An empirical demand model for e commerce

M.A. de Bok, Larissa Eggers*, Sebastiaan Thoen, Gerard de Jong

*Corresponding author for this work

Research output: Contribution to conferencePosterScientific

37 Downloads (Pure)


1. Overview and motivation

The emergence of e-commerce in the past decade and the surging growth during the pandemic, partially at the cost of in-store shopping, have reinforced the need for a better representation of this type of consumer demand and its effects in urban transportation studies (Reiffer et al, 2021). Since this is a recent development, conventional passenger transport models only model the personal mobility for in-store shopping. Standard modelling tools for large-scale demand forecasts for online and in-store shopping are limited. A proper representation of this demand segment first of all requires an estimate of e-commerce demand, and second the simulation of the delivery of the orders. Jaller and Pahwa (2020) developed both an econometric MNL model for in-store and online shopping and applied it to a synthetic population to estimate externalities of the alternatives. The econometric model explains the preferences for type of shopping but not the total level of product consumption, and the delivery of online orders is estimated on aggregate statistics. Other disaggregate simulation studies only focus on e-commerce demand, without considering the trade-off between online versus in-store shopping, such as Cheng et al (2021). In effect, online ordering may reduce physical movements of people to stores, while increasing the delivery of orders to people’s home addresses. This shift is taking place for many consumer products and groceries as well. Weltevreden and Rotem-Minaldi (2007) show early evidence that e-commerce ordering in the Netherlands increases freight transport, while personal travel decreases marginally. On the side of e-commerce deliveries, the simulation of urban freight transport is a well-studied topic in recent literature (Mommens et al, 2021; Hörl and Puchinger, 2021; Reiffer et al, 2021). However, modelling the demand side of e-commerce is often still minimal.

2. Methodology, results and main contributions

We present an empirical e-commerce demand model that is implemented in an urban freight simulator developed in the H2020 project HARMONY (Kamargianni et al, 2020). This new demand model for e-commerce is now a part of the simulator’s parcel module, which generates delivery tours based on the parcel demand by households and businesses. We estimated an ordered logit model with the demand for e-commerce shipments to households as the dependent variable, based on the assumption that one online order equals one parcel, as a function of personal and household characteristics which are known within the simulator.
A second model, connecting e-commerce with the demand for traditional in-store shopping, is also presented here, albeit not yet implemented in the urban freight simulator. In this model we first estimated total consumer demand separately for groceries and non-groceries, and next an adoption model for e-commerce services. The model has the structure of a two-step logit model: an ordered logit model for the total consumer demand, and next a binary choice model for the choice between online and in-store shopping for each of the shopping occurrences that make up a person’s consumer demand.
The models are estimated on the Mobility Panel Netherlands, the MPN (Hoogendoorn-Lanser et al, 2015). The 2017 wave of the MPN contained additional questions regarding online and in-store shopping that can be used for the estimation of choice models. To make the models suitable for application in the freight simulator, we focused on explanatory variables that differ between locations (i.e., zones in a model). The most important variables in the choice models that explain the spatial pattern of e-commerce demand are household income, age of the respondent (in 10 categories) and urbanization level at household location. Other personal characteristics that do not vary spatially are included if they improve the explanatory power of the models (e.g., gender).

3. Conclusion and future works

Age and household income are important predictors for the adoption of e-commerce and the number of parcels ordered. The age-classes 18-39 have the highest preference for e-commerce ordering. Above 40, the preference for e-commerce steadily declines. Persons living in households in the highest income classes (more than 67,000€ per year) are the most likely adopters of online ordering for both groceries and non-groceries. The urbanization level does not affect the adoption of e-commerce services for non-groceries, but strongly for groceries. This can be explained by the limited availability of e-groceries in less urbanized areas, especially at the time of data collection in 2017.
The presented e-commerce demand model has been implemented in the HARMONY Tactical Freight Simulator where it is used to calculate the number of parcels delivered in an area, the subsequent delivery tours and their effects on traffic and emissions. As the explanatory variables differ between zones, we obtain spatially distinct effects. In a next step, the presented model can be linked to a passenger simulator to jointly model and assess the generation of shopping trips and parcel deliveries. Another important research topic is the formulation of representative growth scenarios for e-commerce demand. As online ordering adoption rates evolve over the coming decade, socio-economic developments alone will likely not be sufficient to explain them. Adequately representing the evolution of these adoption rates in transport models requires a tailored calibration approach.
Original languageEnglish
Publication statusPublished - 2022
Event Transport Research Arena 2022: TRA Visions 2022 - Lisbon, Portugal
Duration: 14 Nov 202217 Nov 2022


Conference Transport Research Arena 2022
Internet address


  • e-commerce demand
  • discrete choice models


Dive into the research topics of 'An empirical demand model for e commerce'. Together they form a unique fingerprint.

Cite this