The potential impacts of e-commerce on the mobility of people are usually classified into four types of effects: _substitution_ , or a reduction in travel demand due to the virtualization of activities that motivated travel; _complementarity_ , or an increase in travel demand because the virtualization of the activity allows or promotes other activities that do require travel; _modification_ , or changes in demand patterns not directly related to an increase or decrease in the number of trips; and _neutrality_ , or the absence of impact on travel demand. Studies to date have found both substitution and complementarity effects, with the former being slightly more common (Le et al. 2022). Studies that find complementarity effects suggest that the possibility of doing more information search online (one of the activities of e-commerce) not only promotes more online shopping but also more physical shopping (Xi et al. 2020). The studies that find substitution effects have indicated some factors that may enhance this effect, such as shoppers not having their own vehicle (Shi et al. 2019) and sameday deliveries (Xi et al. 2020). Ferrell (2005) quantified the substitution relationship with data from California, finding that for every 100 minutes of time spent on e-commerce, consumers reduced 5 minutes of time spent on shopping trips. There is no consensus among studies that incorporate other dimensions of travel demand such as trip distance, trip chaining, mode choice, or trip frequency for other purposes. Modification effects have been detected in the use cases addressed in each study, but often with contradictory results from each other (Le at al. 2022).

# **3. Methodology**

The current paper analyzed how the growth of e-commerce is impacting mobility in Bogotá and Buenos Aires and how the patterns in e-commerce changed during the pandemic. A distinction can be made between two types of impacts of e-commerce on mobility: (i) its effects on the mobility of people, insofar as they allow the virtual development of the activity of buying products, which required physical interaction before the arrival of e-commerce that generates displacements; and (ii) its effects on the urban distribution of goods, insofar as they can modify the patterns of generation and spatial and temporal distribution of trips in vehicles destined for the distribution of goods between sellers and consumers. Specifically, the analysis: (i) characterizes the demand for e-commerce in both cities and its evolution during the pandemic, (ii) quantifies the potential reduction in travel demand due to e-commerce, (iii) quantifies the share of e-commerce in urban distribution flows of goods, and (iv) identifies whether the growth of e-commerce has resulted in a reduction in travel demand or, rather an increase in urban distribution flows of goods. The weight of trips for shopping purposes in overall mobility and their composition were analyzed according to the following criteria:

- **_Home-based or non-home-based travel_** : This analysis allows identifying the share of trips that correspond to shopping trips for each of the demand segments of the origin-destination matrices from cell phone data.

- **_Type of trip chain_** . Two types were identified according to the sequencing of the shopping activity with other activities of the same traveler: (i) round-trip, in which the traveler returns after the shopping activity to the same place where the previous activity took place; and (ii) chained, in which the traveler adds a shopping activity in a trip between two other activities. This analysis allows identifying the potential for trip reduction as a consequence of the substitution of shipping trips by e-commerce, since this substitution can avoid the two trips of the round-trip chains, but only one of the trips of the chained chains (Figure 1).

With different types of agents and multiple impacts on mobility, there are a large number of data sources that can contribute to understanding the demand for e-commerce and its impacts on mobility. A distinction can be made between two main groups of data sources: (i) e-commerce sector data sources, which are those that can characterize indicators related to the adoption and use of e-commerce among consumers, spatial patterns of order demand and the properties of the logistics chains that support e-commerce (Buldeo Rai and Dablanc 2022); and (ii) mobility data sources, which are those that can be used to directly measure the impacts of e- commerce on personal travel demand and urban distribution of goods.

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