A simulation framework for evaluating the impacts of urban goods transport in terms of road occupancy

Journal of Computational Science 3 (2012) 206–215

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A simulation framework for evaluating the impacts of urban goods transport in terms of road occupancy Jesus Gonzalez-Feliu a,∗ , Christian Ambrosini a , Pascal Pluvinet a , Florence Toilier b , Jean-Louis Routhier a a b

Laboratoire d’Économie des Transports, ISH, 14 Avenue Berthelot, 69007 Lyon, France Laboratoire d’Économie des Transports, ENTPE, Rue Maurice Audin, 69120 Vaulx-en-Velin, France

a r t i c l e

i n f o

Article history: Received 8 July 2011 Received in revised form 30 March 2012 Accepted 7 April 2012 Available online 17 April 2012 Keywords: Urban goods movement Road occupancy Modelling Simulation

a b s t r a c t This paper proposes a novel approach in order to simulate the impacts of urban goods transport on road occupancy. It combines both inter-establishments flows and households’ motorized shopping trips, as well as B2C flows. After a general description of the method resulting in an estimation of the movements, an actual example is implemented (city of Lyon, in France). Four scenarios are described and simulated, and then their results are compared. These results are promising and they take into account a large variety of flows (about 90% of the urban goods movement flows according to the classification of Patier, 2002). © 2012 Elsevier B.V. All rights reserved.

1. Introduction Since the early 1990s, several studies deal with urban freight transport planning and policy issues [14,59,57,58,4,34,39,36,6,33]. Goods transport has specific characteristics concerning urban logistics. Indeed, in urban zones we observe several operators with a various range of specific logistic chains: various delivery vehicles, different organizations (rounds, direct trips), a large part of own account transport and a large part of subcontracting [37]. This context implies an expensive last mile movement worsened by congestion, more and more constraints in road sharing, and new orientations in regulation and planning by local authorities. Although several authors relate urban goods movement to retail supply flows [62,5,11], urban goods movement involves a wider variety of flows and stakeholders. It is possible to detail the components of urban goods movement, as follows (all ratios are extracted from [42]): • Pickup and delivery flows by vehicles used for interestablishments trade (commercial, industrial, services) and goods vehicles for business trips (artisans carrying goods

∗ Corresponding author. E-mail addresses: [email protected] (J. Gonzalez-Feliu), [email protected] (C. Ambrosini), [email protected] (P. Pluvinet), fl[email protected] (F. Toilier), [email protected] (J.-L. Routhier). 1877-7503/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jocs.2012.04.003

from depot to working site . . .). These flows, known as interestablishment movement trips (IEM), represent between 40% and 45% of the total goods traffic in terms of road occupancy rates.1 • End-consumer movement flows (ECM), including shopping trips made in private car, home deliveries and other proximity teleshopping services, which represent between 45% and 55% of the total goods traffic. • Flows related to urban management movement (UMM), mainly related to waste collection, postal service, removal, hospital and public and building works. They represent between 8% and 10% of the total goods traffic. • Other flows that represent less than 1% of the total goods traffic. Overall goods traffic flows described above amount to 20% of the total road occupancy rates by running vehicles, and to 25% CO2 emissions of the overall urban transport. Ségalou et al. [51] noted that heavy goods vehicles (HGV) represent only one part of the urban goods movement (UGM). In France, HGV (>3.5 t) account for about 50% of the UGM vehicle-kilometres [46,45], whereas a third of light goods vehicles (LGV) with weights below 3.5 tons also carry goods on a regular basis [53]. It is thus essential to identify accurately the contribution of UGM in traffic generation, with the help of a relevant knowledge database. Currently, in urban zones,

1 Road occupancy rates are in general estimated in Private Car Units (PCU): 1 private car = 1 PCU; 1 light goods vehicle = 1.5 PCU; 1 medium truck = 2 PCU; 1 heavy truck = 2.5 PCU.

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activity is significantly linked to operating modes, organizational modes and vehicles size [4]. The transport chain is connected with operating mode and type of activity. Own account trips reach almost the same share of pick-ups/deliveries as professional carriers, while 3/4 of pick-ups/deliveries are carried on rounds and 3/4 of tours consist of single trips [40]. Over 5% of pick-ups/deliveries are served by vehicles less than 3.5 tons [36]. Moreover, the distance travelled depends on operating mode and urban density [42]. Understanding and forecasting urban goods flows is a major issue for public authorities. For this reason, several methods and tools have been proposed in the relevant literature. Demand estimation models are very popular and several commercial tools have been proposed in the literature [3]. However, the definition of what is a urban goods movement (UGM) model does not make the unanimity in the different scientific and practice communities related to urban logistics [3]. Although the aim of this work is not to provide a standard definition of UGM modelling, it is important to define the context and terms we refer, positioning our proposed framework with respect to the scientific literature. According to the last findings presented in the VI International Conference of City Logistics related to UGM modelling [56], two different categories of models are envisaged. The first is that of “fixed demand” simulation models, which need a demand distribution as inputs and simulate different organizational situations, with or without freight distribution optimization [10,56]. These models are mainly related to operations research linear programming problems, and derive on mixed integer mathematical formulations solved using current linear program commercial solvers or with ad-hoc developed heuristics, derived from local search or from evolutionary computing techniques [11,56]. The second category is that of demand estimation models [35], commonly used in people and intermodal transport modelling. Our proposed framework can be included in this second category; it is why we will focus on these models. The first UGM demand estimation frameworks were developed in the 1980s–1990s combining empirical relations to several fast simulation and approximation algorithms to forecast urban goods flows in real contexts. Moreover, several models have been developed for only inter-establishment movement flows. At this level, all models introduced above deal with inter-establishment movements (IEM). Focusing on the computational framework related to those models, we can group them on four main classes: • Four-step derived models: these frameworks propose to model the urban goods movement phenomena by analogy to personal trip or interurban transport, by sequential models inspired on the classical four-step modelling framework. Although many models can be included on this category, we observe several subcategories, which are: • Trip-based models [16,28] are less used on UGM than on interurban transport, due to the fact that urban trips are included on multi-destination routes. However, the two cited approaches have been implemented on commercial tools still used by practitioners. These models are direct applications of a four-step model to freight trips. These approaches need specific survey data to be calibrated and censorial information to define explicative variables. • Commodity-based models, the most popular of the present category. These models use classical trip generation techniques [35] and propose gravity approaches [7] or discrete choice frameworks [48,47] for trip distribution and modal choice. • Shipment-based models [13,31,32] are used when few data is available. The O/D matrix is estimated similarly to trip-based models, but using the shipment as the modelling unit. • Movement-based models ([19,17]) are more complex that shipment ones. They need a good knowledge of economic activities

•

•

•

•

207

and their delivery behaviour. Moreover, they follow a bottomup approach ([44], this approach is described in next section), so to define the functions from the data analysis and not to only use data to calibrate them. Origin–destination synthesis (ODS) models [25] are used when little information is found, and estimate O/D pairs from vehicle counting and secondary data related to network and infrastructure. There exist both trip and commodity versions of these models. Multi-agent simulations models [60] use concepts derived from artificial intelligence and multi-agent modelling to represent the behaviour of UGM stakeholders. Tour-based models [54,26] are generation-route construction models that do not derive from a four-step framework. First, the number of tours generated by each zone is estimated. In the tour generation phase, the origin location and the number of destinations or each tour are defined. Second, using operations research techniques, a route-construction algorithm is used to assign each tour origin to its destination locations. The first model uses a Savings algorithm and the second a logit-based approach adapted to route construction. Bottom-up statistical behavioural models [44,41], built in parallel to data collection for a better explanation of the chosen explained variable. In these models, a typology of establishments is proposed, and the number of movements (defined as pickup and/or delivery operations) is estimated for each establishment using a generation function. This function is estimated using experimental modelling techniques [43]. Moreover, they do not produce directly O/D pairs but estimate the impacts of UGM flows in terms of travelled distances and road occupancy rated within a section, zone or the entire area of study.

Three of the models presented above ([54,28,30]) have been integrated on traffic estimation models in order to add to classical urban transport models (for passengers only) the freight component [3]. Other three [16,44,19] are friendly environment software tools used mainly by public authorities but also by practitioners. Concerning ECM, only few models can be found in the literature. Refs. [52,43] propose a methodology to simulate the shopping trips in order to estimate the end-consumer’s flows from the statement that shopping trips do not follow the same generation variables than work trips [12]. Refs. [50,49,47] propose a general modelling framework that relates retail supply flows to household supply ones. Refs. [22,23,21] complete Segalou’s [52] model by characterising shopping trip generation variables and showing the links between personal shopping trips and freight transport. For a complete survey of these models, see Refs. [3,21]. In any case, the integration between IEM and ECM still remains at theoretical levels [49,47], the existing works and tools making an addition of effect for each category of flows without the possibility of substitution and interaction to simulate new or future trends and ruptures in urban logistics. Fixed demand models, which have this simulation capacity, are in general carrier-dependent, and limited to a small group of transport carrier categories: mainly non-perishable goods LTL carriers, which represent, according to [40], about 15% of the total number of IEM trips in urban areas. The aim of this paper is to propose an integrated framework to simulate the urban goods movement flows in real urban areas, in order to assess possible future situations. More precisely, the contribution of the present work arises on the approach to model end-consumer movements and the link between these flows and inter-establishment movements, and on the integration of IEM and ECM flows using an interactive trip substitution module, allowing scenario simulation with little data formatting and setting efforts. Section 2 is given over the proposed framework, which combines different modules. We pay a special attention to end-consumer

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movements, first describing and calibrating the proposed generation model, then comparing it with the known models of this type used in urban goods movement simulation, and finally introducing the link with all the other modules of the framework. After that, the architecture of the simulation framework is described, focusing on the interaction between the various modules, and on information exchange and management. In Section 3, we give an example of application, followed by the main results (Section 4). In the end some proposals of further work are presented.

2. The proposed framework The following chart proposes the general schema of the framework that includes 90% of the urban goods movement flows: inter-establishment movement (IEM) and end-consumer movement (ECM). This is a novel approach that combines all the flows related to establishment supply (not only the retailing activities but all the establishments of an urban area) and both the motorized shopping trips and the other B2C flows. This approach appears more relevant to estimate appropriately the impacts of urban goods transport on road occupancy. Consequently, the result is a significant improvement of any help for public decision-makers in the matter of urban logistics. Before presenting each module, we aim to take a look on the structure of the integrated framework. The proposed urban goods simulator can be schematized as follows: Input data processing and scenario construction. IEM generation. IEM distance estimation. Shopping trip generation (attraction). Catchment area for shopping-household relation. Substitution of shopping trips by e-commerce deliveries. ECM distance estimation. Substitution of last mile IEM flows related to e-commerce trips. ix. IEM distance re-estimation. x. Road occupancy issues calculation.

i. ii. iii. iv. v. vi. vii. viii.

Each stage needs however important information from the others, as shown in Fig. 1, which is the chart of the proposed interconnected modular architecture. Each module needs data from at least another one, and gives information to the others. This can be possible only if the modelling and simulation unit is the same for each module. For this reason, we assume the movement as the relevant modelling unit. A movement can be defined as either a delivery or a pick-up operation, performed with a given vehicle for an establishment [44]. The movement is the event, which make possible the link between the logistics behaviour of the establishment and the transport system. The movement is usually related to the shipment that can be tracked throughout the transport chain and is used in some studies [13,32]. However, there is a major difference: during its lap, a shipment can be shipped in several vehicles, just as well as it can in the same vehicle, whereas a pickup or delivery operation is directly related to one establishment and to one vehicle. This unit allows the connection between the different modules. In order to pre-process the input data and to assign the appropriate input variables to each module, a specific module has been designed to be used as the Knowledge Management System (KMS) of the framework. This KMS contains a set of seven inter-connected databases, built from three major French urban goods surveys [46,45]. This module has a double function: is allows to pre-process the input data and it results in a database of delivery and shopping trip practices.

Urban logistics practices database

Standard input data (France)

Shopping trip practices database

Data processing and analysis module

IEM movement generation

ECM movement generation

(number of commercial pickups and deliveries at each establishment)

(number of shopping trips at each retailing activity)

IEM distance estimation

ECM distance estimation

Correspondence and substitution procedures

Road occupancy issues estimation Fig. 1. Chart of the proposed framework.

2.1. The IEM estimation The IEM estimation module is issued from the urban goods demand estimation model FRETURB [44,43,41]. A distinctive feature of this model is that it is a bottom-up behavioural-based statistical model of which meta-modelling characteristics and schemes have been developed in parallel with the data collection methodology which derived from three extensive surveys used to feed the model [2]. Analyses of these surveys provided a set of indicators concerning the logistic behaviour of the various types of activities [41]. A typology of 116 categories of establishments could be defined, according to the activity and size sub-classes. The main hypothesis of FRETURB model is that the number of pickups or deliveries at a given establishment depends on its activity (French NACE code), its size and its nature. Therefore, the model is based on the nature of the generators (shippers, forwarders, consignees) and the transport choices carried out by the latter. More precisely, the model is organized as follows: • First, the number of movements (pickup or delivery operations) is generated at each establishment, before to be grouped by zone. • Second, this number of movements is converted into a number of trips, using an empirical approach. • Third, the travelled distances are estimated based on a typology of logistics practices and the geographical configuration of the urban area. 2.1.1. Movement generation You are reminded that a movement is defined in such a context as the operation of transport, which describes a delivery or a pickup, carried out by a type of vehicle in a given establishment. The relevant characteristics that explain the relationship between the urban activity and the generation of deliveries and pick-ups are as follows: ne = ϕ(a, p, o)

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where ne is the number of movements (deliveries, pick-ups and mixed operations) carried out each week for the establishment e, a the industry category (45 types), p the nature of the premises (mainly store, warehouse, office or headquarter), o the number of jobs (15 classes). Each function is given thanks to interpolation techniques from the survey data according to the number of jobs. A comprehensive description of this method and the detail of each function can be found in Refs. [44,43]. 2.1.2. Trips generation Given a freight transport path, three types of stops and trips can be distinguished. If we consider a FTL2 transport, i.e. a direct route, we usually find two stops (one loading and one unloading operation) and two direct trips (go and return). In a LTL3 schema, i.e. round, the main loading/unloading point corresponds to the establishment where the vehicle is loaded for the delivery tours or unloaded for the pick-ups tours. Two trips are connected with this position, named starting and ending trip. Generally, one of them is an empty trip. Finally, we can define the ordinary delivery or pickup positions (dp points), which are hit by a connecting trip (c trips) or by a starting or ending trip. Thus c = dp-1. In a round with n + 1 points, the first and the last point are generally at the same location (which is the case of many routes). Therefore, the number of trips t is equal to n. The number of goods vehicle trips tz generated at zone z can then be defined as follows: tz = 2 × drz + 2 × pz + (dpz − pz ) where drz represents the number of direct rounds starting in z, pz the number of main loading or unloading points in z and dpz the number of deliveries or pick-ups carried out within a round in the zone z. The values of drz , pz and dpz are empirically estimated from a database of delivery practices [38,20]. The details on model calibration can be found in Ref. [41]. 2.1.3. Distance generation In order to calculate the travelled distances, the FRETURB model proposes three types of functions. Each of them related to one of the trips described above (direct trips, starting/ending trips and connecting trips). Concerning the direct trips, the most significant variables are vehicle type, town size and the mode of transport management. The relations for the direct trips length are following: ldt (v) = ˛v × wr(T ) + ˇv where ldt corresponds to the direct trip length and wr to the radius of the town T weighted by the number of dt trips. Concerning starting/ending trips, the main significant variable is the distance of the generator from the centre. A linear function has been defined to adjust the length of the starting trip, and can be defined by the following relation: Lz = ˛ × dc(z) + ˇ where Lz is the length of a starting/ending trip in z and dc(z) corresponds to the “as the crow flies” distance of the zone z from the city centre. Concerning the connecting trips, the main significant variables are the number of stops s in the round, the vehicle type v and the mode of management m. In order to take into account the spatial effects, two other significant variables have to be added: the size of the town T (measured by the radius weighted by the number of

2 3

Full truckload. Less than truckload.

209

movements wr(T)) and the density of movements ız carried out in the zones z (more dense are the zones, smaller are the round-trips, because of congestion and more trading opportunities). l = ϕ(v, m, s, wr(T ), ıi, j ) Three classes of density ı have been identified. The general function is given by: lv,m,ı (s, T ) = ˛v,m,d × log(s) + ˇv,m,ı × wr(T ) + v,m,ı where ˛ < 0. A comprehensive description of the calculus, including all the relations, can be found in Ref. [41]. 2.2. The ECM estimation The ECM estimation module is developed similarly to the IEM module. However, the required data have not been collected in parallel to the method development, but only partial available data have been collected. Indeed, these data are aggregated by two types of retailing activities (small proximity retailers and big stores) and no experimental data concerning the current volumes of ecommerce use could have been found in the same area, like the shopping trips data used to calibrate the model. In previous works [52,22], shopping trip models have been calibrated and tested. The two first works are related and attempt to apply a three step procedure to shopping trip simulation, taking into account only the trips where vehicles contain goods. In the first step, the overall number of shopping trips (all modes) is generated at each zone. Then, the number of private car shopping trips is estimated. Finally, the trips are distributed using a gravity model. The generation results are encouraging but the trip distribution is not well performing according to [52]. In the third one [22], several models for shopping trip generation (attraction and emission) are presented. They are then calibrated using linear regression techniques. Moreover, the guidelines for including them on classical four steps procedures are also proposed. In [23], a two-step procedure is introduced. First, the number of shopping trips (all modes) attracted by a zone j is estimated. This model is calibrated using linear regression. Second, a catchment area gravity model is implemented to estimate the number of trips Tij related to a purchasing activity at zone j from people living at zone i. This model was calibrated using a fitting optimization method [35] with data derived from a household trip survey on Lyon. Moreover, data were aggregated on few zones (about 25), which limits the robustness of the model. The studies being exploratory, lead to the identification of the main variables in shopping trip modelling on a city logistics viewpoint but the validity and further application of these models to other cities need a calibration based on new data and a test of transferability to other contexts. For an autonomous software tool, it is important to have easily processable input data without a long and complex calibration process. For this reason, the proposed model needs to be able to work on any urban area with standard data, so probabilistic models [9] or ad hoc models [35] are not envisaged. The shopping trips model proposed below has been developed in the continuity of these works. The main contribution of the present model is that the shopping trip generation is entirely reformulated to make the model a priori applicable to any urban context. With respect to classical shopping trip models [12,35], we focus on private car shopping trips directly instead of making an “all modes” shopping trip generation, following Gonzalez-Feliu et al.’s (2010) considerations. Indeed, in shopping trip decision processes, the choice of the purchasing location takes place simultaneously to the modal choice. This is different from home-work trips, which

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are the basis of four-step models. The proposed model is organized in three phases: • First, the number of trips in private car attracted by zone j for shopping purposes is generated. In the present work, we focus only on trips made by private car where the driver is the shopper (in precedent works, all shopping trips made by car were taken into account). This choice is made because trips made as a passenger do not generate a new car trip but use an existing one. • Second, a catchment area model [23] is used to relate each attracted trip to a household location. This model derives from a classical gravity distribution model and has not been modified with respect to precedent works. • Finally, according to empirical findings, the travelled distances for purchasing are estimated. In precedent works, distances were estimated on the basis of only household-shopping-household trips. In the current paper, we extend this estimation to all trips, focusing on household-shopping trip chain-household and workshopping trip chain-household schemes. 2.2.1. Trip generation In the shopping trips generation phase the number of attracted trips for purchasing is generated at each zone. In a precedent work [22], two modelling approaches were compared on both emission and attraction trips. The first one assumes that the urban geography does not have an impact on shopping trip generation rates (classical viewpoint). The second one supposes that shopping trips generation rates depend also on the type of the urban space, classified as follows: • The first category of urban space is the main central urban area (CUA), which contains the main city of the urban region and sometimes other urban suburbs, which can be assimilated to the main city, because of a continuity of the urban landscape. This category of urban space is characterized by a dense household area, mainly with a variety of proximity retailing and service activities, and a lack of industrial activity zones. Moreover, the public transport network is very developed and interconnected. • The near periphery (NP) includes the urban zones limiting with the central urban area. This category of urban space is characterized by a mix of dense a non-dense household areas, industrial and commercial activity zones and a developed public transport network that connects it to the city centre. • The rest of towns of the urban area belong to the far periphery (FP). They are characterized by a low density, a predominance of individual housing and high motorization rates. Indeed, public transport is radial and closer to interurban rail and bus services than to urban public transport networks. In the proposed model, we focus on attraction trip rates, i.e. the number of trips which arrive to a zone for shopping purposes. This is made assuming that all trips arriving to a retailing activity for shopping purposes are connected to a second trip, i.e., a purchasing activity is never the final point of a trip chain (see Fig. 2): In Fig. 2 we illustrate different trip chains focusing only on shopping trips that are related inside a given zone. As we observe, a shopping trip is related to a succession of trips with different purposes, and it seems to us important to see them as a part of a more complex trip structure, which can be called the trip chain. From the Household Trip Survey of Lyon [55], we have extracted the main characteristics of shopping-related trip chains. Indeed, in Table 1 we report the number of trip chains which main purpose was a purchasing activity. We observe that the usage of private car for shopping trips is strongly influenced by the urban zone where the household is located. Moreover, 94% of these private car shopping

Other

3

Household

Household Household

2

Retailers

1 Fig. 2. Different types of shopping trips (example).

trips are included in a trip round containing only shopping trips, the initial and final location of the trip round being the household. Note that trips belonging to these rounds represent about 80% of the total shopping trips,4 and work-shopping-household trips represent about 15% of the total number shopping trips and an average distance of 3 km. From these considerations, we propose to generate the attraction shopping trips following the general methodology discussed in [12]; the number of private car shopping trips Tj attracted by a zone j can be formulated as follows: Tj = f(Retj , Xj ); where: Tj is the total number of shopping trips attracted to zone j; Retj the set of retailing activity characteristics vector in section j and Xj the set of socio-economic characteristics of people and households belonging to section j. The trip attraction function can be defined in different ways. We propose a multi-linear function that depends on the category of urban space. This function is defined as follows [22]: T=

 e

ae Rete +



ae Xf

f

In order to make this model transferable to various urban contexts, the explanatory variables have been chosen from standard censorial files. Indeed, the retail characteristics can be obtained by aggregation of French establishments’ census (called SIRENE), for a given city. The socio-economic variables are extracted from INSEE5 population census files. Moreover, while defining the combination of variables and calibrating the model, we have considered the transferability possibilities to avoid a site-dependence behaviour of the model. To calibrate the model, we define each coefficient by linear regression. We have used the R Commander framework [18] to implement the regression calibration procedures. As stated by [52] and [12], factors affecting shopping trip generation (so attraction) rates are mainly related to the retail configuration, the population and other socio-economic variables that can be transposed from one city to another. To confirm this statement, we calibrate the model on data of Lyon, which is about 2,000,000 inhabitants, then tested the model on the area of Dijon, which about ten times smaller (about 250,000 inhabitants) and presents different geographic and demographic characteristics. The data from Lyon is extracted from two different databases. The demographic and geographic data (zoning, distances, population, number of households and motorization rates mainly), as well as the number of private car shopping trips (to calibrate the model) are extracted from the Household Trip Survey of 2006, which follows a standard methodology at national level [8]. Furthermore, the economic data concerning retailing activities (number of establishment and number of employees per size range) are extracted from

4 5

According to a statistical extraction on the same data sample. French National Institute of Statistical Studies.

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211

Table 1 Shopping trips in related trip chains. Household zone

Shopping trips in shopping related chains Total

Shopping trips in household-shopping-household rounds

No private car

Private car

Total

No private car

Private car

CUA NP FP

172,923 206,543 251,865

79.5% 36.3% 29.5%

20.5% 63.7% 70.5%

134,313 158,128 182,621

76.6% 45.3% 30.6%

23.4% 54.7% 69.4%

Total

631,332

45.4%

54.6%

475,061

48.4%

51.6%

Table 2 Linear regression results for the best attraction analysis. Model

R2

3C – CUA 3C – NP 3C – FP

0.77 0.75 0.84

Degrees of freedom n

k

25 20 19

4 4 3

F value

Significance of F

27.29 20.90 58.01

7.4 × 10−8 5.4 × 10−6 1.7 × 10−8

the SIRENE database of year 2005, after a first aggregation made using the data processing and analysis module. In precedent works [22,23], the territory was divided into 34 zones, in order to provide a significant number of surveyed trips per zone, which is set according to Ségalou’s [52] considerations. Since that number of zones is not enough important to develop three models (one per category of urban area) with a suitable level of accuracy [23], a new division of the territory is provided. The area has then been divided into 80 zones: 33 belong to the CUA, 15 to the NP and 32 to the FP. The model has been implemented and calibrated using R Commander [18], combining the different variables in order to obtain the most precise model. The model can be formulated as follows: TjCUA TjNP

=

aCUA .POPj 1

+ aCUA .NrSMCj 2

+ aCUA .Emp − BSj 3

+ aCUA .CCj 4

= aNP .POPj + aNP .Emp − BSj + aNP .CCj + aNP Densj 1 2 3 4

TjFP = aFP .POPj + aFP .Emp − BSj + aFP .CCj 1 2 3 where NrSMC is the number of small and medium stores; Emp-BS the number of employees in the supermarkets and big commercial stores of the zone; CC is a binary variable that indicates the presence of a commercial centre [52]. This variable takes the value 1 if at least one extra-urban commercial centre is located inside the zone and 0 otherwise. These variables give the retailing activity characteristics of the zone. Moreover, POP is the population of the given zone and Dens the population density of the attraction zone. We observe from the equations that each model has a different variable set from the others. This has been made to provide the strongest combination related to each coefficient’s Student’s test [61]. Moreover, the R2 coefficient is close to 1 in all three categories of urban space. With respect to previous work, these results are stronger since the Fisher’s test shows F values is substantially lower and close to zero in all cases (see Table 2): The values of R2 are between 0.75 and 0.85, depending on the category of urban space. Moreover, the degrees of freedom are between 19 and 25. The significance values of F are close to 0, which makes us reject the null hypothesis of the Fisher’s test. Taken into account the degrees of freedom and the quantity of data used for the analysis, the values are coherent and reject in all cases the hypothesis of a simultaneous nullity of all coefficients. However, the linear regression being made on a restricted dataset, it seems important to examine the capacity of approximation of the obtained model as shown below. We present below the main calibration and validation results of the attraction model. First, we present a fitting curve (Fig. 3) obtained from the model calibration on Lyon’s data (as presented above). Note that zones 1–69 have been used to calibrate the model

Fig. 3. Calibration and test results on Lyon (2006).

(training set). More precisely, zones 1–26 correspond to the CUA, zones 27–41 to NP and 42–69 to FP. Then, zones 70–80 (a mix of the three categories of urban zones) are used as a test data. We observe that most of the estimated trips are close to the surveyed ones, although some discording points are found. Note that in many cases, discording points are zones with an insufficient number of surveyed data (samples with less than 30 answers) which make the fitting errors being perturbed by the statistical variability of the population reconstruction process. It appears that the test sample results are better than those obtained by the traditional method [23]. The average error (in absolute value) is lower than 25%, which is considered a good result taken into account the limits of generation models [35]. Following this conclusion, we apply the model to a second test case. The data are extracted from a different urban area (Dijon, which counts about 200,000 inhabitants) and time period (1999). This choice has been made for two reasons. The first is the data availability (household trip surveys are in general owned by local authorities who do not systematically give access to data at a disaggregated level). The second is that Dijon presents different characteristics from Lyon, in terms of size, density, activity geographical distribution and composition of the three urban spaces (there are few far periphery zones). As shown in Fig. 4, except on zones 1 and 20, the model gives accurate estimations of shopping trip attraction rates. The two big exceptions correspond to the city hypercentre (1) and a big peripheral commercial area (20), which seem specific to Dijon. However, the model that has been calibrated on data of another city give a satisfactory global estimation in terms of the total amount of attraction trips (about 10%). In Lyon, we observe the same error rate, i.e. 9%.

2.2.2. Catchment area After trips are generated at their shopping destination, we propose to connect them to households using a catchment area model

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J. Gonzalez-Feliu et al. / Journal of Computational Science 3 (2012) 206–215 Table 3 Weight factor for traffic issues (adapted from [40]). Total on-load weight

Weight factor

Less than 3.5 t 3.5–7 t 7–16 t More than 12 t

1 1.5 2 2.5

2.3. The e-commerce flow simulation method and the substitution procedures

Fig. 4. Test results on Dijon (1997).

[29]. The proposed approach is a gravity probabilistic model, which can then be formulated as follows: Pij =

Tij Tj

˛1

= Aj

Ej

ˇ

˛2

Ei

NrHi cij

where Ei and Ej are the number of employees in retailing activities respectively for zones i and j; NrH the number of households of zone i and cij the transportation cost between i and j. Moreover, we define Aj as follows, in order to ensure the flow balance: Aj =



2.4. Road occupancy issues

1 ˛

˛

The proposed model is able to estimate shopping trips and to relate them to both the purchasing activity and the household location. However, with current data we have no extensive quantitative information about e-commerce and proximity delivery behaviour, so the inclusion of these aspects will be made making several strong assumptions. The goal of the e-commerce module is not to make a real diagnosis of the current situation but to provide a framework to simulate future possible situations in order to support decision makers on their strategic choices. For this reason, we propose to simulate the impacts of different last mile deliveries strategies for e-commerce using a set of substitution procedures developed in a precedent work [21]. These substitution procedures allow transforming a set of shopping trips on proximity deliveries, then the IEM related flows are recalculated to avoid double counts. These procedures are estimated empirically from Refs. [1,40] data on ecommerce deliveries. For a detailed description of these procedures see Ref. [21].

ˇ

(Ej 1 /Ei 2 )NrHi cij

k

˛1 , ˛2 and ˇare parameters to be determined [35]. Although this model derives from that of [23] and uses the same fitting procedure for calibration, we have to note a significant difference: the catchment area model in the present paper refers only to private car shopping trips (no modal choice here because it has been assumed previously at the generation step). This choice has been made in order to increase the accuracy of the model and to provide a modelling framework analogous to the IEM estimation model. This model has been calibrated using a fitting procedure derived from [27]. First, the three parameters are obtained by an econometric procedure [23]. Then, the b parameter is readjusted using an iterative procedure [27] that tries to minimize the error between surveyed and estimated mean distances. For a comprehensive description of the calibration method, see [23]. As shown above, this model is on the continuity of previous works [52,22,23], integrating their best findings into a more robust model. The generation equations are redefined according to three types of urban space and calibrated to be used on any urban area with negligible accuracy changes. Although the modes has been tested on only two urban areas, they appear as very different and the time horizon is not coincident, which make us state that the model is not site and horizon dependent (the main limit of precedent works). Moreover, and with respect to current literature [12,35,47,9], the model has also its particularities. With respect to personal trip models [12,35] we propose an attraction model, as other city logistics related works [22,9]. Moreover, the generation model is combined to a catchment area model [29], but it is related to the number of trips and not to the freight quantities or the expenditure [50,49,29,9]. Finally, the distances are calculated in a similar way to FRETURB [41] and not using classical transportation models.

The total travelled distance per truck can be an interesting performance indicator for a carrier, but has less sense for public planners since the impact of a vehicle on traffic is related to its footprint on the road surface. This footprint can be measures in terms of road occupancy rates. Following Routhier et al.’s considerations [40], we can easily convert travelled distances on road occupancy rates. Indeed, each truck distance can weighted by a coefficient depending on its weight, as stated in Table 3.

3. An example of application In order to illustrate the proposed method, we propose a simulation of four sets of scenarios for long-term issues. All these scenarios are simulated on a real urban area: that of Lyon (France). In 2006, this urban area was consisting of about 2,000,000 inhabitants and 800,000 households. In order to build a reference scenario, we worked on the following databases: the register file of companies (SIRENE file) of the chosen area, the corresponding census database (INSEE file) and the 2006 household trip survey (EML-2006 file), which follows a French standard [55]. In this survey, the urban community of Lyon is divided into 750 zones, according to population distribution. These zones are then grouped into 34 macro zones. The reference scenario (S0) is directly based on this survey data. The proposed scenarios in this paper are the following: S0: Reference situation – a 2050 situation from Lyon 2006 data and economic trends [55]. S1: Population density increases in central areas and remains stable in periphery. S2: With respect to S1, retailing activity evolves to balance the household supply strategies: 40% proximity retailers, 30% supermarkets, 30% hypermarkets.

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S3: With respect to S2, the e-commerce usage is supposed to be important. The usage rates are the following: 50% traditional shopping; 10% drive; 20% home deliveries and 20% reception points. 3.1. S0 – reference situation – Lyon in 2050 The reference situation is a basic situation in 2050 built by forecasting data of Grand Lyon [55] in terms of population evolution. These data forecast a 0.5% annual population increase for the urban area of Lyon for the next decade. With a decrease for the following decades, the overall population of this area goes from 2.0 million (M) inhabitants in 2006 to 2.3 M in 2050, while the number of households increases from 0.85 M to 1.0 M, assuming that the number of inhabitants per household remains the same for a given zone. The motorization rate of the households (i.e. number of cars per household) comes from [55]. The demographic evolution of the economic activities is forecasted from the results of [40]. The number of small shops is similar in 2006 and in 2050 (but the number of employees grows proportionally to the population), the number of other stores and their employment rates grow proportionally to the population [40]. 3.2. S1 – population location policy Fig. 5. Map of Lyon’s urban area.

This scenario supposes a household location refocusing, i.e. a re-location of several households on the central areas of the city. Indeed, the population ratios are different in the scenario S0, in order to increase consequently the population of the main city (Lyon and Villeurbanne) and the first ring, limiting the sprawl of the population in the peri-urban areas. 3.3. S2 – retail location policy The scenario is an urban commercial planning scenario. It is built by changing the number and the location of the retailing activities using equivalence tables [24], in order to affect 1/3 of the freight demand (in tons) to each category of retailing (i.e. small shops, supermarkets and hypermarkets). In 2006, the supermarkets represented 14% of the total household supply (in tons), while the other 86% were almost equally shared between the other two categories. The number of small shops and hypermarkets decreasing rates and stores increasing rates are applied according to the ratios of [24]. This new commercial supply configuration is supposed to meet the population’s needs, so they are spatially distributed over the whole urban area. 3.4. S3 – development of e-commerce In this scenario, e-commerce and proximity delivery services are used by 50% of the population. That means that both people with and without a car will use these services. Based on the data of several experiments in Nantes, France [15], 60% of the people are delivered at home whereas the other 40% use delivery points. Assuming these ratios, we suppose that the small grocery stores are also used as delivery points to reduce motorized trips (Fig. 5). 4. Main results The modules of the proposed framework have been integrated into a simulation platform (FRETURB v.3). More precisely, to the existing platform (the KMS, the IEM and the road occupancy modules) we have integrated the ECM estimation module and the substitution procedures, and updated the KMS to transform the existing version of FRETURB (which was used only for UGM diagnosis) into a simulation software tool. Then, using this framework, we simulate the four proposed scenarios. In Table 4, we report the

Table 4 Traffic impacts (in millions Km.PCU/year compared with the 2006 situation). IEM S0 S1 S2 S3

1300 1340 1339 1269

ECM 1317 1046 973 554

Total

Difference

2617 2386 2312 1823

– −8.8% −11.6% −30.3%

road occupancy impacts of each scenario, in terms of travelled distances. More precisely, we report the trends in millions km.PCU per year, respectively for IEM (column 2) and ECM (column 3). Moreover, we report in column 4 the total amount corresponding to the addition of these two flows. We observe that in the reference situation (S0), both flows are equivalent in terms of road occupancy impacts. The first scenario (S1), which supposes a concentration of the population in the central areas of the city, has opposite impacts on each category of urban goods flows. Indeed, although the travelled distances for shopping decrease in about 20%, the interestablishment flows increase in about 3%. Note that the last mile retailing supply flows represent about 9% of the total IEM [40], which means that IEM increase corresponds to 1/3 of the last mile distribution flows. Note also that the retailing locations in the urban area have not changed with respect to S0. Therefore, the shopping practices have a direct impact on the retailer’s supply strategies. If the retailing offer in the city is reorganized (S2, see above for the rates of each retailing category), we do not observe big differences at the overall level (a 5% gain with respect to S1). However, an in-depth analysis of the travelled distances to cover each zone’s demand (for both establishments and households) is needed to observe if there are significant differences between these two scenarios. Finally, the effects of e-commerce on the urban goods flows are a priori positive. With only organizational changes related to household purchasing practices, we observe a gain of about 5.2% in IEM with respect to S2 (and 2.4% with respect to S0). For ECM, the usage of proximity depots combined with efficient home deliveries and a rational use of shopping drive (i.e. this alternative is combined

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to work-household trips in order to minimize the detour) leads to road occupancy savings of almost 58% for a total saving of 30% (when considering both IEM and ECM flows). 5. Conclusion This paper proposes an integrated framework to simulate and analyze the road occupancy rates of urban goods movement in terms of running vehicles. The aim of the framework is to integrate IEM and ECM flows in a software tool to be used in any urban area. Oppositely to current literature, we insist on using standard data rather than on “ad hoc” data which need a specific calibration on each site. Moreover, we focus on shopping trip generation, modelling and calibrating a general framework on standard data from Lyon (France). The framework has also been applied to Dijon, which results show a performance similar to that of “ad hoc” models. Then an integrated framework is developed, connecting the shopping trip method to an IEM module and a set of substitution procedures in order to build an UGM simulator. This framework has been applied to the urban area of Lyon in order to simulate four possible future scenarios. The main findings of this study consist in showing the importance of simulating both inter-establishment and end-consumer’s movements and their relations. Moreover, this analysis is significant only if an overall approach containing all the establishments of the urban area is taken into account (and not only the retailers). The framework is able to simulate the impacts of location policies (for both households and establishments) and organizational aspects (mainly on e-commerce) on urban goods movement flows. Future extensions and improvements may be considered following two directions. The first one is related to the construction and simulation of scenarios, either realistic or contrasted. The second is the improvement and extension of the current simulation modules. Recent results of surveys and statistical studies on ecommerce development may be used to develop and calibrate the simulation of e-commerce distribution in a more accurate way (mainly on the mechanisms of substitution). Moreover, the current framework covers 90% of the total urban goods movement flows. A further development will include the urban management flows (mainly household garbage collection, establishment waste management, building construction, road works and civil engineering maintenance). Therefore, several extensions and improvements are already in progress: a route simulation module, a freight demand generation module (in kg) and a simulation method to substitute current organizational schemes for new trends and future supply chains. References [1] L. Alligier, Mesurer l’impact du commerce électronique sur la logistique urbaine. PhD Thesis, University of Lumière-Lyon 2, Lyon, France, October 2007. [2] C. Ambrosini, D. Patier, J.L. Routhier, Urban freight establishment and tour based surveys for policy oriented modelling, Procedia Social and Behavioural Sciences 2 (3) (2010) 6013–6026. [3] C. Ambrosini, B. Meimbresse, J.L. Routhier, H. Sonntag, Urban freight policyoriented modelling in Europe, in: E. Taniguchi, R.G. Thompson (Eds.), Innovations in City Logistics, Nova Science Publishing, 2008, pp. 197–212. [4] C. Ambrosini, J.L. Routhier, Objectives, methods and results of surveys carried out in the field of urban freight transport: an international comparison, Transport Reviews 24 (1) (2004) 57–77. [5] S. Behrends, M. Lindholm, J. Woxenius, The impact of urban freight transport: a definition of sustainability from an actor’s perspective, Transportation Planning and Technology 31 (6) (2008) 693–713. [6] BESTUFS, BESTUFS II Bibliographic Overview, Final DVD-Rom, BESTUFS, The Netherlands, 2009. [7] J. Boerkamps, A. van Binsbergen, Good trip—a new approach for modelling and evaluation of urban goods distribution, in: E. Taniguchi, R.G. Thompson (Eds.), City Logistics I, Institute for City Logistics, Kyoto, 1999, pp. 175–186. [8] CERTU, L’enquête ménages déplacements ‘standard CERTU’: Guide méthodologique, MEEDAT, Lyon, France, 2008.

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Jesus Gonzalez-Feliu is a research engineer at the French National Centre of Scientific Research (CNRS), and member of the Laboratoire d’Economie des Transports (LET). His PhD thesis deals with urban freight distribution solutions and two-echelon vehicle routing problems. His research interests include city logistics modelling, public policy decision support systems, shoppping trips and e-commerce interactions, freight transport optimisation and performance, sustainable supply chain management, and collaborative transportation.

Christian Ambrosini is a senior lecturer at the faculty of economic sciences of the University of Lyon 2 and member of the Laboratoire d’Economie des Transports (LET). His main works deal with the economical aspects of interurban and urban road freight transport. His research interests include urban goods movement data collection and analysis and city logistics practice and public policy.

Pascal Pluvinet is a study engineer at CNRS and ENTPE and member of the Laboratoire d’Economie des Transports (LET). His main works are oriented on the development of GIS-based simulation tools for public policy decision support for both people and freight, on geographical approaches and analyses, on data processing analysis, and on simulation tools development, testing and application.

Florence Toilier is a study engineer at ENTPE and member of the Laboratoire d’Economie des Transports (LET). Her main works are oriented on the development of simulation tools for public policy decision support for both people and freight. She is also in charge of the organisation of various national and international research conferences on regional sciences and transport.

Jean-Louis Routhier is a research engineer at the University of Lyon 2 and member of the Laboratoire d’Economie des Transports (LET). His research interests include urban goods movement data collection and analysis, urban goods movement modeling, interactions between people and goods mobility, sustainability indicators, public policy and forecasting, among others. He is the scientific responsible of the methodology of the French national urban goods surveys (1996–1999 and 2010–2012). Moreover, we has published several papers on international journals and books related to city logistics.