Research Collection
Conference Poster
How did micro-mobility change in response to COVID-19 pandemic?
A case study based on spatial-temporal-semantic analytics
Author(s):
Li, Aoyong; Zhao, Pengxiang; Haitao, He; Mansourian, Ali; Axhausen, Kay W.
Publication Date:
2021-01
Permanent Link:
https://doi.org/10.3929/ethz-b-000458649
Rights / License:
In Copyright - Non-Commercial Use Permitted
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ETH Library
Partners
Data
• Micro-mobility trip data
• Point of Interest
Categories
Business hours
• GPS Survey data
Tracking points
Activity
Methodology
1. Spatial changes on micro-mobility services
2. Temporal changes on micro-mobility services
3. Trip duration & Trip distance
How did micro-mobility change in response to COVID-19 pandemic?
A case study based on spatial-temporal-semantic analytics
3 Results
4 Conclusions
The spatial analysis：
• daily trip volume decreases with varying degrees in different PLZs
• e-bikes draw more attention than bikes due to undulating terrains The temporal analysis:
• the trip volumes show remarkable decrease on workdays
• only slight changes are observed on weekends
The statistics of trip duration and trip distance:
• the proportion of long-duration travels increases
• cyclists prefer to ride longer distance during Lockdown period
Trip activity:
• Home, Work, Transport, and Leisure activities occupy of 80% of all the trips during both Normal and Lockdown periods
• the rankings of these activities are unstable
• the share of Home activity increases
• the share of Leisure decreases while the shares of Grocery and Park increase remarkably
• people may prefer take the outdoor activities during Lockdown period
4. The share of Origin and Destination activities in the two periods
1 Introduction
– Many countries adopted the Lockdown policy due to the outbreak of COVID-19, causing changes world-wide
– Sharing micro-mobility services are influenced a lot as an essential transport mode in human daily life
– Question: How the micro-mobility services in response to the pandemic before and during Lockdown period
– An empirical study in Zurich
– Analysis from several aspects: spatial, temporal, statistical attributes and trip activities are compared in Normal (NP) and Lockdown (LD) periods
2 Data and Methodlogy
Aoyong Li
1, Pengxiang Zhao
2, He Haitao
3, Ali Mansourian
2,4, Kay W. Axhausen
11
IVT, ETH Zurich
2GIS center, LU, Sweden
3
Loughborough University, UK
4CMES, LU, Sweden
Period No. of trips
Operator Type Start date End date Normal Lockdown Publibike Docked bike 2020-02-15 2020-04-14 41954 26746 Publibike Docked e-bike 2020-02-15 2020-04-14 13963 8985 Bond Dockless e-bike 2020-02-15 2020-04-06 7259 3079
Tab: Basic information of micro-mobility trip data
• Spatial network analysis
• Semantic analysis based on trip
purpose
Fig: The framework of trip purpose imputation
docked bike docked e-bike dockless e-bike
docked bike docked e-bike dockless e-bike
docked bike docked e-bike dockless e-bike
Docked bike (%) Docked e-bike (%) Dockless e-bike (%)
NP LD Ratio NP LD Ratio NP LD Ratio
Origin Activity
Leisure 20.53 17.83 -13.15 17.20 14.79 -14.02 15.52 12.16 -21.67 Work 19.93 16.66 -16.43 18.61 16.37 -12.05 19.58 16.94 -13.47 Transport 19.53 20.40 4.41 22.04 19.91 -9.66 10.94 10.83 -1.03 Home 17.70 19.77 11.73 18.97 22.94 20.93 35.12 41.87 19.20 Shopping 9.28 8.56 -7.80 8.67 7.73 -10.83 7.64 7.02 -8.08 Grocery 7.58 9.22 21.66 8.21 10.01 21.98 6.71 6.90 2.80 Park 3.17 4.71 48.54 3.17 4.93 55.51 1.76 1.85 5.50 Education 2.27 2.85 25.59 3.14 3.32 5.98 2.74 2.44 -10.95
Destination Activity
Leisure 23.66 21.85 -7.65 23.10 25.06 8.46 40.20 43.62 8.50 Work 21.99 21.13 -3.92 24.45 21.44 -12.29 16.59 15.82 -4.61 Transport 18.34 16.07 -12.39 15.14 13.06 -13.75 13.48 10.68 -20.79 Home 14.94 16.48 10.31 15.10 15.98 5.80 11.11 10.86 -2.33 Shopping 8.14 8.20 0.65 7.49 7.15 -4.47 6.17 5.60 -9.30 Grocery 6.91 8.92 29.06 7.70 9.41 22.19 6.32 7.04 11.34 Park 3.33 4.21 26.32 3.58 4.35 21.74 2.57 3.03 18.14 Education 2.69 3.16 17.27 3.44 3.54 2.96 3.56 3.35 -5.65
