Implications of Activity Classification Aggregation in Urban Freight Trip Generation Linear Models

Implications of Activity Classification Aggregation in Urban Freight Trip Generation Linear Models

Ivan D. Sanchez-Diaz (Chalmers University of Technology, Sweden) and Jesus Gonzalez-Feliu (Ecole des Mines de Saint-Etienne, France)
Copyright: © 2019 |Pages: 16
DOI: 10.4018/978-1-5225-8160-4.ch002

Abstract

This chapter studies the implication of aggregating establishments by categories with different levels of detail for modeling FTG. To this effect, the chapter conducts an assessment of freight trip generation (FTG) patterns homogeneity inside activity-based grouping. The method implemented is econometric in nature, which allows the assessment of the statistical significance of variables representing commercial activity sectors and sub-sectors. The results show that for some sectors the traditional high-level aggregation includes sub-sectors with homogenous FTG patterns and thus produces appropriate models; in some other cases (e.g., retail, manufacturing), the sub-sectors have different FTG patterns and thus more detailed data is needed to calibrate accurate models. This research can be used to enhance the efficiency of data collection, as it identifies some sub-sectors that need larger efforts for data collection, and some other categories where FTG homogeneity allows for less detailed data collection without hampering the quality of the models.
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Introduction

Urban freight demand models are an important tool for urban planners and transportation authorities to assess local needs, design appropriate infrastructure, and define policy interventions. The increasing relevance of this subject in an urbanized world has attracted the interest from both researchers and practitioners with various frameworks and applications. In general, these models start with a demand generation stage which include freight generation (FG) and freight trip generation (FTG). FG models measure the freight flows (e.g., tons, volume) that result from the commercial interaction between a shipper and a receiver, and provide an excellent insight to understand economic interactions and the demand for goods movement. FTG models measure the amount of freight-related trips that result from the demand for goods and the logistics decisions of shippers and receivers. FTG includes both freight trip attraction (FTA) mainly resulting from the reception of deliveries by an establishment, and freight trip production (FTP) mainly resulting from shipments sent-out by an establishment (Holguín-Veras et al. 2012). This paper focuses on FTG models because enhancing these models will produce more immediate benefits for well-informed decision making and for more accurate local needs assessment and infrastructure planning.

As urban freight traffic is a direct consequence of commercial activities taking place at the establishment level, it is important to collect primary data at this level and estimate statistical models to capture establishments’ freight traffic patterns. FTG models can estimate current freight traffic patterns as well as forecast future freight traffic based on planned commercial activity. One salient characteristic of FTG models is, thus, their reliance on commercial activity categorization.

There are a number of publications in the literature focusing on FTG models (Iding, Meester et al. 2002, Beagan et al. 2007, Holguín-Veras et al. 2011, 2012, 2013, Lawson et al. 2012, Alho and de Abreu e Silva 2014, González-Feliu, Cedillo-Campo et al. 2014, Ducret and Gonzalez-Feliu 2015, Aditjandra, Galatioto et al. 2016, Gonzalez-Feliu, et al., 2016; Sánchez-Díaz et al. 2016a,b; de Oliveira et al., 2017; Gonzalez-Feliu, 2018), most of them use either land-use or industry classifications to group establishment that perform similar commercial activities and estimate either constant rates per employee (Iding et al. 2002, Beagan et al. 2007, Holguín-Veras et al. 2011, 2013) or econometric models for each group (Holguín-Veras, Jaller et al. 2011, González-Feliu, Cedillo-Campo et al. 2014, Ducret and Gonzalez-Feliu 2015, Sánchez-Díaz, Holguín-Veras et al. 2016). In general, the classification systems are borrowed from the ones used by National offices to produce economics statistics. These classification systems use a digit-based nomenclature—where a larger number of digits in the code denotes a more disaggregated category with a more specific activity—that provides the framework for collecting and presenting extensive statistical data according to the economic activity used in other fields of economic statistics (e.g., production, employment) (Eurostat 2016). Similarly, when used for modeling purposes these classifications have different levels of detail and entail different modeling implications. Models estimated with more aggregated categories have less detail, high risk of heterogeneity within the modeling group, but require less data; while models estimated with more disaggregated categories have a higher level of detail but require a substantial amount of data (Holguín-Veras, Jaller et al. 2011).

Key Terms in this Chapter

Generation (Transport Modelling): The process of estimating the transport demand at origin (Production) and/or destination (Attraction). It is usually assumed that demand generation models include both production and attraction flows. That stage corresponds to the first of four-step models. In freight transport, generation can be made for number of trips (or deliveries, or shipments), in that case we call this process freight trip generation, or for commodity quantities, in that case we will qualify those models with the term “freight generation.”

Heterogeneity (in Category Classification): When associated to a category classification, the quality or state of a category’s individuals to be diverse in their characteristics. If we consider multi-level category classifications, disaggregated categorizations (i.e. considering the sub-category or sub-sub-category levels) will lead to mode homogeneous categories (since their number increases) whereas very aggregated categories will not account, when defining them by average values, the heterogeneity of their population.

RMSE: Root mean square error; a common measure of error used to assess the accuracy of models and estimators. It represents the square root of the second sample moment of the differences between predicted and observed values or the quadratic mean of these differences.

French Urban Goods Transport Surveys: A set of generic surveys deployed in France between 1995 and 1999 to quantify the main trends in the generation and spatial distribution of urban goods transport flows. They combine an establishment survey, a transport carrier survey and a driver diary follow-up. They were deployed in three cities (Bordeaux, Dijon, Marseille).

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