A disaggregate freight transport model of transport chain and shipment size choice

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ADISAGGREGATEFREIGHTTRANSPORTMODELOFTRANSPORTCHAINAND SHIPMENTSIZECHOICE

E. Windisch

Laboratoire Ville Mobilité Transport, Université Paris-Est, France G. C. de Jong

Significance (The Netherlands), ITS Leeds, CTS Stockholm, NEA R. van Nes

S. P. Hoogendoorn

Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, The Netherlands

1. INTRODUCTION

The field of freight transport modelling is relatively young compared to passenger transport modelling. However, some key issues in freight policy, like growing freight shares on the road, advanced logistics concepts or emerging strict freight transport regulations, have been creating increasing demand for freight transport models that satisfy the increased need for detail. (Tavasszy 2006)

Especially regarding the increased detail needs, shortcomings in existing freight transport models are found. Firstly, mainly due to lack of disaggregate data in freight transportation, most existing freight transport models are aggregate models (De Jong 2007) rather than disaggregate models. This can result in a big loss of precision, particularly if aggregate groups of freight shipments are not homogenous with respect to their attributes. Secondly, logistics concepts, being a key issue in the development of freight transport, are insufficiently incorporated in freight models. Practically every freight transport model system proposed in literature or used in practice suffers from a lack of explicit treatment of logistics choices or explicit incorporation of a logistics costs perspective. Thirdly, although it has been widely accepted that decision makers make their choices of mode and shipment size simultaneously (Abdelwahab 1998), many freight transport models consider key decisions in an oversimplified sequential order, or ignore shipment size altogether.

Table 1 gives an overview of selected freight transport models that remedy one or more of the above mentioned deficiencies of many freight transport models.

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Table 1: Overview of Attributes of Selected Freight Transport Models This study proposes a transport chain and shipment size choice model for freight transport that overcomes all of the mentioned drawbacks simultaneously by combining the following three characteristics:

(1) The model is based on disaggregate data in order to reflect the diversity of decision makers and the individual level of decision making within freight flows between origin and destination zones.

(2) The model comprises a logistics perspective by:

 modelling the shipment size of shipments, which means that decisions relating to inventory strategies are endogenous

 modelling the configuration of the delivery chain (number of legs, mode used for each leg) rather than assuming one single aggregate mode

 incorporating a holistic cost approach, which accounts for all assessable logistics costs of the delivery process.

(3) The model considers the choices of shipment size and transportation chain size simultaneously by incorporating both choices in the endogenous choice set.

Model attributes Model year Ba se d o n dis ag gre gate da ta Lo gis tics co st ap pro ach Sim ulta ne ou s m od e an d s hip me nt siz e ch oic e m od elin g Chiang et al. 1981

•

-

•

Mc Fadden et al. 1985

•

-

•

Abdelwahab and Sargious 1992

•

-

•

SMILE+ (Tavasszy) 1998 -

•

-SCENES incl. SLAM

module 2000 -

•

-Blauwens et al. 2001

•

-EUNET 2.0 (Ying &

Williams) 2005 -

•

-Swedish national freight

model (De Jong et al.) 2008 -

•

Norwegian national freight

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Decision-makers of the modelled choices are sending firms (or their commissioned shippers respectively), making decisions concerning transportation chains and shipment sizes of individual shipments simultaneously.

The estimation of the proposed disaggregate freight transport model is done by applying discrete choice theory. A random utility choice model (RUM) is established. Multinomial Logit (MNL) models and also Nested Logit (NL) models describing correlations between choice alternatives are estimated and evaluated with respect to their goodness of fit.

First, the data sources used are described and an overview of the collected data is provided, giving already an impression of tendencies in decision-making. It is briefly sketched how logistics costs were estimated. Next, the modelling approach, using Multinomial and Nested Logit models, is shown. Thereafter, modelling results are shown and described in more detail. Finally, conclusions of this study and recommendations for future work are given.

2USEDDATASOURCES

The data used for this study stems from the Swedish Commodity Flow Survey (CFS) 2004/05 that was commissioned by SIKA (The Swedish Institute for Transport and Communications Analysis) and carried out by Statistics Sweden. The objective of the survey was to obtain and maintain an overall picture of the need to move goods within and outside of Sweden. Locations between which shipments were carried out are given; information on attributes of sending units and shipments, as well as information on observed choices of these sending units concerning the sizes of shipments and used transport chains is available. Containing this information, the Swedish Commodity Flow Survey is a unique data source in Europe. Almost 3 million entries are reported.

The second data source was used to enrich the data set of the CFS in order to derive the attributes of the choice alternatives. Data from the Swedish national freight model of the Samgods group delivers information on the Swedish transport network and infrastructure. Further, the logistics model project of the national freight model delivered various logistics cost information. Combining this information made the derivation of cost attributes possible.

In the following it is shown which data of the CFS were actually used for model estimation. Thereafter an overview of the data is given. Finally, it is briefly explained how available data sources were used to derive logistics costs.

2.1 Used Data of the Swedish Commodity Flow Survey

A selection of the vast data set obtained by the CFS was made. Only shipments that were stated to have origin and destination within Sweden were selected. Further, only shipments for which complete and valid information on the following variables (necessary for cost and/or model estimations) was available were used:

- origin of shipment - destination of shipment - value of shipment - weight of shipment - cargo type of shipment

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- sequence of transport modes used for the shipment process

- proximity of sending unit of shipment to and use of private siding for rail transport

- proximity of sending unit of shipment to and use of quay for sea transport - time of year the shipment was sent

Since the network data we obtained showed that some transportation chains (sequences of transport modes) as stated in the CFS were not in line with the actual availability of transport modes in the Swedish freight transport network, some logical assumptions concerning misinterpretations of used transport chains by survey respondents were made. It was assumed that starting or ending lorry legs within transportation chains were often not stated in the CFS as they were either assumed to be irrelevant to the survey or as survey respondents were not aware of ending legs of a transportation chain. Transportation chains lacking these starting or ending lorry legs were therefore recoded, which subsequently allowed determining costs of choices made by using available network data. All relevant transportation chain choices (transportation chain choices that remained after the data selection and after recoding with a frequency of higher than 100 in the data set) can be seen in table 2.

Table 1: Relevant Transportation Chain Choices

2.2 Overview of Data obtained by the Swedish Commodity Flow Survey

Using stated information about the cargo types of shipments allowed classifying all observed shipments into containerised and non-containerised shipments. This categorisation was used for determining costs of choice alternatives. The following table shows the modal split of all observed shipments (considering the main mode, which was assumed to be the mode in the middle of the transport chain) as well as their weight and value averages distinct for containerised and non-containerised shipments.

#

1 lorry

2 lorry vessel lorry

3 lorry rail vessel lorry

4 lorry rail lorry

5 lorry air lorry

6 lorry lorry lorry

7 lorry ferry lorry

8 lorry vessel

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Table 2: Containerised and Non-Containerised Shipments per Main Mode From table 3 already important tendencies can be observed. The transport mode

lorry is used most often (with over 97% of all cases for containerised and over 99% of

all cases for non-containerised shipments), followed by cargo vessel for containerised and by railway for non-containerised shipments. The average weight for non-containerised shipments is clearly higher than for containerised shipments. The highest average shipment weights are preferably shipped by the modes lorry and

ferry for containerised shipments and by the modes railway and cargo vessel for

non-containerised shipments.

Table 4 shows the chosen categorisation of shipment sizes. Table 5 shows tendencies of the explicit choices of transportation chain and shipment size that are investigated in this study. Table 5 gives insight in the shipment size distribution of recorded shipments per transport chain alternative. Percentage values (summing up to 100% per transport chain) are given for each defined shipment size category. The different shading of the various choice alternatives gives a clearer overview of how chosen transport chains correlate with chosen shipment sizes (a dash (-) indicates that there was no observation for this choice alternative given in the CFS, 0 indicates that there were observations but with a share of < 0.4%).

It can be observed that (very) large shipment sizes, notably from size category 14 onwards, are not really preferred for any of the chain alternatives. Only two transport chains (lorry-vessel-lorry and lorry-rail-vessel-lorry) show an observable frequency of shipments in shipment size category 18. No transport chain really correlates with high shipment size categories.

When taking a look at mid-range shipment sizes, it can be seen that mainly 4 transport chains correlate with according size categories. A preference of the chain type lorry-vessel for size category 10 can be observed. More than 50% of the shipments being transported with this chain are to be found in this size category.

Containerized Shipments Mode Nb of shipments in % Average weight/ship ment (kg) in % of weight of all OG shipments Average value/ship ment (SEK) in % of value of all OG shipments Lorry 2.275.447 97,11 22.460 99,52 12.329 94,66 Railway 14.626 0,62 3.850 0,11 61.368 3,03 Ferry 670 0,03 26.215 0,03 111.846 0,25 Cargo Vessel 43.928 1,87 3.939 0,34 11.937 1,77 Air 8.516 0,36 83 0,00 10.190 0,29 All 2.343.187 21.916 12.649 Non-Containerized Shipments Mode Nb of shipments in % Average weight/ship ment (kg) in % of weight of all OG shipments Average value/ship ment (SEK) in % of value of all OG shipments Lorry 334.467 99,74 53.039 96,27 139.259 98,81 Railway 630 0,19 808.889 2,77 720.339 0,96 Ferry 44 0,01 18.729 0,00 53.311 0,00 Cargo Vessel 195 0,06 910.064 0,96 516.193 0,21 Air 4 0,00 340 0,00 1.001.701 0,01 All 335.340 54.952 140.569

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Table 4: Categorisation of Shipment Sizes

Table 5: Distribution of Transport Chain and Shipment Size Choices in the CFS Small shipment sizes correlate with a number of transport chains. For altogether even 4 transport chains more than 60% of their assigned shipments lie in shipment size category 1. For the chain types lorry-air-lorry and lorry-ferry-lorry this is not surprising. The chain type including air was expected to correlate with shipments that are very sensitive to time. Since they are sensitive to time and therefore likely to have a high value density, preference is probably given to a high shipment frequency (meaning small sending units) rather than large inventory stocks. The transport chain

lorry-lorry-lorry represents a chain where consolidation activities are carried out

throughout the transport process. Shipments of the larger shipment size categories are less likely to be consolidated though. Therefore it seems obvious that a

lorry-lorry-lorry chain is preferred for shipments of small sizes. An explanation for the

correlation of transport chain lorry-rail-vessel-lorry with small shipment sizes is already less apparent. Also for this chain can be assumed though, that consolidation activities are carried out throughout the shipment process. Otherwise two transhipments in one delivery chain would be unlikely. The high correlation of the chain type lorry-vessel-lorry with the smallest shipment size category is quite

Category from (kg) to (kg) 1 0 50 2 50 100 3 100 200 4 200 500 5 500 1.000 6 1.000 2.000 7 2.000 5.000 8 5.000 10.000 9 10.000 20.000 10 20.000 50.000 11 50.000 100.000 12 100.000 200.000 13 200.000 400.000 14 400.000 600.000 15 600.000 800.000 16 800.000 1.000.000 17 1.000.000 1.500.000 18 1.500.000 or more

Transport Chain Types 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

lorry 23 3 3 4 3 3 4 5 8 43 2 0 0 0 0 0 0 0 lorry-vessel-lorry 87 6 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 lorry-rail-vessel-lorry 82 5 3 1 0 0 0 0 0 1 0 1 0 0 0 0 - 1 lorry-rail-lorry 15 2 2 2 2 5 9 9 18 26 6 2 1 0 0 0 0 -lorry-air-lorry 86 3 2 2 1 1 1 1 2 1 - 0 0 - - 0 - -lorry-lorry-lorry 65 3 1 26 0 0 1 1 0 1 - - - -lorry-ferry-lorry 14 3 3 5 5 7 12 12 18 20 0 0 0 0 - 0 0 0 lorry-vessel 4 1 2 2 2 3 7 7 15 55 1 1 0 0 0 0 0 0 1-5% 5-10% 10-20% 20-40% 40-60% 60-100%

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surprising. Due to the high capacity of vessels and the rather slow speed of this transport mode, a correlation to low value goods and therefore high inventories, less frequent transhipments and large shipment sizes was expected.

Although the variation of chosen shipment sizes per transport chain is observable, the table clearly shows that each transport chain (highly) correlates with a rather limited range of shipment sizes. Whereas for some chain types small shipment sizes got chosen extremely frequently, for others mid-range shipments seem to be preferred. Large shipment sizes are generally not very attractive for either one of the transport chains and seem to be rather exceptional. The correlations shown as they could be observed in the CFS therefore support the hypothesis of a simultaneous transport chain and shipment size choice model as proposed in the research objective.

2.3 Derivation of Logistics Costs

Combining network data and cost information of the Swedish national freight model with information of the CFS about shipments and their sending units allows deriving cost attributes of choices by applying specifically set up logistics cost functions. These logistics cost functions (specific for containerised/non-containerised goods and commodity types) incorporate the following assessable time, distance and cost information obtained by the Swedish national freight model and its logistics model project:

 Time matrices for origin and destination pairs for different transport modes  Distance matrices for origin and destination pairs for different transport modes  Vehicle time costs for each transport mode

 Vehicle distance costs for each transport mode  Costs for (un)loading goods

 Time for (un)loading goods  Waiting times at terminals  Pilot fees charged at terminals

 Technology factor of all available terminals (reflecting the technical advancement of a terminal, which allows to derive approximate loading/unloading times of goods)

 Fairway dues (to be paid for the usage of certain sea routes)  Other link costs (e.g. tolls, fees)

 Interest rates

The costs are determined by assuming that the transport route alternative causing the lowest costs is chosen by decision makers for a transportation chain. Costs for each transport leg in a transportation chain are first determined separately and then summed up in order to obtain the costs of a whole transportation chain.

3MODELINGAPPROACH

This section describes how an individual choice set for every decision maker is determined in a two-step approach. Furthermore, it is shown which kinds of random utility choice models are applied for estimations by briefly explaining their theoretical background.

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The choice sets, describing the available choice alternatives for an individual shipment, are determined in a two-step approach. In the first step a choice set is derived on the basis of choices actually made as stated in the CFS. Depending on the frequencies of observed chosen choice alternatives for which costs could be determined, alternatives are either in- or excluded in the available choice set. The resulting choice set includes all choice alternatives that a decision maker can choose from. It is therefore comparable to a universal choice set that contains all existing and logical shipment alternatives. Table 6 shows the maximal available choice set for decision makers.

Table 6: Maximal Available Choice Set for Decision Makers ('Universal' Choice Set)

The universal choice set obviously does not contain all existing choice alternatives according to the network but rather according to the available observations. On the contrary, it might even contain alternatives that do not exist for certain shipments according to the network. This might for instance be the case if a transport chain alternative got (due to its high observed frequency in the data set) included in the choice set but is nevertheless not available for an individual decision maker, as the sending unit has no access to terminals that would be needed for a certain transport chain type. This is taken care of in the second step of the choice set determination procedure. In this second step the alternatives of the universal choice set get reduced to the choice alternatives that are existent for the individual decision makers according to the network data received by the Swedish national freight transport model. This second step is a result of the process of determining costs for choice alternatives. Whenever costs of a choice alternative could not be determined e.g. due to unavailable transport modes or transhipment possibilities as given by the transport network data, this choice alternative was excluded from the choice set of the sending unit.

3.2 Applied Models

The applied random utility choice models are based on the assumption that the decision maker chooses the alternative from which he derives the greatest utility. For being able to derive the utility of a choice alternative, a utility function has to be defined. For the proposed model the utility of a choice alternative Ui,j (defined for the

combined choice of a transport chain i and shipment size j) subject to the decision maker n, is defined as follows:

Shipment Size Alternatives

Transport Chain Alternatives # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Lorry 1 Lorry-Vessel-lorry 2 Lorry-Rail-Vessel-Lorry 3 Lorry-Rail-Lorry 4 Lorry-Air-Lorry 5 Lorry-Lorry-Lorry 6 Lorry-Ferry-Lorry 7 Lorry-Vessel 8

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 … parameter vector giving weights for all attributes an n

a … vector of attributes describing the shipment and the sending

unit

c

 … parameter vector giving weights for all attributes ci,j,n n

j i

c_, _, … vector of attributes describing costs of choice alternatives

d

 … parameter vector giving weights for dummy variables, determining alternative specific constants

j i

d, … Vector of dummy variables for transport chain and shipment

size choice alternatives

n j i

e_, _, … Random utility component, denoting the unobservable part of the utility of a choice alternative

An alternative (i,j) therefore only gets chosen if the difference between the sum of the observable and unobservable part of the utility of the regarded alternative and the sum of the observable and unobservable part of any other alternative’s utility is greater than 0.

The parameter vectors _a, _c and d, giving weights to each element of the

vectors an, ci,j,n and di,j, were estimated by applying Multinomial Logit (MNL) models

that give readily interpretable choice probabilities of all available choice alternatives by



                



n j i s r d n s r c n a j i d n j i c n a CS s r d c a d c a n j i

e

P

, , , , , , , , ) , ( , , (2)

for all (i,j) in CSi,j,n , where

CSi,j,n … Decision maker n specific choice set of transport chain i and

shipment size j choices

In complex decision making processes it is likely that different choice alternatives within a choice set are correlated. The alternatives are not independent anymore, which is likely to influence choice behaviour and should thus be accounted for in choice modelling. Nested Logit (NL) models are suited to account for some form of correlation of alternatives due to grouping of alternatives by allowing error terms of subsets of alternatives to be correlated (which interferes with the condition of the MNL model that error terms are independent across alternatives).

Since it was also expected that there are correlations of choice alternatives when regarding transport chain and shipment size choice simultaneously, NL models were estimated. Nests are defined comprising alternatives that are assumed to be

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correlated. This implies that a larger degree of substitution of alternatives within a nest is assumed, than for substitution of alternatives across nests. For any two alternatives in the same nest, the error terms ei,j,n of the alternatives are correlated.

If the set of all possible alternatives is partitioned into K non-overlapping subsets Bk )

1

( kK , then the choice probability of a decision maker n for an alternative

k

B j

i,  is given for this study by

 



                             _K l rs B d c a B s r d c a k d c a n j i l l l s r d n s r c n a k k k s r d n s r c n a j i d n j i c n a e e e P 1 ( , ) / ) ( 1 ) , ( / ) ( ) ( , , ) ( ) ( / , , , , , , , , ,    



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where Bl is a subset of alternatives for which lk and where the parameters kand l

 are a measure of the degree of independence in unobserved utility among the alternatives in the nests k and l. A higher value of_kmeans greater independence and less correlation of the choice alternatives in nest Bk. The parameter kcan differ

over nests, reflecting different correlation among unobserved factors within each nest.

For the considered model, a nesting structure, where all shipment size choice alternatives are comprised in nests of transport chain choice alternatives, showed best results when comparing the log-likelihood (LL) value.

The parameter vectors _a, _c and d were estimated using a maximum likelihood

method both for MNL and NL models. Estimations were carried out in the software program ALogit.

4MODELINGRESULTS

Table 7 gives model results of the MNL and NL model that showed the best goodness of fit. Since the NL model showed better results than the MNL model (see the higher final LL value and tested by a likelihood ratio test), NL results are discussed specifically. First an overview of the model and of the way of incorporating variables is given, before specific interesting results are discussed separately in the next sections.

Table 7: Best obtained MNL and NL Model Results

MNL NL Observations 2.225.150 2.225.150 Final LL value -1.601.661 -1.589.420 Deg. of Freedom 33 37 Rho²(0) 0.737 0.739 Rho²(c) 0.314 0.320 S ta ti s ti c s value t-value Coeff1 0.0189 9.8 Coeff2 0.0449 16.5 Coeff3 0.0537 27.2 Coeff4 0.0667 21.3 N e s t C oe ff . NL

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Table 7: Best obtained MNL and NL Model Results (continued)

The third part of table 7 gives estimates of coefficients of all introduced variables. The column ‘alternative’ indicates for which choice alternatives the variables were introduced. Combined costs (meaning costs that comprise all assessable cost components without any further separation or selection) that give the total costs of a delivery process were incorporated in the utility function of all choice alternatives as continuous variable. All other variables were introduced as dummy variables. The

Variable Alternative Coefficient t-value Coefficient t-value Costs Combined all chains -0.0011 -615.2 -0.0012 -607.0

chains 2,3,4,8 all Cat. vd1 Cat 1-5 -5.79 -28.7 -13.2 -33.2 vd2 Cat 6-9 4.49 22.3 11.9 29.9 vd3 Cat 1 0.961 29.4 1.11 31.4 Time of year (summer) all chains except 6 1.02 8.8 49.7 24.3 Bulk Cat 10-18 -0.986 -136.1 -1.01 -138.8 Cat 1-3 1.70 78.0 1.73 77.9 Cat 4-8 2.96 181.2 3.01 181.3 Cat 1-5 -0.322 -14.7 -0.324 -14.7 Cat 6-10 0.300 17.4 0.300 17.2 7.02 4.7 MNL NL D um m y V a ri a bl e s Pre-Slung, Pall. Boxes, other Prox. Rail/Quay 0.729 8.6

Constant Value t-value Value t-value

Chain 1 0 * 0 * Chain 2 -8.34 -67.1 -9.83 -17.0 Chain 3 -6.47 -231.5 -7.03 -25.8 Chain 4 -6.96 -229.5 -7.41 -25.5 Chain 5 -4.91 -135.4 -7.80 -23.5 Chain 6 -2.48 -33.9 -5.97 -8.9 Chain 7 -6.71 -88.2 -9.11 -16.7 Chain 8 -6.33 -37.8 -7.00 -14.0 Cat 1 0 * 0 * Cat 2 -7.08 -550.5 -7.43 -538.5 Cat 3 -8.74 -579.6 -9.17 -567.4 Cat 4 -11.0 -522.1 -11.5 -517.6 Cat 5 -13.0 -562.3 -13.6 -556.1 Cat 6 -19.6 -96.3 -27.7 -69.1 Cat 7 -20.8 -102.2 -28.9 -72.2 Cat 8_9 -22.6 -111.2 -30.9 -77.0 Cat 10 -22.0 -107.8 -30.2 -75.4 Cat 11 -25.3 -123.6 -33.6 -83.8 Cat 12 -29.8 -144.3 -38.3 -95.2 Cat 13 -30.5 -145.9 -38.9 -96.5 Cat 14 -35.6 -77.9 -44.2 -77.1 Cat 15 -34.2 -62.9 -42.7 -66.3 Chain 9 Cat 10 Chain 6 Cat 10 NL MNL -2.90 -4.02 -9.2 8.06 32.1 -5.2 3.53 34.4 C ha in Ty pe C on s ta nt s W e igh t C a te go ry C on s ta nt s S pe c if ic C on s ta nt s

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variable reflecting the proximity of sending units to rail or quay facilities was introduced for the chain types that used the modes rail or vessel. This way it could be observed if there was a preference for these chain types if proximity of sending units to the mentioned facilities was stated. The attribute of the value density of a shipment was introduced by defining three value density categories and introducing dummy variables for each category for different shipment size alternatives. The dummy variable vd1 was introduced for a value density range of 0-10 Swedish crowns/kg, the vd2 was introduced for a range of 10-500 crowns/kg and vd3 was introduced for all shipments with a value density higher than 500 crowns/kg. This way it could be observed which shipment size categories got preferably used for which value densities of goods. A dummy variable introduced for shipments shipped during the summer season got introduced for all chain types other than the lorry-lorry-lorry chain type. The resulting positive coefficient value of this variable suggests that the

lorry-lorry-lorry gets preferred during winter and serves for a replacement of transport

chains that are more difficult to be operated during winter-time (e.g.

lorry-vessel-lorry). Further, three defined cargo group dummy variables were introduced. Each

shipment was assigned to one of the four cargo groups ‘bulk’, ‘pre-slung, palletized’, ‘containerised’ or ‘boxes, other’ by using the stated information in the CFS about the cargo type and commodity type of shipment. The three introduced dummy variables were again inserted for certain shipment size categories only. Conclusions about certain shipment size preferences for different cargo groups could therefore be made. All parameter values are according to their t-values statistically very significant. The forth part of table 7 gives the values of all alternative specific constants. 4.1 Importance of Costs of Delivery

Costs of delivery have a statistically very significant influence on decision-making. Only taking a look at the magnitude of the cost parameter value does not really reveal how large this influence of the costs on the utility of a choice alternative and therefore on the decision making process is. Costs vary across choice alternatives and are specific for every observation depending on the attributes of the shipment and the sending unit. For giving an idea about cost magnitudes, the average costs of chosen alternatives of observations were calculated.

Due to their frequent occurrence, the costs of observations having the following attributes were calculated:

 the sending local unit is not located in the proximity of rail or quay facilities  the value density of the shipped good lies above 500 SEK/kg

 the shipment is transported during winter

 the cargo type of the shipment falls in the group of ‘pre-slung, palletized’ goods

The average costs of the observations stating that shipment size category 1 and transport chain type 1 (lorry) were chosen were calculated. Making this selection of the total data set, the average costs of chosen alternatives could be calculated for a rather large number of observations (namely 95,919). The obtained average value is 1,005 SEK. Knowing this, the average contribution of costs to the utility of the chosen alternatives for the selected data set could be investigated. The left part of figure 1

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shows the proportional contribution of average costs of delivery to the utility of the chosen alternative (shipment size category 1, transport chain type lorry).

(a) (b)

Figure 1: Exemplifying average composition of utilities of choice alternative (a) and (b)

It can be seen that for the chosen sample of observations the utility of the chosen alternative comprises contributions of three attributes. Next to the cost attribute of the choice alternative, also the value density and the cargo group of the shipment influence the choice process: up to 70% of the total utility is determined by attributes of the shipment. Nevertheless, the costs of delivery still play a quite influential role. The right part of figure 1 shows a different example, where the average costs were calculated for shipments with the same above stated attributes that, however, got transhipped by the transport chain alternative lorry-air-lorry instead of lorry. The average costs, calculated for the available sub set of 704 observations amount to 650 SEK. This time the composition of the utility shows that the costs (and all shipment attributes) have considerably less influence on the utility value of the chosen alternative than this was the case before. The main difference is that for this choice alternative, also a chain type constant contributes to the value of the utility. This influence is comparably much larger than the influence of all other attributes. The costs of delivery have the smallest influence on the utility of the chosen alternative.

The magnitude of costs of choice alternatives, defining the impact of costs on the utility of alternatives (and therefore also the impact of costs on decision making), can greatly depend on the chosen shipment size of the observation. If an observations states that the shipment got delivered in a large shipment size, costs of delivery for the chosen, as well for the not chosen alternatives, will be higher than for an observation that states that the shipment got delivered in a small shipment size. They will therefore have more impact on the utility value of a choice alternative.

However, especially for the frequently observed small shipment size choices the impact of costs on the decision making process proves to be rather small.

4.2 Importance of Sending Unit Attributes

Shipm ent size category 1, Transport chain type 'lorry'

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Total delivery costs Value density Cargo Group Pre-Slung, Pall. Etc.

Shipm ent size category 1, Transport chain type 'lorry-air-lorry'

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Total delivery costs Value density Cargo Group Pre-Slung, Pall. Etc. Chain type constant

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Sending units of shipments could be described by the attributes telling if the sending units were in the proximity of rail and/or quay facilities. Model estimations showed that there is statistically significant influence of these proximity attributes on decision-making. A tendency could be observed that choice alternatives, where the modes rail or vessel serve as mean of transportation, get preference when a sending units are located in the proximity of rail and/or quay facilities. Further, it could be observed that the proximity to rail/quay facilities often specifically correlates with larger shipment size choices.

4.3 Importance of Shipment Attributes

By the data obtained by the CFS, shipments could be described by their cargo type and their value density. Especially less complex model estimations (not presented in this paper) performed to investigate the influence of the cargo type showed that shipments falling into the cargo group ‘bulk’ have preference for larger shipment sizes, whereas shipments falling into the cargo group ‘palletized’, ‘pre-slung’ or ‘boxes’ have a preference for smaller shipment sizes. Parameter values of best resulting models (as shown in table 7) do not clearly show these tendencies anymore (parameter signs are partly negative) due to correlations of these variables with other introduced variables such as the variables for value density.

The value density of shipments has quite a remarkable influence on the choice process (as can also be observed in the composition of utilities in figure 2). High value goods often correlate with small shipment sizes: low value goods often correlate with big shipment sizes. The reasoning behind this is that low value goods also cause less storage costs (less costs of working capital, less interest costs) than high value goods. It is therefore more likely for low value goods to be stored and therefore less often transported than for thigh value goods. For low value goods it is therefore easier to profit from economies of scale regarding the delivery process. 4.4 Importance of Alternative Specific Constants

The high values of alternative specific constants suggest that unobserved attributes reflected in those constants play an important role in decision-making. Concerning transport chain specific constants, this suggestion is supported by the right part of figure 1, where the large contribution of the chain specific constant to the utility of the choice alternative is shown. The reason for the high unobserved negative influence on the utility of all transport chains compared to the transport chain lorry might lie in the relatively high reliability of the lorry chain type. Also habits of shippers or frame contracts between suppliers and receivers might play an important role in decision making that support the choice of the lorry chain type. Taking a look at shipment size specific constants, a clear preference towards smaller shipment sizes can be observed. This result is likely to reflect current logistics developments, where more and more suppliers are made responsible for timely delivery of their goods. Suppliers are bound to frame contracts, in which they e.g. agree to a time or just-in-sequence delivery of their goods. These inventory strategies are based on frequent and therefore small-size shipments, to avoid any unnecessary warehousing along the supply chain.

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All nest coefficients are between 0 and 1, implying that there is considerably more substitution between shipment sizes than between transport chain types. The very small values suggest that the alternatives within all nests are very highly correlated with each other. It therefore seems to be very reasonable to introduce a nesting structure for obtaining better model results.

5CONCLUSIONSANDRECOMMENDATIONS

This paper presented a model based on a disaggregate data source from the Swedish Commodity Flow Survey. It was shown that accounting for simultaneous decision-making and incorporating logistics costs yield accurate freight model estimations. Statistical values showed the significance of the estimated models, their parameters and their ability to predict choices on a disaggregate level. The approach of modelling transport chain and shipment size choice in a simultaneous way therefore seems to be fruitful and should be further developed in such a way that also other decisions within the same choice making stage of freight transport modelling can be incorporated. As decision makers in freight transport also show individual attributes and are therefore confronted with different attributes of available choice alternatives, a disaggregate approach to freight transport modelling, as this is common practice for passenger transport modelling, is suggested.

The CFS proved to be a good data source for modelling decisions in freight transport on an individual level. Integrating a logistics perspective into freight transport modelling is inevitable due to logistics developments. For being able to do this in a more appropriate way than it was done in this study, even more information on individual shipments would be necessary. However, a logistics cost approach already proved to be possible and should become standard in freight transport modelling. A nested modelling approach accounting for the high correlations between choice alternatives in freight transport modelling showed to be most appropriate. It showed that there is considerably more substitution between shipment size choice alternatives (within the defined nests) than between transport chain alternatives (across the defined nests).

Modelling results showed that alternative specific constants contribute considerably to the utility of choice alternatives. This suggests the existence of important but unobserved attributes influencing the decision making process. Future work could try to model such unobserved influences by e.g. incorporating attributes reflecting the reliability of transport chains or by gathering and combining information on these unobserved attributes with the data obtained by the CFS. Future work should furthermore be based on an even more holistic logistics cost approach and more elaborate integration of a logistics perspective. Storage locations, storage durations and costs should be considered. Also, estimating different kinds of more flexible models, such as mixed Multinomial Logit models that allow random taste variation for (selected) parameters or continuous-discrete models that allow incorporating a continuous choice variable for shipment size into the model, should be considered. Bibliography

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Abdelwahab, W. (1998) Elasticities of mode choice probabilities and market

elasticities of demand: evidence from a simultaneous mode choice/shipment-size freight transport model, Transportation Research E, 34, pp. 257-266.

Abdelwahab, W., Sargious, M. (1992) Modelling the demand for freight transport, Faculty of Civil Engineering, University of Calgary, Calgary.

Blauwens, G., Janssens, S., Vernimmen, B. and Witlox, F. (2001) De keuze tussen

vervoerswijzen op basis van het ‘total cost concept, Een gevalstudie, Tijdschrift

Vervoerswetenschap, 3(10), pp.28-32.

Chiang, Y., Roberts, P.O., Ben-Akiva, M. (1981) Development of a Policy Sensitive

Model for Forecasting Freight Demand, Center for Transportation studies Report

81-1, Cambridge.

Jong, G. de (2007) A model of mode and shipment size choice on the Swedish

Commodity Flow Survey, Institute for Transport Studies, University of Leeds, Leeds.

Jong, G.C. de, Ben-Akiva, M.E. (2007) A micro-simulation model of shipment size

and transport chain choice. Special issue on freight transport of Transportation Research B, 41, pp. 950-965.

McFadden, D. L., Winston, C. and Boersch-Supan, A. (1985) Joint estimation of

freight transportation decisions under non-random sampling, in: E. F. Daughety (Ed.)

Analytical Studies in Transport Economics, Cambridge.

SCENES Consortium (2000) SCENES European transport forecasting model and

appended module, SCENES deliverable 4 for the European Commission DG-TREN,

ME&P, Cambridge.

Tavasszy, L.A. (2006) Freight Modelling – An overview of international experiences, Paper prepared for the TRB Conference on Freight Demand Modelling: Tools for Public Sector Decision Making, September 25-27, Washington DC.

Tavasszy, L.A., Vlist, M. van de, Ruijgrok, C., Rest, J. van de (1998) Scenario-wise

analysis of transport and logistics with a SMILE, Paper presented at the 8th WCTR Conference, Antwerp.

Ying, J., Williams, I. (2005) Integrated regional economic and freight logistics

modelling: Results from a model for Trans-Pennine corridor, WSP Policy and