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

IF 3.2 3区 工程技术 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Journal of Transport & Health Pub Date : 2024-08-24 DOI:10.1016/j.jth.2024.101892
Yufeng Jin, Jie Liu
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引用次数: 0

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.

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弹性工作制对极端天气条件下交通拥堵的影响:来自自然实验的经验证据
导言气候变化增加了极端天气事件的频率和强度,这可能会导致道路拥堵加剧,因为驾驶员会放慢车速并在危险条件下行驶。本研究以突发公共卫生事件为自然实验,研究了极端天气事件(如大雪)下弹性工作制的交通拥堵时空模式。我们收集了中国哈尔滨的实时交通数据,并提供了一个量化极端天气条件下交通拥堵减少情况的框架。在疫情封锁期间,研究区域内只允许关键岗位的工作人员上班,其他人都在家工作。这是一个系统所能允许的最大灵活工作程度。因此,我们的研究结果为在极端天气事件中减少交通拥堵提供了一个上限。我们构建了三种情景,即基准情景、雪天情景和雪天在家办公(WFH)情景。我们采用时间序列分析和 Kaplan-Meier 生存分析方法,研究了早高峰时段(6:30-9:30)每隔 10 分钟的交通拥堵时空模式。结论这项研究揭示了城市交通拥堵的地理规律,为未来干预、政策变化和研究中引导居民优化雪天出行方式提供了支持。
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来源期刊
CiteScore
6.10
自引率
11.10%
发文量
196
审稿时长
69 days
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