Reviewing the axial-line approach to capturing vehicular trip-makers’ route-choice decisions with ground reality Abhijit Paul Published online: 6 October 2012 Ó Springer Science+Business Media, LLC. 2012 Abstract This paper reports some limitations of the axial analysis theory as a basis for modeling the distribution of vehicular movement with a relationship study between the syntax configuration of a North American city and its vehicular flow pattern. Along with the relevance of the axial-line philosophy of capturing vehicular trip-makers’ route-choice decisions, many general concerns dealing with the effects of network character, land use, traffic congestion, and configuration boundary have been critically analyzed with theo- retical and empirical research results. A few procedural concerns have also been discussed. The conclusions suggest that the inclusion of the real-world variables of traffic and net- work studies into the methodology of generating configuration–movement relationships is expected to make the space syntax approach to modeling vehicular movement networks comprehensive. Keywords Vehicular traffic estimation Route choice Space syntax Axial analysis Network character Mobility characteristics Land use Introduction Space syntax, a technique of space accessibility analysis, was developed with an objective to understand the complexity of spatial arrangement in urban morphology and its effect in urban life (Hillier and Hanson 1984; Paul 2011a). Its application in urban analysis is vast (Hillier et al. 1987a, Peponis et al. 1989). One such application is traffic-assignment without using cost-intensive origin–destination traffic data. 1 In space syntax terms, the A. Paul (&) Department of Architecture, Jadavpur University, Kolkata, India e-mail: [email protected]1 The need of an alternative traffic assignment model, as identified by Penn et al. (1998, p. 59) is that: ‘‘[t]he construction and calibration of traffic models is a costly procedure in which the cost is related to the resolution with which the origin–destination data are gathered and the size of the model measured in terms of the number of nodes and links represented in the network.’’ Apart from this crucial failing, readers are also encouraged to see a recent critique of the author (Paul 2011b), that explains quite a few other limitations 123 Transportation (2013) 40:697–711 DOI 10.1007/s11116-012-9436-3
Transcript
Reviewing the axial-line approach to capturing vehiculartrip-makers’ route-choice decisions with ground reality
Abhijit Paul
Published online: 6 October 2012� Springer Science+Business Media, LLC. 2012
Abstract This paper reports some limitations of the axial analysis theory as a basis for
modeling the distribution of vehicular movement with a relationship study between the
syntax configuration of a North American city and its vehicular flow pattern. Along with
the relevance of the axial-line philosophy of capturing vehicular trip-makers’ route-choice
decisions, many general concerns dealing with the effects of network character, land use,
traffic congestion, and configuration boundary have been critically analyzed with theo-
retical and empirical research results. A few procedural concerns have also been discussed.
The conclusions suggest that the inclusion of the real-world variables of traffic and net-
work studies into the methodology of generating configuration–movement relationships is
expected to make the space syntax approach to modeling vehicular movement networks
comprehensive.
Keywords Vehicular traffic estimation � Route choice � Space syntax � Axial analysis �Network character � Mobility characteristics � Land use
Introduction
Space syntax, a technique of space accessibility analysis, was developed with an objective
to understand the complexity of spatial arrangement in urban morphology and its effect in
urban life (Hillier and Hanson 1984; Paul 2011a). Its application in urban analysis is vast
(Hillier et al. 1987a, Peponis et al. 1989). One such application is traffic-assignment
without using cost-intensive origin–destination traffic data.1 In space syntax terms, the
A. Paul (&)Department of Architecture, Jadavpur University, Kolkata, Indiae-mail: [email protected]
1 The need of an alternative traffic assignment model, as identified by Penn et al. (1998, p. 59) is that: ‘‘[t]heconstruction and calibration of traffic models is a costly procedure in which the cost is related to theresolution with which the origin–destination data are gathered and the size of the model measured in termsof the number of nodes and links represented in the network.’’ Apart from this crucial failing, readers arealso encouraged to see a recent critique of the author (Paul 2011b), that explains quite a few other limitations
roadway structure of a settlement itself bears the ability of recognizing which of its
roadway units, traditionally identified as axial lines (Hillier and Hanson 1984), are closely
accessible, integrated, from all other units. As described by Hillier (1999a, p. 169):
‘‘[in] the study of cities, one representation and one type of measure has proved more
consistently fruitful than others: the representation of urban space as matrix of the
longest and fewest lines [roadway units], the axial map, and the analysis of this by
translating the line matrix into a graph, and use of the various versions of the topo-
logical (that is, nonmetric) measure of patterns of line connectivity called integration.’’
Axial lines are the minimal set of accessible straight roadway units among all possible
combinations that can be drawn from within a given roadway structure representing the
shortest topological distances from one another (Hillier and Hanson 1984). The topological
distance between two such lines refers to the minimum of number similar lines that a trip-
maker needs to travel in-between them. In an axial analysis, the shortest topological distance
from a specific line to all other lines, on average, defines how integrated the line is within a
given roadway structure leading to an important finding on the behavioral theory of trip-
making, that appears to aid in explaining the pattern of movement distribution in the structure
itself. That is, because integrated axial lines provide close connections, on average, to all
other lines, trip-makers tend to select these lines in the first place. As a result, the probability
of receiving movement in these lines increases. However, novel as the theoretical argument
on route choice, described by the axial model appears to be, it lacks a number real-world
parameters that the contemporary theories of traffic-assignment take into account for mod-
eling vehicular movement networks. This paper reports some of the limitations of axial
analysis as a basis for modeling the distribution of vehicular movement with a relationship
study between a roadway syntax configuration and its vehicular flow pattern.
Axial analysis
Let us first see how axial analysis works in evaluating the roadway accessibility at an urban
level. Imagine a set of axial lines that represent a roadway structure as shown in Fig. 1.2
These lines inherently develop the topological relationships with one another, and typically
by using justified graphs (Fig. 2), these relationships are described. As shown in graph 2a,
line 1 is directly connected by three other lines (n1). Let us label them depth 1 (d1)
connections. Similarly, line 2, as an origin-line in graph 2b, is directly connected (that is, at
depth 1) by one line (n1), which is also connected by two other lines (n2) at depth 2 (d2).
So on and so forth. This way, each axial line in the network is labeled in accordance with
how many changes of depth separate the line from the origin-line (Ratti 2004, p. 488) with
an understanding that the measure of depth itself will describe the topological distance
between the origin-line and any other line in the network. Then by averaging the topo-
logical distances between the origin-line and each of the other lines, space syntax deter-
mines how the origin-line is accessible from all other lines. The average topological
Footnote 1 continuedof the equilibrium approach to traffic assignment—a popular traffic assignment model in the transportationplanning research community.2 This example has appeared in Ratti (2004, p. 488). Also there are numerous classical studies (Hillier andHanson 1984; Hillier 1996; Major 1996), where the rationales of determining space accessibility can befound.
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distance, mean depth (d), of an origin-line to all other lines is the primary measure of its
accessibility. According to Hillier and Hanson (1984):
d ¼ Rd n
k � 1k ¼ total number of lines.
That is, a lower measure of mean depth makes the origin-line closely accessible from all
other lines, whereas a higher measure makes the line distantly accessible. However, space
syntax also considers the compositional pattern and size (k value) of the network and
describes the measure of accessibility typically through the notion of integration (Hillier
and Hanson 1984). In the previous example, line 1 (refer to Fig. 2c) is found to be the most
integrated line in the entire network.
Configuration–movement relationship
In a number of syntax studies (Caria et al. 2003; Hillier 1998; Penn et al. 1998, etc),
integrations of axial lines representing settlement roads have been found positively cor-
related with pedestrian and vehicular movements suggesting that the roadway configura-
tion of a settlement itself might aid in explaining the settlement’s traffic flow distribution.
Nonetheless, this axial-line technique of modeling flow distribution has been criticized by
Ratti (2004, p. 488) with an important, and perhaps, a very natural inquisition: ‘‘how is it
possible to tell so many things about the urban environment with such a limited amount of
information?’’ that is, with the configuration that space syntax uses in its analysis? For
instance, how is it possible for axial integrations to predict both pedestrian and vehicular
traffic with accuracy when they show distinct travel behaviors?
Fig. 1 Axial representation of roadway structure. a Roadway structure; building blocks are shown in solidboxes; b axial map; and c movement pattern
Fig. 2 Justified graphs. a Line 1. b Line 2, analogous to lines 3 and 4. c Axial-integration map. Integrationreduces as the color of the lines changes from red to blue. (Color figure online)
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The two modes of transportation, pedestrian and vehicular, are the two distinct factors of
traffic estimation primarily because they bear two different philosophies of trip-making. In
general terms, pedestrian trips are generated for short-distance travel, whereas vehicular trips
are made for long-distance travel (Paul 2012a). It, therefore, appears to be inequitable to
approximate both pedestrian and vehicular traffic with a common integration measure.
Consequently, the configuration–movement relationships, generated by axial integrations,
are expected to be inconsistent and, occasionally, inadequate; see Table 1. Interestingly,
however, the configuration–movement relationships reported in Table 1 give an indication
that, despite generating inconsistent traffic correlations in both cases, axial integrations turn
out to be somewhat better predictors of pedestrian traffic than vehicular ones implying an
argument that vehicular trip-makers might not always consider the shortest topological
distances while selecting routes.
An analogous argument can also be found in the classical theories of travel demand
modeling. While modeling with origin–destination traffic data with Gravity Model (before
1920), travel distance (in metric terms) was believed to be the key parameter of route
choice. Later, Pallin modified the Gravity Model by replacing the distance parameter with
travel time (as appeared in Fricker and Whitford 2005, p. 207) for more accurate traffic
predictions. In the contemporary theories of traffic-assignment, the travel time of a trip-
route is fundamentally judged by its metric length, speed zoning, and traffic congestion.
Hence, the distance parameter alone, in metric or even in topological terms, seemingly falls
short in capturing the trip-makers’ route-choice decisions especially for the purpose of
modeling vehicular movement networks. This indicates that integration measures that are
merely generated by analyzing topological distances between each pair of roadway units,
Table 1 Configuration–movement relationships of axial studies
No. Source Study area r2 Modes
1 Hillier (1998) Baltic House area 0.773 (Pedestrian)
2 Penn et al. (1998) Bransbury, South Bank, Calthorpe Street, andSouth Kensington of London
0.68 (Pedestrian, withcar parking)
3 Hillier (1998) Above areas but without car parking 0.84 (Pedestrian)
4 Hillier et al.(1987a)
Golders Green (sub-urban) 0.645 (Pedestrian)
5 Hillier et al.(1987b)
Bransbury 0.641 (Pedestrian)
6 Caria et al. (2003) Avenidas Novas 0.608 (Pedestrian)
The p-values are lesser than 0.01 unless mentioned in parentheses. The axial flow is the total flow in theentire length of an axial line
3 Occasionally in space syntax studies, logarithmic measures of traffic counts are considered for obtaininghigh traffic correlations and, therefore, for achieving high configuration-movement relationships (Peponiset al. 1997; Read 1999; etc.). However, in the axial analysis of Lubbock, all traffic correlations have beendetermined with unlogged measures of vehicular flows for the purpose of maintaining the purity of theconfiguration–movement relationships.
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arteries of the city are significantly higher (nearly 20,000 and 14,000 vehicles per day,
respectively) than that of the residential streets (nearly 600 vehicles per day). A thorough
analysis of this contradictory configuration–movement relationship that has resulted in
yielding a weak axial traffic correlation for Lubbock is discussed next.
Analysis and findings
Notion of axial line
The concept of axial line has been developed with an understanding that trip-makers tend
to reduce the number of turns to reach their destinations (Hillier et al. 1993; Hillier 1999a,
b). According to Penn (2001, p. 11.1):
‘‘recent research using simulation agents with vision confirms that axial movement
patterns follow from a simple random movement rule combined with a forward
facing visual field.’’
Now, assuming that the trip-makers’ common notion of movement is the average number
of changes of the direction encountered on routes, not to specific destinations, but to all
possible destinations (Penn 2001), the only possible representation of a specified roadway
structure ought to be the minimal set of lines that pass through the connecting roads within
the structure itself; each line here is an axial line (Paul 2012a). However, from the traffic-
assignment stance, in which travel time is the key consideration of a trip-maker’s route-
Fig. 3 Axial-integration map of Lubbock. Integration reduces as the color of the lines changes from red toblue. (Color figure online)
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choice decision (Fricker and Whitford 2005), the notion of axial line, as explained by Penn,
only seems to be reasonable if we accept the idea that the trip-maker’s travel speed always
remains unchanged. Theoretically, when the travel speed remains unchanged, as we see in
pedestrian movement, it is the travel distance that becomes proportionally related to the
minimum travel time or free-flow travel time of a roadway segment. Furthermore, if we
also assume that the trip-maker’s notion of distance is not metric but is ‘‘compromised by
the visual, geometrical and topological properties of networks’’ (Hillier and Iida 2005,
p. 554), then the entire length of a roadway segment itself becomes a unit (that is,
nonmetric). Therefore, the number of roadway units to be crossed becomes the key
consideration for understanding the notion of route choice, and the average number of such
units to be crossed from one unit to reach all other units becomes the accessibility measure
of the former unit. But, from the vehicular trip-making point of view, this notion of route
choice appears to be unrealistic simply because vehicular travel speed cannot be presumed
equal in all units of an urban texture. Apart from traffic congestion, vehicular travel speed
changes because of speed zoning controls, traffic signaling, stop and yield signs, roadway
geometry, etc. (Fricker and Whitford 2005; Transportation Research Board 2000). In this
situation, it does not seem to be reasonable to buy the idea that vehicular trip-makers will
always choose to cross the least number of units even if they will require higher travel time
than an alternative route that is not made of as few a number of units. Because travel
speeds of all units are not found equal in real conditions, it is quite possible that trip-
makers, for the purpose of reducing overall time-costs of travel, may select topologically
longer routes that have high mobility characteristics (also see Paul 2012a for further
explanation of this argument).
For the case of Lubbock, this general notion of route choice has been substantiated by
the actual traffic data of the city roads. The city roads with higher mobility characteristics,
such as freeways and arteries, receive more trips, on average (see column 5 of Table 2),
than ones that have lower mobility characteristics, such as residential streets.
Effect of network types
According to Hillier et al. (1993, pp. 29–30) axial integrations become less deterministic in
predicting movement when the network becomes more like a grid. Quite naturally, the
configuration–movement relationships, in such cases, are expected to be weak. Let us explain
this further with an example. Imagine a pure branch-like roadway structure (refer to Fig. 4a).
In this structure, the problem of route choice does not arise (Figueiredo et al. 2005) as the trip-
maker can only find one route between any pair of trip-origin and trip-destination located in
the network regardless of the route length and geometry. Hence, it is deterministic. On the
other hand, roadway structures, which are not purely branch-like, must contain loop(s) inside
(refer to Fig. 4b, c). Any loop inside a structure creates the problem of route choice, and in this
situation, trip-makers need to consider some other parameters, such as free-flow travel time,
traffic congestion, the physical condition of roads, number of turns, etc., in order to make a
rational decision of route choice. Hence, it is probabilistic.
The critical dilemma of the axial-line approach to traffic assignment, as understood in
the above argument, is the assumption that the two philosophies of trip-making, deter-
ministic and probabilistic, are the same, and thus, the accessibility of settlement roads can
be determined by the unified form of a roadway unit: such as an axial line. The ground
reality, however, is that the two distinct philosophies of trip-making—embedded in the
network typology—cannot be addressed by using a unified analysis unit. If this is
attempted, weak configuration–movement relationships, as seen in the axial analysis of
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Lubbock (r2 = 0.18), are expected to occur especially in the cases where the roadway
structures are more like grids.4
Land use, configuration, and movement relationship
In syntax terms, land use, configuration, and movement generate a strong inter-relationship
with one another. By using the notions of trip-attraction of land uses and syntax configuration,
Hillier et al. (1993, p. 32) introduced the concept of natural movement: a type of movement
that is generated by the configuration itself. Moreover, according to them (pp. 31–32):
‘‘… configuration is the primary generator, and without understanding it we cannot
understand either urban pedestrian movement, or the distribution of attractors, or
indeed the morphology of the grid itself.’’
On the contrary, from the contemporary traffic-assignment stance, land use itself is
considered the primary generator of movement (Fricker and Whitford 2005), and it
influences the actual movement of a roadway unit (or axial line) in two ways. First, the
land-use-generated trips directly contribute to the actual movement of the unit, and then
these trips influence the other connected units (also see Paul 2012b). Syntactically, the
closer the connection, the greater the influence of the latter. Now, with the assumption that
each roadway unit has equal or unit ability of generating trips, it is worth suggesting that
the movement variations in these units are merely because of their accessibility measures.
However, the reality is that roadway units of an urban texture are not likely to have equal
trip-generation abilities nor can the configuration-generated trips be separated from the
actual movement of the units. Therefore, it also seems fair speculating that, merely by
Fig. 4 Deterministic and probabilistic approaches. Origin–destination routes are shown in blue lines.(Color figure online)
4 Tech Terrace, a fairly grid-like residential neighborhood located at the heart of Lubbock, has also beenanalyzed independently in order to check whether the distribution of traffic is differentiated by a sub-area.However, almost no configuration–movement relationship (r2 = 0.03) has been found in the sub-areaanalysis as well.
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using integration measures, the true picture of traffic distribution in roadway units cannot
be figured out (also see Batty et al. 1998). However, that being said, the discussion of this
interrelationships of land use, configuration, and movement relating to the axial analysis of
Lubbock brings out a key finding of this paper. That is, because integration measures
purely reflect configuration-generated movements, these measures are expected to produce
strong traffic correlations only for those roadway units, in which the land-use-access
opportunities are nil or minimal, such as freeways or arteries. On the contrary, the roadway
units that provide moderate or maximum land-use-access opportunities, such as residential
streets, are predominantly expected to receive land-use-generated movements, and
therefore, their integrations are expected to produce weak traffic correlations.
For the case of Lubbock, the traffic correlations of its various roadway classes, as
reported in column 1 of Table 2, reinforce this argument. The freeways and the arteries of
the city show the highest traffic correlations in the set, but the correlation gradually reduces
for the classes of collector and local streets. These statistical results explain that the greater
the land-use-access opportunities the roadway units of Lubbock provide, the more the land-
use generated movements the units receive, and consequently, the weaker the configura-
tion–movement relationships the units generate.
Notion of through-movement
In the contemporary theories of traffic assignment, trips are understood in terms of origin–
destination movements (Fricker and Whitford 2005), whereas in space syntax, they are
typically considered through-movements (Hillier et al. 1993). Some syntax researchers
have considered these through-movements analogous to the total trips that pass by a
specific point within a roadway unit; some counted the encounter rate while traveling with
a uniform speed (Hillier et al. 1993).
However, from the theoretical stance of applying syntax configurations for traffic esti-
mation, the notion of through-movement refers to a specific type of movement that travels a
unit only to access the other connected units, and therefore, this movement cannot be mixed
with those that start from or end at the unit itself. Movements that start from or end at a
roadway unit can only be considered land-use-generated movements, and with this under-
standing, it is also worth suggesting that a roadway unit that is connected to the other
unit(s) only at one roadway intersection can receive no through-movements. This is because
all trips that enter into or exit from such a unit are either produced or attracted by the land uses
that the unit provides access to. Similarly, a roadway unit, which is connected to the other
units at least at two roadway intersections (as shown with A and B in Fig. 5), but has no land-
use-access opportunities, ought to carry only through-movements but no land-use-generated
movements. Because the separation of these two movement types is not considered in the
axial-line approach to traffic estimation, the configuration–movement relationship produced
by the analysis model is expected to be less accurate and, perhaps, less appropriate.
In addition, through-movements can be complete and segmented (refer to Fig. 5). That
is, the former travels the entire length of a roadway segment, whereas the latter does not.
Now, in a typical North American city, where the roadway structure is mostly found to
be grid-like, axial lines, representing straight roadway segments, may run from one city
end to the other. Each axial line here is usually comprised by a number of sub-units (see
Fig. 5b) that carry segmented-through movements. Because of these segmented-through
movements, variation of flow-counts is expected to be seen in the entire length of the
line.
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Fig. 5 Through-movements; A and B are the roadway intersections defining the roadway unit or axial line.a Complete-through; and b Segmented-through
Fig. 6 Frequency of flow-counts in the sub-units of axial lines representing 19th Street, Slide Ave., andUniversity Ave. of Lubbock (data source: LMPO)
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For the case of Lubbock, a number of axial lines, comprised by sub-units, are found in
both the directions (E–W and N–W) of the city texture. Now, because drastic variations of
flow-counts, as reported in Fig. 6, have been seen in the sub-units of each line, their
averages—that have been used for determining the configuration–movement relationships
of the axial study of Lubbock—appears to account for little in portraying the true picture of
the vehicular flow distribution of the city.
Effect of boundary consideration: edge effect
In a syntax analysis, the peripheral axial lines of a structure usually turn out to be segregated
(Penn et al. 1998; Peponis et al. 1997; Ratti 2004, p. 497). But, when the boundary of the
structure is extended, these lines generally become less segregated (or more integrated than
their previous conditions); some other lines, which fall near the periphery or edges of the
extended structure, become segregated. Therefore, the measure of integration, in general
terms, depends on the boundary consideration of the roadway structure as well. The effect of
the boundary consideration on the accessibility measures of the analysis units is known as edge
effect. Because of edge effect, the methodology of traffic estimation merely by using syntax
integrations of roadway units cannot be presumed very realistic (Penn et al. 1998; Ratti 2004).
However, space syntax researchers have recommended some techniques to solve the
problem of edge effect. Penn et al. (1998, p. 61) have suggested that ‘‘one way of over-
coming these edge effects involves calculating the mean depth of all nodes within some
fixed radius of each node in turn.’’ In a restricted radius analysis, commonly known as local
integration analysis, the edge effect of the units located outside of the radius turns out to be
very less or nil. While this process of elimination of edge effect has been found effective in
certain cases for estimating pedestrian movement (Penn et al. 1998), the process still
cannot be generalized for modeling vehicular movement networks. This is because
vehicular trips usually generate for long-distance travel, and they can generate not only
from outside a restricted radius but also from outside the entire analysis area.
For the case of Lubbock, the axial integrations, measured with a restricted radius
(typically with radius 3 analysis), further weaken the configuration–movement relation-
ships yielding the r2 results of 0.11, 0.27, 0.25, 0.11, and 0.13 (r = -0.37) on the samples
accounting for all classes of roads together, and then freeways, arteries, collectors, and
local streets, respectively. Additionally, Lubbock is also connected to all its neighboring
settlements by a number of highways, such as I-27, Levelland, Brownfield, Clovis, Slaton,
etc. All these highways act as the major channels of commutation to arrive in Lubbock
from its neighboring settlements and states (Paul 2012b). Now, because these highway
connections to the other settlements have not been considered in the axial analysis of
Lubbock, the axial-integration map of Lubbock cannot be presumed free from edge effect,
and perhaps for this reason, the overall configuration–movement relationships, measured at
different syntax radii (radius ? and radius 3) have not been found very strong.
Discussion
Voluminous effect of movement
At a theoretical level, vehicular traffic-assignment is a complex and dynamic process
simply because traffic congestion also influences trip-makers’ route-choice decisions.
According to Paul (2011b, p. 267):
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‘‘… the relationship between congestion and route choice appears to be somewhat
interdependent, and quantification of traffic congestion through the link performance
functions of OD routes is only an empirical representation of specific real traffic con-
ditions that, theoretically, seems to be a probabilistic approach to traffic forecasting.’’
Traffic congestion determines the performance of a roadway unit in terms of its level of
service (Transportation Research Board 2000). Because of traffic congestion, the travel
time of a roadway unit increases gradually from its minimum travel time (or free-flow
travel time). In other words, the higher the congestion a roadway unit faces, the longer the
travel delay the unit causes, and in consequence, the poorer the quality of level of service
the unit provides. Now, when the level of service of a roadway unit becomes poor, its
connected units that constitute the alternative routes start becoming attractive to the trip-
makers and, thus, start receiving the additional trips from the original unit. These con-
nected units, usually, are not considered in the first place because of their higher minimum
travel times than that of the original unit. In order to quantify this route-wise traffic-
assignment, an equilibrium (Fricker and Whitford 2005) is established by balancing out the
total travel times of the alternative routes.
As an inherent limitation of space syntax dealing with traffic control systems that relate
to congestion with the roadway capacity, Penn et al. (1998, pp. 74–75), however, inter-
estingly recognize that however integrated a roadway unit is found to be within a con-
figuration, the average traffic flow of the unit does not increase above its capacity. As a
result, the importance of an axial analysis remains limited merely within the realm of
certain aspects of configurational behavior of an urban texture and its relations to the
environments that people build (Penn 2001, Hillier et al. 1987b) without taking much into
account the dynamicity of the travel behavior that emulates existing and future traffic flows
in relation to the obtainable roadway capacity.
The impedance factor
While defining the axial-line end points, both the network and roadway characteristics
describing the impedance of travel on line segments are not also dealt by the traditional
axial model of traffic assignment with much clarity.5 Part of the differences between model
fit for pedestrian versus auto travel relates to the fact that impedance of segments is far
different (or variability in impedance across network segments) for roadway travel than for
pedestrian travel. It seems reasonable to recognize that a theory highly dependent on
network intersections is going to be more robust in contexts where segment characteris-
tics—be they impedance to traffic associated with the phenomena of roadway access
controls (stop or yield signs) and local roadway conditions due to different adjacent
activity patterns and trip-generation—are more consistent across the network. Recent
investigations with the syntax unit-segment theory (Paul 2009, 2012a) and its sensitivity
tests in understanding the segment flows approaching to stop or yield signs and
5 According to Jiang et al. (1999, p. 129), accessibility is a widely used spatial analytic measure defined asthe relative ‘‘nearness’’ or ‘‘propinquity’’ of one place to other places. Usually such measures of nearnessbalance the benefit of locating at or visiting a place with the costs of moving or travelling to that place froma fixed location around which accessibility is being calculated. In this scenario, the measure of impedance istypically considered the distance or travel time of moving from one place to other places (also appeared inJun et al. 2007, p. 289). Interestingly however, dealing with urban digital elevation models (DEMs), Ratti(2005, p. 559) finds that ‘‘another way to generalise accumulated distance maps, beyond travelling time, is tointroduce a cost-of-passage function, also called friction or impedance, which can be stored on a new imageother than the DEM... This function represents the cost of crossing each pixel in DEM, …’’
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simultaneously relating to the trip-generation rates of the adjacent land uses (Paul 2012b)
have thrown some insights into the argument that the factor of impedance differentiates
syntax integrations of roadway units based on the priority control signs and the adjacent
activity patterns the units encounter in the network.
Conclusions
There are numerous space syntax studies, where the limitations of the axial-line approach
to traffic-assignment have been addressed. New developments that emulate human travel
behavior and the configurational properties, such as connectivity, choice (or betweenness,
see Hillier and Iida 2005, Turner 2007), etc., underlying the trip-makers’ route-choice
decisions can also be found in much newer studies published in the recent symposia
proceedings of space syntax as well as in different planning journals (Paul 2011c).
However, the objective of this paper rests on the understanding of the rational reasoning of
vehicular travel behavior from the traditional traffic-assignment stance and its modeling
distinctively with the theory of axial-integration analysis.
In space syntax terms, an urban roadway structure itself bears the ability of recognizing
which of its roadway units – axial lines in this paper—would receive more traffic than
others. While numerous axial studies have been performed in order to establish and then to
reinforce this proposition, the two distinct philosophies of trip-making, pedestrian and
vehicular, have not been distinguished in the framework of the axial-line approach to
have been obtained, especially when the analysis results are used for modeling vehicular
movement networks.
This paper has critically reviewed the limitations of axial analysis as a basis for modeling
the distribution of vehicular movement by examining the route-choice rationales that are
influenced by mobility characteristics of roadway units, network uniqueness, land-use-access
opportunities, and traffic congestion. A few procedural concerns involving movement cor-
relates and boundary consideration have also been discussed. The critique, in this context,
contributes to the field by identifying the key parameters relating to the physical and oper-
ational characteristics of vehicular travel behavior, that might aid in interpreting the trip-
makers’ route-choice decisions through spatial networks in order to make the space syntax
approach to modeling vehicular movement networks comprehensive.
Acknowledgments This paper has been developed from the Ph.D. thesis of the author entitled: An inte-grated approach to modeling vehicular movement networks: trip assignment and space syntax, which wascompleted at Texas Tech University in 2009. The author is grateful to many people who supported the thesiswith their review comments. In particular, the author is thankful to Saif Haq, Paul Goebel, Perry Carter,Hong Chao Liu, Carlo Ratti, Bill Hillier, Alan Penn, Glenn Hill, Jere Hart, and Darrell Westmoreland. Also,the author is thankful to the anonymous reviewers of Transportation for their comments in preparing thispaper.
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Author Biography
Abhijit Paul has a Ph.D. in land-use planning, management, and design from Texas Tech University (USA)and an M. Arch. with urban design concentration from Jadavpur University (India). He also taught at boththe schools. Apart from his registration with Council of Architecture in India, Dr. Paul is also a Fellow ofIndian Institute of Architects and an active member of Center for Built-Environment in Calcutta. Dr. Paul’sworks predominantly deal with evidence-based approaches to urban analysis, quantification of urbanproblems, and development of new tools and techniques in urban land-use-transportation planning.
