Impact of flexible working on traffic congestion in extreme weather conditions: Empirical evidence from a natural experiment

Abstract

Introduction

Climate change increases the frequency and intensity of extreme weather events, which can lead to increased congestion on roadways as drivers slow down and navigate hazardous conditions. Flexible working can be a potential solution because it reduces the need for employees to commute to work.

Methods

This study investigates the traffic congestion spatiotemporal patterns of flexible working in extreme weather events (i.e., heavy snow) by using the public health emergency as a natural experiment. We collect real-time traffic data from Harbin, China, and provide a framework to quantify the reduction of traffic congestion under extreme weather conditions. During the epidemic lockdown period, only crucial workers were allowed to go to work in the study area; everybody else was working from home. This is the maximum level of flexible working that a system can allow. Hence, our findings provide an upper limit for traffic congestion reduction in extreme weather events. We constructed three scenarios, i.e., baseline, snow, and snow with work-from-home (WFH). We use time series analysis and Kaplan-Meier survival analysis methods to study the spatiotemporal patterns of traffic congestion during morning peak hours (6:30 a.m.–9:30 a.m.) at 10-min intervals.

Results

The data analysis identified that significant traffic congestion reduction due to the WFH arrangement. For example, the longest travel duration is reduced from 120 min in the snow scenario to 50 min in the snow with WFH scenario.

Conclusions

This study reveals the geographical patterns of urban traffic congestion, providing support for guiding residents to optimize snowy travel methods in future interventions, policy changes, and research.