Infrastructure improvements and freight transport: a case study on Møre & Romsdal

LOG950 Logistics

Infrastructure improvements and freight transport: a case study on Møre & Romsdal

Adrian Svelund

Number of pages including this page: 290 Molde, 20.11.2017

Master’s degree thesis

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Date: 20.11.2017

Preface

This thesis concludes my Master of Science in Logistics at Molde University College. It was written from January to November 2017 with the supervision of Svein Bråthen, Professor in transport economics at Molde University College, and in cooperation with the National Public Roads Administration, represented by Arne Sæther and Kjetil Strand of Region Midt.

I would like to thank the interviewed companies and their representatives for on short notice finding available time in crammed schedules and gladly offering information as requested. I am also thankful for the NPRA granting me the opportunity to write this thesis and for the help with extracting required data from road registrations and the ferry

databank. These helpers’ names are Iril Helen Ulvøen, Tor Harald Eliassen, and Trygve Haram. I cannot thank Professor Svein Bråthen enough for his invaluable input and patience during the writing process. Without him, this project might never have been finalized.

I would also like to thank family and friends for the necessary respite during these stressful times.

Molde, November 2017.

Adrian Svelund

Abstract

The Norwegian Public Road Administration has observed a relatively high growth in freight transport on the Halsa-Kanestraum ferry transit, which it believes is the result of recent infrastructure investments in the region. This case study employs a mixed method design with linear multiple regressions and semi-structured interviews to determine the extent to which freight transport can be attributed to the infrastructure investments, to estimate the future amount of freight transport on the transit, and how investments in the region have affected freight transporters. The linear multiple regression includes a wide range of data representing economic activity and infrastructure projects that may affect freight flows, and factor analysis is applied to combat multicollinearity among the variables. Despite established theoretical relations between economic activity,

infrastructure investments, and freight transport, this case study failed to create coherent and realistic models for freight transport into Møre & Romsdal. The failure is apparent in the incidental nature of the statistical significance and relative effect sizes of infrastructure investments’ effects on freight transport, and the lack of statistical significance for

theoretically and empirically established relations. Reasons for the failure in creating models based on variables are multicollinearity in the data set, potentially inadequate choice of variables and insufficiently disaggregated variables judging by the low number of significant variables, and the scale of analysis being too small. The three latter reasons also influence the models based on factors, and the accuracy of the models with factors is low compared to models with variables. The interviews were limited to two companies representing different sectors, and neither company has been or expect to be affected by the relevant infrastructure investments. Future studies based on the NPRA’s observation should primarily focus on interviews with a higher number of transporters, and quantitative analyses should be more advanced.

Keywords: E39 Costal Highway Route, freight transport modelling, linear multiple regression, infrastructure investments, distribution networks

Contents

Preface ... I Abstract ... I Contents ... II List of tables ... III List of figures ... VI List of acronyms and abbreviations ... VII

1.0 Introduction ... 1

1.1 Background ... 1

1.2 Research focus ... 3

1.3 Research aim and research question ... 5

1.4 Value of this research ... 7

1.5 Structure outline ... 8

2.0 Literature review ... 10

2.1 Infrastructure investments and regional growth ... 10

2.2 Role of decoupling ... 11

2.3 Capacity and congestion ... 13

2.4 Transportation and the Supply Chain ... 16

2.5 Location theory ... 18

2.6 Transportation costs ... 19

2.7 Freight Generation and Freight Trip Generation Models ... 25

2.8 Summary and implications on the current research ... 28

3.0 Methodology ... 31

3.1 Research strategy ... 31

3.2 Data collection ... 32

3.3 Framework for data analysis ... 36

3.4 Research design quality ... 41

3.5 Limitations and problems ... 43

4.0 Results... 46

4.1 Traffic data ... 46

4.1.1 Road registration points ... 52

4.1.2 Ferry transit statistics ... 56

4.1.3 Traffic data summary ... 77

4.2 Time series analysis ... 78

4.2.1 Notes on the dependent variables ... 78

4.2.2 Relation between registration points ... 80

4.2.3 Data set preparation and analysis of heavy traffic into MR ... 82

4.2.4 Summary of regressions ... 92

4.3 Interviews ... 96

4.3.1 Company A ... 97

4.3.2 Company B ... 98

4.3.3 Summary of interviews ... 100

4.4 Synthesis ... 102

4.4.1 Comparison with previous research ... 102

4.4.2 Answering the research questions ... 106

4.4.3 Notes on the research ... 112

5.0 Conclusion ... 116

References... 117

Appendix 2.A ... 125

Appendix 3.A ... 139

Statistics collected. ... 139

Relevant statistics excluded. ... 144

Statistics in the final data set. ... 150

Statistics used outside time series analyses. ... 151

Appendix 4.A ... 152

Appendix 4.B ... 163

Appendix 4.C ... 164

Appendix 4.D ... 179

Appendix 4.E ... 200

Appendix 4.F ... 204

Appendix 4.G ... 207

Appendix 4.H ... 208

Appendix 4.I ... 211

Appendix 4.J ... 212

Appendix 4.K ... 215

Appendix 4.L ... 224

Appendix 4.M ... 232

Appendix 4.N ... 240

Appendix 4.O ... 249

Appendix 4.P ... 258

Appendix 4.Q ... 266

Appendix 4.R ... 270

Appendix 4.S ... 279

List of tables

Table 2: Curve estimations of heavy vehicle development at Lappestein. ... 52

Table 3: Trendlines and correlations for Gjøra and Steinløysa. ... 54

Table 4: Regression on heavy traffic at Steinløysa. ... 54

Table 5: Trendlines and correlations for registration points in Oppland. ... 56

Table 6: Linear trendlines and correlations for southbound traffic on Halsa-Kanestraum. ... 58

Table 7: Trendlines and correlations for northbound traffic on Halsa-Kanestraum. ... 59

Table 8: Regression with total trips as dependent. ... 60

Table 9: Regression with total number of trips as dependent variable. ... 60

Table 10: Regression with southbound trips on Halsa-Kanestraum as dependent. ... 61

Table 11: Regression with northbound trips on Halsa-Kanestraum as dependent. ... 61

Table 12: Regression with southbound heavy traffic on Halsa-Kanestraum as dependent. ... 61

Table 13: Regression with northbound heavy traffic on Halsa-Kanestraum as dependent. ... 62

Table 14: Trendlines and correlations for southbound traffic on Molde-Vestnes. ... 64

Table 15: Trendlines and correlations for northbound traffic on Molde-Vestnes. ... 65

Table 16: Regression with total number of trips on Molde-Vestnes as dependent. ... 65

Table 17: Regression with southbound trips on Molde-Vestnes as dependent. ... 66

Table 18: Regression with northbound trips on Molde-Vestnes as dependent. ... 66

Table 19: Trendlines and correlations for southbound traffic on Solavågen-Festøya. ... 69

Table 20: Trendlines and correlations for northbound traffic on Solavågen-Festøya. ... 70

Table 21: Regression with total number of trips on Solavågen-Festøya as dependent. ... 70

Table 22: Regression with southbound trips on Solavågen-Festøya as dependent. ... 71

Table 23: Regression with northbound trips on Solavågen-Festøya as dependent. ... 71

Table 24: Trendline functions for light and heavy vehicles on all ferry transits in MR. ... 73

Table 25: Trendlines and correlations for southbound traffic on Lote-Anda. ... 74

Table 26: Trendlines and correlations for northbound traffic on Lote-Anda. ... 75

Table 27: Overview of relations between southbound heavy traffic on Halsa-Kanestraum. ... 80

Table 28: Overview of relations between northbound heavy traffic on Halsa-Kanestraum. ... 81

Table 29: Model summary of std. regr. on aggregate of heavy traffic into MR. ... 84

Table 30: Model summary of stw. regr. on aggregate of heavy traffic into MR. ... 85

Table 31: Communalities in the factor analysis. ... 86

Table 32: Total variance explained and extracted factors from the factor analysis. ... 87

Table 33: Rotated component matrix. ... 89

Table 34: Model summary of the std. regr. on aggregate heavy traffic into MR with factors. ... 90

Table 35: Stepwise regression on aggregate heavy traffic into MR using factors. ... 91

Table 36: Coefficient estimates in stw. regr. on aggregate heavy traffic into MR. ... 91

Table 37: Summary of regressions. ... 93

Table 38: Regional transport of goods by railway. ... 96

Table 39: Overview of coefficients for statistically significant dummy variables ... 108

Table 40: Deseasonalised development of heavy traffic on Halsa-Kanestraum. ... 110

Table 41: Correlations between counties for April. ... 158

Table 42: Regression of the traffic index in MR for April ... 159

Table 43: Regression of the traffic index in MR for April, ex. Hordaland. ... 160

Table 44: Regression of the traffic index in MR for April, ex. Rogaland. ... 160

Table 45: Regression of the traffic index in MR for April using neighbouring counties. ... 161

Table 46: Regression of the traffic index in MR for April, inc. Nord-Trøndelag. ... 161

Table 47: Trendlines and correlations for ingoing traffic on Aukra-Hollingsholmen. ... 180

Table 48: Trendlines and correlations for outgoing traffic on Aukra-Hollingsholmen. ... 181

Table 49: Regression with total trips on Aukra-Hollingsholmen as dependent. ... 181

Table 50: Regression with ingoing trips on Aukra-Hollingsholmen as dependent. ... 182

Table 51: Regression with outgoing trips on Aukra-Hollingsholmen as dependent. ... 182

Table 52: Model summary for std. regr. on Gjøra using variables. ... 215

Table 53: Coefficient estimates for std. regr. on Gjøra using variables... 216

Table 54: Model summary for stw. regr. on Gjøra using variables. ... 217

Table 55: Coefficient estimates for stw. regr. on Gjøra using variables. ... 218

Table 56: Model summary for std. regr. on Gjøra using factors. ... 219

Table 57: Coefficient estimates for std. regr. on Gjøra using factors. ... 220

Table 58: Model summary for stw. regr. on Gjøra using factors. ... 221

Table 59: Coefficient estimates for stw. regr. on Gjøra using factors. ... 221

Table 60: Model summary for std. regr. on Bjorli Vest using variables. ... 224

Table 61: Coefficient estimates for std. regr. on Bjorli Vest using variables. ... 225

Table 62: Model summary for stw. regr. on Bjorli Vest using variables. ... 226

Table 63 Coefficient estimates for stw. regr. on Bjorli Vest using variables. ... 226

Table 64: Model summary for std. regr. on Bjorli using factors. ... 227

Table 65: Coefficient estimates for std. regr. on Bjorli using factors. ... 228

Table 66: Model summary for stw. regr. on Bjorli Vest using factors. ... 229

Table 67: Coefficient estimates for stw. regr. on Bjorli Vest using factors. ... 229

Table 68: Model summary for std. regr. on Lom Vest using variables. ... 232

Table 69: Coefficient estimates for std. regr. on Lom Vest using variables. ... 233

Table 70: Model summary for stw. regr. on Lom Vest using variables. ... 234

Table 71: Coefficient estimates for stw. regr. on Lom Vest using variables. ... 234

Table 72: Model summary for std. regr. on Lom Vest using factors. ... 235

Table 73: Coefficient estimates for std. regr. on Lom Vest using factors. ... 235

Table 74: Model summary for stw. regr. on Lom Vest using factors. ... 236

Table 75: Coefficient estimates for stw. regr. on Lom Vest using factors. ... 237

Table 76: Model summary for std. regr. on Anda-Lote using variables. ... 240

Table 77: Coefficient estimates for std. regr. on Anda-Lote using variables. ... 241

Table 78: Model summary for stw. regr. on Anda-Lote using variables. ... 242

Table 79: Coefficient estimates for stw. regr. on Anda-Lote using variables. ... 243

Table 80: Model summary for std. regr. on Anda-Lote using factors. ... 244

Table 81: Coefficient estimates for std. regr. on Anda-Lote using factors. ... 244

Table 82: Model summary for stw. regr. on Anda-Lote using factors... 245

Table 83: Coefficient estimates for stw. regr. on Anda-Lote using factors. ... 246

Table 84: Model summary for std. regr. on Halsa-Kanestraum using variables. ... 249

Table 85: Coefficient estimates for std. regr. on Halsa-Kanestraum using variables. ... 250

Table 86: Model summary for stw. regr. on Halsa-Kanestraum using variables. ... 251

Table 87: Coefficient estimates for stw. regr. on Halsa-Kanestraum using variables. ... 252

Table 88: Model summary for std. regr. on Halsa-Kanestraum using factors. ... 253

Table 89: Coefficient estimates for std. regr. on Halsa-Kanestraum using factors. ... 253

Table 90: Model summary for stw. regr. on Halsa-Kanestraum using factors. ... 254

Table 91: Coefficient estimates for stw. regr. on Halsa-Kanestraum using factors. ... 255

Table 92: Model summary for std. regr. on Halsa-Kanestraum using variables. ... 258

Table 93: Coefficient estimates for std. regr. on Halsa-Kanestraum using variables. ... 259

Table 94: Model summary for stw. regr. on Halsa-Kanestraum using variables. ... 260

Table 95: Coefficient estimates for stw. regr. on Halsa-Kanestraum using variables. ... 260

Table 96: Model summary for std. regr. on Halsa-Kanestraum using factors. ... 261

Table 97: Coefficient estimates for std. regr. on Halsa-Kanestraum using factors. ... 261

Table 98: Model summary for stw. regr. on Halsa-Kanestraum using factors. ... 262

Table 99: Coefficient estimates for stw. regr. on Halsa-Kanestraum using factors. ... 263

Table 100: Overview and coefficients of statistically significant variables. ... 266

Table 101: Overview and coefficients of statistically significant dummy variables. ... 268

Table 102: Overview and coefficients of statistically significant factors. ... 269

Table 103: Model summary for std. regr. on traffic into MR using final data set ... 270

Table 104: Coefficient estimates for std. regr. on traffic into MR using final set ... 271

Table 105: Collinearity diagnostics for the final set of variables. ... 272

Table 106: Model summary for std. regr. on traffic into MR using final factors. ... 272

Table 107: Coefficient estimates for std. regr. on traffic into MR using final factors. ... 273

Table 108: Model summary for std. regr. on Halsa-Kanestraum using final variables. ... 274

Table 109: Coefficient estimates for std. regr. on Halsa-Kanestraum using final variables. ... 274

Table 110: Model summary for std. regr. on Halsa-Kanestraum using final factors. ... 275

Table 111: Coefficient estimates for std. regr. on Halsa-Kanestraum using final factors. ... 275

Table 112: Overview of Adjusted R2 in the regressions in subchapters 4.2.3 and 4.2.4... 276

List of figures

Figure 1: Dependencies between infrastructure, transportation, and economic development. ... 6

Figure 2: Theoretical framework. ... 28

Figure 3: Research model. ... 32

Figure 4: Overview of all registration points originally included. ... 47

Figure 5: Registration points in MR, ST, Oppland, and SF. ... 48

Figure 6: Registration points originally included in the SF/Hordaland region. ... 49

Figure 7: Registration points in Rogaland and Agder originally included. ... 50

Figure 8: Traffic development at Gjøra, Rv70... 53

Figure 9: Traffic development at Steinløysa, Fv62. ... 53

Figure 10: Traffic development at Bjorli Vest, E39. ... 55

Figure 11: Traffic development at Lom Vest, Rv15. ... 55

Figure 12: Southbound traffic development at Halsa-Kanestraum. ... 57

Figure 13: Northbound traffic development on Halsa-Kanestraum. ... 59

Figure 14: Directional comparison of light and heavy traffic on Halsa-Kanestraum. ... 62

Figure 15: Directional balance of traffic on Halsa-Kanestraum. ... 63

Figure 16: Southbound traffic on Molde-Vestnes. ... 63

Figure 17: Northbound traffic development on Molde-Vestnes. ... 64

Figure 18: Directional comparison of traffic on Molde-Vestnes. ... 67

Figure 19: Directional balance of traffic on Molde-Vestnes. ... 68

Figure 20: Southbound traffic development on Solavågen-Festøya. ... 68

Figure 21: Northbound traffic development on Solavågen-Festøya. ... 69

Figure 22: Directional comparison for Solavågen-Festøya. ... 72

Figure 28: Direction balance on Solavågen-Festøya. ... 72

Figure 24: Southbound traffic development on Lote-Anda. ... 74

Figure 25: Northbound traffic development on Lote-Anda. ... 75

Figure 26: Directional comparison of traffic on Lote-Anda. ... 76

Figure 32: Directional balance on Lote-Anda. ... 76

Figure 28: Scree plot of eigenvalues per component number. ... 88

Figure 29: Ingoing traffic development on Aukra-Hollingsholmen. ... 179

Figure 30: Outgoing traffic development on Aukra-Hollingsholmen. ... 180

Figure 22: Polynomial trendlines for traffic on Aukra-Hollingsholmen. ... 183

Figure 32: Directional comparison on Aukra-Hollingsholmen. ... 184

Figure 24: Directional balance on Aukra-Hollingsholmen. ... 184

List of acronyms and abbreviations

NPRA Norwegian Public Roads Administration

MR Møre & Romsdal

ST Sør-Trøndelag

ÅDT Average traffic per day for one year

MDT Average traffic per day for one month

SCGE Spatial Computable General Equilibrium

GDP Gross Domestic Product

GVA Gross Value Added

EU European Union

USA United States of America

OLS Ordinary Least Squares

JIT Just-in-Time

UK Unites Kingdom

FHWA US Federal Highway Administration

PBE Person Vehicle Equivalent

VOT Value of travel time

VOR Value of travel time reliability

WTP Willingness to pay

SCM Supply Chain Management

O/D Origin/Destination

FG Freight Generation

FTG Freight Trip Generation

FA Freight Attraction

FP Freight Production

FTA Freight Trip Attraction

FTP Freight Trip Production

MCA Multiple Classification Analyses

MRIO Multiregional Input-Output

RFID Radio Frequency Identification Tags

SF Sogn & Fjordane

WFE Whole Fish Equivalent

VIF Variance Inflation Factors

ANOVA Analysis of Variance

CA Company A

CB Company B

HoReCa Hotels, Restaurant, Café

LSP Logistics Service Provider

1.0 Introduction

1.1 Background

The basis for this research is an observation made by the Norwegian Public Roads

Administration (NPRA) on the Halsa-Kanestraum transit, namely a steady increase in the southbound transportation on this transit, particularly among actors in the food and drink sector. The NPRA connects this increase with the increased capacity of E39 in Møre &

Romsdal (MR hereafter) and Sør-Trøndelag (ST hereafter), specifically the ferry transit Halsa-Kanestraum and the distances Renndalen-Staurset and Harangen-Høgkjølen.

E39, from Kristiansand to Trondheim, is 1,031 kilometres long with seven ferry transits, and requires about 20 hours to drive (NPRA, 2012, p. 35). The distance from Trondheim to Molde is 202 kms long with one ferry transit, and requires almost 200 minutes to drive.

About 1,100 to 17,000 vehicles drive the Kristiansand-Trondheim distance each day depending on the season. Of the two possible “ambition” scenarios in the E39 Coastal Highway Route, the most extensive scenario yields a reduction of transportation time from Kristansand to Trondheim of 530 minutes/8.5-9 hours, and requires fjord crossings and overall improvements of the road network; the less extensive, which is delimited to fjord crossings, gives a reduction of 297 minutes/5 hours. For the Molde-Trondheim distance, the figures are 48 minutes and 31 minutes, respectively. A case in point regarding the time disadvantage of transportation along E39 is the fact that much of the traffic between Rogaland and MR prefer a detour via Oslo, which often increases the distance driven by more than 400 kms (ibid, p. 52).

Primary challenges of using E39 include transportation time, transportation costs,

predictability, and traffic safety (ibid, p. 36). Reasons for the long transportation time are the number of ferries, which are slower moving than vehicles, induce waiting times before and after the crossing, and require users to adapt to the ferry departures/arrivals; low speed limits when passing through the many towns along E39; poor road quality; and limited capacity (ibid, p. 37). Transportation cost for a large/heavy vehicle (≥5.6 metres (NPRA, 2017a) or >3.5 tonnes (NPRA, 2014a; Hovi, Caspersen, & Ørving, 2017, p. II)) is NOK 20,962 (Kristiansand to Trondheim) and NOK 5,628 (Ålesund to Trondheim).

Predictability relates to problems in driving to the ferries, the promptness of the ferries, and whether there is enough capacity on the ferries. Many stretches of E39 lack decent

detours in case of emergencies, of which there were 2,900 in the years 2000-2009 (NPRA, 2012, p. 39).

NPRA’s experience from earlier projects that replaced ferries with bridges or tunnels shows a short-term traffic increase of 35-100%, and a somewhat lower short-term effect when removing toll stations on the same distances. In comparison, traffic growth on E39 has at best been 5.8% per year on average (NPRA, 2012, p. 47). The average ÅDT (average traffic per day for one year) on E39 is 6,000; each hour of reduced travel time will lead to a benefit to these 6,000 vehicles valued at roughly NOK 600-900 million per year (ibid, p. 51). Another source of traffic increases is the characteristics of the markets (e.g. customers’ structure, market shares among logistics providers and customers, and potential for new markets), and politics may play a role by offering economic incentives to alleviate bottlenecks in the transportation system (Askildsen & Senneset, 2006, p. 23).

Using an SCGE model to analyse macroeconomic effects of the E39 Coastal Highway, Hansen (2014) estimated the benefits of the project to mostly befall long interregional travels. For MR, the monetary benefit in 2020, measured by 2014-NOK and including toll fees, is NOK 48 million in the transportation of goods; for Sør-Trøndelag, the benefit is NOK 23.1 million (ibid, p. 33).

A precise definition of infrastructure is not widely accepted, but it is common to regard infrastructure as publicly owned stock providing a service to users and being fundamental for societies and economies, as the term encompasses e.g. transport, health, education, and communication (Rietveld & Bruinsma, 2012, ch. 2.2; Torrisi, 2009). Attributes of

infrastructure include (a) indivisibility, i.e. high costs of serving the first user and low costs of serving the following users; (b) non-substitutability, meaning the costs of infrastructure are high enough for the private sector usually being unable to establish alternatives; (c) immobility, which means infrastructure usually cannot be moved to another location; (d) polyvalence, which refers to the multi-purpose nature of infrastructure in production and consumption; (e) non-rivalness, as users do not have to compete over using the

infrastructure; and (f) non-excludability, or the impossibility to discriminate against users if one user already uses the infrastructure (Rietveld & Bruinsma, 2012, pp. 18-9).

Infrastructure investments¹ generally lead to a regional increase in productivity and economic growth (Fox & Porca, 2001, p. 66), and demand for transportation is derived

1 Infrastructure is in this thesis short for transport infrastructure unless otherwise specified.

from economic activity (Rodrigue, Comtois, & Slack, 2013). Transport infrastructure investments usually have positive effects on firms’ and regions’ productivity and accessibility, and improved infrastructure networks may cause redesigns of distribution networks by reducing transportation costs or enabling new distribution solutions (Rietveld

& Bruinsma, 2012). The observed increase in amount of traffic on Halsa-Kanestraum may indicate such adaptations. The goal of this thesis is to find the drivers of the amount of heavy traffic into MR through linear regression analyses using economic and infrastructure data, and interviews with two large transporters.

1.2 Research focus

Economic Geography tries to explain the geographically uneven distribution of human activity and agglomeration of economic activity, and accessibility is a primary cause of firms’ and consumers’ locations (Lafourcade & Thisse, 2011). For goods, two central constituents of accessibility costs are transport and time costs. New Economic Geography (NEG) considers interregional relations, and holds transport costs as an important location factor (ibid). Improvements in transport infrastructure lead to reduced transport costs, which has positive implications on a firm’s productivity, which subsequently increases regional or national gross domestic product (Melo, Graham, & Brage-Ardao, 2013;

Rietveld & Bruinsma, 2012, p. 38). Distribution, as it relates to supply chains and logistics, is the “steps taken to move and store a product from the supplier stage to a customer stage in the supply chain (Chopra & Meindl, 2013, p. 80). Distribution costs and times are deciding factors in the design of a distribution network, particularly when determining inventories, transportation, and facilities. Infrastructure investments may change

transportation costs sufficiently for firms to alter their distribution networks. Two parts of the theoretical framework of the thesis is thus economic geography and location theory, insofar the theories help the research and synthesis. Distribution networks will be a relevant topic in the qualitative research.

The concept of accessibility is the “potential of opportunities for interaction”, or the

“attractiveness of a node in a network taking into account the mass of other nodes and the costs to reach those nodes via the network” (Rietveld & Bruinsma, 2012, p. 33). Improving infrastructure tends to increase accessibility (ibid, p. 43). Increasing capacity of a

congested road system by adding another link might cause average transport costs to increase, because users of the system would change their routes to their new optimum

route, and contribute to congesting the road for other users (Pas & Principio, 1997). Part of this thesis’ analysis is therefore to examine redirected traffic flows.

The benefits of infrastructure investments are often related to the productivity in affected regions, as the investments generally lead to increases in regional productivity and economic growth, but the strength of this causal relationship is questionable due to

insufficiently critical evaluations (Fox & Porca, 2001, p. 66). The productivity effects from transport infrastructure investments are highest for road transport and mainly affect

manufacturing, construction, and the primary sector of the economy, and the effects are lesser in European countries compared to the USA (Melo, Graham, & Brage-Ardao, 2013).

Economic development can be defined as “the observed pattern, across countries and across time, in levels and rates of growth per capita income” (Lucas, 1988, p. 3), and economic performance is measured by employment, personal income, and value added (Fox & Smith, 1990, p. 53). Economic growth and development refer in Banister &

Berechman (2001) to boosted economic activity that is traceable to particular infrastructure investments and that can be proven to be beneficial beyond the direct monetary values of transportation gains. The terms “economic development” and “economic growth” seems to be used interchangeably in most literature, and will be used interchangeably in this thesis.

The economic performance of a geographical region may refer to measurements such as the overall average wage or the employment growth (Porter, 2003). Performance, as it relates to a firm’s logistics, is the measure of the extent to which objectives are achieved;

these objectives include e.g. effectiveness, efficiency, quality, profitability, and

productivity (Chow, Heaver, & Henriksson, 1994). Productivity is the relation between inputs and outputs, i.e. outputs divided inputs (Tangen, 2002; Diewert, 1992, p. 317).

Productivity differences are either measured by the change in maximum output using a specific level of inputs, or the change in minimum input to yield the same level of output (Caves, Christensen, & Diewert, 1982, p. 1402). Productivity is not measured in monetary terms, and should not be mixed with performance, efficiency, effectiveness, or

profitability. Ulstein, Skogstrøm, Aalen, & Grünfeld (2015) looked at the macroeconomic effects of the entire E39 project. They estimated that counties not subject to E39-related investments will experience positive productivity effects, but carry a proportionally large amount of the costs. The negative effects of prioritizing transportation infrastructure in the western part of Norway will be allocated to a larger part of Norway compared to the regions that will have an increase in construction activity. Transport infrastructure

improvements may also have adverse effects on national production and employment by changing the optimal location of economic activity (Rietveld & Bruinsma, 2012, p. 58).

Though this thesis will not examine effects of the E39 investments outside possible transportation pattern changes, it will include measures and proxies of economic performance in various sectors in the data analysis, and firms’ performance will be touched upon in the interviews.

1.3 Research aim and research question

The overall aim of this study is to examine effects of E39 investments on heavy traffic into MR. With the NPRA’s traffic observations and chapter 1.2 as basis, the research questions are:

RQ1) To what extent can the observed changes in transportation be attributed to infrastructure investments along E39 in MR and Sør-Trøndelag?

This question will be approached with regression analyses, discussed in chapter 3. The goal is to find a model that includes regional and economic figures along with

infrastructure projects to estimate each figure’s and project’s contribution to heavy traffic into MR.

RQ2) Will the amount of transportation between ST and MR continue to increase?

This question can only be answered if a well-functioning model for heavy traffic into MR is found.

RQ3) How have infrastructure investments related to E39 in the MR region affected freight transporters?

This question will be answered by the qualitative research, which forms the second method of data triangulation.

As seen above, the observed changes may be the result of distribution network redesigns.

A related question is therefore what factors determine distribution network design. This is not a research question, but it is central to the research as it helps in the data collection, analysis, and interpretation.

There are four main approaches to investigating the issue of infrastructure’s productivity (Torrisi, 2009): (a) Modelling using production functions, making infrastructure an input;

(b) Modelling using cost functions, making infrastructure a cost-saving factor; (c)

Modelling economic growth using infrastructure as a factor for boosting growth; and (d) modelling data to analyse the relationship between data series such as infrastructure, GDP, and employment. McKinnon & Woodburn (1996, p. 148) state that the most accurate method of determining what causes changes in freight movement is to collect information about transportation requirements directly from a large number of firms (though

quantitative data may be difficult to acquire, as tonne-km statistics are irrelevant for many firms). In this thesis, the fourth approach above is combined with interviews with a small number of firms to find the drivers of heavy traffic into MR.

The research starts with an examination of heavy traffic into, within, and out from MR.

The primary analysis is a time-series analysis with various traffic registrations as

dependent variables, and the independent variables are primarily based on findings in the literature review. Interviews with two firms responsible for relatively large transport volumes on a national level supplement the time-series analysis.

There are a few complicating facets of the selected quantitative approach. One is

multicollinearity among the explanatory variables, which likely leads to the contribution of infrastructure being wrongly estimated (Rietveld & Bruinsma, 2012, p. 359). Another complicating facet of the quantitative approach is the directional causality dubiety and feedback loops between infrastructure and regional economic development, and regional economic development and transport flows, illustrated by the following model (Torrisi, 2009; Rietveld & Bruinsma, 2012, p. 334):

Figure 1: Dependencies between infrastructure, transportation, and economic development.

(Rietveld & Bruinsma, 2012, p. 334).

The causality will cycle back-and-forth, as economic development is continuous while infrastructure improvements can be described as stepwise. Each infrastructure

improvement thus boosts economic activity over an unknown number of years and economic activity causes infrastructure improvements when it reaches a certain level (presumably when congestion hinders further development; this will be looked into in chapter 2). As there have been changes in the transport infrastructure during the time period used in this research, and assuming there is a statistically significant relationship between transport infrastructure and economic development in MR, the directional causality may have shifted during the past ten years with a consequential implication on the main determinant of transport flows. Before the infrastructure investments, transport infrastructure was static whereas economic activity was (and still is) dynamic, meaning transport flows were directly and primarily determined by economic development and less so by the existing infrastructure, and economic activity occurred within the bounds of the existing infrastructure; after the infrastructure investments, transport flows are expected to adapt to the new infrastructure network, which means the infrastructure directly affects transport flows and indirectly through boosting economic development. The problem of directional causality is exacerbated by measures of both infrastructure and economic development being explanatory variables in the analysis, and suggests the time series analysis could be split into periods of the old and improved infrastructure to better see the improvements’ effects, but that is not possible because of lack of data. Using dummy variables for infrastructure should remove potential collinearity between infrastructure and economic development measures in the analysis, and alleviate the causality problem.

Rietveld & Bruinsma (2012, p. 363) found that, in general, economic activity locations are affected by infrastructure projects to a small degree, and that the effects depend on the characteristics of the existing network and regional economies. Alleviating bottlenecks or constructing new links will have the greatest effects if the infrastructure network is

properly functional in the first place, certain economic sectors rely on the type and quality of infrastructure, and macro-economic downturns makes the private sector less responsive to infrastructure investments. These characteristics complicates comparability between studies as they need to be examined, but that is beyond the scope of this thesis; only the result of the analysis can be compared.

1.4 Value of this research

There are four aspects of the research related to the E39 project. The Fjord Crossing aspect relates to the technological possibilities of crossing the fjords; the Energy aspect evaluates

the potential of combining fjord crossings with renewable energy production; the Implementation strategies and contracts aspect encompasses the applied approaches towards the E39 project planning, procurement, and contracting; and the Society aspect highlights the effects of a ferry-free E39 on the development of economies, housing and labour markets, value creation, and structural changes of societies in general (NPRA, 2012, p. 47). As this thesis looks at the transportation effects, partly from higher frequency on Halsa-Kanestraum, it is a part of the Society aspect.

Case studies are often criticized for being difficult to generalize from (Yin, 2009). Banister

& Berechman (2001, p. 211) note that specific case studies on the effects of infrastructure investments on economic development need to focus on the micro-level, i.e. where

investments occur, but case studies of multiple levels and locations need to be combined in order to enable generalizations from and for all levels (micro to macro). The rationale for local case studies is that economic development tends to occur in the vicinity of

infrastructure investments; consequently, case studies need to be on a local level in order to register the changes, as the changes would disappear in aggregated figures. These changes may be e.g. economic development at the local level, level of income, employment figures, and accessibility; purely considering transportation costs is too

narrow for assessing effects on economic development, which require a wide array of costs and benefits. This current research adds to the existing stock of research by studying the transportation effects of infrastructure investments at a regional level.

1.5 Structure outline

This case study will examine the observed transportation changes into MR. The design is an explanatory mixed method, which entails first collecting and analysing quantitative data, use the results and literature review to guide the qualitative data collection, and finally compare the results of both methods. In the quantitative phase, data on traffic, infrastructure, and economic activity will be collected from the NPRA and Statistics Norway to examine how economic activity and infrastructure investments relate to transportation into MR. The qualitative phase will draw on the literature review and quantitative analysis to guide the qualitative data collection, and will be considered up against the quantitative results. For this qualitative phase, the plan is to examine the effect E39 investments have on transporters’ distribution network.

The thesis is structured as follows. Chapter 2 - Theory and Literature Review provides the theoretical context of the research and draws on relevant studies to show the current

knowledge in the research area. The studies act as inspiration for the methodology and data analysis, and are summarized at the end of the chapter. Chapter 3 – Methodology presents the data collection approaches, the theory underlying the data analysis, and how the data analysis will be performed. Chapter 4 – Results describes the collected data and data analysis, discusses the results, and answers the research questions. Chapter 5 summarizes and concludes the thesis.

2.0 Literature review

This chapter highlights theory and research within various topics briefly touched upon in the introduction. There is a plethora of theory related in some capacity to the research at hand, and one goal in writing the literature review was to collect only what is applicable from e.g. Economic Geography while ensuring its relevance to the research questions and methodology. The utility of chapters 2.1 (Infrastructure investments and regional growth) and 2.5 (Location theory) is mainly in establishing context and in data collection; chapter 2.2 (Role of decoupling) to expand on the relation between infrastructure, economic activity, and freight transport; 2.3 (Capacity and congestion) and 2.4 (Transportation and the Supply Chain) to help the qualitative analysis and research synthesis; 2.6 in data

collection, interviews, and research synthesis; and 2.7 (Freight Generation and Freight Trip Generation Models) in conducting the quantitative analysis. Chapter 2.8 summarizes the literature review and findings relevant to the quantitative analysis and interviews in chapter 4.

2.1 Infrastructure investments and regional growth

Transport infrastructure is often seen as the primary tool for the economic growth of a region and nation (Elburz, Nijkamp, & Pels, 2017), due to infrastructure affecting unemployment rates, personal income, transportation costs and time, access to suppliers and buyers, and relieved congestions (Agbelie, 2014). In their meta-analysis, Elburz, Nijkamp, & Pels (2017) found that studies using data from the USA are more likely to find public infrastructure to have a negative impact on regional growth, while studies using data from members of the EU likely find positive effects. Their results support the notion that infrastructure has positive effects up to a certain level of development; the USA are past this level, while some EU member states are not. If the infrastructure in question is related to air, rail, or sea transport, regional outcomes are more likely to be negative.

Research on the link between infrastructure investments and economic growth reveals large differences in the relation: some find investments had negative effects on economic growth while others find positive effects regardless of whether the infrastructure is new or improved, and erroneous methodology is usually to blame for the varying results (Fox &

Porca, 2001, p. 66). Another reason may be the scale of analysis, as Munnell (1992) notes smaller scales will lower the effects of infrastructure on growth. Furthermore, regional economic and demographic characteristics determine how effective infrastructure

investments in the region will be for economic growth (Bougheas, Demetriades, &

Mamuneas, 2000). By extension, infrastructure investments’ catalytic effects on traffic volume through economic growth will at best be equally difficult to determine.

Previous national-level studies on the infrastructure effects have used time series or cross- sectional data usually in the form of a Cobb-Douglas function with variables related to labour, private capital, and infrastructure expenditure (Agbelie, 2014). The economic indicators Agbelie uses as variables were GDP, producer price index, the contribution of the service sector to GDP, and demographic data (employment and unemployment rates);

the variables for transport infrastructure data were infrastructure expenditures and

maintenance, and route-kilometres (highway density, measured as km/km²). Agbelie used a logarithmically transformed production function in an OLS (Ordinary Least Squares) model. The parameter estimates for Norway were 0.083 for highway expenditure (1%

higher expenditure leads to 0.083% higher GDP) and 0.002 for railway expenditure (0.002% higher GDP from 1% higher railway expenditures); other variables’ parameters were only presented as averages for the 40 examined countries.

The scale of analysis is either national, regional, or local (Banister & Berechman, 2001).

The national scale is concerned with the national infrastructure network with its nodes and international connections, the network quality, and the quality of economic externalities and political factors. Accessibility is a fundamental aspect of analyses at the regional level, and can be measured by changes in employment activities, housing, retail locations, and access to skilled labour. Modelling the effects of infrastructure investments on regional economies is difficult because of the investments and adaptations often occurring at

different times; the markets may adjust their processes after, during, or before investments, which makes the direction of causality and magnitude of effects ambiguous. The scale of analysis potentially affects the estimated output elasticity of infrastructure investments, in that the more local the scale of the analysis is, the lower the elasticity is (Munnell, 1992;

Berechman, Ozmen, & Ozbay, 2006).

2.2 Role of decoupling

Economic activity generates freight transportation, but the magnitude is unclear (Müller, Klauenberg, & Wolfermann, 2015). The authors claim no economic indicators other than GDP (Gross Domestic Product) and GVA (Gross Value Added) are appropriate for analysing economic activity’s generation of freight transportation. Changes in the strength

of this relation is referred to as coupling/decoupling, and it is advisable to avoid using aggregated GDP if possible (Meersman & Van de Voorde, 2013). McKinnon (2007) found, based on aggregated data, three causes to be responsible for two thirds of the decoupling: amount of foreign road transport operators, truck utilization shifting to other modes, and higher road freight prices.

German traffic prognoses draw on disaggregated economic structure and development along with population to determine freight volume, but the resulting freight transport depends on the applied model (IWH, 2006; Müller, Klauenberg, & Wolfermann, 2015).

Müller, Klauenberg, & Wolfermann investigated the relation between disaggregated GVA and transported goods tonnage in Germany. Weighted GVA for 24 categories of goods described economic activity in 59 industries, and the authors found statistical significance for the relation in 91% of sampled goods.

While GDP and transport performance (in tonne-kms) are closely related as transportation is derived from economic activity, the use of GDP to forecast freight transport may be ill- advised. Meersman & Van de Voorde (2013, ch. 2) demonstrate that it is unwise because (a) the constituents of GDP are under constant change; (b) several methods of connecting GDP and transport are inappropriate; and (c) the relationship between economic activity and transport is changing. These changes are caused by economic globalisation, policies intended for decoupling of economic activity and transport, and behavioural aspects in business. Examples of the poor forecasting capabilities of GDP for the amount of road haulage are the forecasts for Belgium, Germany, France, the Netherlands, and the United Kingdom, for which forecasted road haulage growth with GDP growth of 1% were 50% of actual road haulage growth (ibid, p. 22). McKinnon (2007) indirectly supports the

argument for not using GDP, as he shows the GDP of the UK grew 20% from 1997-2004 while road freight volume was stable. Using a fully modified least squares approach to estimate the relation between total freight, manufacturing production index, import penetration, and export performance, Meersman & Van de Voorde (2013, p. 38) find that manufacturing sector growth is the main driver of short-run freight transport growth rate, and both international trade indicators and manufacturing sector activity affect freight transport performance, and they conclude that disaggregation of economic activity indicators needs to be based on microeconomic motivations of freight transport and shipper companies.

2.3 Capacity and congestion

There are two kinds of congestion: recurrent, which is a constant overloading of capacity;

and non-recurrent, which is caused by random events such as accidents (Rao & Grenoble, 1991, p. 4). The three perspectives on congestion are the shippers’, carriers’, and

governments’. The viewpoint of carriers is through timing and routing decisions; of governments, through aggregate traffic performance and infrastructure improvements; of shippers, through transit time and service reliability. With numerical examples of the effects congestion can have on JIT deliveries, Rao & Grenoble show that alternatives for reducing mean and standard deviation of transportation times is to change facility location and the channel structure. Freight congestions can be alleviated by increasing capacity, maintenance of infrastructure, and improving efficiency (U.S. Government Accountability Office, 2008). The American Transportation Research Institute (2008) measures

congestion, or Total freight congestion value, as the “sum of the hourly product of miles per hour below free flow and vehicle population by hour” (Long & Grasman, 2012, p. 97).

Direct costs of congestion are a function of transit time, while indirect costs are primarily related to unreliability. In the UK, 5% of total transit time can be attributed to congestion- related delays, and resulting industry costs (related to freight) attributable to congestion can amount up to £1.3 billion, 54% of which are indirect (McKinnon, 1999, pp. 112-3).

However, these figures are indicative, and considered inaccurate and not representative of the real costs because of poor methodology. Other estimations find a direct cost increase to shippers of 8-11%, which translates to about £96-132 million indirect costs in the UK. A drawback of these two estimations is the assumption that the managers asked possess correct and plenty information needed to estimate congestion costs (ibid, p. 113).

Respondents in McKinnon’s survey claim that congestion does only influences their logistical systems operations to a small extent (ibid, p. 118). On a scale from 1 (major modification) to 5 (no change), the average response is 3.9 for inbound and 3.7 for outbound logistics. Close to all managers claim congestion does not influence the

distribution centre inventory levels, and thus low impact on inventory costs (ibid, p. 121).

Serious congestion problems at the operative level can have strategic impacts, such as the allocation of demand to distribution centres. However, McKinnon found that such changes were made for other reasons than congestion (ibid, p. 123). The number of terminals, however, may increase as a result of congestion and a shift towards medium- and long- term planning horizons (ibid, p. 124). Logistics Service Providers are likely to suffer most

adverse effects from congestion, but McKinnon notes that his survey is not large enough to be generalised from (ibid, p. 126).

Regan & Golob (1999) investigated the perception freight operators in California have of capacity, congestion, and intermodal connections. The research aim was, among others, to uncover how the operators experienced congestion’s impact on trucking operations and the operators’ views on infrastructure investments. Representatives for the companies were asked questions related to possible effects of congestion on their operations, and how they would rate the effectiveness of different congestion relief measures. 18% of respondents do not think congestion is a serious problem; 64% and 18% think congestion is a

somewhat serious and critically serious problem, respectively. 40% of these experience higher costs, and 50% experience scheduling issues from unreliability. 27% of respondents often miss schedules because of congestion, 62% miss schedules sometimes, and the schedules of 11% are unaffected by congestion. On the question of congestion relief, respondents agree that adding more freeway lanes would be very effective (median score 5 out of 5 possible). In the long run, the extent of this effectiveness is limited by induced demand.

Induced demand, in the context of congestion and increased road capacity, refers to improved infrastructure attracting more users and thereby reduce the new infrastructure’s effect on congestion (van der Loop, Haaijer, & Willigers, 2016). It is related to latent demand, which refers to a user’s cost outweighing the benefit of using a certain road;

latent demand may become realized demand, or induced travel, after infrastructure improvements. The US Federal Highway Administration define induced travel as “the observed increase in traffic volume that occurs soon after a new highway is opened or a previously congested highway is widened” (FHWA, 2013), and note that much of the induced travel is simply redirected traffic, which means the net increase of vehicle kilometres is minimal. Van der Loop, Haaijer, & Willigers (2016, p. 74) define induced demand as “increase in car use per day on the total network, in terms of the vehicle kilometres resulting from road expansion”; induced demand thus refers to the usage of light vehicles, while induced traffic refers to the traffic changes on the infrastructure. In the Netherlands, 119 lanes were added from 2000 to 2012, and benefits for companies and citizens are estimated at EUR 470 million, and EUR 480,000 per lane kilometre in 2012 (ibid). 83% of benefits are in travel time and the rest in travel time reliability. 73% of the travel time and 81% of reliability is estimated to benefit passengers, while 27% and 19%

benefit freight transport, to which only 8% of hours saved can be attributed. The induced demand following infrastructure capacity increases in the Netherlands is similar to that of other countries. An increase in lane metres of 10% increases car use by 3-5% over the following five years. It is unclear how much of this increase is caused by changes in route choice, but in the Netherlands it is estimated to be 40% of a 5% car use increase (ibid, p.

75). The changes in car use before and following the addition of 119 lanes was estimated using a regression model that included control of changes in factors other than lane metres that can influence car use; these factors include policy measures, taxes, jobs, and car ownership rates per municipality. The regression was performed on 30,000 km of road divided into 3,000 individual sections, and traffic data was monthly for the years 2000- 2012.

2.3.1 Ferry transit capacity

Jørgensen, Mathisen, & Solvoll (2007) wrote a report on capacity as it relates to the ferry industry. Capacity on a ferry transit refers to the number of ferries, the capacity on each ferry, and the transit frequency. Increasing the frequency is the primary method of reducing total travel time as the potential waiting time decreases, though this effect is subject to decreasing returns to scale. Without increasing the frequency, the two primary methods of increasing the service level of a transit are to add more ferries and substitute for larger ferries (ibid, p. 36). Frequency without adding ferries is mostly relevant to increase if the current frequency results in ferries idling. The difference between the different capacity management strategies, measured as the maximum PBE (person vehicle equivalent) per km of the transit distance, decreases as transit distance increases because the distance limits number of possible round-trips (ibid, p. 38). For transporters, higher frequency is likely to lower the generalised transportation costs more than larger ferries (ibid, p. 39). On the Halsa-Kanestraum transit, the transit time is 20 minutes with ten minutes for discharging/loading vehicles, and there are three departures per hour (Fjord1, 2017). Moving from two to three ferries has therefore yielded a reduction in waiting time at the quay from 30 minutes to 20 minutes for a truck arriving immediately after a

departure. Capacity on the transit is insufficient as of September 2017, as 2.6% of traffic had to wait for the next ferry (Lehman, 2017).

2.3.2 Value of travel time and reliability

Carrion & Levinson (2012) reviewed studies on the value of travel time (VOT) and of travel time reliability (VOR) in passenger transport. VOT is the willingness to pay (WTP) for reduced travel time, in monetary terms, and VOR is the WTP for enhanced travel time predictability/reduced travel time variability. Demand and supply side fluctuations affect traffic operations on the infrastructure, which affects the travel time. On the demand side, influencers include seasonal effects, demand on connected infrastructure, population characteristics, traffic mix, and users’ response to traffic information. On the supply side, influencers include incidents and road projects, weather, road design and regulations, and traffic control and management. Carrion & Levinson show that the VOR mean is about

$40 per hour for a commuter.

Shams, Asgari, & Jin (2006) performed a meta-analysis of VOR in freight transportation.

Reliability is increasingly important because supply chains are becoming less based on an inventory/push system and more on a timely replenishment/pull system. Shippers’

competitive edge therefore relies on the reliability of their supply chains; carriers benefit from higher time reliability to minimise costs and maximise efficiency; and customers avoid costs related to untimely deliveries (ibid). The meta-analysis is inconclusive because of a low number of relevant studies, and the studies’ outcomes are of little value because of differences in units and definitions.

Feige (2007, p. 104) concludes that, based on interviews with transport managers, there is a strong preference for transport service quality among customers, and reliability is of about the same importance as direct transport costs. Feige measured the likelihood of delay to be of similar importance as the magnitude of actual delay, while average transportation speed is of far lower importance; other important infrastructure characteristics are

predictability and punctuality, and all these characteristics need to be accounted for when making infrastructure investments in order to maximise effects on economic growth.

2.4 Transportation and the Supply Chain

The structure of a supply chain reflects the measures taken to manage and integrate

material and information flow from earliest suppliers to end customers (Morash & Clinton, 1997). Transportation is integral to maximising customer value and minimising supply chain costs (ibid). Transportation influences value and costs through e.g. time

compression, reliability, standardisation, information systems support, flexibility, and

customisation (ibid, p. 5). Time compression refers to better operational strategies and coordination and information sharing, all of which enable higher speed of products through the chain. Reliability, as it relates to transportation, refers to the variability in shipment dispatches and arrivals and the integrity of deliveries (correct items, correct number of items, etc.). Standardised processes, practices, and policies increase predictability and efficiency of supply chain flows. Incorporating flexibility with regard to e.g. time and location helps fulfil customer demands. Higher degree of supply chain integration can also be achieved through customising transportation solutions for certain market segments or parts of the supply chain.

Determinants of the design of distribution systems are inventory and transportation costs, service quality, and responsiveness; the design is ultimately a trade-off between costs and responsiveness (Nozick & Turnquist, 2001). Responsiveness is a strategy for dealing with uncertainty, and is driven by demand rather than forecasts; it is built on flexibility in supply chain processes and the ability to provide customised solutions in the shortest time possible (Christopher, 2011, p. 23). A primary determinant of both fixed and variable inventory and transportation costs is the number of facilities (Nozick & Turnquist, 2001).

Freight transport decisions include, among others, location of plant, supplier, and shipment sizes; in the medium term, distribution centres locations, inventory, allocation of supply and demand to distribution centres, and shipment sizes; in the short term, transport modes and means, shipment sizes, scheduling, and routing (Tavasszy & De Jong, 2013, p. 7). The four logistics costs categories are transport, storage, ordering and handling, and risk costs (ibid, p. 67). Transport costs include expenses related to driver, fuel, vehicle, use of infrastructure, and capital cost during transport. Storage costs include capital, building and equipment, land, and energy. Ordering and handling costs include handling, picking and packaging, ordering, and order processing. Risk costs include loss and damage, spoilage, obsolescence, and being out of stock.

There are five main categories of distribution network structure drivers: demand, supply, goods, logistics system, and resources (Tavasszy & De Jong, 2013, p. 68). In the demand category, drivers are the volume of demand, spatial distribution of facilities and customers, volatility in demand, and customers’ required lead time. In the supply category, the drivers are also volume of supply and spatial distribution, along with reliability and possible lead time. In the goods category, drivers are the value density, handling requirements, and perishability. Logistics system drivers are automation of processes, scope of planning, and

planning capabilities. Drivers in the resources category include transport infrastructure characteristics, available real estate for facilities, and energy.

2.5 Location theory

Location theory, a part of Regional Science, “deals with what is where” with the intention of explaining why economic activities occur at certain geographical locations (Thisse, Button, & Nijkamp, 1996, p. xvii). Location models are often applied to distribution network design, and many models base calculations on transportation cost from facility/- ies to customer/-s, transportation cost from supplier/-s to facility/ies, transportation times between nodes in the distribution network, and capacity at nodes and links between nodes;

the models’ objective is usually to minimise total system cost (ReVelle, Marks, &

Liebman, 1970; Crainic & Laporte, 1997). Location problems at the tactical level of decision making are always related to a transportation system in which one vehicle of convoy transports freight of (potentially) different origin to (potentially) different customers (Crainic & Laporte, 1997, p. 418). The problems can be classified as either service network design problem or vehicle routing problem. The result of tactical planning is the transportation plan, which daily guides the transportation system’s operations and aids in examining consequences of strategic planning. Problems at the operational level are usually the scheduling of services, repositioning or distribution of empty vehicles, crew scheduling, and resource allocation (ibid, p. 429).

Location models can also have a different objective, namely to maximise profits (Greenhut, 1952). An example is the solution to the Weber problem; this profit- maximising location is not necessarily the location at which total system costs are

minimised, but it is the location at which total system costs are minimised given a certain level of sales. The Weber problem can be transformed into a gravity model, which can be used to explain inter-regional trade flows (Tavasszy & de Jong, 2013). Gravity models can be adapted to a formula for inter-regional/bilateral trade, and the formula becomes a function of the size of the regions’ GDPs, the distance between them, and a factor to represent bilateral resistance (e.g. a function of variables such as costs). Moses (1958) placed Weber’s location theory within economic theory by making it a part of production theory. Moses then found that the following primary determinants ultimately decide the location of the firm: the price of inputs; transportation rate of inputs and outputs;

geographic location of suppliers and markets; production function; and the demand