Simulating Urban Flows of Daily Routines of...
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Simulating Urban Flows of
Daily Routines of Commuters
Tomas Crols and Nick Malleson
School of Geography & Leeds Institute for Data AnalyticsUniversity of Leeds
http://surf.leeds.ac.uk/
ABMUS 2018, Stockholm, 15 July 2018
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Ambient population
Not (only) where people live...
But where they are throughout the day
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An ABM of the Ambient Population
1. Simulating urban flows
2. Agent behaviour and activities
3. Study area and data
4. Results and conclusions
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Objectives
How many people at urban locations?
Which activities are they doing?
Calibrate with (( live ) ‘big’ ) data
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Possible applications
Crime risk: more people, more crime?
Air pollution risk: more people, more people affected
Transportation: e.g. optimising public transport
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‘Big’ and ‘traditional’ data for calibration and validation
Footfall
Smart cardTwitter / Apps
Census Survey
Phone signal data
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An ABM of the Ambient Population
1. Simulating urban flows
2. Agent behaviour and activities
3. Study area and data
4. Results and conclusions
![Page 11: Simulating Urban Flows of Daily Routines of Commuterssurf.leeds.ac.uk/p/2018-07-15-commuters-slides.pdf · Leisure (sports, going out, …) Agents Individuals » Not yet households](https://reader033.fdocuments.in/reader033/viewer/2022041520/5e2da2348900d065f94fb56b/html5/thumbnails/11.jpg)
Individual agents that move around to do these activities:
Being home
Working
Shopping
Lunch / dinner in restaurants
Leisure (sports, going out, …)
Agents
Individuals» Not yet households
Agents and their activities
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Agents and their activities
Intensities as a behavioural framework
No daily schedules for agents
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An ABM of the Ambient Population
1. Simulating urban flows
2. Agent behaviour and activities
3. Study area and data
4. Results and conclusions
![Page 14: Simulating Urban Flows of Daily Routines of Commuterssurf.leeds.ac.uk/p/2018-07-15-commuters-slides.pdf · Leisure (sports, going out, …) Agents Individuals » Not yet households](https://reader033.fdocuments.in/reader033/viewer/2022041520/5e2da2348900d065f94fb56b/html5/thumbnails/14.jpg)
Study area
Leeds
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Study area
Leeds
Otley
Bradford
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Data
Census data (for calibration)
Flows between home and work for Output areas
Focus on commuters / workdays in the model
Activity survey (for calibration)
UK Time Use Survey 2014-2015
WiFi sensors (for validation)
Count individual phones passing by in Otley
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UK Time Use Survey 2014-2015
– 8278 respondents
– Demographic and household information
– 10 minute intervals
– Main and secondary activities
– Type of location
– Only 2 days per person
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Working at the office
08:00 13:00 18:00 23:0004:00 08:00 13:00 18:00 23:0000:00 03:00
Start time End time
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Working at the office
8
Duration in hours
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Shopping on a workday
08:00 13:00 18:00 23:0003:00
Start time
About 30% of respondents do some shopping on a workday
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Shopping on a workday
Duration in minutes
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An ABM of the Ambient Population
1. Simulating urban flows
2. Agent behaviour and activities
3. Study area and data
4. Results and conclusions
![Page 23: Simulating Urban Flows of Daily Routines of Commuterssurf.leeds.ac.uk/p/2018-07-15-commuters-slides.pdf · Leisure (sports, going out, …) Agents Individuals » Not yet households](https://reader033.fdocuments.in/reader033/viewer/2022041520/5e2da2348900d065f94fb56b/html5/thumbnails/23.jpg)
Footfall observations vs. model results
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People in age group 65+ in the study area
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Conclusions and future work
Modelling the ambient population
Commuters vs. total population
Combining ‘traditional’ and ‘big’ data
Modelling the behaviour of agents
Future work
Generalising the model
Dynamic calibration
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Thank you!
Simulating Urban Flows of
Daily Routines of Commuters
Tomas Crols and Nick Malleson
School of Geography & Leeds Institute for Data Analytics – University of Leeds
http://surf.leeds.ac.uk/
ABMUS 2018, Stockholm, 15 July 2018