Online shopping has been growing in popularity for several decades, and the pandemic has accelerated its adoption. The environmental and congestion effects of a shift to delivery services are unclear, and depend on a number of parameters about the delivery services. However, understanding the relative impact of a shift to online shopping also requires understanding the environment and congestion implications of current shopping behavior. The amount of travel associated with shopping is often calculated by adding up the total mileage of trips to/from shopping destinations, or by computing the round trip distance from home to store. Both methods may significantly overestimate shopping travel since much shopping travel occurs on the way to another destination. In the limit, stopping at a store that is directly on the way to another destination may add no additional travel, but traditional travel survey methods would attribute at least a portion of the travel to the shopping trip. Using geocoded travel survey information from the Transportation Secure Data Center, this project will estimate the marginal travel associated with shopping trips. These surveys include precise geographic locations for all stops made by respondents. We will use a network analysis algorithm to compute travel distances for 'counterfactual' tours where the shopping destination was skipped. From this, we will calculate the marginal travel associated with shopping—which is likely to be much lower than current estimates of shopping travel, making delivery services relatively less attractive. We will disaggregate these results by time of day and location on the network to better understand congestion effects of shopping travel.
|Effective start/end date||01/19/17 → 03/31/23|
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