constrained to match observed CSRGT and rail statistics

Intermediate calculations Estimate the supply characteristics (cost and time) along the intermodal path between zone pairs

Vehicle operating costs both stopped and in movement - function of vehicle type, link travel speed, link length, etc.

Allocation of vehicles to links along their path - a non-equilibrium congested assignment

Generation / attraction

Distribution leg proportions / zone choice

Port choice

Mode choice

Value to volume

Vehicle type choice

Matrix matching

Path building

Cost estimation

Assignment - path choice

zone

zone pair

zone pair

zone pair

zone pair

zone pair

zone pair

zone pair

link

link

This modelling methodology follows closely the Spatial Input-Output (SIO) methodology already tested successfully within the EUNET2.0 Pilot Study. This SIO model provides the joint sub-division by commodity type, main mode and distribution leg that is required in the base matrices in order to aid in distinguishing the spatial pattern of movements by different types of road vehicles and on rail.

This modelling methodology ensures that:

4 BYFM Final Report

the known effects of freight volume under-estimation within the CSRGT are corrected;

the pattern of demand for freight movements is connected back to the spatial economy of Great Britain and to the logistical structure that determines the resulting pattern of goods movements, so that it could provide a behaviouraleconomic platform that could potentially be used to forecast the future demand for freight transport and how this could be influenced by policy measures.

GENERATION AND ATTRACTION

There are no national comprehensive, accurate data sources directly available that measure zonal freight generation and attraction tonnages or vehicle numbers. Instead, these volumes needed to be estimated indirectly using a variety of sources and methods specific to individual commodity types and distribution stage.

Two broad approaches to estimation are adopted.

Bottom up: this is the preferred approach and is used in those cases where there are data sources that can provide direct estimates of tonnes lifted or dropped at the local authority district or county level. This is the minority of cases and is mainly used to measure directly the zonal production or consumption of bulk commodities such as cement, coal, petroleum products and power stations. This production will often be concentrated within a relatively small number of locations, each with large production levels.

Top down: this is used wherever the data to implement the bottom-up approach is not available for a specific commodity type. Starting from a known national or preferably a regional or county total of tonnes produced or consumed, the subdivision of this volume between individual zones is estimated indirectly based on relevant zonal data, such as: Valuation Office Agency (VOA) data on non-domestic floorspace or rateable value; Annual Business Inquiry (ABI) data on employment numbers by industry type; and CSRGT and national rail statistics on tonnes lifted or dropped.

Within the top-down approach, in those cases where it is difficult to obtain direct regional information on the tonnages lifted and dropped, the UK Input-Output Tables is used to identify the consumption patterns of each type of commodity in value terms. This value estimate in pounds is then converted to a volume estimate measured in units of tonnes or containers based on national estimates of volume-to-value ratios. THE SPATIAL PATTERN OF DISTRIBUTION LEGS

The method adopted is to use a EUNET-type Spatial Input-Output (SIO) model to generate the P-C matrices of freight in a form that distinguishes the distribution legs1 appropriate to each commodity type. The travel costs and

1 The distribution legs denote intermediate movements between where goods are initially produced and where they are ultimately consumed, i.e. from a computer assembly factory to a distribution centre and from that distribution centre to the office at which the computer is to be used.


times between zone pairs that are used in the creation of these legs are derived from the modal networks.

There are wide differences in supply chain organisation among industry sectors, and even among different types of goods produced and consumed within a given industry sector. It can change through time in response to improvements in operations and to changes in transport, freight handling and storage costs.

The pattern and number of distribution channels that are relevant will vary substantially between commodity types. In general for low value bulk commodities such as coal or sand and gravel, many movements will be direct on a single distribution leg from producer to consumer and few will use more complex distribution channels that require a sequence of distribution legs. In contrast, for import / export goods or for higher value finished goods there may be a wide range of distribution channels in use. MODE, PORT AND VEHICLE TYPE CHOICE

In order to bring the demand data from the various economic, zonal and zone pair data sources together into a consistent overall matrix framework that can subdivide the distribution legs, it is beneficial to model mode and vehicle size choice within an integrated structure.

The choice of mode and of vehicle type on road depends on the costs and operating characteristics of the competing options. These in turn can differ strongly between different commodity types. Furthermore, for a specific commodity type the costs and carriage requirements can be very different for different logistic legs as a result of the differences in consignment sizes and number of drops that will be required.

Primary distribution legs (e.g. from producers to distributors or to major individual consumers) will typically be single drop, regular, large consignments moved on large vehicles that are heavily loaded, whereas the tertiary legs (e.g. from distributors to dispersed small-scale consumers) will be spread as part loads across the smaller vehicle sizes which may only be partially loaded, depending on the particular combination of the size and urgency of the consignment and on the access conditions for the pick-up and drop. Although the primary legs will include rail as an option within their modal split stage, this is not generally the case for tertiary legs.

The choice of an appropriate vehicle size for a commodity is determined by a combination of the cost per tonne-km and the suitability of the vehicle for that type of logistic leg. It would be difficult to model the choice of vehicle size in a behavioural fashion, without taking direct account of these different characteristics of distribution legs, which is why this latter dimension needs to be explicitly distinguished as part of the process of freight matrix creation. There is an associated step of conversion from tonnes to vehicles that makes allowance for the average load factor per loaded movement for that combination of vehicle type, commodity and distribution leg as well as creating any associated empty running movement in the reverse direction. Within the design of this model the movement of goods by LGVs is mainly determined at the vehicle choice stage in a form that is fully integrated with the modelling of HGVs. . However, some LGV movements that are not in competition with larger vehicles are generated directly.

BYFM Final Report 6

The mode and vehicle choice stages are modelled using logit discrete choice models. VALIDATION OF THE MATRICES

The full model has been successfully implemented and calibrated to produce OD matrices of tonnes and of vehicles for all domestic freight movements on road and rail within Great Britain for 2006.

Aggregate statistics on vehicle kilometres segmented by vehicle type, road type and region have been compared against the published National Road Traffic Estimates. DfT’s Annual Average Daily Flow (AADF) traffic counts (which include both freight and non-freight taxed HGVs) accumulated across selected cordons have been used to validate the main vehicle flows against key screenlines of national importance.

RECOMMENDATIONS AND CONCLUSIONS

The matrix creation method used in this study is based on a robustly specified underlying model that has already been tested in the EUNET2.0 study. Freight flows are a derived demand from production, consumption and trade, mediated by the supply chain and distribution system. This means that the model represents and is consistent with the economic and logistic basis of observed freight flows.

This model incorporates observed local data on the generators and attractors of goods where it is available. The observed freight data available from the various data sources inevitably is partial in its geographic coverage and variable in its sectoral and spatial resolution, so that the underlying model provides a systematic basis on which freight demand matrices are established and refined.

The study makes recommendations to DfT and to other agencies on how future data collection and processing might be adjusted to serve the needs of freight modelling more effectively, and lists potential further modelling developments to enable forecasts of future freight transport patterns to be soundly based and to make most effective use of cheap, widely available sources of vehicle count data.

The key improvements that are provided in these base matrices are:

Making effective use of a wider set of economic and land-use data sources when generating the matrices at the district level and to major freight hubs, so as to provide improved estimates of the locations of the starts and ends of goods vehicle movements;

Sub-dividing the goods movements into their distribution legs so as to facilitate realistic modelling of mode and vehicle type choice, all of which are observed to differ strongly in behaviour between these distribution legs;

Explicit inclusion of major freight hubs to facilitate implementing each of the previous two steps;

The first detailed approach to the modelling of goods movements in LGVs (approximately 30% of all LGV vehicle km are for carriage of goods);

Providing a logical and consistent foundation for the development of future year freight matrices as part of a potential subsequent study.


There are however a number of aspects of the current model and its output base year matrices that could be further improved and refined through applying more resources to investigate local mismatches and to adjust the calibration of individual demand segments more precisely. The areas where particular improvements could be achieved include:

Rail trip length and mode split calibration, particularly for intermodal rail, as well as rail network assignment;

Road assignment adjustments between motorway and other trunk routes;

Improving the O-D matrix matching. This helps the model matrix match the observed through applying a matrix matching procedure; due to pressure on resources the road matrix matching has been implemented in the model only in a quite broad-brush fashion. Improving this requires careful consideration of issues such as sampling rates and commodity classifications in the CSRGT, the locations and commodities carried by foreign registered vehicles, and the role of freight vans in some sectors;

Improving the representation of multi-drop delivery routes, particularly for LGV movements.

Nonetheless, the validation results for the model generally show a relatively good match to the observed pattern of aggregate statistics. This is impressive, given that the model is entirely synthetic in construct, rather than being a scaled and adjusted "observed" matrix. The SIO model creates an underlying multidimensional synthetic matrix of the tonnes moved between zones. This matrix is segmented by commodity type, distribution leg, mode and vehicle type. The summary calibration and validation output statistics that have been presented are all aggregations of this same unique underlying matrix or of its conversion to vehicle loads. This provides a level of consistency within the model in its response to external economic and logistics developments that is powerful for policy testing and for future forecasting.

A particular strength of this modelling approach is that it provides a natural foundation that can later be used both to update these matrices to a new base year, and to forecast future year matrices under different policy scenarios. This means that this investment in the creation of base year matrices provides an efficient and relatively low cost method for production of matrix updates and of forecasts.

This study has provided the following main deliverables:

a flexible operational model foundation from which a useful, policy sensitive, freight forecasting system could be developed;

a set of P-C matrices in units of tonnes or value, segmented into 23 commodity types and 5 types of production consumption relations;

a set of O-D matrices in units of tonnes, segmented into 23 commodity types and a large number of distribution leg types;

a set of O-D matrices in units of vehicles, segmented by mode and vehicle type by 10 commodity types and distribution stage.

BYFM Final Report 8

1 Introduction

1.1 THE OBJECTIVES OF THE STUDY

1.1.1 The main objective of the study is to develop and validate a set of base year freight matrices for 2006 which include the representation of logistics within freight supply chains. Matrices are created by origin-destination (O-D) zone for each of the distribution stages along the supply chain that leads from where goods are initially produced through distribution centres and warehouses to where the goods are ultimately consumed. The matrices are required in two forms: in units of tonnes segmented by commodity type and distribution leg; and in units of vehicles, segmented by main mode (road or rail) and by vehicle type within each mode.

1.1.2 These matrices represent all domestic freight moved within Great Britain by heavy goods vehicles (HGVs), light goods vehicles (LGVs - vans) and by rail. The matrices do not include domestic transport by water, air or pipeline. Most such movements are not in significant competition with road freight movements (especially primary petroleum and chemical products) and were not a priority for base year modelling.

1.1.3 The matrices cover all road and rail freight movements within Great Britain, whether by UK or by foreign registered vehicles, bearing in mind the rapid growth in the latter over recent years. The representation of movements to or from overseas is primarily from the point of view of ensuring that the spatial pattern of the movements within Great Britain is correct. This requires that major airfreight terminals, the Channel Tunnel, ferry and short-sea container ship movements be explicitly represented in order to take account of their impact through terminal choice on the pattern of traffic on the domestic leg of overseas movements. The creation of overseas legs of matrices of freight movements is not otherwise an aim of this study.

1.1.4 The zoning system adopted for the matrices is at the local authority district level, augmented by extra zones to represent individual freight ports, airports and major agglomerations of distribution centres. This strikes a balance between providing sufficient spatial detail to enable freight transport movements to be represented realistically, but not requiring input data at level of spatial detail that is difficult to obtain comprehensively at the national level.

1.1.5 Road Traffic Estimates (DfT, 2007d) are adopted as the primary validation statistics that need to be matched by the estimated base year matrices when they are assigned by vehicle type to the national road network and then aggregated across the links of a given type within a region. DfT’s Annual Average Daily Flow (AADF) traffic counts are also accumulated across selected screenlines to validate the main vehicle flows.

1.1.6 Although, the primary aim of this study was to create matrices for 2006, the subsidiary aim was where feasible to develop the system to produce these matrices in a form that could subsequently be used as the foundation for a flexible freight forecasting model. A London-focused variant of this model has been created for TfL which is run in forecasting mode through to 2031.

1.2 STRUCTURE OF THE REPORT

1.2.1 The tasks of this study were divided into two phases, both of which are documented in this Report:


Phase 1, Scoping and Design: Specified the methods to be adopted in building and validating the Base Year Freight Matrices (BYFM), together with the data to be used in their construction. The results are documented fully in the report: Model Design and Specification: BYFM, WSP (2008). Its proposed approach has been updated and summarised in this current Final Report, with much of the detail being moved to the Appendices.

Phase 2, Implementation and Validation: Implement the methodology to create validated zone pair matrices of both tonnes and vehicle movements, segmented by commodity type, which cover the whole of Great Britain. These should be in a suitable form for use as an input to the creation of spatially detailed matrices in local studies.

1.2.2 The rest of this Report falls in four parts: firstly Chapters 2 to 5 specify the model structure and summarise its underlying methodological and implementation details. In Part 2 Chapters 6 to 10 document the main sources of data available on which to develop, calibrate and validate the matrices, while Part 3 from Chapters 11 to 14 specifies the calibration of the model and then the validation of its output matrices, followed in Part 4 in Chapters 15 and 16, respectively, by recommendations and conclusions from this study. The separately bound Appendices A to M contain greater technical detail on a number of the topics that are summarised in the main body of the Report.

1.2.3 Part 1 of this Report specifies the design of the model and its implementation details, starting in Chapter 2 with an introduction to the concepts used in freight modelling. Chapter 3 explains how the spatial input-output modelling approach is used as the core formulation of the demand for freight transport. Chapter 4 outlines the BYFM model structure and its main component stages. The segmentation details of the commodity types and logistic legs used in the model are presented in Chapter 5,

1.2.4 In Part 2, Chapter 6 describes the zoning system that is adopted to create the freight matrices. Chapter 7 outlines the transport networks and other transport cost and supply characteristics that are used as inputs to the model. Chapter 8 describes the data used for the spatial location of the entities (firms, households, buildings, distribution centres, etc.) at which goods are picked up or delivered. Chapter 9 describes the main data sources used for observed transport movements for each transport mode in turn (road, rail and maritime) as well the data sources on logistics that were used to separate movements into the individual distribution legs of the supply chain. Chapter 10 presents the particular features that are used to estimate those LGV movements that are related to the carriage of goods.

1.2.5 Part 3 of this Report provides in Chapters 11 and 12 a description of the calibration of the model and the output matrices that it produces. Assignment validation statistics are presented in Chapter 13, noting that the main study objective was the production of matrices, and the objectives with regard to assignment validation were limited. Chapter 14 provides an overview of the implementation and run time details for this model.

1.2.6 In Part 4, Chapter 15 outlines recommendations for future actions to enhance the model. It makes recommendations to DfT and to other agencies on how future data collection and processing might be adjusted to serve the needs of freight forecasting more effectively, and lists potential further modelling

10 BYFM Final Report

developments to enable forecasts of future freight transport patterns to be soundly based. Chapter 16 draws some general conclusions from this model building and matrix construction exercise. To aid the reader, Chapter 18 provides definitions and cross references to all of the main technical terms that are used in this report.


Part 1 Model Structure and Design


2 Introduction to Freight Modelling

2.1.1 This Chapter first discusses the main previous studies from which the current study has benefitted. It then introduces some key concepts related to freight logistics that need to be understood in order to master the added complexity that arises in freight modelling in comparison to passenger modelling.

2.2 BACKGROUND STUDIES

2.2.1 This work builds on a number of past studies on freight modelling, namely:

Review of Freight Modelling (WSP et al., 2002) for UK DfT: to examine methods for improving freight modelling in Great Britain. This review provided the impetus for much of the freight modelling development over the last decade.

Feasibility Study of SCGE Models of Goods Flows in Sweden (WSP, 2002) for SIKA, Sweden: to examine the role that Spatial Computable General Equilibrium (SCGE) models could play in improving freight modelling in Sweden. This study developed the methodology through which spatial input-output models could be used to model the complex distribution structures that arise in freight logistics.

EUNET2.0 (WSP, 2005) for UK DfT to create a pilot freight and logistics model in the Trans-Pennine Corridor to demonstrate how the demand for transport can be derived through a regional economic model. The current study takes this model as its starting point for the development of the base matrices.

GBFM Upgrade Study (MDS Transmodal, 2008) for UK DfT: to update and recalibrate the existing GBFM model to create an improved new version 5.0. This has provided data inputs for the development of the base matrices.

2.2.2 Use has been made in this report of the findings from the “Review of Data Sources” (Katalysis & ME&P, 2002) that was part of the Review of Freight Modelling Study. It aimed to list all of the main sources of relevant freight data for Great Britain at that time. It should be consulted to provide further details on a number of the data sources discussed below, though details of the key sources have been updated within this current Report. A major report has been produced by University of Westminster and Freight Unit, TfL (2008), which provides a comprehensive review of the availability of freight data specifically within London.

2.3 FREIGHT AND LOGISTICS CONCEPTS AND TERMINOLOGY

2.3.1 Many past freight transport models have been based directly on matrices presented in units of goods vehicle movements. This approach is suited to studies where the primary requirement is to assess the impacts of changes in infrastructure supply on vehicle routing and on congestion levels.

2.3.2 However, when examining more wide-ranging transport policy measures the disadvantage of this vehicle based approach is that freight is a derived demand. The pattern of goods movement arises from the interplay between: the spatial pattern of the supply and of the cost of goods; the spatial pattern of the demand for goods; and the logistics structures that are in place to ship goods from producers (supply) to consumers (demand) via distribution


centres (or warehouses). The changes in the patterns of freight transport that arise in response to wider policy measures are best forecast by having a proper understanding and representation of the underlying freight distribution system, as now outlined.

2.3.3 The economic transactions between suppliers and consumers, and the logistics operations that actually deliver the physical goods, are the two main drivers behind the observed pattern of freight movements. In order to understand the relationship between transport and the economy, it is helpful to draw a distinction between two separate matrices of movements that are used conceptually in the analysis of the demand for goods transport.

The pattern of economic trade in commodities from the initial producer to the ultimate consumer; this is called the Production-Consumption (P-C) matrix of trade. Changes in this matrix are strongly influenced by economic changes outside the transport and distribution sectors. This P-C matrix is not observable in practice other than through access to data on intercompany sales transactions which are not normally available to model builders.

The actual set of physical transport movements generated by the logistics structure that distributes and transports these P-C trades in practice; this is called the Origin-Destination (O-D) matrix of shipments. Changes in this matrix are strongly influenced by changes within the transport and distribution sectors. This matrix can be observed either through surveys of industry (e.g. CSRGT2 or rail freight statistics) or through roadside interview surveys that record for passing vehicles the commodities carried and their origin and destination zones.

Figure 2.1: One P-C movement and its associated two O-D legs

2.3.4 The example in Figure 2.1 illustrates the particular case of a P-C movement of a fridge. The delivery from the factory to the retailer travels firstly by rail to a warehouse, and then after storage is reloaded onto a lorry for the final delivery leg of the journey to the retailer. Here one P-C movement creates two O-D legs.

2.3.5 The reason for considering a P-C matrix in addition to an O-D matrix for the same commodity, is that the impact of trends in logistics and of the responsiveness of logistics to policy initiatives can best be understood realistically if these impacts are considered within the context of their underlying P-C matrix. The resulting O-D matrix is then what is captured in the standard statistical surveys of vehicle movements. For example, the lengthening of P-C trades does not automatically imply a lengthening of O-D shipment lengths since there may be a succession of separate intermediate distribution legs between the initial production and the final consumption of a good, with the good being warehoused in distribution centres between these legs. An industry-led policy to increase the number of distribution centres could, perhaps, generate a larger number of shorter distribution legs even at a time when there is an overall lengthening of trade. However, in the past the broad trend has certainly been

2 CSRGT: Continuing Survey of Road Goods Transport (DfT, 2007b) 14 BYFM Final Report

towards a smaller number of larger distribution centres, which has had the effect of increasing the average length of haul of O-D shipments / distribution legs.

2.3.6 In discussions on logistics the wide variety of types of distribution legs are often grouped into three broad categories that have distinct transport characteristics:

Primary distribution legs (e.g. from producers to distribution centres or to major individual consumers) – these will typically be single drop, regular, large consignments moved on rail or on large road vehicles that are fully and efficiently loaded (pallets) and often operating on a 24-hour basis,

Secondary distribution legs (e.g. from distribution centres to major retailer outlets or to local distributors / cash-and-carries) - these will typically be relatively large consignments with few drops per vehicle but delivering using roll-cages rather than pallets,

Tertiary distribution legs (e.g. from distributors to dispersed small-scale consumers) - these will typically be spread as multi-drops of smaller consignments, packages and parcels often using smaller vehicle sizes that may perhaps be only partially loaded and operating only during the daytime, when the receiving shops and offices are open for business.


3 The Spatial Input-Output and Economic Model

3.1 INTRODUCTION TO THE SIO MODEL

3.1.1 This Chapter provides an overview of the Spatial Input-Output (SIO) model that is the fundamental tool used to create the BYFM model and matrix. It first introduces below the main concepts and vocabulary of this model to provide a simplified example of how its individual components operate. A full formal mathematical specification of the SIO model is provided in Appendix I. Section 3.2 summarises the strengths of this SIO modelling approach, while the treatment of the input-output coefficient matrices is presented in Appendix J.

3.1.2 The standard manner to relate the economic linkages between different industry sectors is through use of the input-output (I-O) model framework developed by Leontief in 1936. The fundamental concept is that the production of each commodity, an output, consumes a variety of other commodities as inputs. Each such input itself needs to be produced, and so in turn they also will each consume inputs of other commodities in the course of their chain of production, and so on. The term "commodity" here denotes both goods and services (e.g. transport, warehousing, insurance, etc.).

3.1.3 The SIO model (Williams, 1979) is a generalisation of this standard aspatial economic I-O model that integrates within this I-O modelling framework, the features of a discrete choice based spatial distribution model for the sourcing of goods that are to be consumed. In essence it uses the same approach as in the standard I-O model but it explicitly recognises that the production of a commodity will frequently take place in a different location to that in which it is consumed, so that the process of transporting commodities from where they are produced to where they are consumed should be represented explicitly. In this way differences between locations in their cost of production or in their cost of consumption can be calculated explicitly.

3.1.4 The SIO model equally provides a natural formulation to represent the individual intermediate distribution legs that occur along the way when transporting goods from the original producer through to the final consumer via one or more distribution centres and depots.

3.1.5 Williams and Echenique (1978) presented the original formulation of the SIO model, based on a freight model they had developed for the State of Sao Paulo. The solution algorithm was then generalised in Williams (1979) to take account of zonal constraints, and to demonstrate how the method could be used to model the spatial competition governing the location patterns of firms, of households or of agricultural crops. It has been used in a variety of operational models since then including:

to model the location patterns of households and services, including an endogenous estimation of the zonal rents for residential and commercial floorspace, within the LASER integrated land use and transport model (Williams, 1994; Jin, Williams and Shahkarami, 2002);

to model the transport of passengers and goods in the EUNET Trans-Pennine corridor model (Jin and Williams, 2000);


to model the transport of goods throughout all of the EU in the SCENES model, which includes separate I-O tables for each of the EU member states (SCENES, 2000).

3.1.6 The spatial I-O model provides a flexible system that can be used to represent in an integrated fashion:

the functional economic linkages between activities: - the production and consumption of goods and services and their associated costs - this was the focus of the original aspatial I-O model;

the spatial competition for location of activities: - constrained local supply of land and building stock, planning policies and rents;

the spatial linkages between activities: - transport, logistics and communication.

3.1.7 The operation of the I-O structure is illustrated in a simplified form in Figure 3.1.

Figure 3.1: The I-O relationships between industries and to households

3.1.8 This Figure shows how the purchase (consumption) of, say, a fridge by a household gives rise to production activity in various industry sectors. The dark blue arrows represent the consumption of durable goods, of electronic components and of energy by households (termed the Final Demand). The red and green arrows represent some of the consumption by industries of the inputs that have been produced by other industries (Intermediate Demand). The orange dotted lines represent the flow of money in the opposite direction to pay for the intermediate goods that have been consumed. For simplicity, the costs of purchasing goods by households have been omitted from this Figure.

3.1.9 The production of a fridge at a factory will require inputs of steel, electronic components and energy to be delivered to it. The first two of these inputs (red arrows) will use transport networks and so will give rise to freight transport demand - if the energy source is electricity it is transmitted through power lines but electricity transmission (green arrows) is not included within freight transport models. Each blue circle potentially denotes a set of production zones. There may be different companies producing fridges in different parts of the UK or abroad.

3.1.10 The distinctive feature of the SIO model, as opposed to the traditional I-O model, is that it explicitly represents the location of each of the production and consumption zones and of the transport movements between them. Thus each red or blue arrow in Figure 3.1 above represents a collection of movements between separate zone pairs that are distinguished individually within the SIO model.

3.2 MAKING EFFECTIVE USE OF THE SIO MODEL 3.2.1 This explanation in outline of how the SIO model operates illustrates the potential strengths of this modelling approach.


It represents the functional linkages between all of the activities in the economy.

It has the potential capability to represent the spatial competition for the location of industries and warehousing and the impacts on rents, though this is a topic on which as yet there has been little experience in practice with operational models. Though the theoretical structure to represent these features is outlined within Appendix I, this study has not sought to implement them in practice within this BYFM-oriented model development phase.

It represents the spatial linkages between the activities of the economy in terms of their transport, distribution and communication requirements as explained below in Chapter 4 - this is the main focus of the current study.

It is able to maintain a detailed level of segmentation for commodity types this aids the forecasting of transport demand.

It operates at the zone pair level and so enables transport supply availability (e.g. costs, times, quality) to be measured in a spatially discriminating fashion, based directly on transport network descriptions - this again aids the forecasting of transport demand.

It provides a natural method for integrating the representation of logistics as a part of the overall modelling system - this facilitates the distinction between P-C matrices and O-D matrices. Accordingly it connects trade patterns indirectly to the O-D vehicle movements that are observed in surveys of goods transport.


4 BYFM Model Structure

4.1 WHY IT NEEDS A MODEL TO CREATE THE MATRICES

4.1.1 No single data source covers all of the component elements of the base year matrices in sufficient detail for our needs. Accordingly, a modelling methodology has been developed that enables the key information from these various datasets to be extracted and merged in a consistent fashion so as to create the base matrices in a form that provides maximum detail and accuracy. This consistent merging of these data sources is achieved through creating a freight transport model that takes the various sources of observed data as its inputs and uses them to create the required set of base matrices as its output. The MEPLAN software package, which has already been used to create a similar style of model in the EUNET2.0 Pilot Model study (WSP, 2005), has also been used here because it already includes the features required to implement this freight and logistics model structure.

4.1.2 The tasks carried out in setting up and running this model to generate the required matrices include:

minimising the effects of sampling errors in the input data;

identifying and addressing inconsistencies between the data sources used as input data;

infilling missing data observations;

increasing the dimensions of the cross-categorisation of the segmentation in the matrices, in particular to distinguish the distribution leg of each movement within the overall supply chain.

4.1.3 The main model outputs are matrices of zone-to-zone movements of freight in the year base year of 2006. These are produced in three forms:

Production-Consumption matrices of tonnes, segmented by

o commodity type o by type of producer (domestic factory/farm/quarry or import) and of

consumer (domestic factory/farm/quarry or household or export);

Origin-Destination (O-D) matrices of tonnes, segmented by

o commodity type o main mode (road/rail) o distribution leg;

O-D matrices of vehicle movements, segmented by

o main mode (road/rail) o for road, segmented by vehicle size.

4.1.4 One important extra benefit from this model-based approach is that it provides most of the components needed to provide a solid foundation for the development of a freight forecasting model, though completing that development lies outside the scope of this current study.


4.2 MODEL OVERVIEW

4.2.1 This Section provides an overview of the structure and design of the model being used to deliver the required base year freight matrices.

4.2.2 The model is built using MEPLAN software to arrive at a transparent model that is defined through a series of input files, rather than through designing a bespoke programme. This also has the advantage that it will link better with the MEPLAN-based highway assignment module in the Department's existing National Transport Model, which may be a useful feature in the future to simplify joint assignment of passenger and freight O-D matrices to the highway network.

4.2.3 The EUNET2.0 (WSP, 2005) regional model demonstrated that the demand for transport can be derived through a regional economic model. The BYFM study builds on this earlier research and applies its key concepts at a national level to create a spatial input-output (SIO) model. The broad formulation of the SIO model was presented in Chapter 3, while its detailed mathematical formulation is fully specified in Appendix I.

4.2.4 The main components of the overall model used to create the base year matrices are summarised in Table 4.1, while the methodology for each component is subsequently presented in greater detail in this Chapter within the corresponding Section indicated in column 3. The Table also lists the main output from each stage of the model. Each stage that involves choice among alternatives, as indicated in column 1, operates as some form of logit discrete choice model within a larger multi-level choice hierarchy.

Table 4.1: Summary of the main model components

Model stage Space Sctn. Main outputs

Generation / zone 4.3 attraction

Distribution zone 4.4 leg pair proportions / zone choice

Port choice zone 4.5 pair

Mode choice zone 4.6 pair

Value to zone 4.7 volume pair

Vehicle type zone 4.8 choice pair

Matrix zone 4.9 matching pair

The total volume of freight by commodity type that is produced, distributed or consumed in a zone

Uses an SIO model to build up P-C relationships through a fixed proportional split of distribution legs appropriate to each commodity type, including those legs to, from or between distribution centres, together with the spatial allocation of each leg across competing zones to generate O-D matrices of goods movements

For unitised movements between the UK and the rest of Europe, uses an SIO model to subdivide between the UK ports for imports and exports

Subdivides the transported total for each O-D movement into main modes, including their associated feeder legs to and from intermodal terminals

Convert the units of O-D movement from value (£) to volume units (tonnes)

Subdivides the total road tonnes for each O-D into road vehicle types (artic, rigids [4], LGV) Includes: conversion from volume (tonnes) to vehicles

The total modal volume between a pair of zones as estimated synthetically, is constrained to match observed CSRGT and rail statistics


Model stage Space Sctn. Main outputs

Intermediate calculations Path building zone

pair 4.10 Estimate the supply characteristics (cost and time) along the intermodal

path between zone pairs

Cost estimation

link 4.10 Vehicle operating costs both stopped and in movement - function of vehicle type, link travel speed, link length, etc.

Assignment - path choice

link 4.10 Allocation of vehicles to links along their path - a non-equilibrium congested assignment

4.3 FREIGHT GENERATION AND ATTRACTION

4.3.1 The activities that initiate freight movements include five broad categories of producers, consumers and logistic operators (see Figure 4.1). These are listed below in order of sourcing, starting with the consumers who attract this freight demand (shown in the centre of the diagram):

Consumers of goods such as firms, institutions, households and tourists;

Local distribution depots, including those of regional suppliers, stores and shops, delivery consolidation, e-retailing and waste/recycling;

National and regional distribution centres mostly for road and rail operations;

Producers within Great Britain, and port and airport terminals for imported goods;

Producers and consumers outside Britain, who provide imports to and demand exports from Britain.

The black downwards arrows in Figure 4.1 denote the direction of movement of the flow of goods; from producers, via intermediate distribution stages, through ultimately to consumers. These intermediate distribution stages act both as generators and attractors of goods movements, depending on which type of distribution leg is being considered.

Figure 4.1: Producers, consumers and logistic suppliers of freight: summary

4.3.2 Although the raw materials and finished products are delivered towards consumers, the recycled materials, packaging, returns and waste are collected from them to be processed by the supply chain, as illustrated by the grey arrows in Figure 4.1. This reverse logistics is not treated directly in BYFM at present, although some of the “empty” vehicle back-flow generated by the model may in reality be carrying some kinds of packaging or reverse logistics materials.

4.3.3 Obviously not all freight follows the same sequence of supply chain distribution. Many types of bulk goods are delivered directly from the producer to the consumer, whilst many types of food and consumer goods are handled by more than 3 or 4 intermediaries before being delivered to shops. At the model implementation stage the distinct supply chain configurations are defined for


each commodity and producer/consumer type combination, based on handling factor data. Further discussion on distribution legs is provided in Section 4.4.

4.3.4 Initially, the model estimates the zonal production and consumption of goods for each of 23 product groups (indicating the industry sector in the economic model) in units of value (£m/year). This is carried out in one of two broad ways, depending upon the spatial level at which observed data are available for the product group. The zoning structure is discussed in Chapter 6, but to aid discussions at this point the reader should assume around 400 zones based on local authority districts.

4.3.5 For a small number of product groups, estimates of tonnes produced / generated and / or of tonnes consumed / attracted to individual zones are available and so are used directly as an input to the base year total for that zone. Such data are beneficial to making sure that major movements of bulk commodities are correctly allocated, and that the movements to and from major freight ports, airports and other facilities are accurate. However, such data are generally not segmented to the degree of detail in logistic legs that is used in the model. Accordingly, the estimated zonal volume (or value) of attraction is first summed over all logistic legs before being constrained to match the observed tonnes (or value) lifted in the zone. This makes use of a feature available within the MEPLAN software that can apply trip end constraints to aggregates of distribution legs3.

4.3.6 In all other cases, it is necessary to synthesise the zonal production / generation and consumption / attraction of each product group. This is achieved by using relevant zonal data to subdivide national, regional or county totals down to the level of the individual zone.

4.3.7 For example, the final consumption by households of each type of good is computed through estimating the monetary value of consumption, based on the number of relevant consumers (taking account of their patterns of expenditure) within each zone. Higher income and larger households will generate different patterns of consumption of goods to lower income or smaller households.

4.3.8 The zonal consumption of goods by industry for use as inputs to the manufacture of other goods (i.e. intermediate demand) is estimated using Input-Output based methods that are outlined in Section 3.1 and then specified in detail in Appendix I.

4.3.9 For non-bulk goods the spatial pattern of producers, distributors and especially of consumers can be widely dispersed. Data on the scale of movements to and from manufacturing and distribution centres has been derived indirectly from the Valuation Office Agency (VOA) zonal data on factories and warehousing floorspace.

3 The MEPLAN software provides considerable flexibility in its application of zonal constraints to control trip ends. It allows constraints to be applied to a combined set of zones (for example when control totals may only be available at the county rather than the district level) and/or to a combined set of logistic stages for a given commodity type, to cater for the restriction that neither the CSRGT nor rail nor port data provide segmentation by logistic stage.


4.3.10 The approach taken to estimate the relevant production and consumption zonal totals is outlined in Chapter 8 and then discussed in greater detail in Appendix A, which lists for each product group the data sources selected as most appropriate for zonal sub-division of higher level data.

4.3.11 The output from this component of the model is the total freight by product group that is produced and consumed in each zone in units of value (£m/year). This is very much like the generation/attraction stage of a typical transport model, though here our totals are in million pounds of goods, segmented by product group rather than being trips segmented by trip purpose.

4.4 THE SPATIAL PATTERN OF DISTRIBUTION LEGS

4.4.1 The next stage must join together these producers and consumers of product groups to build a complete matrix of movements. Each movement will correspond to a particular distribution leg of the supply chain for each product group and hence be based around production-consumption relationships.

4.4.2 There are wide differences in supply chain organisation among industry sectors, and even among different types of goods produced and consumed within a given industry sector. It can change through time in response to improvements in operations and to changes in transport, freight handling and storage costs. For example, , the various logistic legs that may arise in the course of moving manufactured goods from the zone of production to the zone of consumption are illustrated in the three diagrams shown in Figure 4.2:

Production - consumption link The distribution chain Distribution channels

Factory/Farm

Factory/Farm

Distribution Centre B

Distribution Centre A

Depot

1

5

4

3

2

6

Factory/Farm

7

Depot

Factory/Farm


1

54 3

2

6

Factory/Farm

7

Depot

1

Depot

1

Depot


6

7 7

Factory/Farm

Depot Depot Depot

Distribution Centre B

Figure 4.2: The production-consumption, distribution chain and distribution channels for raw materials and parts used in production

The left hand diagram shows a single link between production (i.e. supplier of the goods) and consumption (the industrial user of the goods), in other words, from the factory/farm where the good is produced to another


factory/farm where it is used as an input to the production of some other goods. This left hand diagram is the P-C matrix representation of the movement.

The middle diagram shows the various distribution legs within the distribution chain for this single production-consumption relationship. The goods are first moved to a depot to be consolidated so that they can be moved onwards efficiently using large vehicles; some to a distribution centre A and others to another nearby depot for use in adjacent factories. Some of the goods at the distribution centre A will be moved to another distribution centre B if they are being shipped to distant locations, others will be moved straight to depots for transfer to the factories where they are consumed. As shown in the diagram, each type of distribution leg is assigned a distinct trade code number that is used to distinguish the type of entity at its start and end point, the level of costs faced, and the mode/vehicle type mix used on that type of distribution leg. Each distribution leg corresponds to a movement within an O-D matrix.

The diagram on the right shows how these combinations of distribution legs, when set up in the model will generate a variety of distribution channels. Each sequence of distribution legs connecting from the producer through to the consumer is a separate distribution channel.

4.4.3 The SIO model in BYFM sends through each relevant distribution channel a proportion that is common across most or all zones. This proportion is differentiated by product group by five broad types of P-C relations:

Domestic factory or farm goods consumed by final demand / households (DoHH)

Imported goods consumed by final demand / households (ImHH)

Domestic factory or farm goods consumed by domestic industries (DoCI) (intermediate goods e.g. raw materials and components)

Imported intermediate goods consumed by domestic industries (ImCI)

Exported goods to be consumed in other countries (Ex)

4.4.4 Appendix K provides a comprehensive set of charts illustrating the differences between product groups in the complexity of their distribution systems. It also presents lists of trade codes for each product group for the set of P-C relationships and distribution chains that it uses.

4.4.5 The proportions using each distribution channel were initially determined in the EUNET2.0 study, based on a range of data sources including the REDEFINE study and industry specific KPI studies carried out for DfT. They have since been recalibrated to reproduce national tonnes lifted as described in Section 11.7. The split of traffic between these channels is therefore a calibrated input to the matrix estimation. The MEPLAN software is capable of modelling a range of elastic functions which would cause this distribution channel split to respond to cost changes in a forecasting context, if the relevant elasticities were specified.

4.4.6 Decomposing distribution chains into legs in this manner has significant advantages in model development and calibration: the distinct patterns of


sourcing by consumers and by logistic providers can be represented transparently, as are the patterns of mode choice, which are heavily dependent on the consignment characteristics on each distribution leg. The model structure also makes it possible to distinguish the O-D movements on particular distribution legs that when aggregated across distribution legs can be compared with observed road and rail traffic as part of the model validation.

4.4.7 The pattern and number of distribution channels that are relevant will vary substantially between commodity types. In general for low value, bulk commodities such as coal or sand and gravel, many movements will be direct from producer to consumer leading to a low handling factor (defined as the number of times a particular good is lifted between the producer and the consumer/retailer). In contrast, for import / export goods or for higher value finished goods there may be a wide range of distribution channels in use resulting in higher handling factors. This topic has been explored in detail within the EUNET2.0 Study (Chapter 13, WSP, 2005) where a range of examples are presented.

4.4.8 For each combination of product group and distribution leg, the model is calibrated to match to its expected average length of haul. For a given distribution leg type to a specific destination zone, the choice of the origin zone from which the good is supplied is determined by a discrete choice, logit model of spatial distribution. The attractiveness of each supply zone is determined by its production or warehousing capacity for that product and by its generalised cost of travel per unit of that product for that type of distribution leg. The scale parameters that multiply the generalised cost of travel are adjusted so as to match the average length of haul for the product group as a whole. On average, primary logistics movements from producers to national distribution centres (NDCs) and regional distribution centres (RDCs) tend to be long, whereas tertiary logistics movements from regional distribution centres to individual retailers or to households tend to be much shorter.

4.4.9 The method adopted is to use an SIO model to generate the origin-destination (O-D) matrices of freight in a form that distinguishes the distribution legs appropriate to each product group. An initial estimate of the travel costs and times between zone pairs is required; this is derived from the modal networks and is discussed in Section 4.10.

4.4.10 The output from this component of the model is an O-D matrix in units of value of domestic movements between zone pairs, which is segmented by product group by distribution leg type. For primary distribution movements of bulk goods it is further segmented by main mode, as explained in Section 4.6.

4.4.11 For reasons of efficiency of calculation and data storage the SIO model does not store the onward zonal pattern used in subsequent distribution legs, when calculating the zonal production pattern from which goods will be sourced for a distribution centre. This computational simplification makes it a major task to generate for a commodity the P-C matrix that is associated with the set of O-D matrices that are estimated by the SIO model for this commodity. Instead a separate utility has been created to produce such P-C matrices, taking as input the component O-D legs that are output by the SIO model.


4.5 PORT CHOICE

4.5.1 The reason why port choice for movements to/from Europe needs to be modelled as part of this exercise to produce freight matrices for Great Britain is in part because there is no reliable road data available on the inland pattern of movements from ports. The CSRGT lacks sample size, other than from the largest ports, while both the CSRGT and the IRHS necessarily exclude the foreign hauliers that now comprise 75% of the total accompanied movements. The pattern of inland movements has been evolving in recent years, including the rapid growth in availability of direct Ro-Ro services from the North to the continent, particularly through the port of Immingham. This will have captured a significant part of the unaccompanied traffic of the North that previously would have used ports in the South East of England.

4.5.2 The supply data used for port choice in GBFMv5 has been adopted as the foundation for the modelling of short-sea movements of unitised goods between Great Britain and the rest of Europe through ferries, container ships and via the Channel Tunnel. GBFM assigns port choice by taking into account the relative cost of each of the competing services via alternative ports as well as the degree to which each service is perceived by customers as being inherently different from the other options. Although it has not been possible as yet to calibrate a full port choice model in BYFM, this supply data provides the foundation for one should that be required.

4.5.3 For the remaining shipping movements, which comprise all bulk movements and the unitised cargos on deep-sea services, the choice of port is taken as an exogenous input to the model. In essence only the road and rail traffic to/from the ports is included within the base year matrices that are created.

4.5.4 In most cases the choice of port is modelled using the SIO model in the same fashion as the choice of origin zone of supply described in Section 4.4 above. In the case of exports of bulk goods or of exports outside Europe, the starting point is the estimated export volume from that port so the choice within the SIO model is of the inland origin zone supplying the goods.

4.5.5 The output from this component of the model is an O-D matrix in units of value of the domestic legs between ports and inland zones for international movements. It is segmented by product group by distribution leg type.

4.6 MAIN MODE SPLIT

4.6.1 Main mode split divides demand between road (HGV+LGV) and rail using nested logit models. An important difference is that for rail there is an essentially complete observed matrix of movements, although BYFM was only able to use the 2004 matrix scaled to 2006 totals. Also large rail movements are often dependent upon the behaviour of a small number of specific firms and the location of sidings etc., so that it is difficult to model the choice of bulk rail versus road using just the techniques described above. Therefore main mode split in BYFM is done by subtracting the observed rail matrix trip ends from the modelled “road+rail” trip ends for each zone and product group. However, this is done by adding constraints to the choices in a full nested logit model, so that the volumes of goods are conserved and rail is integrated into the logistics


structures described above. Main mode split is performed in one of two ways, depending upon the commodity.

4.6.2 For “bulk” rail (coal, minerals and aggregates, cement and building materials), and metals and automotive (cars and vehicle parts), the logit hierarchy is with mode above destination choice. For bulk commodity types, unless the operator already has available the appropriate infrastructure and rail sidings to load the bulk goods onto and from rail, it is unlikely that rail would be viewed as an effective option for that zone. Although the primary distribution legs will include rail as an option within their modal split stage, this is not generally the case for tertiary distribution legs.

4.6.3 This hierarchy also makes it possible within the model to doubly-constrain the rail trip ends to the observed matrix of bulk rail movements. In a few cases there is still a small mismatch between the rail tonnes estimate and the endogenous SIO model, which could do with a little more polishing. However, in most cases the model reproduces the tonnes lifted and tonnes dropped in each commodity exactly.

4.6.4 The inland legs of intermodal sea containers are modelled, in contrast to bulks, as a mode choice nested below destination choice on the primary “port to distribution centre” legs of certain commodities (food, machinery, miscellaneous consumer goods). The data about the commodity split within intermodal rail is very limited.

4.6.5 The output from this component of the model is an O-D matrix in units of value between zone pairs. It is segmented by product group by distribution leg type and between road and rail main modes. For bulk commodity types, it is a vector segmented by main mode rather than an O-D matrix, as their main mode split occurs above the spatial distribution stage within the nested logit model choice hierarchy.

4.7 CONVERSION FROM UNITS OF VALUE TO TONNES

4.7.1 This model derives freight demand patterns using the SIO model that represents the flow of different commodities through the economy. This flow is generated from the final demand from household

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