Aggregation level, variability and linear hypotheses for urban delivery generation models

Transcription

1 Aggregation level, variability and linear hypotheses for urban delivery generation models Jesus Gonzalez-Feliu (Corresponding Author) Environnement, Ville et Société, UMR 00 CNRS ENSMSE-PIESO Team Institut Henri Fayol Ecole Nationale Supérieure des Mines de Saint-Etienne 1 cours Fauriel 0 Saint-Etienne cedex, France Phone : + jesus.gonzalez-feliu@emse.fr Iván Sánchez-Díaz Urban Freight Platform Technology Management and Economics Chalmers University of Technology Vera Sandbergs Allé 1 Gothenburg, Sweden Phone: + 01 ivan.sanchez@chalmers.se Christian Ambrosini Laboratoire d Economie des Transports ISH 1 Avenue Berthelot 00 Lyon (France) Phone: + Fax: + chistian.ambrosini@univ-lyon.fr Number of Words:, words + Tables + 0 Figures =,1 words Submitted to the Transportation Research Board th Annual Meeting, Washington, D.C. on July 1, 01 1

2 Aggregation level, variability and linear hypotheses for urban delivery generation models Abstract Category classification models are nowadays one of the main approaches in urban goods transport demand generation, and we see applications of those frameworks in different contexts, both in Europe and the USA. Those models are developed for a fixed category classification, which can be more or less disaggregated. However, those models depend on the data quantity and quality, and the category construction can have a real impact on the model quality. This paper seeks to analyze how the aggregation of category classifications affects the significance and the quality of the model deployed. To do this, a dispersion analysis is combined with the search of linear relations between the number of deliveries and the employment, for each category given. After motivating the proposed study via a synthesis of the literature, this paper present the methodology used to assess the relations between data aggregation and model quality. The authors also present the data used to carry out the analysis and make a first description of sample size and dispersion via the estimation of coefficients of variation for each category, regarding the number of deliveries and the number of deliveries per employee. Then, the results of the analysis are presented. The presented results are obtained from the assessment and comparison between a constant model, a linear model with constant and a linear model without constant. Each model prediction quality is estimated using the root mean square error (RMSE) as indicator. The best model is then selected. Finally, results are discussed and the choice of a category aggregation is proposed. Keywords: urban goods modelling, freight trip generation, delivery model, dispersion analysis. Introduction Urban freight demand modelling is one of the main subjects in city logistics and urban freight distribution research. This subject has also many applications, as shown by a wide number of methods, software tools and practical studies made with the support of demand estimation models. One of the most important stages of demand modelling is that of demand generation, since most models (four-steps, activity-based, bottom-up, route construction, etc.) start by generating demand, even if not expressed in the same way. In this paper we aim to focus in freight trip generation (FTG). We observe two main approaches to construct this type of models. The first approach is to apply, via a more or less complex categorization (1, ), a constant number of deliveries per employee (see () for a discussion on the possibilities and limits of such approaches). In this type of models, all categories are assumed to follow the same generation pattern; the differences arise on the value of the parameter that multiplies the number of employees for each establishment according to the category. The second approach is to consider different generation patterns which are replicated from the modelling point of view through different functional forms for each category. Holguín-Veras et al. () and Lawson et al. () make a first analysis using linear regression to determine if the generation pattern of each industry sector and land use category is constant per establishment or a function of the employment. Further analyses are found in Holguín-Veras et al. (), who focus on the possibilities of transferring both

3 constant ratio and linear function approaches. Sánchez-Díaz et al. () show that freight trip attraction (FTA) is better modelled using nonlinear models, and identify spatial effects for different industry sectors. The authors also found that land-value plays a role in the intensity of FTA for food services and accommodation, and that there are strong spatial autocorrelation effects in FTA of retail establishments. Jaller et al. () show the importance of identifying freight intermediaries in urban environments to estimate correctly freight trip production, and suggest some modelling implications. Those works focus on US generation patterns using standard data mainly from New York. On another side, González-Feliu et al. () make an analysis based on () on French data but applied to freight trip emission (FTE, i.e. trips starting from the selected locations, representing expeditions and not receptions). In general, the studies presented above are made on a selected category classification, without a detailed assessment on the impact that the aggregation level has on the quality of the estimation. A first exploratory analysis of the impacts of aggregation in the quantity of data to model and the dispersion is made in Ducret and Gonzalez-Feliu () to select the most suitable aggregation level to combine a demand generation model with a spatial categorization model. However, the analysis on data aggregation remains in this paper exploratory and is mainly used to select a category classification to analyze possible interactions between urban form and urban freight needs. In essence, this paper focuses on the choice of a good data aggregation, which is crucial for the significance and coherence of a model (). From this synthesis of the literature, we observe that exploring the possible generation alternative functions for different categories of establishments is mainly made by econometric analysis, but the choice of the aggregation level is often limited by the amount of data on each category or is simply disregarded. The approaches described before can also be applied to identify the best level of aggregation for the data (i.e. number of categories). Moreover, econometrics analyses can be complemented by descriptive statistics techniques, such as dispersion and variability analyses, to enhance their accuracy. The aim of this paper is to analyze the implications of using different aggregation levels on the classification systems. The authors approach it from a qualitative perspective, in which they study what is comprised within each classification, and from a quantitative perspective by assessing the performance of the models from different aggregation levels, assessing the different alternatives of modelling and comparing them via root mean square error (RMSE) comparisons then associating the results to the coefficient of dispersion, and using an econometric approach to assess the homogeneity within each industry sector. The paper is organized as follows. We present the general methodology used for the present analysis as well as a description of data and an analysis based on dispersion to give a first conclusion on the potential of linearity related to the level of aggregation. Then, we present the results of the analysis, more precisely the selected models for each category. Lastly, we discuss the results and propose the choice of a category aggregation. Methodology The methodology used assesses the specification of the FTG models using an econometric approach. Following the methodology described in (,, ), the models can take three different forms: (i) a

4 constant number of trips per establishment (does not depend on employment), (ii) a model where the number of trips is directly proportional to the number of employees, and (iii) a combined model where there is a constant number of trips per establishment and a term that shows the increase of the number of trips depending on the number of employees. The criteria used to select the model are the conceptual validity, the statistical significance of the constant and the employment variable, and the RMSE. This study of the functional forms brings about enhancements in the modelling quality, as well as contributes to shed light on FTG patterns for different industry sectors. In the case that the best functional form for an industry sector is (i), a dispersion analysis is conducted to assess whether the constant FTG per establishment means that all establishments in the sector attract the same FTG, or that there is a high variability within the industry sector which is not correlated with business size. To account for the latter, models with a constant will also include FTG values for the quartiles and the median. In the case of models with the functional form (ii), FTG is proportional to employment; establishments with a small number of employees have a negligible FTG, while establishments with numerous employees have significant FTG. In the case of models with the functional form (iii), small establishments tend to have higher FTG per employee than large establishments. This is the typical case of establishments that can take advantage from larger shipment sizes that can be transported in more efficient modes (i.e., larger trucks). In essence, for this cases an increase in the amount of freight attracted can be handled by a change in shipment size, which could lead to a change in the vehicle mode chosen, and a drop in the FTG per employee (, ). Additionally, the authors estimate a set of econometric models to assess the homogeneity between industry sub-sectors (ST and ST ) within the same industry sector (ST). To this effect, the authors create binary variables for the sub-sectors and assess their significance as well as the significance of their interaction with the number of employees working in the establishment Data description The first step in the analysis is to define and present the data, as well as the classification system. The authors chose to deploy this analysis framework on the French Urban Goods Surveys of 1-1, for a detailed description of the survey refer to (1). This quantitative survey consists of three main parts: the first one describes the establishments attributes, the second one studies pickup and delivery operations and the third one presents the truck driver s patterns, vehicle rounds and paths. For FTG, the authors use the first two parts of the survey. The data was collected from three cities of different characteristics (Bordeaux, Dijon and Marseille) and includes,0 establishments and, delivery operations (including both receptions and expeditions) without missing data concerning freight transport demand. Although this dataset is outdated, it provides a unique opportunity to assess the modelling implications of different aggregations of classification systems, because of the number of observations within each sector:,0 establishments with at least one operation of any type, 1 establishments with at least one reception, and 1,00 establishments with at least one expedition. Moreover, two new urban goods surveys were carried out between 0 and 01 (1) but data are however not yet available for public use as confirmed by corresponding engineers of the Région Ilede-France and Communauté Urbaine de Bordeaux, who are part of the financing bodies respectively of the 01 Paris Survey and the 01 survey of Bordeaux. We will then focus on the surveys made between 1 and 1. Those surveys present information on both expeditions and receptions (as shown above); however, in this study we will focus on FTG, i.e. mixing both attraction (receptions)

5 and emission (expeditions). This is due to two reasons: first, FTG without specifying the nature of the operation can be useful for some planning purpose (identification of truck types and mileages for congestion estimation, definition and dimensioning of parking and delivery bay facilities, etc.) and making no distinction between the type of operations give us a larger dataset, i.e., about 00 establishments more than only receptions and more than 0 more than only expeditions, which enhances the statistical significance of the results. Moreover, we concentrate only on the influence of aggregation level at the category level (i.e. the typology of establishments) and not on other variables such as type of vehicle, type or city or mode of management Hypotheses and methodological choices Although FTG can be related to several variables and elements (see literature overview above), one of the main questions that can be asked is that of the role of data aggregation in the form and quality of the generation model. Since considering many elements can affect the identification of direct impacts of classification on the model s accuracy and robustness, we aim to focus only on the categorization of establishments. In this context, and in terms of establishment nature, we use an adaptation of the classification used in the French urban goods surveys, which is also adopted in most works dealing with such data (,, 1-1). This classification can be based on two elements (): The category of activity of the firm, eventually coupled with the main function of the establishment (, or activities distinguished); The class of workforce (to pass from to 1 categories); The authors detail below the logic of the classification as well as the way the different levels are obtained. The first level of disaggregation (the most aggregated classification) distinguishes categories of establishments (ST), which has been set in correspondence with the most aggregated classification of French NAF s (the French declination of European NACE classification). Then, each ST category is divided into one or more categories. However, the NAF declination has not been followed here, since the level of disaggregation for retailers is not enough for modelling purposes in urban areas. This classification (ST), which has categories, presents new s with respect to the ST ones. In other words, there is not a subdivision-based notation, but in both classification s are entire numbers. Moreover, this classification corresponds to a second level of disaggregation in the sampling phase of the surveys. The third classification aggregation (ST) is the third level of disaggregation of the survey s sampling framework, and its notation consist on repeating the ST s then adding sub-indexes only when the category is subdivided (see table 1). In all three classifications, the only discrimination element is the economic activity of the establishment. The last level of disaggregation (ST) is obtained from the S level by including as discrimination element also the range of workforce and the main function of the establishment. Note that, as stated in Bonnafous et al. (01), not all combinations ST-range of workforce-main function include individuals, so the resulting number of categories retained is. A synthesis of the three first levels of disaggregation is presented in Table 1 (the fourth level, i.e. the category classification, is presented in appendix).

6 Table 1: Synthesis of the three first level of disaggregation (ST-ST-ST) ST ST ST Description Description Description 1 Agriculture 1 Agriculture 1 Agriculture - Repair activities Craftsmen - Manufacturing or installation - Light repair Craftsmen/ Ha Tertiary services: high flows services Mi Tertiary services: mixed flows Services M Tertiary services: average flows o Chemical industry Chemical industry Construction - Repair industry industry - Construction Manufacturing or installation - Production and intermediate basic bulk Production and - Production and intermediate small objects Industry intermediate - Production and intermediate bulk Wholesalers Consumption goods Intermediary products Non food Food products Department stores Department stores Retailers Tertiary/ offices Warehouses/ transport - Fragile foodstuffs - Non-fragile foodstuffs - Non-fragile consumer goods, equipment of the house and the individual - Fragile intermediate products - Oher intermediate products - Non-food fragile consumer goods - Non-food non fragile consumer goods - Fragile food consumer goods - Other food consumer goods Hypermarkets and big department stores Supermarkets 1 Specialized department stores 1 Minimarkets 1 Minimarkets 1 Clothing, shoes, leather 1 Retail trades, clothing, shoes, leather 1 Butcher's shops 1 Butcher's shops 1 Small groceries 1 Grocer's shops 1 Bakery retailers 1 Bakeries Cake shops 1 Ho.,Re.,Ca. 1 Ho.,Re.,Ca.: Hotels, Restaurants, Cafés 1 Pharmacies 1 Pharmacies 0 Hardware stores 0 Hardware stores 1 Furnishing shops 1 Furnishing shops Bookshops Bookshops Other retail shops Other retail shops Street trading Street trading (outdoor trading centers and marketplaces) Pure transport Transport (except storage) Pure tertiary (offices) Pure tertiary sector (offices) Other tertiary Fa Other tertiary activities with low flows - Not tertiary offices (agriculture, wholesales) Offices not pure Not tertiary offices (retail trade, industry, tertiary - transport, administration) Warehouses - Warehouses (bulk) - Warehouses (of which transport)

7 The first question that can be asked when dealing with data aggregation is the statistical significance of the sample, in number of observations. In other words, before making statistical or econometrics analysis, it is important to know how many observations contain each category. To this purpose, we present in Table for each classification system the percentage of categories that present a quantity of data considered as enough to carry out statistical analyses. This number of observations is set to 0 in classical statistical approaches (1) but in some cases it can be necessary to carry out statistics with a lower number of observations. For this reason, we define two types of significance: the classical one, called here strong significance, which threshold is 0 individuals, and the weak significance, which threshold is obtained by dividing the strong significance threshold by, i.e. by fixing it to 1 observations. We present in Table the number of categories having respectively at least or 1 individuals, and this for each aggregation of categories (see below). Table : Aggregation level and data quantity requirements for statistical modelling purposes Category of establishments Number of categories with observations or more Number of categories with more than 1 observations ST classes / (0%) / (0%) ST classes / (0%) / (0%) ST classes / (%) / (%) ST1 1 classes 1/1 (%) /1 (1%) As shown, the data requirements are respected for categorizations with high aggregation of data, i.e. those that present few categories. Indeed, the ST and the ST classifications present at least 0 individuals in all defined classes. The two most disaggregated categories (ST and ST1) do not present all categories with at least 0 individuals. However, although in the ST, the number of categories with at least 0 individuals is high (more than 0%), in the ST1 this number is very low (only %, which means 1 out of the 1 categories), although it increases to about 1% when downscaling to 1 individuals. However, almost 0% of the categories are presenting less than 1 individuals (i.e. out of 1); we can state that statistics in this classification remain uncertain and difficult to lead to conclusions with current databases. For those reasons in the rest of the paper we will compare the three first levels of aggregation (i.e. the ST, ST and ST category classifications), without entering in details on the ST1 classification. In other words, we consider that data is not enough to provide a statistically significant set of categories for ST1, so results at this level of disaggregation will not be robust enough to conclude on solid generation patters. On the other hand, the three first levels of classification present enough data to propose robust results and analysis for almost all categories, in terms of statistical significance. After presenting the data, we describe in detail the methodology followed for the proposed analysis 1. For each category, a dispersion analysis is carried out, as in (). In other words, and following the hypothesis of a model based on a constant, we calculate for each category the average number of deliveries E(n) (independently of the employment), as well as the corresponding standard error σ(n). Then, the coefficient of variation CV is estimated to state on the significance of the use of average values and on the invariant nature of good trips that is the main hypothesis behind the constant model. CV is estimated as follows: CV=E(n)/σ(n). For each category, at the same aggregation level, an econometrics analysis is carried out (as on Holguin-Veras et al., 01). Two linear regression analyses are provided, one for the form

8 y=a.x+b and one for the form c.x. Those two models are estimated via the least squares method. Results and discussion This section presents the results from the FTG modelling. The selection of the final models was based on the criteria described in the methodology. Table presents the dispersion analysis for the three first category classifications: ST Description Table : Dispersion analysis for the three different category classifications CV Deliveries/ establishment CV Deliveries/ employee ST CV Deliveries/ establishment CV Deliveries/ employee ST CV Deliveries/ establishment CV Deliveries/ employee 1 Agriculture 1,0 0, Craftsmen/,,0 Ha.. services.. Mi. 1. Mo Industry 1, 1, Wholesalers,1, Department stores Retailers 1,0 1, Tertiary/ offices Warehouses/ transport ,1 0, , 1, Fa , 1,

9 As shown, the dispersion of the weekly number of deliveries per employee is in all cases higher than 0.. Same conclusion is made for the weekly number of deliveries per establishment. Since no invariance can be defined at this level (in all cases, standard errors are higher than half the average, so the data cannot be considered as compact and the data distribution cannot be modelled via a symmetric law). These results suggest that the mean value is not a good estimator for freight trip generation in most of the cases Assessment of the functional form Those results are complemented with an econometric analysis. The results in Table shows the models for each industry sector for each of the three aggregation studied (i.e., ST-, ST-, ST-). Table presents a short description of the groups by industry sector and sub-sector, the number of observations for each group, the parameters for the FTG models, the RMSE, and the adjusted coefficient of determination when applicable. As explained in the methodology, the FTG models can take three different functional forms: (i) a constant per establishment, (ii) a constant per establishment and a coefficient for the number of employees, or (iii) an employment rate. Table : Selected Freight Trip Attraction Models ST ST ST Model Description Obs. RMSE R² constant employment Agriculture 1,0 0,1, 0,1 All All Craftsmen and services, - 1, n.a. All Craftsmen 1,1 1,01, 0, - Repair activities,0-1, n.a. - Manufacturing/ installation 1-1,,1 0, All Tertiary services: craftsmen 1,1 1, n.a. Mi Tertiary services: high flows, -,0 n.a. Mo Tertiary services: mixed flows 1, -, n.a. Ha Tertiary services: average flows, - 1, n.a. All All Industry,1 0, 0, 0,1 Chemical industry, 0,1 0, 0,1 All Construction industry 1, 0,1 0,0 0,1 - Construction repairs, 0, 1, 0,1 - Construction manufacturing/installation,1 1,, 0,0 All Primary and intermediate products, 0, 1,0 0, - Basic bulk 1-0, 1,1 0, - Small objects - 0,, 0, - Bulk -,,0 0, All Food and non-fragile consumer goods 1, 0,, 0, - Consumer goods (fragile foodstuffs) - 0,1,0 0, - Non-consumer goods (fragile, 0,1, 0, foodstuffs) - Non-fragile consumer goods (house), 0,1 1, 0,

10 ST ST ST Model Description Obs. constant employment RMSE R² All All Wholesale 1 1, 0,, 0,0 All Intermediary products 00-1,0, 0,0 - Fragile intermediate products 0-0,0 1, n.a. - Other intermediate products, 0,0,0 All Nonfood consumer goods,0 0,0 0, 0,0 - Non-food fragile consumer goods, 0,0 1, 0,0 - Non-food non fragile consumer,0 0,0, 0,0 goods All Food 1, 0,,0 0, - Fragile food consumer goods - 0,1, 0,1 - Other food consumer goods - 0, 0,1 0, All Department stores - 0,, 0,1 Hypermarkets and department stores 1-0,, 0, Supermarkets 0, 0,0 1, 0, 1 Specialized department stores 1-0,, 0, All All Retailers 0, 0,, 0, 1 1 Clothing, shoes, leather,01 0,1,1 0, 1 1 Butcher's shops 0, 1,1, 0,0 1 1 Small groceries, 1,0,0 0,0 1 1 Bakery retailers,1 -, 0, 1 1 Hotels, restaurants, cafés 1, 0,1,1 0, 1 1 Pharmacies 1 1, 1, 1,1 0, 0 0 Hardware stores, 0,, 0,1 1 1 Furnishing shops, 0,,1 0,1 Bookshops 0, -,0 n.a. All Other retail shops,1 0,1, 0,1 1 Minimarkets 1-0,1, 0, Other retail shops 1-0, 1,01 0,0 Street trading (marketplaces) 0, -, N²².a. All All Tertiary/ offices, 0,0 1, 0,0 Transport except storage, -, n.a. Fa Other tertiary activities with low flows,1 -,01 n.a. All Offices, 0,0,0 0,1 Pure tertiary sector (offices) 1,1,1 1,0 0,0 - Non-tertiary offices (agriculture, wholesale), -,1 n.a. - Non-tertiary offices (retail trade, industry, transport, administration) 0 -,1, 0, All Warehouses/ transport, -,0 n.a. - Warehouses (bulk) 1, - 1, n.a. - Warehouses (with transport) - 0,0,0 0,1 As shown in the table, a substantial majority of the models display a functional form with a constant and an employment term. In the case of aggregation ST-, % of the groups display a model with a constant and an employment term, while % of them display a constant FTG per establishment, and 1% display an employment rate. In the case of aggregation ST-, % of the groups display a model with a constant and an employment term, % display a model with a constant per establishment, and % display an employment rate. In the case of aggregation ST-, % of the models display a model with a constant and an employment term, % display an employment rate, and % display a

11 1 1 constant FTG per establishment. The predominance of models with a constant shows that employment rates which are the most popular models in the literature are not the most appropriate specification and its application is bound to produce misleading estimations. In terms of the fit of the models, the adjusted R² varies between 0.0 and 0.1, with most of them having an adjusted R² higher than 0., which reveals a high variability in the results across groups and an appropriate accuracy in most of the models. This is confirmed by the RMSE, for which the mean is.0, but the standard deviation is.. This heterogeneity is a consequence of the high RMSE in some groups, mainly in the group of warehouses and transport. To analyze the homogeneity within the industry groups studied, the authors conducted a statistical study to assess if establishments within different sub-sectors (using ST and ST) have significant different patterns, and if including fixed effects for those sub-sectors would improve the performance of the model. To confirm those results, it is important to go in-depth into the analysis of the homogeneity within categories, presented below Assessment of the homogeneity within categories This section presents the results from the homogeneity assessment within categories using an econometric approach with fixed effects. As presented in the methodology section, each sub-sector in the classifications ST and ST are represented by binary variables, as well as their interaction with employment. For each sector in the classification ST a pooled model is estimated, and only the binary variables and the interactions that are significant are conserved. The resulting models are described using a set of equation where the t-stat is presented under each parameter. For the categories corresponding to agriculture (ST-1), department stores (ST-) and warehouses including transport (ST-), no binary variable or interaction is found significant, meaning that the final model is identical to the ones presented in Table. In the case of craftsmen/services, the final model is as follows: FTGC..ST + 1.1E 1.0E _ ST Mo 1.1E _ ST Mi = (1) (.) (-.) (.) (-.) (-.) Observations: ; RMSE: 1.; R adjusted: 0.0 Where, ST - corresponds to the binary variable for establishments in the manufacturing or installation sub-sector; E represents the number of employees at the establishment; E_ST Mo represents the number of employees for establishments in the tertiary sub-sector with average flows; E_ST Mi represents the tertiary sub-sector with mixed flows. As shown, a typical establishment in the craftsmen/services sector has in general. deliveries per week, plus 1.1 more deliveries per employee. However, this behavior is not homogeneous within the sector, as a typical establishment within the manufacturing or installation sub-sector has 1. weekly deliveries (i.e.,. less than other establishments in the sector) plus the same 1.1 per employee. In the case of establishments in the tertiary sub-sectors, the difference is that a typical establishment in that sector tends to produce. weekly trips, independent from the number of employees. It should also be noticed that the RMSE decreases from 1. to 1., when the effects per industry sub-sector are taken into account.

12 Equation () shows the equation for the industry sector (ST-). FTG I =.0 1.ST 1.0ST 1.0ST.ST + 1. E (.1) (-.0) (-.0) (-.0) (-.1) (.00) 1.E _ ST 1.E _ ST 1.0E _ ST 1.0E _ ST () (-.1) (-.) (-.0) (-.1) Observations: ; RMSE: 1.; R adjusted: 0. Where, ST - corresponds to the binary variable for establishments in the construction and manufacturing installation sub-sector; ST corresponds to the binary variable for industry establishments in the consumption goods sub-sector; ST corresponds to the binary variable for industry establishments in the primary and intermediate products sub-sector; ST - corresponds to the binary variable for industry establishments in the primary and intermediate bulk goods sub-sector; E_ST represents the number of employees for establishments in the chemical sub-sector; E_ST - represents the number of employees for establishments in the non-fragile consumer goods sub-sector; E_ST represents the number of employees for establishments in the construction industry subsector; and E_ST represents the chemical sub-sector. In terms of the RMSE, there is a decrease from 0. to 1. when using the pooled model with fixed effects. For the wholesale sector, the final model is shown in equation : FTG W = 1. 1.ST + 0. E () (.1) (-.) (.) Observations: 1; RMSE:.0; R adjusted: 0.0 Where, ST - corresponds to the binary variable for wholesale establishments in the non-food nonfragile sub-sector. In this case the model is very similar to the one in Table, except for the non-food non-fragile goods sub-sector, that attracts about trips less per week than other establishments in the sector. The RMSE does not improve significantly. For the retail sector, the final model is shown in equation : FTG R =. +.ST 1 +.ST 1 +.ST +.ST + 1.0ST 1 (.1) (.) (-.) (.0) (.0) (.) + 1.E 0.E _ ST 1 1.0E _ ST 1.E _ ST 1 1.1E _ ST 1 (.0) (-.) (-.) (-.) (-.0) 1.1E _ ST 1 1.E _ ST () (-.) (-.) Observations: 0; RMSE:.; R adjusted: 0.1 Where, ST 1 corresponds to the binary variable for retailers in the furniture sub-sector; ST 1 corresponds to the binary variable for retailers in the bakery sub-sector; ST corresponds to the binary variable for the other retailers sub-sector; ST corresponds to the binary variable for retailers in the books sub-sector; ST 1 corresponds to the binary variable for the pharmacies sub-sector; E_ST 1 represents the number of employees for retailers establishments in the hotel, restaurants, and cafés sub-sector; E_ST represents the number of employees for the other retailers sub-sector; 1

13 E_ST 1 represents the number of employees for retailers in the bakery sub-sector; E_ST 1 represents the number of employees for retailers in the clothing, shoes and leather products sub-sector; E_ST 1 represents the number of employees for retailers in the furniture sub-sector; and E_ST represents the books sub-sector. In terms of the RMSE, there is a decrease from. to. when using the pooled model with fixed effects. In the case of the tertiary and offices sector, equation () represents the FTG: FTG T / O =..ST + 0.0E () (.) (-1.) (.) Observations: ; RMSE: 1.; R adjusted: 0.0 Where, ST corresponds to the binary variable for the pure tertiary sub-sector. In this case the model is very similar to the one in Table, except for the pure tertiary sub-sector, that attracts about trips less per week than other establishments in the sector. The RMSE does not improve significantly. In essence, these results show that there is a certain degree of heterogeneity within the traditional industry sectors, but in about half of the cases the model used for the higher aggregation level can also be used for more detailed sub-sectors without losing significant accuracy. It is noteworthy that for some sectors (e.g., industry and retail sectors) the difference between sub-sectors is substantial, suggesting the need for more detailed data to avoid large estimation errors. Conclusion This paper is a first step into understanding how the classification of data impacts the quality of the estimators for urban freight trip generation (FTG). A combination of dispersion-based and linear regression analyses was deployed to compare a constant model and two linear models (respectively of type y=a.x+b and y=c.x). Those analyses show the difficulties to conclude on the best categorization for the aggregations proposed. In all cases, the performance of the estimators is low, but those models remain applicable for general estimations in an aggregate level. In this case, the three categorizations produce similar results, which can be interesting when no detailed data is available; in this case, an - category based model can give coherent results with a little effort of data disaggregation. The most disaggregated category will be explored in future research, but the lack of categories with at least 0 points limits the significance of the analysis. The results from the heterogeneity assessment confirm the diverse FTG patterns that coexist within a category. Capturing these differences explicitly can enhance the quality of the model, although they also increase the efforts required for data collection. The analysis also reveal that for about half of the models the aggregate model can be used to fairly estimate FTG without the need for more detailed models. This study contributes to identify the industry sub-sectors requiring more attention and more detailed data. Further improvements of the paper would be mainly related to two directions: the refinement of the analysis methodology and the comparison and integration of French and US data to produce an integrated and transferable methodology for urban FTG. Moreover, the introduction of nonlinear functional relations would be an important step to adapt the model to the effects of shipment size and 1

14 mode changes on FTG. In the second direction, two possibilities are considered: the comparison of French and US models, for example by applying both approaches to the same dataset and examining if there are similar generation patterns by category, and the comparison of US and French data (for example, New York and Paris) and making in-depth statistical analyses of data dispersion and correlation, directly on primary data of both countries, then searching for similarities in FTG determinants. 1

15 Appendix: 1 category classification Table Details of the ST1 category and corresponding dispersion analysis results ST1 Sample CV CV ST1 Sample CV CV Description size Del Del/emp Description size Del Del/emp 1a all b and a a b b 1 or c and c and a a b b 1 or c and c and a Ha-a b Ha-b 1 and c and Mi-a a Mi-b b Mi-c c and Mi-d 0 and a Mo-a b 1 or Mo-b and c a d b and e 0 and a a b and b a c and b and a a b b and a a b b and c 0 and a a b and b a c and b and a a b b and c a d 0 and b and all a a b.1. b c and +..0 c a d b e c and f 0 and a Fa-a b Fa-b c and Fa-c a Fa-d 0 and b a c and b and a a b.1. -b and c and a a b b c and c and a all b and all a a b and b 0 and

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