Han Yan

Cédric Gueguen

Bernard Cousin
email: bernard.cousin@irisa.frhan.yan

Jean-Paul Vuichard
email: jeanpaul.vuichard@orange.com

Collaborative Overlay Power Management based on the Delay-Power Tradeoffs

Keywords: Home network, Energy saving, Green networking, Overlay control network, Low Power, Collaborative system

Achieving an energy-efficient home network while providing high quality user service is and will continue to be a significant need for the future residential environment. In this home network environment, numerous home appliances are widely used and also integrally turned on to contribute to the home network service. But these appliances are used partially in different service requirements. Therefore, in this paper, we propose a collaborative overlay power management system in which appliances can be partially turned on depending on the request of the services. Moreover, by learning a users behavior, the collaborative overlay power management could trend to minimize power consumption or trend to minimize the waiting delay. Regarding research on the different power consumption and waiting delay tradeoffs, our proposition allows for satisfying different user requirements on power consumption and waiting delay.

I. INTRODUCTION

Energy saving is recognized as a key issue in global warming and climate change. According to the recent report of the European Commission, Eurostat, there are three dominant energy consumption categories: transport, household and industry. Household energy consumption increased to 26.7% [START_REF] Eurostat | Consumption of energy[END_REF] of total energy consumption in 2010 and this category of energy consumption is greater than industry energy consumption. Furthermore, the price of electricity is constantly increasing, with European residential electricity prices increasing on average 2% faster than inflation in 2012. The greatest price increases from 2012 to 2013 were observed in Romania (26%) and in Estonia (23 %) [START_REF]European residential energy pricing report 2013[END_REF]. It is therefore crucial to reduce energy consumption in home environments.

The first challenge is that, in home environments, in recent decades, there has been a proliferation of connected devices and the number of connected devices has led to a sharp increase in energy consumption in the home. With many different types of appliances in a home network, such as home gateway (HGW), Set-Top Box (STB), network attached storage (NAS), laptops, etc., household energy saving approaches should take these different connected appliances and their different usages and functionalities into account.

In the literature, techniques have been proposed to reduce energy consumption at the device level. Using dynamic power management [START_REF] Benini | A survey of design techniques for system-level dynamic power management[END_REF] [START_REF] Chung | Dynamic power management for non-stationary service requests[END_REF], devices can be switched to a lower power mode when the service demand is reduced. In addition, algorithms have been proposed minimizing the energy consumption of device components. For instance, Maruti proposed a method that reduces the power supply when there is less traffic over Ethernet links [START_REF] Maruti | Dynamic ethernet link shutdown for energy conservation on ethernet links[END_REF], and there are other proposals aiming to control the memory in order to be more power efficient [START_REF] Khargharia | An adaptive interleaving technique for memory performance-per-watt management[END_REF] [START_REF] Fan | Memory controller policies for dram power management[END_REF]. It is not sufficient, however, to save energy only at the level of each individual device. The power status of each home device is independent of the others. For example, when all family devices are not operational or not in use, it can be concluded that the home gateway no longer needs to provide a local network, and its Ethernet and WiFi components can be turned off. The activity or power status of one appliance is not independent information; this information can be used to manage other appliances. Consequently, our solution provides a collaborative system to control the power status of home connected devices at the network level and the power states of functional blocks in these collaborative devices at the device level.

In earlier works, Youn-Kwae Jeong et al. proposed a solution that controls home network devices by reconfiguring the power control element (PCE). Their proposed solution only supplies power to the devices and the functional elements that are related to requested services [START_REF] Jeong | A network level power management for home network devices[END_REF]. In their approach, all functional elements are turned on at the beginning of the service, despite the fact that early functional modules are not needed at that time. The UPnP AV use case [START_REF] Ritchie | UPnP AV Architecture:1, The UPnP forum Std[END_REF]] is a good example to illustrate why there is a time lapse between requested functional blocks in one service: the user controls the home devices with an UPnP Control Point (laptop, smartphone, iPad or other tablet) and wants to watch a film on his UPnP Media Renderer (STB). This film is saved on his UPnP Media Server (NAS). For this service, the content directory functional block on the UPnP Media Server is needed at the beginning of the service. The decoder functional block on the UPnP Media Renderer is required later by the service. Therefore, our solution provides collaborative management according to the service request on each functional block in order to control the right function block at the right moment.

Another challenge is that user behavior is an important factor in the home network energy control system. By learning user behavior habits, the power management system could anticipate the requests of the functional blocks while one service is launched. Accordingly, the power management could make more precise power control decisions that are adaptive to the learned behaviors of a given family. Moreover, different users have different requirements: some prefer to be more energy efficient, some do not want their service experience to be affected, and some want both. Therefore, our solution proposes a learning power management approach for the purpose of satisfying the user service demand.

A final challenge is that, in order to be power-efficient and reactive to user demand, the solution should be able to control any device at any time. This requires an always-on connection. In order to ensure power management, the solution proposed by Youn-Kwae Jeong et al. needs a permanent connection (Ethernet or WiFi) which is said to consume more than 1 Watt. Unlike earlier power management solutions which need to maintain a WiFi connection or an Ethernet connection, in our former works [START_REF] Yan | Green Home Network based on an Overlay Energy Control Network[END_REF] [11], we proposed a low power overlay network for the centralized monitoring and control of home network devices by using the following technologies: ZigBee [START_REF] Farahani | ZigBee Wireless Networks and Transceivers[END_REF], UPnP network [START_REF]UPnP Low Power Architecture, The UPnP forum Std[END_REF], 6LowPan [START_REF] Hui | Extending ip to low-power, wireless personal area networks[END_REF], and Bluetooth low energy [START_REF] Mackensen | Bluetooth low energy (ble) based wireless sensors[END_REF]. These can be considered for an infrastructure of a green overlay control network. Home connected appliances can nowadays be turned on by a command from Wakeup on USB [START_REF] Flannery | Method and apparatus of providing power management using a self-powered universal serial bus (usb) device[END_REF] or Wake on LAN (WoL) [START_REF] Ryu | Method and apparatus for controlling power of computer system using wake up lan (local area network) signal[END_REF].

The main contributions of our collaborative overlay power management are: 1) the proposition of a collaborative home network. 2) Our power management approach takes into account the user behavior. 3) The control messages are sent over a low power overlay network.

The rest of the paper is organized as follows. Section II describes our collaborative overlay power management. Section III presents the service pattern, the power consumption model and the delay model. Section IV presents the setup of the simulation. Section V. analyzes the results of the simulation. Section VI draws conclusions from our findings.

II. PROPOSED COLLABORATIVE OVERLAY ENERGY CONTROL POWER MANAGEMENT SYSTEM

In this section, we detail the collaborative overlay control system, as shown in Fig. 1, which is composed of the power management system, the collaborative home network connected devices, and the low energy overlay control network.

A. Power management

Power management: power management comprises a database and a decision algorithm entity. The database is used to store records of user habits when they use services. When the service is requested for the first time, the database gathers the information relating to the user request services in order to learn the habits of this family and the information relating to the family user reaction. According to the information collected by the system, the power management controls the device with fine granularity. The granularity of the control is said to be fine because the control can turn on/off the functional blocks that are necessary for the collaborative service at the point at which they are requested. We assume each home network collaborative service involves one or more devices that cooperate together to meet the service demand of the family.

A typical collaborative UPnP audio video service pattern within four devices is shown in Fig. 4 (below). The user uses their UPnP Control Point (laptop) to search for a film, which is stored on the UPnP Media Server (NAS) in order to watch it on the UPnP Media Renderer (STB). Each service occurrence requires different Functional Blocks (FB) in different devices. The user firstly needs the connection between their laptop and their NAS to be guaranteed by the HGW. Then, when the user has found the film saved on the NAS, the STB should be turned on in order to play the film. The content directory (FB1), connection manager (FB2), transfer server (FB3) functional blocks in NAS and the video stream decoder (FB1), display interface (FB2), authentication (FB3) and transfer client (FB4) functional blocks in STB are requested. The HGW provides the connection block during the entire service.

This typical UPnP AV use case requires different connected devices to participate at different points in the service. When the user decides to start the service, according to the information saved about this service, the power management sends control messages to each device as they are required. The requested information can be pre-saved by the user in the power management, or by a process of auto-learning in the power management. With the collaborative system, only the required components are turned on, and the components which are no longer needed when the service is terminated are turned off.

B. Home network connected devices

In our proposal, we not only assume that collaborative services involve several devices, but also that each device is collaborative into one or more functional blocks. It is necessary to detail how one functional block pattern works in one device during one service as shown in Figure 3. If the power management decided to turn on the functional block before the request of the service, as shown in Fig. 3(a), FB has three phases: starting, idling and operating. The starting phase defines the necessary starting time that begins when the functional block is turned on (t dec-on ) and lasts until the functional block is available (t available ). Then FB may be in the idle phase during a period of no activity until it is requested by a service. Once this FB is requested (t request ), it could execute the operation immediately since it is already operational. On the contrary, as shown in Fig. 3(b), the request of this functional block may happen before this FB is available. FB will begin the service execution immediately (t dec-on ) upon becoming available (t available ) without having an idle period. In this case, the starting phase becomes a waiting delay before executing the service operation.

C. Low energy communication overlay network

Low energy communication overlay network: On each home network device, we propose an overlay low energy node by considering the characteristics of the device. These low power nodes form a low power overlay control network. The control message can be sent via ZigBee, Bluetooth Low Energy (BLE) or an UPnP Low Power (UPnP LP) network, depending on the capacity of the device. For example, it is possible that a new generation tablet will be equipped with BLE instead of having to add a ZigBee dongle to this tablet. The power consumption of a ZigBee module or BLE chipset is about a few milliwatts, which is much less than that of an Ethernet or WiFi network card, which consumes about 1.5 Watts. In our system, we assume that the overlay power control messages will be sent by a ZigBee or BLE module in order to ensure a low-power and always-on network.

In this section, we have described our overall architecture of our collaborative and collaborative overlay power management (COPM). The power-delay tradeoff (PDT) algorithm of this COPM architecture will be presented in the following section.

III. PROPOSED ALGORITHM

Before describing the collaborative overlay power management power-delay tradeoff algorithm in detail, it is worthwhile to know how the power energy and delay models are constructed.

i i ∈ {N b d n } N b F B d Number(N) of available FB in one device(d) j j ∈ {N b F B d } F B(i, j) j t h functional block (FB) in i t h device. N bs Number of service(s) occurences. k k ∈ {N bs} service(k) k th service instance P F B (i, j)
Power consumption of the Functional Blocks (FB)

A. Energy and delay models

The notation of the parameters is described in the Table I. The energy consumption of one service occurrence can be calculated using the following formula (1): where the D on (i, j, k) is the on duration of the functional block in formula [START_REF]European residential energy pricing report 2013[END_REF], which is composed of the starting duration, the utilization duration of F B(i, j) in service(k) and the idle duration. The starting duration is the necessary period to launch a FB. The utilization duration depends on the service requirement. The idle duration may be null when the service request t request (i, j, k) is earlier than the FB being available at t available (i, j, k).

E(k) = N b d n i=1 N b F B d j=1 P F B (i, j) × D on (i, j, k) (1)
D on (i, j, k) = D starting (i, j, k) + D idle (i, j, k) + D utilization (i, j, k)

(2) The Delay(k) is the total waiting time for the service(k), described in formula (3). There are two cases: if the FB is available before the arrival of the service request , there is no waiting time for the user. Thus, the delay is null in this case. Otherwise, if the service request arrives before the FB is available, the waiting time is the period from the t request (i, j, k), to thet available (i, j, k). The delay is the difference between these two moments.

Delay(i) = N b d n i=1 N b F B d j=1 { 0, t request (i, j, k) < t available (i, j, k) D starting (i, j, k), else (3)
After modeling our service patterns, function block patterns, and energy and delay calculations, we applied our COPM-PDT propositions and other power management systems to the models.

B. Power-Delay Tradeoff (PDT) algorithm

The proposed COPM Power-Delay Tradeoff (COPM-PDT) algorithm is illustrated in Fig. 4. The COPM-PDT is launched by a daemon process which checks if one service is launched. If yes, the COPM-PDT will check if the service description has already been saved in the database. If this is a new occurring service, the power management will learn the functional blocks that were used by this new service and the service request time for each functional block t request-learned (i, j). The formulas (4) calculates how the COPM-PDT learns the t request-learned (i, j). If the service has already been learned by the power management, there will be two cases. In the first case whent COPM-PDT detects a request for one FB, the FB will be turned on immediately. User needs to wait that the required FBs are ready. In the second case when COPM-PDT does not detect an request for an FB, it controls the FB based on the learned user behavior. It means that each FB will be turned on t dec-on , in the formula (5):

t request-learned (i, j) = N bs k=1 t request (i, j, k -1) N b s (4) t dec-on (i, j, k) = t request-learned (i, j) -t tradeof f (5)
t tradeof f (i, j) = t request-variance (i, j, k) × α ( 6
)
The decision is based on the mean value of all recorded learned requests. Based on the learned mean value, COPM-PDT turns on the FB in advance of t tradeof f in order to minimize the waiting delay. The t tradeof f will be configured by the user satisfaction requirement α and the variance of the request (t request-variance ) as shown in formula [START_REF] Khargharia | An adaptive interleaving technique for memory performance-per-watt management[END_REF]. The variance of the request depends on how is user behavior. For example, one user each day turns on his STB at around 8 pm; sometimes it could be earlier or later depending on the users behavior. For the user who is more concerned about power consumption and willing to wait, the user satisfaction requirement coefficient will be small. And the t tradeof f will be relatively small. On the contrary, the t tradeof f will be relatively greater if the user wishes to start the service immediately. This value will be investigated in the simulation. In both two cases, after receiving the FB requests, COPM-PDT will update his database in order to calculate the new mean value of the request (t request-learned (i, j)) and the tradeoff value (t tradeof f ). And this new calculated t request-learned (i, j) and t tradeof f will help the power management system control the devices for the next time when the service will be called. At the end of the service, COPM-PDT will turn off the FBs that are not needed by this or other services.

IV. SETUP OF SIMULATIONS AND ANALYSIS OF RESULTS

In this section, we will firstly present the setup of our simulation and the scenarios, and then we will analyze the simulation results in different scenarios.

A. Simulation setup

In order to accurately measure power consumption and the waiting delay of each power management, we implemented a typical home network, which was capable of executing a collaborative service in omnet++. In this typical home network, we had 5 devices that cooperate to provide services in the home network. The service inter arrival time follows an exponential distribution whose mean value is 5,000 seconds. Each arriving service requires different components in the device to achieve the complete service. The use of each component also follows an exponential distribution whose mean value is 1,000 seconds. It is also possible that there is a time lapse between the beginning of the service and the request of each FB. This time lapse follows a normal distribution. The parameters are described in TABLE II. Firstly, for the purpose of measuring the power efficiency and the waiting delay of our version of power management, we will tune the mean value of the request of the functional block. In this case, during the simulation limit time, our power management could have 3 scenarios: α=0.99, 99% of services are executed with a minimum waiting delay.

α=0.75, 75% of services are executed with a minimum waiting delay.

α=0.50, 50% of services are executed with a minimum waiting delay.

Secondly, in order to analyze the solutions, by tuning the tradeoff percentage, we will investigate the different powerdelay tradeoff. We compare our COPM-PDT proposition versus User control power management and PCE power management:

User control power management: We assume a user who is mindful of energy conservation. This user turns on each device integrally when its service is needed, and turns off each device when the service is no longer required. This version of power management could be seen as an ideal behavior for the point of view of delay and the worst behavior on the user which have to trigger every events. PCE: The service is started with all necessary power control elements on at the beginning of the service. They will be turned off when the user finishes using the services.

B. Simulaton Results

1) The request of the functional block: In this study, we first examined the changes on the request of functional blocks in 3 different tradeoff scenarios. To study the relationship between the t request and the power consumption and waiting delay. The distribution law of the request time of FB ( t request (i, j, k)) follows a normal distribution whose standard deviation is 100 seconds. We varied the mean value of the distribution of the requested functional block from 0 seconds to 1,000 seconds.

From Fig. 5 we can see that the energy consumption of the user control power management is almost stable because the devices are turned on integrally when the service requests arrive and the devices are turned off integrally when the services are terminated. Therefore, the energy consumption of the user control power management corresponds to the service utilization duration. In this experience, the utilization duration does not change, thus, the energy consumption of the user control remains stable. The energy consumption of the PCE power management increases continuously, since the PCE turns on all participating functional blocks from the beginning of the service. The increasing of the t request signifies that the requests of functional blocks are arriving increasingly later. The functional blocks will to consume more energy in the no-activity period. So, with the PCE, the total energy consumption in one service increases as the t request increases. The COP M -P DT 99 , COP M -P DT 75 , COP M -P DT 50 are configured to assure respectively 99%, 75%, 50% functional blocks should be ready while the request of the functional block is arriving. Therefore, the functional blocks in the COP M -P DT 99 are turned on much earlier than the COP M -P DT 75 and COP M -P DT 50 . Thus, the energy consumption of COP M -P DT 99 is higher than COP M -P DT 75 and COP M -P DT 50 . Fig. 6 shows the average waiting delay for each service. The user control power management turns on the devices when the service request arrives. Thus, there is always a waiting delay in the starting time before the device becomes available. The PCE has the smallest waiting delay because all of the functional blocks are turned on at the beginning of the service. The waiting delay of the PCE comes from the functional blocks that are used first. Although some of the functional blocks are needed later, they are already turned on. As explained before, the FBs controlled by COP M -P DT 99 are turned on much earlier than the COP M -P DT 75 and COP M -P DT 50 in order to minimize the waiting delay. Therefore the COP M -P DT 99 has almost the minimum waiting delay that we could achieve. COP M -P DT 75 has a higher delay since the functional blocks are often turned later than the COP M -P DT 99 .COP M -P DT 50 has the shortest delay.

After seeing the trends of energy consumption and delay by varying the request of the functional block, we will zoom at the moment that the mean value of the request is 1,000 seconds in Fig. 7 and TABLE III. We can see that our proposition COP M -P DT 99 is the most energy efficient and a minimum delay: COP M -P DT 99 consumes 74.7 watt-hours, compared with PCE we could have a 23.62% energy gain. In the same scenario, COP M -P DT 99 cause only 0.5% increased delay compared with PCE. For the users who prefer to have more energy gain at 31.29 % and may accept 20.06% delay more than PCE, they could choose COP M -P DT 75 . For those who want to obtain the maximum energy gain without considering the delay, the energy gain could be more than 34.15%.

2) The tradeoff coefficient: In this study, we will investigate the tradeoff percentage parameter from 0.01 to 0.99. . We fixed the mean value of the distribution of the requested functional block at 1,000 seconds with a standard deviation fixed at 100 seconds. Fig. 8 shows how energy consumption changes while we tune the tradeoff coefficient. Fig. 9 shows how waiting delay changes in the same condition. These two figures can be divided into 3 regions. In the first part, while the coefficient is smaller than 0.25, the COPM-PDT trends to maximize the power consumption with a relatively high delay (Between 210 seconds to 400 seconds per service). The home network user may find this difficult to accept. The second region of tradeoff coefficient is between 0.25 and 0.6. The energy efficiency is almost the same as in the first region. Moreover the delay decreases almost by half compared with the first region. The tradeoff coefficient in the last region is from 0.6 to 0.99. Although the COPM-PDT is a little bit less energy efficient than the second region, the algorithm reaches almost the minimum waiting delay that we could achieve in one service. Based on this experience, the COPM-PDT provides 3 regions of choices for different user requirements in energy efficiency or waiting delay or a tradeoff between these two factors. In this paper, we developed a collaborative overlay power management that offers several possible tradeoffs between the power consumption and the waiting delay in the home network. Based on the learned knowledge of the user's behavior, our COPM-PDT proposal is capable to control collaborative Our proposed COPM-PDT algorithm exploits the tradeoff of power consumption and waiting delay. The results show that our proposition could be energy efficient and provide a low waiting delay for different user requirements. The results also show that COPM-PDT could achieve the minimum waiting delay or the maximum energy efficiency which would depend on the user exigencies. There is always a tradeoff between power consumption and waiting delay. The future work will be to extend the proposed algorithm for more collaborative services that may have dependences between each other, which may provide a better energy efficiency and less waiting delay home environment.

Fig. 1 .

 1 Fig. 1. Collaborative home network service management

Fig. 2 .

 2 Fig. 2. A service pattern example

Fig. 3 .

 3 Fig. 3. Functional block is turned on (a) in advance (b) by the service request

Fig. 4 .

 4 Fig. 4. Algorithm of COPM-PDT TABLE II SIMULATION SETUP Notation Value Number of device = 5 Number of FBs in one device = 10 Mean value of duration utilization = 1,000 seconds Starting duration = 100 seconds Power consumption of one FB = 10 watt Simulation repetitions for each experiment = 10 runs Simulation limit time = 100 hours

Fig. 5 .Fig. 6 .

 56 Fig. 5. Energy while varying the request of Functional Block(FB)

Fig. 7 .

 7 Fig. 7. Energy consumption and delay while the request at 1000 seconds

Fig. 8 .

 8 Fig. 8. Energy while varying the request of tradeoff coefficient

Fig. 9 .

 9 Fig. 9. Delay while varying the request of tradeoff coefficient

  The number(Nb) of devices(d) in the home network(n)

		TABLE I
		NOTATION SUMMARY
	Notation	Definition
	N b d n


Jesus Gonzalez-Feliu

Eichi Taniguchi

Bruno Faivre D'arcier

Financing urban logistics projects From public utility to public-private partnerships

Keywords: urban logistics, finance, public-private partnerships, cost-benefit analysis

Urban goods movement and urban logistics started to be defined as a scientific discipline 20 years ago, where several actions in research, development, policy and deployment were started to be coordinated. However, most of the innovations and projects presented in that field are stopped or reduced because of a common constraint that becomes its worst enemy: the financing mechanisms. Although many studies deal with urban logistics, only a few of them show the difficulties at financing, but without entering in detail on the financing mechanisms. This chapter aims to present the main financing issues in urban logistics. First, the main categories of funding strategies that can be applied to urban logistics are presented, focusing on public-private interactions and collaborations. After that, a scenario assessment using a cost benefit analysis framework shows the different interests and issues of each category of stakeholders, and the main advantages and limits of each category of investment and financing strategies. Then, the fields of urban logistics that seem the most adapted to public-private collaboration in terms of financing are identified and commented. As a conclusion, guidelines for researchers and practitioners to take into account financing issues in urban logistics decision support are proposed.

Introduction

Planning and management issues in terms of urban goods are not something new. The first documented proof of the interest of public authorities on regulating the traffic flows for urban supply is found in the Ancient Rome [START_REF] Quak | Sustainability of Urban Freight Transport. Retail Distribution and Local Regulations in Cities[END_REF]. Indeed, the oldest known urban freight transport restriction dates from the first century BC: pickups and deliveries were banned from the ancient city of Rome during the day by an edict attributed to Julius Caesar (i.e. the 'Lex Iulia Municipalis'), based on references in conserved letters of Cicero to a comprehensive law of Caesar that deals with municipal affairs [START_REF] Smith | A dictionary of Greek and Roman Antiquities[END_REF]. However, it is not the only ancient example of urban logistics interest by public authorities: several researches in history and archaeology show the importance of urban goods management to feed big cities like London or Paris [START_REF] Britnell | A Commercialising Economy: England 1086 to C. 1300[END_REF]. Also the Islamic medieval cities accorded a particular importance to urban goods distribution [START_REF] Boone | Archeological and historical approaches to complex societies: the Islamic states of medieval Morocco[END_REF]. Moreover, the economic context in the late medieval period had a direct impact on the urban consumption, then on the flows of goods in urban zones, having an indirect impact on employment. This lack of planning concerning the supply of cities is one of the consequences of what is called the "medieval decline" [START_REF] Bailey | Demographic Decline in Late Medieval England: Some Thoughts on Recent Research. T. S. Ashton Prize: Joint Winning Essay[END_REF].

Focusing on recent and contemporaneous event, the integration and development of urban goods strategies passes through different stages. After the 2 nd World war (1944)(1945)(1946)(1947)(1948)(1949)(1950)(1951)(1952)(1953)(1954)(1955)(1956)(1957)(1958)(1959)(1960), Europe and Japan are on a reconstruction period. Although delivering primary materials is an important, it is seen as an emergency case by public authorities that make big economic efforts on rebuilding cities and primary infrastructures. With the development of the industry during the Cold War (mainly in the U.S.A., Europe and Japan) and the popularization of private cars, the period between 1960-1980 is characterised by two related phenomena (Routhier, 2001): City expansion and need of understanding personal mobility. The aims of local authorities and even several countries' governmental offices are focused on expanding urban areas and creating core infrastructures to incite internal mobility (of both people and goods). However, local economies remain important and cities start to be populated resulting on a constant increase of cities' supply flows.

It is why between 1980 and 1990 the first congestion problems start to be observed. Urban goods trips co-habit with personal trips, and the first conflicts are in general solved by punctual interventions or market regulations [START_REF] Gonzalez-Feliu | Models and Methods for the City Logistics. The Twoechelon Capacitated Vehicle Routing Problem[END_REF]. It is during the 1990-2005 period that the main concepts of urban logistics will be developed. The notion of Urban Goods Movement is introduced by [START_REF] Ogden | Urban Goods Movement: A Guide to Policy and Planning[END_REF] and extended by Ambrosini and Routhier (2004). Several coordinated actions take place in different countries during those years (mainly in Germany, France, Japan, The Netherlands), supported by the research community (Taniguchi and Thompson, 1999;[START_REF] Bonnafous | Les marchandises en ville : le problème méthodologique de l'appréhension statistique[END_REF]2004;2006;2008;2010;2012;[START_REF] Maccharis | City Distribution and Urban Freight Transport: Multiple Perspectives[END_REF]. Public authorities being the most active during that period, the private stakeholders start to strongly being implicated in urban logistics projects since 2004, mainly in France, Germany and the Netherlands.

However, we observe that after almost 20 years of coordinated urban logistics researches and studies, most innovations and projects are stopped or reduced because of a common constraint that becomes its worst enemy: the financing mechanisms. Although many studies deal with urban logistics, most of them are related to regulation, optimization and management issues, and only a little set show the difficulties at financing, without entering in detail on the financing mechanisms.

For this reason, and to support this field in urban logistics research, this chapter proposes to present the main financing issues in urban logistics. First, the three categories of funding strategies common to infrastructure investment are presented and applied to urban logistics, focusing on public-private interactions and collaborations. Moreover, several examples are presented to illustrate those concepts. Then, an example is presented to show the different interests and issues of each category of stakeholders, and show the main advantages and limits of each category of investment and financing strategies. To do this, an example of deployment of delivery space booking systems is proposed, as well as a cost benefit analysis taking into account three modes of financing: an "all private", an "all public" and a "public-private partnership". After that, the fields of urban logistics that seem the most adapted to financingbased public-private collaboration are identified and commented. As a conclusion, guidelines for researchers and practitioners to take into account financing issues in urban logistics decision support are proposed.

Urban logistics funding strategies

In urban logistics several funding strategies can be identified. Since the variety of stakeholders is high and their interactions frequent, the financing actions can take different forms.

The first is public funding, as happens in several city's infrastructures like delivery bays or reserved lines.

For private initiatives, private funding is the most common strategy. However, public intervention is possible. We find three main forms of public intervention:

• Delegation: public authorities cover a part of the investments (or not) and give a private company the structures to make a service. Sometimes (like in public transport) they cover a part of operational costs, in other cases (like Vicenza's UCC) they cover only the investments and give free usage of the structures, but the operational costs have to be covered by the private company. • Subsidies: subsidies are economic helps that must not be refunded back • Public loans: this is the case of low interest credits to help the development of urban logistics systems. Those economic helps must be refunded back to the public authority. In South-West Europe, the concept of delegation is one of the most popular when referred to the deployment of urban logistics solutions. Indeed, many operational systems derive from trials and demonstrations financed by local authorities (with the contribution of regional, national or European subsidies). The most significant cases are the UCCs of La Rochelle (France), Monte Carlo (Monaco), Ferrara, Parma, Venice Mestre and Vicenza (Italy). In all those cases, the logistics terminals and the vehicles have been funded by the public authorities (who remain the owners). Also failed projects have followed this funding strategy, like Strasbourg (France), Genova and Bologna (Italy), Regarding logistics facilities, such projects use mainly on de-used public publics like gross marketplaces, warehouses or industrial pavilions, reconverted into urban crossdocking terminals (and sometimes, like in Parma, warehouses). The vehicles are in general built in association with the public transport operator, but remain property of the city council. Once the investments are funded (in general without a real return of investment), a carrier is contracted to operate the system. This can be a consortium of existing operators (Bologna), a municipal service (Monte-Carlo, Parma, Venice Mestre), a specific society created with public funds (Vicenza), a mixed-capital company (Genova) or a public transport operator (La Rochelle).

Subsidies are in general applied in many cases. From R&D projects (like those promoted by the European Union) to public actions like those of the Emilia Romagna region in Italy, we find in several countries cases of subsidies. They cover in general the following application fields:

• Subsidies to local authorities by national or international entities to cover a part of structural and infrastructural investments (Emilia Romagna Region subsidies, European Commission projects). • Financial support of local, regional and national governments to carries for equipping them with green vehicles. • Technological subsidies, when dealing to the deployment of vehicle and driver support technologies. • In some cases, social subsidies can be used to recruit fragile populations and to prepare them to urban logistics. Although some French stakeholders like La Petite Reine or Alud have promoted those practices, this field remains less developed than the others.

The case of public loans is less common. However, the French politics in terms of environment have promoted several actions where funds are not given to private carriers as subsidies but as loans that have to be refunded back. Although most actions concern industry and long haul carriers, we can find also applications regarding urban logistics, mainly related to intermodal transport in urban areas o to technological innovation to support urban goods transport.

Public economics re-funding strategies

Traditionally, two main families of re-funding approaches have been applied by public authorities, mainly for infrastructure investments: that of collective utility and that of users' refunding. Those two families have been seen as opposite so being in direct conflict. However, a third family of approaches that mix both precedent families have been observed in the last years, mainly in transport infrastructures [START_REF] Bonnafous | Le cofinancement usager -contribuable et le partenariat public-privé changent les termes de l'évaluation des programmes d'investissement public[END_REF] but also in urban logistics projects [START_REF] Browne | Urban freight movements and public-private partnerships[END_REF]. This family includes all mixed approaches where a part of the investments are covered (or refunded) by public authorities on the basis of collective utility and the rest must be obtained by the economic benefits of the systems, paid by its direct users or their customers. In this section we will examine all three families of approaches, illustrating them by representative examples in urban logistics.

Collective utility

By collective utility we intend the socio-economic interest that a project can bring to a society. In collective utility viewpoints, the initial investments and operational costs are paid by public authorities. The funds come from the public taxes, either local or national, and in general no refunding is allowed. In some cases, it can be asked to a private partner to invest a part (in case of PPPs) with a total refunding by public funds, although this case is less common. Collective utility is motivating the construction of free infrastructures, like national and regional public roads, public parking (with no fees) or, regarding urban logistics, delivery bays or electronic accesses to limited traffic areas. To justify public utility, a system must be proven socio-economically viable. In other works, it must prove to bring a quantifiable socioeconomic benefit to the collectives of the city or country it is deployed.

The decision to invest is then conditioned to a quantifiable analysis of the balance between the used (and sometimes destroyed) resources and the created richnesses, which are advantages of different nature (economic, environmental, cultural, social, societal, etc.) directly or indirectly lead by the project [START_REF] Bonnafous | Le cofinancement usager -contribuable et le partenariat public-privé changent les termes de l'évaluation des programmes d'investissement public[END_REF]. To do this analysis, a socioeconomic assessment via Cost Benefit Analysis is a valid alternative, as seen in infrastructure investment [START_REF] Hayashi | International comparison of background concept and methodology of transportation project appraisal[END_REF].

To make a first example related to urban logistics, we will cite the deployment of delivery bays and other free parking infrastructures for trucks in urban centres, common to several European cities 1 . Indeed, delivery bays in city centres are in general located next to fee-based parking spaces (so no free parking zones) and their deployment imply two types of resource usage: one is that of real costs, both for its construction (signalling, painting and sometimes small civil works) or for enforcement controls (sometimes assimilated to car parking controls); the second is the lack of savings that suppose blocking private car parking economic benefits to make a delivery free parking space. To justify that resource usage, it is important to quantify the socio-economic benefits: impacts on congestion, social benefits (mainly to delivery workers and private car drivers related to the decrease of double line parking) and economic benefits for carriers (mainly related to global time savings). Because the socio-economic benefits are higher than the destroyed resources, the development of delivery bays is justified.

Another case of collective utility is that of Bologna (Italy), where the city centre has rigorous restrictions but presents also parking facilities to "clean freight transport vehicles" [START_REF] Trentini | Developing urban logistics spaces : UCC and PLS in South-Western Europe[END_REF]. In that case, vehicles have to be identified (mainly by RFID) and automatic control devices are installed at the "gates" of the limited traffic zone. Those investments are not refunded back, because they are of collective utility (the decongestion of the city centre due to such controls, on both private cars and business/goods vehicles led to significant socio-economic savings). However, they have been financed by different public bodies: the city, a regional contribution and a national subsidy.

User's refunding

The user's refunding strategy is that of making the user (in this case the transport carriers, the retailers or the freight senders) for using the service, i.e. to pay a fee for using an urban logistics service. This strategy is mainly motivated for economic reasons and the systems in this category need to be economically viable. This is the case of German UCCs, most of them stopped when carriers found less costly delivery schemes. Indeed, in Germany, consolidation has followed an economic logic, and carriers adopting such schemes have done only because they allowed economic savings. The main example is the Postdamer Platz distribution centre of Berlin, which is still operational. Another example is that of Dresde's cargo-tram, developed for private interests of a Volkswagen manufacturing plant. The system, which was an example of user's refunding, stopped in 2010 because considered as less rentable that a classical truck-based delivery system due to the global economic situation.

An alternative is to use refunding mechanism applied to a larger set of individuals (urban tolls, negotiable permits, etc.) to make the less virtuous pay for the virtuous delivery system. This alternative, although envisaged for delivery space booking services, has not been adopted by the city of Bilbao (Spain) because of both cultural and legal issues.

Mixed approaches

In urban logistics, the main re-funding approaches are mixed because of a common factor of most projects: investment costs are difficult to be entirely refunded. For that reason, public authorities accept to partially finance them, then to make them operational and economically viable (for operational costs and a part of the investments). However, mixed approaches are various in nature and structure and it is not always easy to properly identify all of them. We however propose a categorisation of mixed approaches:

The most common strategy is that of a partial contribution of authorities as a subsidy. In other words, when developing urban logistics solutions, some investment costs are covered by a public subsidy. In general, those costs cover feasibility studies, part of the investments (mainly related to civil works and technological issues, including clean vehicles) and a demonstration period. In many cases, they are not refunded back (like in Genova and in several cities from the Emilia Romagna Region in Italy). In other cases, a part is refunded back but not the entire subsidy.

Another strategy is that of giving concession (with or without asking for retribution) of infrastructures and/or vehicles. Indeed, since platforms and vehicles constitute the main investment costs, public authorities can give concession of them to the service operator. In Vicenza (Italy), a specific carrier was created to operate the UCC, freely having the right to use the logistics facilities and the vehicles given by the city's authorities. The management, maintenance and other management costs are covered by the fees the system asks the transport carriers to deliver the city centre. Being compulsory to a large number of fields to use the UCC [START_REF] Ville | The limits of public policy intervention in urban logistics: Lessons from Vicenza (Italy)[END_REF], the economic benefits are enough to make the operator continue managing the system, and ensures its continuity.

Also indirect subsidies are found in practice. In Paris, Chronopost developed an urban logistics space in a central part of the city (Place de la Concorde). Since the facility prices are high, the city of Paris gave a subsidy to one of the main real estate stakeholders to make reduce the price asked to Chronopost to the values seen in near periphery areas, and allow the operator have an economically viable system. The platform is still working but the indirect subsidy needs to be maintained because of the economic situation.

Concerning Private-Public Partnerships (PPPs), the most common action related to financing transport systems is that of making a mixed investment, where public funds are used to cover a part of the global costs and the rest must be funded by the private company, or vice versa. That strategy starts to be popular in urban people transport but remains quite unusual in urban logistics.

Finally we find approaches combining various strategies, like in Padova's UCC, where the facility was already owned by the operator. In that case, costs for feasibility analysis and demonstration were not refunded (as paid directly by public authorities), but vehicles were bought on the name of the public transport operator, and given free to the operator. The system is operational and economically viable (for only operational costs) since its second year. For new vehicles, subsidies have been given, but original vehicles are step by step rebought to the public transport company in order to refund a part of the initial investments. New investments, like the increase of storage surface and the development of fresh products logistics, are funded by the operator (which is a main logistics real estate stakeholder in the area).

A comparison of funding strategies

In this section, we present a comparison among four funding strategies. To do this, we will follow a classic cost-benefit analysis (CBA) method after defining each scenario. All the scenarios are defined on the same basis, i.e. a deployment project arising on implementing and making operational a network of delivery space booking (DSB) systems.

A note on Cost Benefit Analysis (CBA)

Generally, a CBA method consists on listing on one side all investment and operational costs, year after year, for a given time horizon (in general 10 years for infrastructure projects, i.e. DG [START_REF] Dg Regio | Guide to Cost Benefit Analysis of Investment Projects[END_REF]. Then benefits are also listed in the same time horizon. After that, for each year, benefits are confronted to costs and their difference is updated using an update rate in order to take into account the money updating year after year. Finally, an Investment Return Rate (IRR) after the project's time horizon is calculated. In order to take into account the pluri-annual time horizon, it is important to define an updating rate "a" which allows comparing two quantities of money at two different periods. Taking the value of a quantity of money V t at time t, and V n the value of this quantity at horizon n, they are related by the following equation:

V t = Vn/(1+a)n
Then, year by year, benefits are confronted to costs and their difference is updated using an update rate of 4%. Finally, an Investment Return Rate (IRR) is calculated, in a 10-year horizon.

To simulate the scenarios, we need to have a unique basis on which only parameters related to who invests would change. We assume a hypothetic city, making abstraction of the country. All simulations are then made on the same city, a virtual 2.000.000 inhabitant urban area created from real data (the details on how the virtual city is constructed and how the freight demand is forecast, see MODUM, 2011). Using the tools of evaluation in this context, i.e. generalising local effects to a city point of view, we estimate the costs and the benefits for the two main stakeholders: the city (or the collective community) and the transport carriers (or individuals).

We assume a VAT of 20% and, for each system personnel fees equal to those of employees working during the pilot implementation, operation and evaluation phases (in case of pilots in different cities, the retained costs will be précised in the corresponding section). Another important assumption concerns the time period where investments are made. Oppositely to public transport infrastructures (tramways, subways, urban-suburban trains), investments are not made in the first two years, but the systems are introduced gradually. This assumption enforces that of money availability.

The CBA will be made on a 10-year horizon, which is enough long to ensure a return of investment and enough short to not need a strong technology change or replacement during the operation period. We also assume the level of operating costs and revenues as constant over this period. The discount rate is assumed to be the French public one, i.e. 4%. This rate varies from one country to another, and can be updated (as well as personnel costs and VAT) when adapting the scenario assessment to cities of one precise country. Moreover, we define a target internal return rate (IRR) of 15% for the private company and 4% for the public entity.

Last but not least, we assume that invested money is available by each investor, so no hypotheses on how the money is obtained are made.

Context, scenario characteristics and assumptions

When assessing a scenario, for a CBA or other forecasting analyses, it is important to explain well the context and the input variables by defining all the parameters and setting the various assumptions that allow building the scenario. In this section we present the main characteristics of the scenario that will simulate the deployment of a DSB service in the virtual city as well as the assumptions that have been made.

First, we aim to make a quick synthesis of how a DSB system works. Such system is installed into a surface parking machine which is located next to the delivery space that can be booked. The user that aims to book has to be registered and have an identification card that needs to be introduced in the machine each time the vehicle is stopped on the delivery space. Two booking alternatives are proposed: either by internet (both punctual and periodical reservations are allowed) or directly on the machine is the delivery space is free. When a vehicle is stopped on the delivery bay, lights on the floor indicate if it is reserved (red) or not (green). In any case, the vehicle needs to be identified. If the delivery space was free, the vehicle automatically books the place. If not, if the introduced card corresponds to that of the reservation, the lights change colour (to green). If an unauthorised vehicle is stopped on a reserved delivery space, lights indicate it and a message is sent to the police station to inform about the irregular situation.

To build a deployment scenario under realistic commercial, tactical and operational conditions, we suppose that the solution tested in Bilbao has been further developed and can be applied to existing parking machines in order to allow the possibility to make private car parking payment (for private parking places around the DSB) and booking operations for the DSB systems on the same machine. In that way, existing machines can be used for both private parking and DSB services. We suppose that all delivery bays with the DSB technology are deployed in a central area (about 3.5 km²). A total number of 100 DSB will be operational in 5 years, and we assume a total number of users (per year) of 1200 vehicles. We assume that one user corresponds to one vehicle and then one vehicle uses only one card. Because the cards can be lost, broken or stolen, we estimate that 15% of the users will need to replace their cards each year. The deployment trends of the system and the number of vehicles consequently using it are reported on the following table: In all scenarios, we make the assumption that operational costs will not change. The details of those costs are found in Gonzalez-Feliu et al. ( 2013). The cost structure can be summarized as follows: To finance the system and also to make it more efficient, it is necessary to ask a fee to the users, in order to both contribute to its development and be more involved in using it as well as in defending its good usage. The following CBA (one for each scenario) will be made to find the better fee to ensure a viable system and then continuous in time.

Transport carrier benefits

Before making a CBA for each scenario, it is important to define the benefits of transport carriers to estimate the values that fees to ask to carriers can take. This value corresponds to the maximum fee transport carriers would accept to pay. To obtain this value, we need first to estimate the benefits of a DSB for a transport company. In this case, we can identify four direct benefits for a carrier:

• Fuel savings, directly translated into economic gains (money savings related to fuel consumption). • Time savings, also directly translated into economic gains (money savings related to timetabling and working hours).

• Distance savings, indirectly translated into economic gains (money savings related to vehicle usage).

• CO2 savings, which can be related to economic gains if a Carbon Tax is assumed.

We assume that the DSB areas will be created in order to consent the loading and unloading operations for carriers that are not DSB customers, i.e., to be developed in a noncongested situation. We extrapolate the results of Bilbao's DSB evaluation with a small calibration concerning small vehicles, the category the less concerned by the system (their characteristics and delivery behaviour show the need of stopping even no place is available and the possibility to make double lines without significantly perturbing the traffic and the environment). In this context, we assume a unitary fuel and CO 2 savings per vehicle per DSB stop as follows: We make the following assumptions:

• The deployment of the DSB allow an average usage of the system, per vehicle, as • Savings related to double line avoiding are negligible for drivers in terms of fuel consumption and CO2 emissions. However, a speed gain related to congestion decreasing can be assumed. This gain is estimated to be about 2 km/h in average in the considered area, i.e. an average gain in route of 20 min., corresponding to a time savings of 6% with respect to total travel time.

• Fuel savings are estimated in gram, then converted into liter using an average volumetric mass for fuel of 750 g/l. Moreover, a fuel cost of 1.3 €/l is assumed (this is the current value in France, according to CNR (2012), it can be updated to the current value for each country).

• Concerning CO2, we assume a carbon tax for each transport carrier. Although the current value is 17€/ton, we aim to set it to 100 €/ton, according to the last European Considerations (French Ministery of Land Use and Transport, 2005). In this configuration, a carrier having a standard route (see Pluvinet et al., 2012, for more information about routes using DSB in Bilbao) would pay about 1175 €/truck each year (for trucks making urban distribution as those of DSB pilot). On the another hand, the direct benefits are small since the gain of CO2 and the current carbon prices give an average gain of 16 €/truck each year.

11

The benefit table for the transport carrier is the following: With these assumptions, after year 5 and that each transport carrier would have an average benefit of 450 €/vehicle each year, mainly due to the congestion reduction (which is traduced into time savings). However, it is important to take into account margins. Since the main impacts are related to time savings because of congestion reduction and traffic estimations have in general errors of 20-30%, a 25% margin seems reasonable. We set then the maximum fee that can be asked to transport carriers to 360 € per vehicle and year, including VAT.

Collective benefits

After defining individual benefits for transport carriers, it is important to define the collective benefits in order to estimate the interest of municipal authorities on investing on such systems. Some of those benefits derive from those of transport carriers but others have to be estimated by taking into account global traffic on the DSB influence areas. The main benefits that have been identified are:

• Time savings of drivers (both for personal or commercial trips), which can be translated into economic gains (money savings related to timetabling and working hours). However, since it is difficult to make this estimation, we assume an average cost of time according to World Bank (2005) for monetary value estimation of travel time. • Distance savings, indirectly translated into economic gains (money savings related to vehicle usage) are as for transport carriers savings, negligible.

• CO2 savings, which can be related to economic gains if a Carbon Tax is assumed. The estimation method is similar to that of heavy vehicles, using an estimation of the current distribution of vehicle types on the considered city and translating it to the traffic in the parts of the city where we supposed to have DSB systems operationally working.

The collective benefits table is the following: The overall benefits by year are estimated to be comparable to those of investments, so that will justify a collective utility vision.

Scenario simulation

We propose a scenario simulation on the basis of 5 scenarios of public and/or private funding of the DSB system. The scenarios are:

• S1: Investment and management costs covered by the public authority on the basis of a public utility. No fee is asked for carriers and public authority assumes all costs by using the public funding mechanisms (local tax derivation or national funds). For this reason, only socio-economic CBA is regarded for this scenario. • S2: Investment and management costs covered by the public authority on the basis user's refunding. Public funds are only loaned for a part of investment costs then refunded back on the basis of the public funding interest rates (about 4%). For this reason, an IRR of 4% is necessary to justify a balance. • S3: Investment and management costs covered by a private company, on the basis of public delegation of No financing is made by public funds, and the company needs to ensure a minimum benefit that can be translated on an IRR of at least 14%. • S4: Investment and management costs covered by a private company, on the basis of public delegation of service, with a public subsidy that covers all investment costs for years 0 to 5. That amount is funded by a public mechanism and no IRR is asked. The company needs to ensure however an IRR of at least 10%. • S5: Public-Private Partnership. Public authorities cover 60% of the costs (on an IRR of at least 4%) and private carrier the 40% remaining (on an IRR of at least 15%). In this case, both stakeholders get an economic return but it allows a better management (by a private carrier) and a contained fee for the transport carriers. We observe from the table the main differences in terms of cost assumed by each stakeholder, in terms of internal rate of return (IRR), socio-economic rate (i.e., the theoretical economic benefit that should be obtained if all benefits considered in the study would be monetized) and the yearly fee that has to be asked to transport carriers (per vehicle) to reach the expected IRR. Scenario S1 defines the socio-economic issues and shows the global benefits in case of a total funding by public authorities. In the current economic context, this scenario is not viable, since at least a part of the costs (most operational costs and if possible a part of investment costs) should be refunded by the user. In that context (Scenarios 2 to 5), different possibilities are shown. The lowest fee is obtained by a system managed and financed by a private carrier with a public subsidy which cover the investment costs, representing about 18% of the total costs of the system in a 10 years operational configuration (S4). Then, a system totally financed by public funds but with a user's refunding strategy (S2) results on a similar but lightly higher fee (220 € for S4 and 250 for S2). An only privately financed system (S3) needs a fee of 280 €, almost 30% more expensive that the best case. HJowever, in S4, public authorities need to finance almost 350 000 € without any economic return of investment, whereas in S2 and S5 the invested capital can be refunded. The difference is that in S2, the total amount (more than 1.8 million €) needs to be invested, and in S5, the amount to invest is about 60% of the total (about 1.1 million €). Moreover, having a private partner guarantees a constant need of ensuring the system's efficiency and reaching in the best way the expected IRR.

Which urban logistics fields seem the most adapted to public-private partnerships?

From the example, we observe that PPPs can be a valid alternative to classical funding strategies. Moreover, other forms of public-private collaboration seem interesting for different types of urban logistics solutions, whereas for commercial applications or private actions they are few recommended. Furthermore, also in the case of public utility cases (mainly related to infrastructural or policy actions) the collective utility thinking dominates the other strategies.

In this section we present several fields of urban logistics where public-private collaboration for funding seems a good alternative to consolidate their deployment and operability. We distinguish three categories of fields:

• Urban logistics facilities.

• Urban logistics systems based on ITS and ICT • Resource sharing-based logistics schemes.

Urban logistics facilities

Urban consolidation centres

Urban consolidation centres (UCC) have been considered as the main example of urban logistics [START_REF] Allen | Good Practice Guide on Urban Freight[END_REF]. However, most of the planned facilities are nowadays not operational [START_REF] Gonzalez-Feliu | FREILOT. Uban freight Energy efficiency Pilot. DL. 6.4 -Cost-benefit analysis[END_REF]. This lack of operability is in general related to the difficulty to maintain the system due to a sub-usage of the platform. However, we observe different cases where the systems are working.

The first category of systems is that of UCCs with a strong public intervention. Either in totally public funding cases, like Vicenza [START_REF] Ville | The limits of public policy intervention in urban logistics: Lessons from Vicenza (Italy)[END_REF] or Monaco (ref.) or on private management with public subsidies, like La Rochelle [START_REF] Trentini | Toward a Shared Urban Transport System Ensuring Passengers & Goods Cohabitation[END_REF], the UCC remains operational or continuous because of an explicit implication of public authorities. In the first two cases, the public authorities ban the access of all freight transport vehicles (allowing however some exceptions) using public ordinances, which is at the limit of free competition [START_REF] Ville | The limits of public policy intervention in urban logistics: Lessons from Vicenza (Italy)[END_REF] but has been allowed by National Juridic instances. In the second case, the support to the UCC is not only policy-based but also economic. Indeed, La Rochelle municipality has grouped three service (two for personal transport and the UCC delivery system) into a lot, and given to a private company. The deficit of the UCC is compensated by the benefits of the other two services [START_REF] Trentini | Toward a Shared Urban Transport System Ensuring Passengers & Goods Cohabitation[END_REF], and in the first years of the contract a yearly public subsidy was given to the management operator to make the system operational. A similar case is that of Milan, where the public transport operator used the public transport infrastructures and their service facilities and vehicles to deploy an urban delivery system. The system was stopped after the municipality retired its support, not allowing the delivery vehicles of the system going on the bus lanes [START_REF] Trentini | Developing urban logistics spaces : UCC and PLS in South-Western Europe[END_REF].

In the second category we find private-managed initiatives with public subsidy where municipalities have not been supporting the UCC outside the initial subsidy for investment and release costs. This is the case of Padova (Gonzalez-Feliu and Morana, 2010), However, this strategy is less used than the others, but when public authorities do not interfere it appears to be efficient and sustainable.

In the third category a number of UCC have been operated by private sector without any subsidies by public authority. We can find these cases in Motomachi, Yokohama, Japan and Shinjuku, Tokyo, Japan (PIARC report, 2012). The Motomachi joint delivery system using UCC started in 2004. The Motomachi Shopping Street Association (MSSA) carried out management of the system asking a nutral freight carrier to operate UCC and collect/deliver goods to about 1,300 retail shops and 500 homes in place of 20 freight carriers. The success factors were that about 95% of retails shops in the area have participated in the joint delivery systems and MSSA effectively coordinated stakeholders involved in the system. Shinjuku UCC was another successful joint delivery system using UCC which started in 1992. The system mainly collect/deliver goods to offices and retail shops in high rise buildings in Shinjuku are of Tokyo. It is very hard and costly for freight carriers to collect/deliver goods to individual office in high rise buildings with about 50-60 stories in very busy area. In addition to the congestion on streets, the bottleneck of delivering goods in such situation is the shortage of loading/unloading space and elevators dedicated for goods delivery in the buildings. They established an association named Shinjuku Matenro stuff for operating UCC near Shinjuku station. In both cases of Japan UCC has been successfully operated without any subsidies by public authority, because private associations had good business models with the excellent leadership of management as well as enough amount of goods to be delivered by UCC.

Urban consolidation terminals can be useful if the demand is well identified (Danielis et al., 2010) and a good business model is found. However, to reach operability, such systems often need public help. Instead of forcing the usage, the best approach is that of partially funding the system at its investment phase (for example providing help to by vehicles) and make an access restriction policy that helps the establishment of an UCC without forbidding the rest of operators (instead of a limiting policy, an incentive action). However, it is important to ask the UCC operator to ensure a robust business model and a good operational management follow-up in order to ensure the balance of operational costs by the service benefits.

Urban multimodal terminals

Close to UCC we find the case of urban multimodal terminals. Despite the failures of Cargo trams (Amsterdam and Dresde), the subject starts to be popular and the cities of Lyon and Paris aim to develop freight systems with trams to access the city centres (not for final deliveries but for city access from important peri-urban logistics zones). Moreover, a case of urban combined transport is that of of Samada Monoprix. In all three cases, a specialised operator (of public or private origin) is needed to run the system, but since terminals have to be constructed in the city centre, a help of public authorities is needed. However, in the case of Dresde tram and Monoprix train service, the initiative was private and related to the main customer of the system (respectively an automotive manufacturer and a grocery distribution group), and fopllows the competition rules. The public intervention is more related to indirect subsidies to make the real estate costs decrease.

A similar case is that of soft mode transport, like Chronopost (postal chariots and small electric vehicles) and La Petite Reine (electric-supported cargo cycles). In both cases, a private initiative started the system, with the goal of an operational system. To do this, a help of public authorities in terms of real estate advantages is needed. Outside of those indirect subventions, no other advantages, neither political nor financial, are given to operators.

In all those cases, the collaboration seems to take place in the form of private initiatives with the need of public subsidy strategies.

Proximity delivery points

Another interesting facility, mode related to e-commerce and proximity delivery services is that of proximity delivery points [START_REF] Augereau | An evaluation of recent pick-up point experiments in European cities: The Rise of two competing models?[END_REF][START_REF] Durand | Impacts of Proximity Deliveries on e-Grocery Trips[END_REF]. Nowadays, two types of reception points are seen: small shops acting as reception points for food and non-food parcel deliveries, which are in general private networks without public funding, and automatic delivery machines for non-food parcels, mainly located in passage points of cities (terminal public transport stations, commercial areas, etc.), which need in general a help of the land owner's to be developed. Although some public-private partnerships can be made in the first category (to use postal offices as delivery reception points, for example), the most interesting collaboration is found in the second category, where public-private partnerships can increase the competitiveness of its business models and provide benefits to both public and private stakeholders.

In Japan we have a large number of small convenience stores in urban areas which are used as proximity delivery points using e-commerce. The individual delivery of products to home with designated time windows can generate a burden for e-commerce company. A freight carrier very often has to visit customer more than one time, since no body is at home. To avoid such inefficient delivery, customers can obtain their products at a convenience store near their home, which is generally open for 24 hours. These systems are operated without 16 any subsidies by public sector. As e-commerce and home delivery is increasing in aging society, proximity delivery points play an important role to reduce costs of home delivery.

Introduction of ITS and ICT for urban logistics

The example is a clear example that illustrates it. In addition to delivery space booking, truckbased intersection control systems, like those of Helmond, the Netherlands (Pluvinet et al., 2012) can be deployed. Both systems need the implication of cities since there is a communication between he vehicle (a login card or an on-board unit) and the infrastructure (respectively parking machines and delivery bays with a reservation system), so public authorities need to invest on it and users should also pay for the service. When a public interest is found, cities can invest higher amounts of money. For example, intersection control increases security for fire brigade, police and ambulance vehicles that need to cross lights even when they are red, decreasing the number of red light crossings. For that reason, the city of Helmond promoted the installation of almost 40 systems in key crossroads of the city. Since such technologies need to evolve and be maintained, a management company which is also a technological actor is needed. The public-private collaboration is trivial in that case.

Logistics sharing systems

Last but not least, the collaborative systems amont transport carriers seem to be a valid alternative to UCCs (Gonzalez-Feliu and Salanova, 2012) that can derive on an overall cost decrease, having important economic, environmental and social benefits (see chapter 14 of the present book). This is also the case of e-commerce distribution sharing systems (Gonzalez-Feliu et al., 2012;[START_REF] Wygonik | Evaluating the Efficacy of Shared-use Vehicles for Reducing Greenhouse Gas Emissions: A US Case Study of Grocery Delivery[END_REF], where synergies can be found among operators to strongly decrease overall costs of physical distribution. However, stakeholders are reticent to share (Gonzalez-Feliu and Morana, 2011), mainly for competition and confidentiality reasons. To deal with that issue, logistics networks seem a good compromise, but to develop them, a neutral partner is needed.

It is then necessary to constitute a consortium with the different operators, a neutral management company and a public authority that ensures the neutrality of the management operator and the collective utility of the system. The most suitable way to make the system work is then to involve the different partner (public and private stakeholders) at financing levels, in order to make them participate to the development (but also to the benefit sharing) of the system, ensuring a good operational management and tactical and strategic development of the logistics sharing approach.

Conclusion

In this chapter we have overviewed the main financing strategies in the context of urban logistics, translating the main public economics concepts from transport infrastructure planning to urban logistics deployment. After that, we have simulated several scenarios for financing a delivery space booking system that illustrate the main concepts and show, through a cost-benefit analysis, the main economic returns for both public and private stakeholders. We observe that public-private collaboration can be a good option if both parts share the risks and are disposed to share the benefits. Opposing to transport infrastructure planning (Bonnafous and Faivre d'Arcier, 2013), in urban logistics both parties can see immediate benefits of adopting a system, and PPPs can be a good alternative to share costs and risks.

However, this does not apply to all urban logistics solutions. The most suitable application fields seem to be related to infrastructures, but not to linear ones (i.e. road or railway lines) but to nodal facilities: urban consolidation centres, proximity delivery and reception points and delivery bay related systems. Furthermore, collaborative transport systems have an important potential but need a good partnership between public and private stakeholders to be efficiently deployed and ensure then their continuity.

  First year (16 DSB): 5 stops/route at DSB. o Second year (40 DSB): 8 stops/route at DSB. o Third year and more: 11 stops/route at DSB.

Table 1 . Deployment trends for the chosen scenario

 1

		Year 0	Year 1	Year 2	Year 3
	Number of installed DSBs during the year	16	40	60	80
	Number of vehicles using the system	0	150	450	850
	Percentage of replaced cars	15%	15%	15%	15%
		Year 4	Year 5	Year 6 and after
	Number of installed DSBs during the year	100	100		100
	Number of vehicles using the system	1150	1250		1250
	Percentage of replaced cars	15%	15%		15%

Table 2 . Cost structure (in k€)

 2

			Investment costs
	Cost type	Year 0	Year 1	Year 2	Year 3	Year 4	Year 5
	Technologic investments	27,00	0,00	0,00	0,00	0,00	0,00
	Infrastructure and civil works 40,25	60,37	50,31	50,31	50,31	0,00
	Other investment costs	10,00	10,48	16,00	11,00	10,50	10,46
	Total investment costs	77,25	70,85	66,31	61,31	60,81	10,46
		DSB -OPERATIONAL COSTS
	Cost type	Year 0	Year 1	Year 2	Year 3	Year 4	Year 5
	Functional costs	0,00	40,00	40,00	40,00	40,00	40,00
	Maintenance	0,00	57,90	80,56	99,44	118,32	137,20
	Other operational costs	0,00	0.11	0,34	0,64	0,86	0,94
	Total operational costs	0,00	98,02	120,90	140,08	159,19	178,14

Table 3 . Fuel and CO 2 savings for DSB in a deployment situation

 3

	Vehicle type	Fuel savings (ml)	CO 2 savings (g)
	Van	0	0
	Small truck	32	82
	Big truck	40	101

Table 4 . Benefit monetary conversion, for each savings category

 4

	Type of gain	Stakeholder	Economic gain per vehicle (€/year)
	Vehicle usage	Transport operator	0 €/year
	Time savings	Transport operator	350 €/year
	Fuel savings	Transport operator	85 €/year
	CO2 reduction Transport operator	15 €/year
	Total savings	Transport operator	450 €/year

Table 5 . Collective benefit monetary conversion, for each savings category

 5

	Type of gain	Stakeholder	Overall economic gain (€/year)
	Time savings	Transport operator	150 000 €/year
	CO2 reduction Transport operator	50 000 €/year
	Total savings	Transport operator	200 000 €/year

Table 6 . Scenario simulation synthesis Scenario Stakeholder Total costs Economic IRR Socio-economic rate Yearly fee 2 (per vehicle)

 6

	S1	Public	1 853 428,91 €	n.a.	5.7%	0 €
	S2	Public	1 853 428,91 €	4.6%	90.9%	250 €
	S3	Private	1 853 428,91 €	16,2%	n.a.	280 €
	S4	Public	346 974,33 €	n.a.	5.7%	0 €
		Private	1 506 454,57 €	14.3%	n.a.	220 €
	S5	Public	1 112 057,34 €	4.6%	62.0%	260 €
		Private	741 371,56 €	17.6%	n.a.

The following statements are made after a quantitative and a qualitative analyses of two delivery bay and road parking behaviour surveys (one in Bilbao, Spain and one in Lyon, France) made between

and

in the context of the FREILOT project. For a detailed description of the surveys and their main results, see[START_REF] Blanco | FREILOT. Urban Freight Energy Efficient Pilot[END_REF]

Fees include a Value Added Tax (VAT) of 20%, which is a reallistic value for European countries (Gonzalez-Feliu et al., 2013).