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Suggested Citation: Marcucci E., Musso E., (2010), “Urban Freight Modelling and Policy Analysis” in Van de Voorde, E. and Vanelslader, T., (Edittors), Applied Transport Economics. A Management and Policy Perspective, de Boeck, Antwerpen,

Urban Freight Modelling and Policy Analysis

Enrico Musso,

University of Genoa,

DIEM, Via Vivaldi, 2 – Genoa - Italy

Edoardo Marcucci�

University of Roma Tre,

DIPES, Via Chiabrera, 199- Rome Italy

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1 Introduction

Cities historically and functionally are characterised by relevant economies of density

and proximity; they produce ideas, innovations and generate economic growth that

irradiates to other areas. At the same time, they consume more than they produce and

need to be supplied form outside. Cities are characterised by research and service

production, high standards of living but also by congestion, environmental and

acoustic pollution (Camagni 2007). The latter elements are linked to the high

concentration of both people and economic activities generating a consistent, strong

and, usually, rigid demand for passenger and freight transportation. To decouple

economic growth from transport demand, decision makers have adopted policies to

optimise the movement of both passengers and freight so to guarantee an

environmentally sustainable development. This paper describes the most frequently

used urban freight policies with the aim of analysing: their characteristics, and the

likely impacts, the proper level of analysis of the phenomenon, and which are the

most appropriate modelling methods to estimate policy impacts.

The paper is structured as follows: after defining and characterising urban freight

transportation (UFT) (section 2) two main modelling approaches are described

(section 3). The most frequently implemented policies are illustrated (section 4) along

with their impacts. After reviewing past and present modelling approaches we

emphasize the need to refocus the standard approach to UFT modelling and planning

and argue for a stronger behavioural stance. These arguments will be discussed in

section 6 that concludes.

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2 Urban Freight Transport Definition and Characteristics

Before defining urban freight policies one needs to define the context within which

they are applied. It is important to define UFT since the literature uses different terms,

such as, city logistics, urban distribution and urban goods movement, to refer to the

same phenomenon of the transportation of goods within cities. City logistics often

indicates the whole range of policies and initiatives implemented to trim down

negative impacts of urban freight transport (Taniguchi and Thompson, 2002). In this

paper, UFT is used to identify the final part of the overall transport chain (Pearman

and Button, 1981).

It is not easy to clearly define UFT due to its intrinsic complexity mainly ascribable to

the differentiation of the activities as well as the number and heterogeneity of the

stakeholders involved. Allen et al. (2000) proposes a multidimensional definition of

UFT explicitly considering its complexity. The main facets of the phenomenon worth

recalling are:

o physical and marketing characteristics of goods: goods can be characterized

according to several physical dimensions (e.g. physical and value density,

perishability, vulnerability, rate of replenishment, gross margin and stage in

product life-cycle, etc., Binsbergen and Visser, 2001);

o origin/destination of the flows: a) within the city traffic, b) into the city traffic,

c) across the city;

o type of origins/destinations: senders/receivers located in, near, or out of the

city will originate differentiated flows (e.g. manufacturing industry, consumer

goods, postal services, courier home deliveries);

o distribution channel: distribution channels differ according to the number and

type of intermediaries within the channel (Binsbergen, Visser, 2001). UFT is

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generally divided in three different types of distribution channels: 1) de-

centralized: receivers obtain their goods from different operators (goods

suppliers or transport operators); 2) company-centralized: a distribution

company (wholesaler or importer) delivers to different receivers; 3) self-

centralized: receivers themselves (e.g. big retail chains) organize their own

deliveries (Schaffeler, Wichser, 2003).

o mode of transport: road is prevalent in UFT given its flexibility and

connectivity, rare are the cases where other modes are used;

o typology of vehicles: two main vehicle-types are used in UFT: heavy good

vehicles (HGV, ≥ 3.5ton.) and light goods vehicles (LGV ≤ 3.5ton). In Italy,

in 2003, LGVs represented approximately 76% of the total (Confetra, 2003).

o types of activities considered: delivery and collection of goods to/from

premises by means of commercial vehicles characterized by number and/or

frequency of deliveries (Binsbergen, Visser, 2001).

o type of delivery/collection performed: two delivery/collection-types are

typical in UFT: direct or multi-drop/ multi-pick operations.

o transport characteristics of freight flows: length, time and speed.

o actors involved: the number of actors increases with the kind of goods/flows

considered and are classified in two categories: actors of the city (i.e.

authorities, inhabitants, tourists) and those in the transport chain (e.g.

receivers, shippers, producers, transport providers, logistics companies).

The synthetic, realistic and detailed definition reported indicates there are multi-

faceted elements to consider when dealing with UFT policies. Freight transportation

is characterised by quite heterogeneous actors and elements. Company size, goods

flows, shipments, usually vary by several orders of magnitude (de Jong et al., 2002)

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and reactions to polices can be diverse since agents modify their behaviour just to

escape policy effects.

2.1 Urban Freight Transport in context

In October 2000 the Bestufs-consortium and the Polis-network administered a survey

in several European cities to understand, from the city planners’ and development

departments’ point of view, which were the main problems characterizing UFT

(Ruesch and Glucker, 2000). The study produced a ranking of UFT problems. In the

following a description of the main issues identified is reported.

2.1.1 Urban freight flows

City planners perceive in-transit and to/out of the city freight flows less disruptive

than freight flows generated within the cities themselves which, in most cities,

represents the highest share of UFT. City planners considered the access of goods to

pedestrian zones or historical centres a critical problem. Timing of urban

deliveries/picking operations, the type of vehicles used and the overlapping with

passenger transport during delivery operations are connected to this problem. Timing

is an important issue due to the increase in network congestion freight vehicles

provoke; UFT peaks are usually almost coincident with passenger traffic (08:00-11:00

and 15:00-17:00).

2.1.2 Urban freight vehicles

City planners perceive the huge number of LGV more important than HGV even

tough these damage road infrastructure the most. In different European countries and

cities LGV represents the vehicles most widely used for distributive operations. 50%

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of ton/km in France are transported by LGV while, in Italy, 68% of the total

vehicle/km of urban traffic are performed by LGV (Schoemaker et al., 2006). Lack of

freight fleet compliance with Euro norms is critical for reducing the environmental

impact LGV produce since there is still a high share of vehicles registered before

1996 not conforming to the minimum Euro-2 standard (Schoemaker et al., 2006;

Colombo, et al., 2005). Indeed it is recognized that many of the negative impacts

produced by the freight road transport industry are concentrated in the cities (Dablanc,

2007).

2.1.3 Vehicle ownership

Lack of delivery optimization is evident if one differentiates according to the

ownership of vehicles representing a proxy of how the vehicles are employed in

multi-drop and combined deliveries. In the UK, characterised by a highly developed

logistic system (many outsourced activities and big logistics providers) the balance

between public haulage and own-account transport is in favour of the former (DfT,

2007). In Italy, in 2003, 87,2% of vehicles fleet was registered as own-account and

was even higher (97%) counting only LGVs (Confetra, 2003). Own-account is

synonymous of lower performance. In the city of Bologna third-party transport

providers account for 61% of the total daily deliveries and perform, on average, 15

deliveries on each trip whereas own-account transport, representing 39% of the total,

performs, on average, just 9 deliveries per trip. Paradoxically, however, the number of

stops per trip increases vehicle fuel consumption especially for LGVs.

2.1.4 Loading and unloading operations

City planners perceive the conflicts between private road users and transport operators

during delivery operations as relevant, especially when it comes to loading and

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unloading operations. Partitioning delivery time into three components: drive into the

city, drive within the city, and dwell time (load/unload the cargo) one notices that the

last two components account for most of vehicle operation time (Schoemaker et al.,

2006). Time spent driving within the cities varies according to city size can be

interpreted as an indicator of congestion. In Italy UFT is responsible for almost 30%

of the total congestion costs (Amici dell Terra, 2003). Dwell time varies according to

the type of product (Allen et al., 2000); combined with quantities delivered it can

provide a measure of the distribution process performance (Schoemaker et al., 2006)

and UFT space occupancy so it might be considered a conflict indicator between

private road users and transport operators especially if loading and unloading is not

performed within dedicated bays. In the LTZ of Bologna the reported share of illegal

parking is 57% where 23% of the transport providers stated that there were no loading

bays available (Colombo et al., 2005).

2.1.5 Coordination among agents

The promotion of cooperation between public authorities and freight transport actors

can be more important than other tangible issues. UFT is typically characterised by

the mismatch among the interests of different supply chain agents and, in particular,

between demand and supply actors. The complexity of implementing policy

optimisation measures is due to the interaction among different agents (e.g. shippers,

carriers, retailers, large or small transport operators, local authorities and

communities) all having contrasting interests and perceptions of UFT problems.

Dablanc (2007) argues that logistic decisions are typically taken without any specific

consideration of the local environment. The lack of distribution coordination and the

low harmonization of regulation within cities might have a great impact on the overall

distribution process. Supply chain actors can be distinguished in demand and supply

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agents. Furthermore one can distinguish two main groups of UFT-demand: own-

account and third-account. Among the former one can differentiate between those

transporting goods as a necessary activity for their business (e.g retailers) and those

transporting goods as an ancillary activity (e.g. electricians and maintenance of public

service suppliers).

Both UFT-demand and UFT-supply actors can be considered the traffic “demand”

component of the market whose suppliers are the infrastructure and resources services

providers and operators (Binsbergen, Visser, 2001).

3 Urban Freight Transport Modelling

To evaluate policy intervention impacts one has to model the phenomenon first.

Policy evaluation and modelling are strictly intertwined. The next subsections will

describe the traditional urban freight modelling approach and illustrate recent

behavioural developments.

3.1 The past and, most of, present

UFT modelling describes the linkages among its main components and agents. In

particular it illustrates specific issues such as: freight vehicles presence and road

congestion; UFT organisation and environment/social impacts; optimal vehicle

routing; UFT impact on overall supply chain performance; urban freight markets and

efficient distribution; effects of pricing/regulatory mechanisms on UFT organisation

and performance (D’Este, 2000). Notwithstanding the issues involved it is reckoned

sufficient to model the aggregate patterns of UFT. The level of analysis adopted has

strong implications for congestion, travel times and UFT policy impact analysis. This

subsection delves on the methods employed for modelling UFT aggregate patterns.

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UFT is both more complex and simpler when compared to passenger transport

analysis. Simpler since only road transport is, in practice, used due to short distances,

time sensitiveness, nature of deliveries, etc.; more complex, since it represents part of

a logistic chain that often influences and pre-determine strategic choices. UFT

intrinsic complexity suggests considering it as a multi-market context where only

partitioning the different market segments guarantees a sufficient level of within-

market consistency. The partitioning can be performed according to different

elements. The most commonly used being: commodity (e.g. consumer goods,

industrial inputs, construction materials, waste, etc.); load type (full track load –FTL–

and less than truck load –LTL–); vehicle type/function (e.g. HGV or LGV),

origin/destination within/outside city boundaries (outside-the-city originated trips are

usually performed using HGV with fewer trip ends, the opposite holds for within-city

originated trips). Considering relevant all the characteristics enumerated a consistent

and homogeneous market partitioning could include several segments.

One needs to specify urban freight movements measures before describing the

modelling framework. In fact this technical aspect will also impact on the modelling

approach. Freight movements at a urban scale can be measured in various ways. The

most frequently used are: amount of goods (units consigned, tons delivered);

number\type of vehicles used for deliveries; ton-kilometres needed to satisfy demand.

The appropriate unit of measurement is usually linked to the stage of the modelling

process. If we are interested in measuring the effects of the policies implemented or

envisaged (e.g. number of trucks employed, effects on traffic flow, environmental

pollution, etc.) since the impacts are practically independent of the type of commodity

carried or whether it is a FTL or LTL, in most cases, freight consignments and

tonnage are converted into equivalent vehicle-trips via some assumptions on the

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packing of the goods derived from average vehicle payloads, commodity densities

and average weights inferred from survey data.

UFT as well as freight transportation modelling in general has, in the past decades,

been approached in different ways (Meixell and Gargeya, 2005), (Crainic and

Laporte, 1997), (TRB 2008), (Cambridge Systematics, 1998) (Fischer et al. 2000).

However the most widely used method is the Four Stages Approach (FSA) (Pendyala

e al., 2000; Cambridge Systematics, 1996) and this is described in more detail in

3.1.1.

3.1.1 Aggregate four stages approach

The methods used to model UFT differ in complexity and practicality. The FSA was

adapted from techniques originally developed for urban passenger movements and

subdivides the modelling process in four parts: 1) trip generation, 2) trip distribution,

3) mode split, 4) trip assignment (FHWA, 1998). Most of the notions and algorithms

are derived from passenger transportation but there are some peculiarities that deserve

specific attention. The elements that need clarification are: 1) market segmentation; 2)

zoning system; 3) network representation; 4) trip generation; 5) trip distribution; 6)

trip assignment.

In more detail: 1) UFT is heterogeneous with respect to some fundamental

characteristics; to achieve internal consistency is useful to segment the market; 2) the

zoning used for passengers is, in general, adequate also for inter-urban and intra-

urban freight movements when specific attractors are included (e.g. ports, airports,

etc.); 3) overlaying the zoning system on to the road network allows modelling the

movements between zones permitting, in certain cases, the matching of market

segments to specific road network subsets; 4) one can estimate, via trip generation,

the overall freight movement generated/attracted by each zone on the base of:

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previous trends, economic forecasts (GDP growth rates), regression techniques and

other methods; 5) the gravity model is usually employed to determine how the volume

of total freight movements is distributed between each pair of zones generating a

inter-zone demand table1; 6) algorithms derived from transport modelling are

employed to assign the trips to the network considering, among other specificities, the

loading of each freight vehicle trip category and the conversion of truck trips on each

network link into car-equivalent trips.

3.2 The future: behavioural freight modelling

Freight modelling is still usually performed by FSA. The approach is aggregate in

nature and provides no satisfactory account of the critical role individual actors play

in the actual decision making process. This represents a relevant limitation especially

for policy interventions aimed at changing the reference scenario and altering the

relative convenience of past actions and choices. FSA may produce biased results

inducing erroneous policy measures and investments. This subsection illustrates some

recent achievements based on a behavioural approach to freight modelling in general

and to UFT in particular. This innovative approach considers the most relevant

complexities deriving from modern logistic supply chain activities. More realistic

estimates are derived by explicitly considering shipping behaviours, supply chain

specifications, shipment characteristics, transport network conditions and many other

aspects previously ignored. Some authors have contended that supply chain, logistic

and technological swift transformations have rendered the FSA to freight modelling

obsolete (Hensher and Figliozzi 2007). They argue FSA is not capable of fully giving

account of the complexity of freight movements at different geographical scales.

1 Modal split modelled explicitly for passenger transport is not usually considered relevant given that freight movements are almost uniquely performed by road.

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Southworth (2003) argues that an adequate freight forecasting apparatus should

integrate fast supply chain changes in the modelling approach. New delivery methods

(e.g. JIT) and customer driven freight services have made UFT more complex paving

the way to third-party logistic providers. Within the group of disaggregate models

(e.g. inventory models and logistic optimisation) behavioural models explicitly

consider stakeholders’ utility maximization efforts. When dealing with behavioural

models one has to clearly identify key decision makers to develop a modelling

framework if an actor-based micro-simulation approach is followed (Liedtke and

Schepperle, 2004). Many authors consider UFT a privileged field of application for

developing appropriate actor-based micro models, these include Gray (1982),

Southworth (2003), Wisetjindawat et al. (2005), de Jong and Ben-Akiva (2007),

Hensher and Figliozzi (2007), Samimi et al. (2009), Yang et al (2009) and Roorda

(2010). Recent developments in freight modelling mainly revolve around the analysis

of decision makers’ choice procedures. Freight movements are relevant but also the

underlying motivations and relative convenience of each stakeholder participating in

the freight movement process is analysed. Structural behavioural improvements with

respect to standard modelling techniques have come from network and micro-

simulation modelling, land use/transport network with feedback effects, specific and

explicit acknowledgment of the relevance of physical characteristics of logistics

networks. Previous modelling approaches have simply abstracted from these aspects.

The innovations introduced have introduced greater realism in the treatment of the

behavioural aspects influencing and motivating freight stakeholders when: 1)

choosing among different strategies, 2) dealing with specific constraints, 3)

accounting for incentives, 4) interacting with others. These aspects are important

when dealing with UFT policy analysis. In fact, interactions between existing and

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prospective constraints posed by new policies; motivations to choose a particular

strategy or a set of constraints may change when new policies determine new states of

the world. For example, policy changes related to the price of fuel, land use patterns,

pricing strategies imply a shift in the constraints and alter the relative convenience of

alternatives. Puckett and Greaves (2009) argue that it is important to jointly consider

both the instruments available to policy makers and the set of drivers influencing

freight travel behaviour so to acquire a better understanding of the potential impacts

the policies implemented might have on market outcomes. This is exactly what policy

makers would like to know ex-ante before actually implementing a given policy. It is

not only important to identify a type of incentive/disincentive with a relevant impact

but also be able to understand and quantify its impact given the reference context. To

do so one has to understand which type of decision makers are involved, how they

interact, under which constraints they operate, on which specific freight service

attribute they negotiate and what sort of interaction is actually going on among them.

Some new approaches recently developed to tackle the issues just raised will be now

described. Their illustration indicates the critical areas where specific efforts are

needed to gain a better understanding of UFT related decision making.

3.2.1 Land use modelling and micro-simulation

Freight modelling describes and predicts freight movements. Freight modelling

originates from passenger modelling (Hunt and Stefan, 2007) and its level of

sophistication has increased by accounting explicitly for logistics and transport

functions (Liedtke 2009). Abstracting from the underlying motivations that induce

freight movements can be acceptable at an aggregate level where heterogeneity in

preferences is not duly considered; however, this is no longer acceptable if one is

interested in determining specific responses to policy mixes influencing the level of

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service produced. In this case, in fact, the role of competition among transport and

logistic service providers cannot be overlooked; it represents a critical element for

adequately representing freight flows and, it has a determinant impact on scenario

analysis when evaluating policy mixes. To summarise, FSA lacks policy response

capability analysis and, more than supplanted, it should be integrated with

behavioural models (de Jong and Ben-Akiva, 2007; Hunt and Stafan, 2007).

Behavioural aspects should be included by adopting an agent level focus

incorporating behavioural elements that ultimately influence travel choices. Liedtke

(2009) adopts a disaggregate behavioural approach when simulating market

interactions that impact on logistics functions determining commodity flows. The

model accounts for interaction effects and relative power among agents which is not

homogeneously divided among shippers, receivers and carriers. These considerations

are reinforced when acknowledging that freight transport is no longer considered a

mere derived demand since it has a functional role in the interdependent relations

between goods distribution and materials management.

3.2.2 Freight characteristics and negotiations

Not all goods need the same level of service quality nor the same combination of their

characteristics. Agents with different preferences interact on the market and negotiate

to accommodate for differences in preferences. If two agents have antagonistic

preferences for a given distributive strategy they need to negotiate over a given

outcome. Modelling such process is important in itself and also represents an

information revealing mechanisms helpful when evaluating policy interventions given

the paucity of real-market evidence concerning these issues. The need for a better

understanding of freight related negotiations among shippers and carriers was already

acknowledged back in the late eighties (Rinehart, 1989). Understanding negotiation

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behaviour within sampled groups, especially when policy outcomes depend on

interdependent choices, benefits both transport analysts and policy makers alike.

Recently both Ramsay (2004) and Krause et al. (2006) have underlined the need for a

deeper understanding of inter-company interaction (collaborative –increased net

benefits– vs. competitive –zero-sum–) analysis since there is a disproportionate

interest in competitive interaction and insufficient research is dedicated to cooperative

bargaining which, on the contrary, is the core of inter-organisational literature. In

other words, one should test rather than assume the non-cooperative model as

representative for all interaction-types. This issue should be characterised in terms of

degrees of co-operation/non-co-operation rather than interpreted within a framework

of auto-exclusive dichotomous assumptions. In fact, the role of information sharing,

one of the potential disruptive issues hindering co-operative solutions, assumes a

varying degree of relevance in different markets and relationship-types, for various

goods, different market relationships, etc. Market interaction often implies

information disclosure along various dimensions (e.g. preferences for transaction

specifications and organisational structure). Co-operation and competition are not

necessarily irreconcilable, they can characterise different parts of a supply chain,

various types of goods or market relations, and, furthermore, they can also co-exist in

different phases of the relationship. A co-operative stance can be adopted to get extra

benefits and, in a subsequent phase, the firms might compete to get the desired share

of the extra benefits achievable that, to materialise, need cooperation in the first

phase. Negotiation characteristics and tactics are relevant since they may have

important impacts on final outcomes. For example, trading off concessions over

bundles of issues is a tactic whereby agents, at the beginning, concede over attributes

of low importance for the final outcome thus avoiding disadvantageous compromises

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in later negotiation stages. In fact, were one party to reveal his preferences completely

in the collaborative stage this would entail a weaker position in the competitive phase

where strategic counter offers could secure larger extra-gains to the second party.

3.2.3 Empirical policy studies

Freight modelling explores freight activities assuming that specific policies have

altered the status quo situation. Various researchers have addressed these issue and,

among the most recent contributions, one recalls Danielis and Marcucci (2007),

Holguin-Veras et al. (2007a,b), Anderson et al. (2005), Fowkes (2007), Browne et al.

(2007). Various are the issues covered, the most interesting being: identification and

estimation of freight attributes for specific actor-type categories, road pricing and tax-

incentive policies, impact analysis of agent interdependency, contemporaneous

achievement of growth sustainability and urban logistic objectives, policy targeting

to freight related outcomes etc. This subsection provides a summary of the most

relevant UFT modelling and policy issues linked to these aspects. Holguin-Veras et al

(2007a,b) scrutinized the effects of freight management policies for the city of New

York using a game-theoretic framework that accounts for the choice of time of travel

as well as for the relative shippers’ and carriers’ power and weakness with respect to

the policies implemented. Interdependence among agents is considered in studying

UFT determinants. Not all the policies equally impact on all agent-types thus

producing differentiated responses. This framework could well be adapted to other

policies such as carbon emission charges; road pricing or even long-term impact

policies such as land use planning. This approach applies for validating or

disconfirming strongly held hypothesis on policy implications. Holguin-Veras (2006),

for instance, discovers that road pricing may have a reduced impact on freight tours

re-scheduling due to the tight time constraints shippers set coupled with a reduced

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capability transporters have to pass-on the charges which are usually small compared

to the operational costs of expanding delivery windows. The relevance of this aspect

is related to the actual balance of power between shippers and carriers and is channel-,

customer- and freight- specific (Holguin-Veras 2007b). In this case only one third of

the carrier changed their behaviour while the others did not due delivery time

restrictions set by the customers. The role of discontinuities in the evaluation of

service attributes cannot be underestimated (Swait, 2001). Shifting consignments

outside peak travel times cannot even be considered without an ex-ante availability of

the receivers to do so which is mainly dependent on the actual costs incurred for

doing so.

Fowkes (2007) demonstrate, using adaptive stated preference techniques, that

thresholds for specific attributes (e.g. travel time, overall duration, punctuality, etc.)

are relevant for shippers. The evaluations for different shipper-types is heterogeneous.

What is important is the heterogeneity of specific shippers’ preferences which, in

certain cases, are particularly sensitive even to small changes in travel time and

reliability, even if, this is not usually the case suggesting that improvements due to

infrastructure investments need to cross a threshold to be considered. These

considerations indicate that using standard techniques for evaluating project

improvements may provide distorted results and, in particular, overestimate, what

truly matters to decision makers, benefits. The non-linearity of the attributes impact

on utility is also found by Danielis and Maruccci (2007) that show how threshold

effects when neglected, can produce highly distorted estimates.

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4 Urban Freight Transport Policy Measures and Analysis

4.1 Type and Range of Measures

UFT is characterized by different problems such as: impact of freight traffic on traffic

congestion, low efficiency of the distribution tasks, and non compliance of vehicles

with Euro standard or traffic regulation.

The list of policy interventions and private measures identified could indeed be quite

long. A classification of these policies and initiatives includes: fiscal measures

(market based incentives), regulatory measures (command and control regulations),

land use planning measures, infrastructural measures, new technologies and ICT

measures, management and other measures. Public Authorities are progressively

recognizing the importance of private involvement in the policy-making process in

order to reach their public objectives as well as taking into account private interests

when defining measures. The current trend is to use consultative planning. Policy-

makers and private agents collaborate to identify the measures to implement for

achieving the objectives jointly set. The planning process is based on the institution of

consultation forums between public and private agents so to combine the advantages

of both top-down and bottom-up approaches for the promotion and implementation of

the measures. The need for behavioural modelling is evident if such an approach is

followed. A good example is the so-called Freight Quality Partnership (FQP)

implemented in several cities in the UK. This strategy primarily aims at involving

private agents in policy decision-making but do not allow in-depth analysis of the

supply chain agents’ support for each specific policy instrument which is the

objective of the approach we suggest.

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4.2 Market based measures

Market-based measures aim to alter the market prices of the goods whose production

generates negative effects (Maggi, 2007). The most appealing measure in this

category is represented by congestion charging. There are two main types of

congestion charging commonly applied: cordon and area charging. The classical

examples being respectively those of Oslo and Trondheim, and that of London.

Park pricing foresees the payment of a toll for leaving a car idle in an area where land

is scarce. An example is the park pricing strategy of the city of Rome where parking

fees change according to the zone considered with the inner city centre being the most

expensive.

4.3 Command and control measures

Command and control (CC) measures are essentially a bundle of rules and

prohibitions, supported by a control system. These measures are usually not

compulsory for freight traffic only but refer to traffic in general. However, there are

specific measures aimed at freight traffic, such as: volume or weight restrictions on

vehicle access into the city, physical or temporal restraint of traffic in specific zones

(e.g. LTZ), mandatory use of low polluting vehicles, etc.

Time windows and vehicle access restrictions are the most frequently used. They can

be differentiated according to the characteristics of the vehicles (volume, weight,

length, emissions, propulsion) or according to other parameters such as the load factor

or the organization of the transport services (own-account or third party).

Other CC measures refer to the appropriate allocation of existing loading/unloading

bays (LUB) and the possibility to perform night deliveries. The scarcity of LUB and

their inappropriate/illegal use suggests to enforce their use for commercial vehicles.

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4.4 Land use planning measures

There is a bi-directional relationship between transport and logistic measures on the

one side, and land use ones on the other where the latter identify all interventions that

change the use of space. Land use measures with the largest impact on UFT are

zoning of economic and non-economic activities and relocation of freight generators

(e.g. logistics or industrial activities, hypermarket). Indeed the concentration of

commercial activities might allow the rationalization of deliveries benefiting both

private operators and the community (Maggi, 2007).

The planning and provision of LUB as well as reserved parking areas also represents

a land use planning policy as in the case of Barcelona where a system of multi-

purpose lanes was implemented converting the equivalent of 44 on-street parking

spaces into LUB during the prescribed (between peak) hours while, during peak

hours, the lane is used as a priority bus lane and, during the night, they are used for

private parking. The difficulty to integrate conjoint planning, land use measures and

UFT measures explains why there still are very few applications of these measures.

4.5 Infrastructural measures

Infrastructural investments represent a typical measure implemented by public

authorities. This is partially due to the high cost of planning, implementing and

maintaining such kind of infrastructure. Infrastructural measures aim to: promote,

whenever possible, a modal shift, away from the road alternative, towards other

modes and foster the rationalization of freight flows through the installation of

logistics platform aimed at consolidating deliveries and collection operations.

The Monoprix experiment in Paris represents an interesting example of the use of rail-

road for goods delivery. A retail chain of urban supermarkets with 60 shops located in

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Paris receive part of their supplies (non-food products, non-alcoholic beverages) daily

by rail, combined with trucks for final distribution to the stores.

4.6 New technology and ICT measures

The implementation of new ICT technologies is affecting all areas of transport. They

are proving powerful tools to support UFT and help public and private decision-

makers to achieve sustainability. Telematics tools can be classified according to type

of application, dimension and user-type. The most relevant telematics applications in

the context of UFT are: freight and fleet management systems, on-board-computers

for delivery vehicles, traffic monitoring and traffic control (enforcement), electronic

access control, automatic fee control system, electronic zone management, dangerous

goods transport management systems (Egger and Ruesch, 2003).

These applications, among other things, promote the exchange of information

between agents, support the routing and scheduling of vehicles according to the

degree of congestion characterizing the transport network, allocate efficiently LUB

and to increase the load factor of vehicles.

4.7 Management and other measures

This category includes both measures implemented by private and public agents

aimed at reducing the social impact of UFT and promoting cooperation between

operators. Regarding private-led measures these refer to activities favouring the

optimization of the distribution process such as the consolidation of orders (i.e.

logistics demand) or consolidation of deliveries/collections (i.e. logistics supply),

reduction of the frequency of deliveries/collections, increase in the load factor of

vehicles, reduction of duration of distribution operations (driving times as well as

loading/unloading time), etc.

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The promotion of cooperation among agents, private and public, is usually pursued by

the establishment of local or national forums to discuss about UFT measures to

implement.

A specific approach implemented for the promotion of partnership between public

and private agents that has reached such a huge success to raise the willingness to

export it in other (national) context is the FQP activated in several cities in UK. FQP

are basically local forums (http://www.dft.gov.uk) where local government and

representatives of local and environmental interest groups as well as supply chain

representatives join together to discuss and pursue several objectives in the context of

UFT.

4.8 Policy impact analysis

Concentrating the analysis on a smaller and more focused set of policies, following

Danielis et al. (2009), we give account of the specific and differentiated effects that a

given policy can have on UFT. In particular, we concentrate on: goods vehicle time-

access regulation, vehicle type restrictions, LUB, fiscal policies and the promotion of

UDC and provide information on how each of the policy may be implemented and on

what are their main effects for given supply chains (Danielis et al., 2009).

Local authorities increasingly use time-access regulations. Quak and de Koster (2006,

p. 6) quote a survey among the 278 largest municipalities in the Netherlands showing

that the proportion of municipalities that use time-window regulations increased from

41% in 1998 to 53% in 2002.

To curb congestion and pollution, cities often regulate vehicle access according either

to their dimension, weight, loading factor, emission factor or fuel type. The limits of

the truck dimension or the weight dimension are aimed at decreasing congestion, road

occupancy and the large emissions of air pollutants that characterize large trucks. A

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potential side effect is the reduction of consolidation possibilities. There is not much

research on the effect of these restriction on freight transport operations and costs.

Exceptions are Allen et al. (2003) who found that the effect depends on the size of

the fleet and the width of the serviced area and Quak and de Koster (2006b) who find

that the use of the vehicle weight restriction results in decreased transport efficiency

and, in almost all cases, in a considerable increase of CO2 emissions.

With a limited number of space available there exists a competition between

passenger and freight parking needs. Usually LUB are used by cars as a solution for

missing car parking spaces. In Paris survey work has demonstrated that LUB are

occupied illegally for 47% of the time, are empty for 47% of the time and are used by

goods vehicles for collections and deliveries for only 6% of the time (Mairie de Paris

2006, quoted by Browne et al. 2007). In Rome LUB located in the LTZ were found

free only in 13% of the cases (Stathopoulos et al. 2009). Signalling and enforcement

is hence an important issue to solve. City authorities need to determine how many

LUB to make effectively available for freight distribution and where and how to

position them.

Fiscal policies comprise both taxes or subsidies. The former could be imposed as

congestion charges or area licensing and be defined according to vehicle type (load

factor, size, Euro emission standard), and/or to time window. There are a few

examples of congestion pricing in European cities (London, Stockholm, Milan)

involving urban freight transport. Not much is yet known on the effects of charging.

In some cases it also resulted that road pricing would affect private (own account)

trucks with relatively low loading factors more significantly implying that cargo

would shift from private trucks to commercial (third-party) ones to some extent

24

(Browne et al., 2004). It is quite unlikely that fiscal policies could be the solution to

UFT although it could be part of it.

The introduction of UDC is an appealing policy aiming at changing the quantity and

quality of deliveries. Many cities, mainly in continental Europe (Germany, The

Netherlands, France, and more recently, Italy) have conducted studies, trials and

experimented specific schemes. In 2008 the Italian government financed 4.8 million

euro UDC schemes in 18 Italian cities. Yet, their results have been so far

disappointing. Only in few cases the UDCs survived financially without public

subsidies or strong political commitment. Dablanc (2005) discussing the French

experience with UDCs underlines that one of the difficulties is financial since UDCs

need high subsidies from municipalities. We now combine the characteristics of the

specific policies previously described to provide some tentative conclusions about the

likely impacts of urban distribution policies on specific distribution channels localized

(see for details Danielis et al. 2009) within city centers and to underline the need to

use a behavioral approach to UFT modeling. The main conclusions is reported in

Table 1 where the policies, the main general impact, the relevant factors which

determine the specific impact and the specific impacts on the distribution channels are

summarized.

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Table 1 Urban freight policies and distribution channels: general impacts, relevant factors, specific impacts

Policies General Impacts Relevant factors Expected impacts on distribution channels (DC)

Access-time restrictions

reduction of time available for delivery more vehicles needed more lorry drivers needed suboptimal routing decreased load factor

third-party vs. own account multi-drop vs. single drop self-implied time windows delivery frequency distance from the shops

no impact on pharmaceutical products DC since exempted heavy impact on fresh food DC with the exception of fruit and vegetables stores with own-account procurement impact on some segments of Ho.Re.Ca DC marginal effects on clothing&footwear DC

Vehicle restrictions (weight, engine type)

fleet size fleet renewal rate

third-party vs. own account carrier size delivery size

no impact on pharmaceutical products DC since exempted heavy impact on fresh food DC when distributed via large retail organizations (weight) or via own-account (engine type) impact on Ho.Re.Ca DC when supplied via own-account impact on the drop size of clothing&footwear DC

LUB consignment costs and times delivery frequency delivery size existence of l/u private facilities

potential impact on pharmaceutical products DC since deliveries are frequent but require short l/u times high impact on fresh food DC since deliveries are frequent and require medium/long l/u times small impact on Ho.Re.Ca. DC since deliveries are less frequent with short/medium l/u times small impact on clothing&footwear DC since deliveries are occasionally large with large l/u times, more frequently small with short l/u times

Fiscal policies

consignment costs and times multi-dropping routing loading factors

consignment frequency third-party vs. own account TL vs. LTL goods value

no impact on pharmaceutical products DC since exempted small impact on fresh food DC since they require frequent deliveries high impact on Ho.Re.Ca. DC and clothing&footwear DC especially for occasional deliveries performed via own-account

UDC consignment costs and time consignment consolidation use of more environmentally friendly vehicles

existing regulation physical characteristics of the good third-party vs. own account logistics coordinator

no impact on pharmaceutical products DC since it is not compatible no impact on fresh food DC since it is not compatible potential impact on clothing&footwear DC for occasional orders potential impact on Ho.Re.Ca DC for non perishable products

5 Concluding remarks

This paper has defined the concept of UFT evidencing its multi-faceted nature and

described the commonly applied (FSA) modelling framework. Policy evaluation

26

cannot be separated from modelling issues especially when analytically sound results

are requested for objective policy evaluation purposes. FSA is described and

constructively criticised by showing both its weaknesses, given the fast changing

phenomena to which it needs to be applied, and also indicating possible

improvements via a behaviourally based approach. The most recent contribution

along this stream of research have been surveyed indicating the potential

contributions derivable from a behavioural approach. One can conclude that an

integration and complementation of the FSA is needed to improve our understanding

of the basic drivers explaining why freight within cities is moved the way it is and

which are the likely results policy interventions would achieve once put into place.

Under this respect a major innovation relates to the sub-division of the analysis to

consider three different agent-types: carriers, retailers and own-account (Marcucci et

al. 2010). In fact, most of the recent literature on UFT illustrates, in principle, the

importance of agent-specific policy measures, however, no study, to the best of our

knowledge, has acquired the necessary data to formulate analytically sound and

empirically verifiable proposals incorporating knowledge of agent-specific behavior.

The approach proposed represents a first attempt of carrying out, define and validate

agent-specific freight policy-mix analysis. The problems and potentially feasible

solutions identified in the FQP are extremely useful in the progressive specification of

the various attributes conceived to map the preferences of each agent-type. The data

allow for the estimation of agent-specific models useful for analyzing the most

promising and potentially acceptable policy-mixes. The results obtainable are not only

reliable but also relevant under a policy implementation and evaluation scenario. The

approach described is not only innovative under several aspects but also provides

socially relevant results especially since several researchers have argued that the

27

impact of the commonly implemented urban distribution policies should be analysed

more in detail given its effects are likely to be differentiated by type of goods and

distribution channels. So far, unfortunately, there is no clear understanding of how

and why this happens, nor have these impacts been described in detail or measured

empirically. There is still a lot of research to be carried out in this specific field.

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