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9 to 5 or a new‐normal? Cluster analysis of pre and post pandemic vehicle and cycle diurnal flow profiles.

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Title: 9 to 5 or a new‐normal? Cluster analysis of pre and post pandemic vehicle and cycle diurnal flow profiles.
Authors: Burke, Matthew Edward1 (AUTHOR) m.burke1@newcastle.ac.uk, Bell, Margaret1 (AUTHOR), Dissanayake, Dilum2 (AUTHOR)
Source: IET Intelligent Transport Systems (Wiley-Blackwell). Dec2024 Supplement 1, Vol. 18, p3041-3057. 17p.
Subjects: Flexible work arrangements, Cycling, Telecommuting, Traffic flow, Demand forecasting
Abstract: Commuting traffic associated with the "9 to 5" workday shaped the morning and evening peaks across the world. The COVID‐19 pandemic led to unprecedented changes in travel behaviour such as an increase in cyclists and telecommuting, where employees worked from home during lockdown periods. Transport modellers, planners and policy makers need to know whether the 9 to 5 has returned, or we have entered a "New‐normal" of more flexible working arrangements and increased cycling, key for delivering sustainability targets. In this research, the unsupervised machine learning technique k‐means clustering investigates temporal patterns across the day and week, comparing the pre‐ and post‐pandemic era across both motorised vehicles and bicycles. Results show that the total daily traffic flow has returned to pre‐pandemic volumes, but more spread across the day. Mondays and Fridays have less‐pronounced peaks compared to pre‐pandemic, having implications for air quality modelling and assessment, traffic management and transport planning. Meanwhile, cycling has increased in volume and the time‐of‐day people are travelling has changed. Policy makers need to consider whether the additional capacity on the road, brought about by reduced peak traffic, could be reallocated to make roads safer for and reduce delay to cyclists, contributing towards net zero goals. [ABSTRACT FROM AUTHOR]
Copyright of IET Intelligent Transport Systems (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Commuting traffic associated with the "9 to 5" workday shaped the morning and evening peaks across the world. The COVID‐19 pandemic led to unprecedented changes in travel behaviour such as an increase in cyclists and telecommuting, where employees worked from home during lockdown periods. Transport modellers, planners and policy makers need to know whether the 9 to 5 has returned, or we have entered a "New‐normal" of more flexible working arrangements and increased cycling, key for delivering sustainability targets. In this research, the unsupervised machine learning technique k‐means clustering investigates temporal patterns across the day and week, comparing the pre‐ and post‐pandemic era across both motorised vehicles and bicycles. Results show that the total daily traffic flow has returned to pre‐pandemic volumes, but more spread across the day. Mondays and Fridays have less‐pronounced peaks compared to pre‐pandemic, having implications for air quality modelling and assessment, traffic management and transport planning. Meanwhile, cycling has increased in volume and the time‐of‐day people are travelling has changed. Policy makers need to consider whether the additional capacity on the road, brought about by reduced peak traffic, could be reallocated to make roads safer for and reduce delay to cyclists, contributing towards net zero goals. [ABSTRACT FROM AUTHOR]
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  Label:
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  Data: <i>Copyright of IET Intelligent Transport Systems (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1049/itr2.12558
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Cycling
        Type: general
      – SubjectFull: Telecommuting
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      – SubjectFull: Traffic flow
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      – SubjectFull: Demand forecasting
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            – D: 02
              M: 12
              Text: Dec2024 Supplement 1
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              Y: 2024
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