Transcript PPT - Space Syntax Symposium 8

Pedestrian agent modelling
A visual approach
The 8th Space Syntax Symposium
Santiago de Chile
January 2012
Pete Ferguson
UCL, Space Syntax Limited
Eva Freidrich
Foster +Partners
Dr. Kayvan Karimi UCL, Space Syntax Limited
Presentation outline
Here we present some key extensions to the EVAS agent based pedestrian modelling
framework for Depthmap v10 used within the Space Syntax Ltd office.
The tools include the ability to set origin and destinations to each pedestrians'
journey and to export individual journey information in the form of a vector trace of
the journey path.
In this paper we first discuss the structure of the original EVAS agent modelling
framework and describe various additions and refinements that have been proposed
for the program.
We then go on to show how another important component of an agent's
environment - the distribution of land use activities - can be incorporated as a matrix
of trip origin-destination information.
We go on to show why such an approach is needed in the practical requirements of
masterplanning pedestrian environments and show how the tool can potentially be
applied in the design process.
Introduction to Agent Based Modelling (ABM)
In Agent Based Modelling (ABM) the city is viewed as an emergent phenomenon arising from
the microscopic encounters of its individual citizens with resulting descriptions having moved
from top-down to bottom-up (Turner, Mottram and Penn 2005).
Agent based models relate to this paradigm in that they afford agency to the individual actor
and attempt to understand aggregate implications of micro-level behaviour. They provide
information on the individual’s experience and how that contributes to collective phenomena
rather than providing a description of the system as an abstract object in its own right.
Agents are imbued with general behavioural traits but their specific actions are not predetermined. This implies a degree of freedom in decision making which is most frequently
operationalised through a form of random action constrained by the pre-defined behavioural
framework. It is this randomness or variability that not only distinguishes one agent from
another as a unique individual but enables such agents to engage in exploration of their
environment.
Introduction to ABM
There are various methods for incorporating the physical environment into an agent model. Often
the environment is represented as pre-defined paths through space rather than a two or three
dimensional field of opportunity.
The routing of agents along these paths is also often pre-determined, based on calibration from
observed data or on sophisticated but deterministic route-choice mechanisms. This approach may
be suitable for models where there is little scope for independent exploration but at a pedestrian
level where movement is unregulated and contain no specific goal, this is limiting. (Turner and
Penn 2002).
Origin- destination agents
Pedestrian path
Mega Ellhniko, Athens
Introduction to ABM
Another way of incorporating an environment is by engraining its physical bounds with a
repulsive force, enabling the environment to influence agent behaviour in a way that
complements the social forces inherent in many agent to agent interactions. Many micro
simulation agent models such as the seminal social force approach by Helbing and Molnar
(1994) contain such active forces and in so doing view the environment in analogy to a
physical force that must be adapted to and overcome.
Introduction to ABM
The EVAS agent modelling framework used by the space syntax community, views the
environment in a slightly different way again. Following the theory of affordances (after
Gibson 1979) agents are programmed to view space as a resource to be explored rather than
a constraining factor and individuals seek available space rather than avoid occupied space
(Penn and Turner 2002).
EVAS agent modelling
The basic EVAS agent modelling framework operates on a simple concept of affordance
whereby the natural reaction of an individual in response to the availability of space is to
walk toward it.
Reproducing this simple exploratory behaviour requires agents to recognise the relative
availability of space through a virtual form of vision. Agents are guided by an exosomatic
visual architecture (EVA) that is constructed prior to model processing in the form of a densegrid visibility graph.
The visibility graph is computed by overlaying a two-dimensional grid (at some arbitrary
resolution) over a layout in plan view, and calculating which points within the grid are able to
see which other points. The resulting visibility graph can be used to calculate the
approximate viewable area, or isovist, from each point on the grid and the set of visible
locations for each point are stored in a lookup table (Turner and Penn 2002).
EVAS agent modelling
In the case of both the original EVAS framework and the extended OD agent model
presented here, the standard field of view is a subset of 170° from the original 360° isovist as
this corresponds to the approximate occlusion of human vision.
When used in an agent model, the set of visible locations from any given point is subdivided
into angular segments and the agent is assigned a heading and field of view in relation to the
segments that are visible from its current location.
170
°
EVAS agent modelling
Once the field of vision has been defined, an
agent simply chooses a destination that lies
within the field at random, walks toward it for
a set number of grid space and then repeats
the decision making process.
Longer site lines will contain a larger number
of visible spaces so the random draw of a
destination means that an agent is
probabilistically more likely to choose its next
step in line with the visual continuity of
space.
The emergent patterns of space use formed by multiple agents correlate well with
pedestrian movement in the Tate Britain Gallery, with a coefficient of up to R²=0.76 (Turner
and Penn 2002).
Origin- destination agents
Turner, Mottram and Penn (2005) observed that agent behaviour following the pure
affordance mechanism lead to a concentration of movement in larger public spaces, simply
driving them to congregate in open areas if the program is left to run to a steady state.
To overcome the adherence to open space over time, they applied the simplest form of origindestination matrix where each agent would start at a random location in open space, have a
random destination in open space and movement toward the destination employed a visual
field that was always directed to the destination rather than the current heading. When
applied to Barnsbury, the origin-destination agents, generated a better correlation with
observed pedestrian movement.
We conjecture that a better representation of possible origin and destinations in the
environment would further improve correlation.
Origin- destination agents
In this paper, we present a simple augmentation of the core EVAS agent modelling framework,
which enables bespoke trip origin-destination information to be assigned to individual agents
and incorporates both locational and visual cues in the agent decision making process. In this
sense it is an incremental extension of work already undertaken by Turner, Mottram and Penn
though its implementation here enables a number of useful applications in everyday design
contexts.
Underlying the visual component of the model is the same dense-grid visibility graph that
supports the original EVAS implementation. In addition however, location is introduced in the
form of a second look up table of origin and destination information where the inputs and
outputs into the system are proportional to the category and intensity of activity at opposing
ends of a journey.
Origin- destination agents
To set up the OD agent model, available data on the number and location of individual
journey origins and destinations is required. If detailed information on absolute numbers is
not available, the OD matrix can be used as a weighting system where the number of entries
or exits at a given location is proportional to the 'attraction' of a plot defined by another
parameter.
Rather than applying a single weighting to each location both an origin and destination value
can be assigned, enabling more specific journey scenarios to be constructed. The images
below show trip origin and destination data applied to a simple pedestrian environment.
Each location receives a value as an origin and a destination relative in this case to the
number of building occupants expected on completion of the project.
Origin- destination agents
Next, a second look-up table is
constructed, complementing the visual
graph and included to provide each agent
with information on its position relative to
its eventual destination.
The topological and geometric distance
from a destination to every grid space is
required so that an agent can understand
its location at any point on the grid.
This pre-calculation process only has to be
completed once even if individual origindestination weights change.
Origin- destination agents
The agent runs through the following calculation at each time step:
1.Calculate my visual field from my current heading (e.g 170° from the original 360° isovist)
2. Take a subset of space from the visual field that lies as close as or closer than my current
position to my eventual destination.
3 If there are no available spaces that meet the criteria of the subset, choose a space from the
full 360° isovist
4. Choose a space at random from this final subset (from step 2 or 3)
5. Walk toward the chosen location for a set number of grid spaces and repeat
Origin- destination agents
Agent path
The result of a few agents
progressing from a single origin
to a couple of destinations in a
simple urban environment
Origin- destination agents
Agent path
Multiple origins to a single
destination showing the
emergence of recognisable
patterns from their collective
behaviour
Origin- destination agents
Morning
Pedestrian navigation is still
influenced by exploratory
behaviour. As such the
resulting pattern of
movement is unique - even
when the origin-destination
pairs are symmetrically
reversed.
Path overlap
high
low
Origin- destination agents
Evening
Note that the pattern of flow in
the evening commute is not the
reverse of the morning commute.
Path overlap
high
low
Application
The EVAS extension is being used on various consultancy projects undertaken by Space
Syntax Ltd offices.
Much of the tool was developed to offer design support to Foster and Partners during the
masterplanning of the Masdar City development in Abu Dhabi.
A range of further developments are underway that build in more functionality for a wider
range of application.
Here are a few examples of how the tool has been put to use…
Application
Masdar Eco City in Abu Dhabi is being developed as an independent settlement of 60,000
inhabitants adjacent to Abu Dhabi international Airport. The vision is for the city to be
carbon neutral and as such encouraging sustainable modes of transportation, in particular
pedestrian movement, is an important aspect of the masterplanning process.
Application
Existing
Application
Vision
Application
Our initial input was concerned with understanding the likely volume and
distribution of pedestrian movement across the whole development.
For this we developed further model extensions to incorporate origindestination factors into angular segment models… but that’s another story!
The intention was to gauge the likely success of the proposed pedestrian
promenade under a range of development scenarios…
Pedestrian forecast
(No PRT usage)
Application
Airport
Kalifa City
Movement
high
low
Pedestrian forecast
(Intermediate PRT usage)
Application
Airport
Kalifa City
Movement
high
low
Pedestrian forecast
(Heavy PRT usage)
Application
Airport
Kalifa City
Movement
high
low
Application
Agent path
The origin destination agent model was used
in Masdar to enable us to understand:
-where people would be likely to concentrate
- which places would be busier or quieter
- which routes would be used and which
wouldn’t
and track changes in distribution as different
phases of the development are completed.
Again origin-destination information was
provided on proposed land use categories and
intensity.
Application
Footfall
Low
High
Retail
The resulting pattern of pedestrian
movement within each phase could be
used to align the land use components
of the masterplan. E.G Retail shown in
grey adjacent to high movement flows.
Application
Building outline
Shading
More and more layers of influence could then
be included, tested and optimised, in this
case, the impact of shading structures on
individual route choice.
Application
Building outline
Footfall
Low
Shading
High
…this can be seen to change over time as activity patterns and the
location of shaded pedestrian realm shifts throughout the day.
Application
Building outline
Footfall
Low
Shading
High
…this can be seen to change over time as activity patterns and the
location of shaded pedestrian realm shifts throughout the day.
Application
Building outline
Footfall
Low
Shading
High
…this can be seen to change over time as activity patterns and the
location of shaded pedestrian realm shifts throughout the day.
Application
Building outline
Footfall
Low
Shading
High
…this can be seen to change over time as activity patterns and the
location of shaded pedestrian realm shifts throughout the day.
Application
Building outline
Footfall
Low
Shading
High
…this can be seen to change over time as activity patterns and the
location of shaded pedestrian realm shifts throughout the day.
Application
Footfall
Low
High
Movement volumes for individual
streets could be derived from the
origin-destination data provided.
150 People Per Hour
700 People Per Hour
450 People Per Hour
Executive summary Key findings What will Phase 1 look like?
Application
Building outline
Landscaping
Agent path
The paths of specific journeys could
be selected and assessed. In this
case, the journey to the Mosque at
mid-day
Application
Building outline
Landscaping
Office workers
Students
Shoppers
This can then be further
disaggregated to look at the
journeys of specific groups of
pedestrians
Application
The tool can be used quite effectively in
internal environments.
Here, the movement of office workers in the
lobby of an office tower is being assessed
showing higher concentrations of flow at the
entrance to lift banks.
Low
Agent Movement
High
Application
Again individual groups of pedestrians can
be identified and points of potential capacity
constraint and journey conflict identified.
Points of conflict
Entering Classroom
Leaving Classroom
Application
From this Level of Service
can be derived
Level of Service
Application
The tool has also
been used on UK
masterplanning
projects as a means
of rapidly testing the
implications of
design options
Footfall
Low
High
Application
The tool has also
been used on UK
masterplanning
projects as a means
of rapidly testing the
implications of
design options
Footfall
Low
High
Application
The tool has also
been used on UK
masterplanning
projects as a means
of rapidly testing the
implications of
design options
Footfall
Low
High
Conclusion
Here we have presented a simple extension of the EVAS agent modelling
framework that combined the exploratory nature of most pedestrian
movement with the purposive nature of specific origin-destination journeys.
The tool has enabled Space Syntax Ltd to offer more traditional agent
modelling design advice such as levels of service, conflict analysis and land use
planning.
The tool is still in its infancy and many further developments are required to
provide a full range of ABM services.
More extensive validation is also required and comparisons made with the
original EVAS modelling and alternative approaches to footfall prediction.
Thank You!
The 8th Space Syntax Symposium
Santiago de Chile
January 2012
Pete Ferguson
UCL, Space Syntax Limited
Eva Freidrich
Foster +Partners
Dr. Kayvan Karimi UCL, Space Syntax Limited