A Chinese study finds that e-commerce delivery services have potential to reduce traffic congestion by as much as 1.4 percent over baseline congestion.

An international sudy on e-commerce services.


Summary Information

E-commerce services (e.g. Amazon, Alibaba) have become ubiquitous in recent years and for transportation planning one of the most important questions is if these services are increasing or decreasing traffic congestion. Instinctually they may decrease traffic congestion by consolidating goods delivery and reducing the need for individuals to drive to the store by themselves. However, e-commerce may also increase congestion by introducing more delivery vehicles onto the roads.


Single’s Day (Chinese: 光?节) is a major unofficial holiday in China, somewhat comparable to Black Friday in the United States. It is generally considered to be the largest 24-hour shopping period in the world and online retail giants like Alibaba and heavily promote it.

Taking advantage of this event, researchers at the London School of Economics gathered data before, during and after Singles Day to better understand if online retail usage has a relationship with traffic congestion. Researchers collected traffic data from a "GPS navigation company in China" and from the Baidu Map API. Researchers used Baidu Search Index data to approximate online shopping demand. Data was collected for 94 major Chinese cities and was collected for all days between November 5th and 23rd, 2018. Researchers then fed this data into an econometric model to estimate the overall impact of online shopping activity on traffic congestion.

  • A 10 percent increase in online shopping activity decreased traffic congestion by 1.4 percent.
  • Effect is strongest during peak hours and in cities that have high, baseline levels of online shopping activity.

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Does E-Commerce Reduce Traffic Congestion? Evidence from Alibaba Single Day Shopping Event.

Author: Peng, Cong

Published By: London School of Economics

Source Date: 01/08/2019

Other Reference Number: ISSN 20142-2695



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Benefit ID: 2019-01428