NETWORK, NETWORK, NETWORK: NEW TECHNIQUES IN PEDESTRIAN
MOVEMENT ANALYSIS
Martin Wedderburn
Independent Transport Consultant
Alain Chiaradia
University of Cardiff
1. INTRODUCTION
While walking is a universal mode of travel and an essential element of any
successful transport system, for many years walking was the invisible mode in
transport planning practice and analysis. The fact that every single journey we make
involves an element of walking meant that traditionally walking was taken for granted
or ignored. And as pedestrians (at least the most able-bodied ones) are arguably the
most flexible transport mode, they were often treated as an afterthought and simply
accommodated where space allowed.
Today walking is increasingly recognised as generating a wide range of benefits in
terms of health and wellbeing, environment, social inclusion, liveability and local
economic vitality. As a result, pedestrian movement analysis tools have become an
integral part of mainstream transport planning practice in just a few years.
The term pedestrian movement analysis covers a range of tools to model patterns of
pedestrian movement. The analysis and forecasting of walking behaviour takes
place on a number of scales from strategic area-wide models to the micro-simulation
of small public spaces. The paper presents several project examples illustrating the
latest developments in pedestrian movement analysis, including innovative
applications of direct models and pedestrian route assignment, and discusses
challenges in relation to data needs.
1.1 Pedestrian behaviour
Research and experience has shown that walking patterns are predictable but that
pedestrian movement differs from other transport modes.
Walking patterns are affected by people’s perception of their own physical strength
and endurance. Distance is a constraining factor for pedestrian movement and
walking speed is largely unaffected by the infrastructure provided. Factors such as
age, gender and journey purpose will affect people’s walking speed. Distance
constraints can also be expressed as a journey budget, defined either as a time or
distance radius (e.g. within 400m or 5 minutes walking time).
There is also a strong cognitive dimension that is essential to understand route
choice. People use both perceptual information (what they can see, hear, etc.) and
inferences (guesses about things they cannot directly perceive) to construct mental
maps of an area. These mental maps then inform route choice plans across a
movement network, and they change in response to new information encountered.
These ‘way-constructing’ and ‘way-finding’ processes allow people to organise public
spaces and their attributes into a safe and easy spatial pattern to navigate.
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Spatial accessibility is the interface between an individual’s ability to navigate
through an urban environment and the configurative qualities of that environment.
This element of individual ability is constantly evolving, increasing as spatial
information becomes more complete. As a result, the more an individual resides in a
location, the more their spatial abilities increase. In fact it is possible to categorise at
least three levels of spatial ability in individuals (Meilinger, 2008; Montello, 2005).
The first level is landmark knowledge. Persons with landmark knowledge are able to
recall the characteristics (cue and function) and location of a place. The second kind
of geographical knowledge is route knowledge, where people are able to link
landmarks with directions for getting from place to place. Route knowledge includes
directions for navigation (sometimes called procedural knowledge). The third level is
map knowledge, survey knowledge or configurational knowledge. Persons with this
ability know the interrelationship of places and routes with each other. Map
knowledge often includes information about distances (proximity) and angles
between features (legibility) and the capacity to project the possible route
(sometimes called inference knowledge).
While transport is generally described as a derived demand, walking activity ranges
from utility walking (including access to other transport modes) to purely recreational
walking. Yet there is also a substantial grey zone between these categories, and this
element of discretion suggests that environmental quality can be a decision factor.
Furthermore, when walking people can stop at any time, they do not have to park a
vehicle and therefore most socio-economic transactions are pedestrian-based.
Similar to other transportation facilities, the level of crowding or congestion of
walking infrastructure can be measured, for example using a 6-digit scale from A to
F, with A indicating free flowing conditions and F indicating extremely crowded
(Fruin, 1971). However, it has been argued that a strict application of this approach
is inappropriate for pedestrian projects since user perceptions of congestion and
crowding depends on the type of environment and the types of activities they are
engaged in (Transport for London, 2010).
2. PEDESTRIAN MOVEMENT ANALYSIS
2.1 Three levels of analysis
Methods for pedestrian behaviour modelling can be classified by size and scale of
analysis. There are essentially three types of tools that can be classed as pedestrian
movement analysis. These are:



Direct modelling - referring to all forms of analysis that predict movement
patterns through a statistical relationship between spatial or other variables
and observed movement.
‘Traditional’ 3-stage modelling – referring to all forms of analysis that estimate
movement between origins and destinations, and assign this demand to
routes.
Micro-simulation – referring to agent-based modelling tools to simulate in
detail the interaction between pedestrians.
Table 1 summarises the relevance of each of the three levels of analysis.
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Table 1: Three levels of pedestrian movement analysis
Direct modelling
3-stage modelling
Micro-simulation
Purpose
Establish a statistical
link between spatial
and other variables
and movement
Incorporate pedestrian
movement into a
traditional transport
modelling and
evaluation framework
Understand pedestrian
comfort and safety at a
detailed level
Role in the
design
process
Option generation and
testing
Planning, feasibility,
appraisal
Detailed planning and
design
Scale
Area or neighbourhood
wide
Area or neighbourhood
wide
Individual station or
junction
Method
Calculation of the
statistical relationship
between activity
density and network
Calculation of change
in trip generation /
attraction and
distribution
Simulation of pedestrian
movement and
interaction
Calculation of potential
movement
Pedestrian route
assignment model
Low
Medium
Cost
Calculation of density
measurements
High
All three forms of analysis therefore have a distinct role to play for transport planning
and are relevant for particular tasks and at specific scales. This paper discusses the
potential role for direct modelling and 3-stages modelling in particular.
2.2 Representation of space
Pedestrian space can be represented in a number of different manners. Whereas in
transport planning all networks are generally based on a traditional link and node
structure, many applications of direct modelling stem from architectural theory and
have used the concept of axial lines. Axial lines (Figure 1) are criss-crossing straight
lines used to represent pedestrian paths through spaces.
Figure 1: Representation of an urban network as figure-ground, axial lines, and network
segment
Source: Geospatial World Forum, May 2013, Tim Stonor
http://www.slideboom.com/presentations/762551/Tim-Stonor_Create-Space-Create-Value
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The use of axial line networks has several difficulties. At a practical level, existing
network maps cannot be used for the analysis and the network needs to be drawn
from scratch for each new location studied. Furthermore there is some disagreement
about the definition of the method for generating these maps, which makes it
extremely difficult to transmit the concept convincingly to non-specialist audiences.
Conversely, recent tools such as the Spatial Design Network Analysis software
(sDNA) developed by the University of Cardiff employ a standard network definition
(Chiaradia, et al., 2011; Cooper & Chiaradia, 2011). This approach means that
existing data and standards, such as the Ordnance Survey Integrated Transport
Network (ITN) in the UK can be employed. sDNA has been thoroughly tested in
urban environment (high streets and residential neighbourhoods) and in urban dense
complex multi-level environments. The link standard used enables sDNA to
outperform previous software when empirically tested against pedestrian and
vehicular counts from standard datasets. sDNA ranks separately Euclidean distance,
directness and route overlap for the complete route choice set using the link node
network standard according to a user defined walking budget in Euclidean distance
equivalent to travel budget i.e. 400m ≈ 5 min walk, 800m ≈10 min walk. sDNA also
includes network density, junction density and severance indices. The analysis
based on this network representation is easier to convey to a transport planning
audience, and the same network can subsequently be used for assignment
modelling.
2.3 Links and zones
Transport models are traditionally built using zones as spatial units for the calculation
of trip generation and attraction. Zones are spatial areas (typically derived from
administrative boundaries) for which production and attraction values are calculated
using attributes relating to the population and land uses situated there. Zones are
generally connected to the network by means of one or more connectors. Some
authors have noted that in pedestrian models where the scale of movement can be
very local, the assignment can be overly sensitive to design of the zone connectors
as a result (Iacono, et al., 2010).
While one response to the zone scale problem is to seek more disaggregate data
sources, an alternative response can be found in many forms of direct modelling that
do not employ zonal representations of the production and attraction of demand.
Instead it has been observed that there is a correlation between the density of
activity and certain network characteristics. As discussed in this paper, the analysis
can be thought of as the sum of multiple accessibility calculations using the number
or length of routes as a proxy for density of activity.
A hybrid model type can be built to incorporate the benefits of both types by
transferring the zonal information to the network of links. The following figure
illustrates this process. The transfer of data to links can be undertaken on the basis
of link length and can incorporate frontage factors as a means of identifying those
links with frontages relating to certain types of land use. The resulting output is a link
network with production and attraction parameters attached to links.
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Figure 2: Using links as zones: Transferring zonal information to links
Aerial view
Population (2011 UK census) by Output Area
Link network overlaid on Output Areas
Population (2011 UK census) by links
This form of model can be fitted within a wider suite of transport modelling
applications, making use of standard transport demand modelling inputs while
maximising the advantages of link-based models in terms of modelling pedestrian
route choice assignment. This approach was demonstrated by Colin Buchanan
(2010) in the pedestrian model developed for the Vauxhall Nine Elms Battersea
Opportunity Area.
3. STRATEGIC PEDESTRIAN MODELS
3.1 The role of direct models
The discussion of the opportunities presented by direct models is not new in
transport modelling (Ortuzar & Willumsen, 2004). Of particular interest in relation to
pedestrian models are the underlying conceptual approaches of the interveningopportunities model, direct demand model, and marginal demand model.
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Intervening-opportunities model
The basic concept of the intervening-opportunities model is that trip making is not
explicitly related to distance but to the relative accessibility of opportunities for
satisfying the objective of the trip. This concept is not new and was first proposed by
Stouffer (1940) and Schneider (1959).
Direct and quasi-direct demand model
Drawing on general econometric demand models, this form of direct demand model
uses a single equation to relate travel demand to transport mode, journey purpose
and person characteristics. It can be argued that by calibrating trip generation,
distribution and mode choice in a single step, the scope for error in the sequential
sub-models is minimised.
Marginal demand model
The objective of this form of direct model is to concentrate on marginal changes in
transport demand brought about by a specific project or policy.
Direct model techniques have long been employed in pedestrian movement analysis.
The principle of intervening opportunity is adopted in the use of accessibility-based
measures to forecast pedestrian route choice, where the accessibility characteristics
of all links in a given network are systematically calculated and ranked.
3.2 Accessibility measures
The concept of accessibility has been discussed in transport planning since the
1950s. Hansen (1959) described zonal weighted accessibility as the ease of
reaching destinations and therefore “the potential of opportunities for interaction”.
From the perspective of individual we generally speak of access to destinations,
whereas we use the term accessibility to describe a location (Geurs & van Eck,
2001). Accessibility between zones as spatial units can be described as weighted
accessibility (also termed weighted closeness in some pedestrian models).
Other authors have employed a measure of network accessibility based purely on
the relationship between spatial units (Shimbel, 1951; Ingram, 1971). These
measures are unweighted and Pooler (1995) employs the term structural
accessibility (also termed unweighted closeness in some pedestrian models).
While the term accessibility is used to describe the sum of destinations that can be
reached within a given travel time or cost, the calculation of this measure also
produces a second measure of flows on the network. By calculating how many
shortest paths traverse each link, a probabilistic estimate of flows on the network can
be derived. This measure has also been termed betweenness centrality. Figure 3
shows examples of relative accessibility and relative flow displayed as heat maps
(where the hotter colours represent greater accessibility or greater flow).
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Figure 3: Examples of relative accessibility and relative flow at 800m radius
Accessibility (closeness)
Flow (betweenness)
The concept of shortest path calculation for pedestrian route choice on network is
discussed in a range of literature (Hill, 1982; Bovy & Stern, 1990; Verlander &
Heydecker, 1997; Penn, et al., 1998; Shimbel, 1953; Dijkstra, 1959). For the purpose
of this paper it is sufficient to stress that empirical studies of pedestrian route choice
have identified a number of explanatory factors including distance, directness
(angular distance) and other qualitative factors. Pedestrian generalised time can be
calibrated using these factors for use in a range of pedestrian movement analysis
applications.
The application of flow measures (betweenness centrality) derived from accessibility
(closeness centrality) as a measure for predicting movement flows is demonstrated
using cordon count datasets from four London case studies (Barnsbury, Clerkenwell,
South Kensington and Brompton) as shown in Figure 4. The four case study areas
combined include a total sample of 231 vehicular cordon counts and 300 pedestrian
cordon counts.
Three definitions of unweighted shortest path are employed in this analysis:



Geometric (angular directness)
Euclidean (shortest)
Hybrid (shortest and most direct)
Overall the hybrid measure is the most powerful predictor of vehicular flows (r2 =
0.79) and pedestrian flows (r2 = 0.65) as shown in Table 2.
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Figure 4: Four London case study areas (OS Meridian network)
Table 2: Accessibility on network variable Betweenness
Correlation
2
r with vehicle flow
2
r with pedestrian flow
Case study area
Angular
Hybrid (angularEuclidean)
Barnsbury
0.69
0.93
Clerkenwell
South Kensington
Brompton
0.80
0.61
0.58
0.86
0.67
0.69
Mean
0.67
0.79
Barnsbury
Clerkenwell
South Kensington
Brompton
0.60
0.73
0.58
0.41
0.72
0.71
0.64
0.53
Mean
0.58
0.65
3.3 Weighting of accessibility measures
The analysis of the four case study areas shown above uses flow measures derived
from unweighted accessibility measures, i.e. structural accessibility (Shimbel, 1953;
Ingram, 1971; Pooler, 1995). The same analysis of shortest paths between links can
also be undertaken with weightings attached to each link. For example, weighting by
links by their length can be used as a rough proxy for activity density. Alternatively
links can be weighted by factors driving demand at origins and destinations using
population statistics, land use density or public transport data. This information can
be transferred from zonal representation to a link-based representation as described
in section 2.3.
One example of the practical application of weighted accessibility measures was
undertaken for a recent project in London. Mitcham is a town centre located in in
London Borough of Merton. A pedestrian choice model was originally developed for
the town centre to predict changes in pedestrian footfall in different parts of the town
centre as a result of traffic management measures to improve pedestrian crossings
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and the relocation of bus stops. The Mitcham pedestrian model was calibrated
against a range of pedestrian count data (27 sites in total) and a pedestrian tracking
survey.
The links were weighted by three different sources of origin-destination:



All links in the 800m buffer around the town centre were weighted by
population data from the UK census 2011;
All links in the core town centre were weighted by retail floor space data
derived from published UK Valuation Office Agency data; and
Daily bus stop boarding and alighting numbers for all bus stop locations in the
core town centre area were obtained from Transport for London data.
Analysis of the bivariate correlation between pedestrian flows and accessibility
measures (betweenness) clearly shows that the weighted measures correlate more
strongly with observed pedestrian flows. For example, Table 3 shows the
comparison of Pearson correlation for predicting patterns of town centre access at
600m or 800m radii.
Table 3: Pearson correlation with pedestrian flows (600m and 800m radii)
Accessibility measure
r2 using number r2 using link
of links
length weighting
(unweighted)
Betweenness (Euclidean, 600m)
0.585
0.637
Betweenness (Angular, 600m)
0.665
0.664
Betweenness (Euclidean, 800m)
0.706
0.688
Betweenness (Angular, 800m)
0.734
0.687
All correlations significant at 0.01 level
r2 using OD
weighting
population-retail
0.806
0.832
0.792
0.829
Using multiple linear regression, several models of total pedestrian flows were
tested. The best fit model without origin-weighting weighting, and therefore excluding
bus stop access trips, has an R2 value of 0.55). The equivalent best fit model using
population and retail weighting (R2 = 0.854) employs a retail-retail weighted measure
of local movement within the town centre (200m radius) and a population-retail
weighted measure of strategic movement (800m radius). By including public
transport boarding/alighting data the model fit improves to an R2 value in excess of
0.90.
3.4 The role of 3-stage modelling
While direct models can play an important role in many types of pedestrian
movement analysis, there are also certain limitations to their application in transport
planning. Firstly, in order to incorporate pedestrian movement analysis into a multimodal forecasting and modelling framework, it needs to fit within the sequential 3stage or 4-stage approach. This includes disaggregating the land use and transport
related determinants of model impacts.
Secondly, the findings of direct models may not meet some requirements of
transport appraisal frameworks. In order to understand the distribution of transport
impacts, it is generally necessary to disaggregate modelled flows into, for example,
existing versus new users or public transport access trips versus walk-only trips.
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The Vauxhall Nine Elms Battersea study (Colin Buchanan, 2010), as well as recent
work for the Bow interchange in London are examples of practical applications of
pedestrian movement analysis within a wider transport forecasting and appraisal
framework. These studies employ trip generation derived from multi-modal strategic
models and local data on development plans. The distribution of trips is undertaken
using simple gravity models. In the case of the Vauxhall study, a mode choice submodel predicted walk versus walk-bus mode choice for access to rail stations. The
pedestrian route assignment employs calibrated route choice algorithms (including
geometric, Euclidean and other route quality factors).
4. NETWORK DATA NEEDS
From the examples of direct and bespoke pedestrian movement analysis described,
it is clear that a robust and consistent representation of the walking network is key to
successful applications. Currently in the UK there is no single definitive data source
that provides an adequate pedestrian network representation for pedestrian
movement analysis applications. As illustrated in Figure 5 there are several products
available in the UK that can assist in the production of reliable pedestrian networks
but some manual adjustments are still necessary.
Figure 5: Sample of pedestrian network datasets
Ordnance Survey Integrated Transport
Ordnance Survey ITN with Urban Paths
Network (ITN)
Open Street Map
Actual pedestrian network (green) and Open
Street Map
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The Integrated Transport Network (ITN) dataset from Ordnance Survey (British
national mapping agency) represents the road centre-line. As such it provides an
adequate representation of many smaller streets. However, this dataset does not
include pedestrian footpaths, shared paths or pedestrian crossing facilities.
Ordnance Survey has subsequently released a new product, Urban Paths, which
includes some publicly accessible footpaths, although the dataset is incomplete and
not fully compatible with the ITN network.
Crowd-sourcing applications such as Open Street Map represent a potential source
of pedestrian network mapping. Figure 5 also shows the equivalent section of the
Open Street Map data. This shows a hybrid network combing road centre line in less
busy streets with detailed representation of crossings at busy intersections. As such
it is arguably the best representation of what people perceive when navigating
through public space. However, it also illustrates the potential issues of quality, for
example as observed in the confusion around the edge of the garden in the
roundabout.
5. CONCLUSIONS
While many early pedestrian movement analysis tools were developed in isolation
from other transport and land use planning methods, the forecasting of walking
patterns can now sit comfortably within the standard transport planning and
modelling toolbox. This has been achieved because new pedestrian movement
analysis tools have adopted compatible networks and concepts and, last but not
least, simply because they have adopted the language of transport planning.
Different models have a role to play at different scales and at different stages of a
planning and design process. The use of direct modelling techniques can provide a
cost-efficient and adequately predictive method to forecast pedestrian flows for many
applications. However, some transport planning projects require the use of
‘traditional’ 3- or 4-stage models where greater disaggregation of impacts is
necessary. Common to both levels of analysis is that simple link-based models can
overcome the challenges of zone-based pedestrian analysis.
Importantly, there is a clear need for a recognised standard of pedestrian network
representation. This coordination of data needs for pedestrian movement analysis is
required to guarantee a minimum level of consistency and comparability of
approaches.
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Acknowledgements
The authors would like to thank the Placemaking & Public Realm team at the London
Borough of Merton for allowing us to use the Mitcham town centre model for this
research. The original town centre movement study was commissioned by Merton in
2013/14 to inform emerging proposals for a major town centre public realm scheme.
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