TransportaTion

SOME EMPIRICAL RELATIONS BETWEEN TRAVELSPEED, TRAFFIC VOLUME AND TRAFFICCOMPOSITION IN URBAN ARTERIALS
Eleni I. VLAHOGIANNI, Ph.D.
Institute of Transportation Studies
University of California, Berkeley
California, USA(Received November 6, 2006)

The effects of traffic mix (the percentage of cars, trucks, buses and so on) are of particular interest in the speed-volume relationship in urban signalized arterials under various geometric and control characteristics. The paper presents some empirical observations on the relation between travel speed, traffic volume and traffic composition in urban signalized arterials. A methodology basedon emerging self-organizing structures of neural networks to identify regions in the speed-volume relationship with respect to traffic
composition and Bayesian networks to evaluate the effect of different types of motorized vehicles on prevailing traffic conditions is
proposed. Results based on data from a large urban network indicate that the variability in traffic conditions can be described by eightregions in speed-volume relationship with respect to traffic composition. Further evaluation of the effect of motorized vehicles in eachregion separately indicates that the effect of traffic composition decreases with the onset of congestion. Moreover, taxis and motorcycles are the primary affecting parameter of the form of the speed-volume relationship in urban arterials.
Key Words: Speed-volume relationship, Urban signalized arterials, Traffic composition, Emergent self-organizing maps, Bayesian
networks

1. Introduction and Objectives
Mathematical relationships between speed and volume measurements are considered fundamental to traffic
operations. Their importance lies in speed being an essential indicator of freeway service quality; it is used as acongestion or mobility indicator in freeways and it is aparameter of primary importance in advanced traveler information systems. Moreover, the functional form of the
speed-volume relationship provides valuable information
regarding the onset of congestion and incidents1.
Most efforts in constructing mathematical relationships between speed and volume measurements are applicable to uninterrupted flow conditions (particularly infreeways)2,3. From a methodological standpoint, startingwith the Greenshields model4, various models and approaches have been developed to approximate the functional form of the speed-volume relationship; among
them are the model-based5-10, macroscopic through curve
fitting11,12, regression1 and simulation approaches13,14.
Regardless of the method used or the mathematical function utilized, the literature agrees on certain findings;
speed-volume relationship has the free-flow and congest-

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IATSS Research Vol.31 No.1, 2007ed branches and a middle area where sudden speed
changes occur15-17. Although the discontinuous form of
the speed-volume relationship is widely supported, thecritical points at which speed changes suddenly or transitions to congestion occur have not been fully resolved1,7,16.
This is probably due to the difficulty in establishing relationships between speed and volume that can account for
traffic’s spatiotemporal evolution, geometric and environmental condition variability11,18.
The above comment is critical in urban arterials;
the speed-volume relationship can be described as complex because of the mixed traffic conditions and transitional nature of the functional relationship that governs
most traffic variables (volume, speed, occupancy and soon); This is, at least in part due to the effect of traffic composition that is the mix of passenger cars, trucks, buses
and so on19. Traffic flow in urban signalized arterials ischaracterized by a high degree of manoeuvrability andlane changing, a result of the interaction of fast-moving
and slow moving vehicles14. In urban areas, mixed trafficis also affected by substantial pedestrian movement, violation at intersections, taxi circulation, on-street parking
and narrow roads. This heterogeneity of traffic flow im-

SOME EMPIRICAL RELATIONS BETWEEN TRAVEL SPEED, TRAFFIC VOLUME AND TRAFFIC COMPOSITION IN URBAN ARTERIALS

posed by traffic’s composition is recognized in various
fields of urban traffic operations particularly near urban
signalized intersections (for example saturation flow estimation, delay at signalized intersections and so on)14, 2022. These approaches are related to specific geometric andcontrol conditions. Moreover, in most of these studies,
only the effect of trucks in traffic flow is considered10,23.
The combined effect of traffic’s composition in urban
traffic flow has not been treated in the literature.
The present paper utilizes a data-driven approachto evaluate the contribution of different types of motorized vehicles to the various traffic conditions as describedby the speed-volume relationship. We present a framework based on emergent self-organization and Bayesianlearning to identify regions in the speed-volume relationship in urban arterials under mixed traffic and evaluate
the effect of traffic composition in each of the regions
separately. The remainder of the paper is organized asfollows: in the next section the proposed methodologicalframework is concisely presented. Then, the implementation concerning data and algorithmic calibration issues
and the results are presented. The final section summarizes the findings of the paper and offers some concluding remarks.

2. Methodology
The proposed methodology has two goals; first to
identify prevailing regions on the speed-volume relationship related to traffic composition and, second, to evaluate the influence of traffic composition on each revealed
speed-volume region. The first step is achieved through aneural network approach that aims at revealing the emergent structures in complex traffic datasets by self-organization. The final step is based on a probabilistic Bayesian
network. The next two sections will present the conceptual and mathematical aspects of the proposed methodological framework.
2.1

Relationships between traffic variables by emergence
The problem of relating speed-volume relationshipto traffic composition is complex due, first, to speed andvolume’s spatial and temporal variability and, second,
because traffic composition is time dependent and inhomogeneously distributed in an urban network17,20,24-26.
One of the most robust approaches to revealing complex
structures in multidimentional data is the Kohonen SelfOrganizing Map (SOM). Given an input dataset {x1, x2,…, xd} with xi D, the SOM training generates a set of

E. I. VLAHOGIANNI

prototype vectors (output neurons) M = {n1, n2, …, xk}(with associated weigh vectors W = {w1, w2, …, wk}) representative of the original data and carries out a topology
preserving projection of the prototypes from the highdimensional input space usually onto a two-dimensional
grid27. Each data point xi is mapped to its best-match neuron bm(xi) = nb   M such that d(x, wb) d(x, wj) wj
W, where d is the distance on the data set. Each output
neuron ni has a set Ni of neighboring neurons on the map.
This ordered grid can be used as a convenient visualization surface for showing different features of the SOM(and thus of the data), for example, the cluster structure27.
Apart from the common approach to utilizing a numberof output neurons that is significantly smaller that theoriginal data, the present paper implements the Emergent
SOM (ESOM) that utilizes a larger number of neurons
than original data to replicate the emergent behavior of
traffic flow. Such use of ESOM has been proven to disentangle cluster structures that are linear not separable28.
The emergent structures characterizing speed, volume and traffic composition can be observed by accounting for the distance of each output neuron from its intermediate neighbors in a map called U-matrix29. The
U-matrix is the graphical representation of the average
distance uh(i) of ni’s weight vectors to the weight vectorsof its immediate neighbors Ni30:
uh(i ) =1nd ( wi , w j ), j

N (i ), n = N (i ) ..................(1)jThe resulting map is a “landscape” where weightvectors of neurons with large uh(i) are distant from othervectors in the data space and weight vectors of neurons
with small uh(i) are surrounded by other vectors in thedata space. The resulting topology can be used to visualize the data structure such as best-matching neurons(placed at depressions), outliers (at funnels), cluster
centers (valleys) end so on. The above graphical representation of the emergent structures observed in multidimensional datasets are visualizations of clusters that
include specific knowledge regarding the relationshipsthat govern the data variables under study. Based on thesestructures, algorithms have been developed that produce
the desired clusters; a simple and efficient way to discover clusters in an emergent SOM is to segment theresulting map by utilizing image processing techniquessuch as a area-filling algorithm in the U-matrix as discussed31,32.

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Augmented Naïve Bayesian Network Classifier
By acquiring a set of clusters/ regions of speed-volume relationship that are influenced by traffic composition, it is possible to construct a classifier, meaning afunction that, given a set of features (for example speed,
volume, traffic composition), can assign data to a specificclass. This paper implements a probabilistic classifier
with demonstrated simplicity and predictive performancethat belongs to the category of Naïve Bayesian Classifiers. A Naïve Bayesian Classifier learns the conditional
probability of each feature Ai given the class C; Classification is conducted by predicting the class ck that maximizes P(C = ck | A), for a data vector A, under the restrictive
assumption that each feature Ai is conditionally independent of every other feature given the class membership33:

3. Implementation and Empirical
Findings

2.2

P (A C = ck ) =i

P (Ai C = ck )................................(2)In order to overcome this unfeasible assumption,
the naïve bayes classifier is implemented in a Bayesian
network framework; A Bayesian network B = < N, A, >is a directed acyclic graph < N, A > where each node n   Nrepresents a random variable (a dataset feature for example), and each arc a   Abetween nodes represents aprobabilistic dependency, quantified using a conditional
probability distribution i  
for each node ni 34. The
lack of an arc between random variables denotes independency while a node in the network for a variable Ai
represents the probability of Ai conditioned on the variables that are immediate parents of Ai , denoted (Ai ).
Nodes with no parents represent the prior probability forthat variable. In a network of the above type the independence assumption can be overcome by allowing the network to develop relations between features; an arc from
Ai to Aj implies that the influence of Ai on the assessmentof the class variable also depends on the value of Aj 35.
These structures called Augmented Naïve Bayesian Networks have increased computational time regarding training, but, provide more feasible probabilistic structure
among data features.
To evaluate the contribution of each feature Ai tothe class membership the conditional mutual information
is used that is defined by35:
I (X; Y ) =P (x, y) log
Ai , A j

3.1

Regions of travel speed-volume relationship
The ESOM architecture implemented to reveal regions in speed-volume relationship relating to traffic
composition is sensitive both to its structural characteristics (for example the extent of the map), as well as to thelearning process. Regarding structural issues, a boundless toroid grid with 4,100 neurons (with a ratio of rows
and columns different from unity in order to avoid border

P (x, y ) . ........................(3)P (x ) P ( y )The Equation (3) accounts for the information thatY provide to X.

112

The field data inputs required for the model are: (a)traffic speed, (b) traffic volume, (c) traffic composition.
In order to collect such data, an extensive survey was
conducted in the center of Patras, the second major port
in Greece (Fig. 1). The road network under study consistsof 1900 arterial links of various geometric and control
characteristics. Floating car runs were used to collect
travel speed measurements and counters were utilized tocollect volume and traffic composition data. The resulting dataset consists of volume (pcu/h) and travel speed(km/h) measurements, as well as information on the percent of different types of motorized vehicles that empirically are said to affect urban traffic flow. These are:passenger cars, trucks, buses, motorcycles and taxis.

IATSS Research Vol.31 No.1, 2007

Fig. 1 A graphical representation of the arterial network
under study

SOME EMPIRICAL RELATIONS BETWEEN TRAVEL SPEED, TRAFFIC VOLUME AND TRAFFIC COMPOSITION IN URBAN ARTERIALS

effects and topology errors) is implemented; literature indicates that the above structural specifications are adequate for the ESOM to generate valid results30. Moreover,the learning algorithm implemented is a combination of
an online and a batch learning algorithm: online learning
exhibits better performance with respect to data representation than batch learning, but is time consuming. For
this, the k-batch learning algorithm that provides thesame quality of representation as online learning but
maintains several important properties of batch learning
is implemented36. The concept is to update the map structure after processing k < n, where n is the number of data
points, instead of only once per learning cycle as done inbatch learning (literature indicates the use of 15% of
learning data as a good choice for k). Figure 2 depicts the
U-matrix of the trained ESOM that is further clustered to
produce the characteristic regions of speed-volume relationship with regards to traffic composition.

E. I. VLAHOGIANNI

Results, presented in Table 1 and Figure 3, indicate
the existence of eight different speed-volume regions in
the urban arterial network under study. Interestingly,
there are three major traffic patterns regarding the manner in which travel speed and volume evolve; the firstpattern reflects free-flow conditions where travel speed
exhibits a weak oscillating behavior around a mean value,
but volume exhibits stronger variability. This pattern is
described by four different regions in the speed-volume
relationship where volume is kept below 250 pcu/h for amean speed of 30 km/h and 40 km/h (Regions I and II)
and below 1,850 pcu/h for mean speeds of 50 km/h and
70 km/h (Regions III and IV). The second pattern suggests the opposite traffic behavior; volume oscillates lessaround mean values while speed varies greatly. This behavior is described by three regions with volume exhibiting stability at 400 pcu/h, 500 pcu/h and 650 pcu/h
respectively (Regions V to VII). The second pattern prob1.00000
0.96038
0.892390.83162
0.766410.69970
0.566620.49999
0.43332
0.36665
0.299990.23332
0.10000
0.09999
0.03333
0.00000

Fig. 2 U-matrix of ESOM based on speed, volume and traffic composition measurements.
The scale of distances between the prototypes in the map is seen to the right:
low to high distances is equivalent to dark to light color.
Table 1 Regions of characteristic traffic behavior in the speed-volume relationship
Characteristic

Travel Speed (km/h)

Volume (pcu/h)

Region I

30

< 250

Region II

40

< 250

50

< 1,81870< 1,830

< 35

400

< 45

500

Region III

Stability in Travel Speed

Region IV
Region V
Region VI

Stability in Volume

Region VII
Region VIII

Low Speed and Volume

< 60

650

< 15

< 250

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ably depicts the congested branch of speed-volume relationship (Region VIII).

90

90

75

75

speed (km/h)

speed (km/h)

ably describes the areas of abrupt speed changes1,5. The
last pattern is when speed takes on low values and prob-

60
45
30
15

60
45
30
15

0

0
0

150

300

450

volume (pcu/h)

600

750

0

150

300

90

90

75

75

60

60

45
30
15

45
30

0
0

250

500

750

1000

1250

1500

1750

0

250

500

750

1000

1250

volume (pcu/h)volume (pcu/h)

Region III

Region IV

90

90

75

75

speed (km/h)

speed (km/h)

750

15

0

60
45
30

1500

60
45
30
15

15

0

0
0

100

200

300

400

500

600

0

700

100

200

300

400

500

volume (pcu/h)volume (pcu/h)

Region V

Region VI
90

75

75

speed (km/h)

90

60
45
30
15

600

700

600

700

60
45
30
15

0

0
0

100

200

300

400

500

600

700

0

100

200

300

400

500

volume (pcu/h)volume (pcu/h)

Region VII

Region VIII

Fig. 3 Graphical representation of regions of speed-volume relationship

114

600

Region II

speed (km/h)

speed (km/h)

Region I

speed (km/h)

450

volume (pcu/h)

IATSS Research Vol.31 No.1, 2007

SOME EMPIRICAL RELATIONS BETWEEN TRAVEL SPEED, TRAFFIC VOLUME AND TRAFFIC COMPOSITION IN URBAN ARTERIALS

3.2

Effect of traffic composition on the travel speed
and volume relationship
In order to assess on the contribution of traffic composition to each resulting region in speed-volume relationship, an augmented naïve Bayesian network is developed. The seven available variables describing traffic
conditions (Travel Speed, Volume, Passenger Cars(%),
Motorcycles(%), Buses(%), Trucks(%) and Taxis(%)), as
well as the produced regions (Classes) are depicted as
nodes; the scope is to produce a set of links between thenodes having as target value the Classes. The above aregraphically presented in Figure 4(a). The network is thentrained. Results can be seen in detail in Table 2; rows reflect the actual and columns the predicted classes. The
number in cells is the percentage of the correct predictions of the target variable (class). The Bayesian classifier
developed can use the associated data for the predictionof the Classes with high precision (function of the number of correct predictions of the target variable) of 97.5%.

E. I. VLAHOGIANNI

Figure 4(b) shows the graphical representation of theresulting relationships among speed, volume and traffic
composition after training. In a preliminary analysis, themutual information is computed for each link of the network between the parent (start of link) and the child (endof link) in order to evaluate the contribution of each variable used to the one that is related. The mutual information, as expressed by the Equation 3, provides the amountof information or else the contribution of each node (inour case travel speed, volume or percent of motorized vehicles) to the knowledge of traffic flow regime. Table 3
summarizes the revealed relations with respect to theirimportance, in decreasing order. As can be observed, thestrongest relations are those of speed and volume withthe classes (regions of speed-volume relationship). Interestingly, the effect of trucks on the motorcycle movements
seems to be significant. Moreover, taxis seem to havemore effect to truck movements than the passenger cars do.
Furthermore, relations between the targeted value

ClassesClassesTravel Speed

VolumeTravel Speed

VolumeMotorcycles (%)

Buses (%)

Motorcycles (%)

Trucks (%)
Passenger Cars (%)

Taxis (%)

Buses (%)

Trucks (%)

Taxis (%)

Passenger Cars (%)

Fig. 4 (a) Bayesian Network before training: Traffic variables and types of motorized
vehicles depicted as nodes and classes or else the resulting regions inspeed-volume relationship depicted as target node in the analysis.
(b) Bayesian Network after training: Links indicate relations between thevariables, the types of motorized vehicles and the classes.
Table 2 Results (predicted versus actual class) of augmented Naïve Bayesian
Network
Predicted (%)
Actual (%)

I

II

III

IV

V

VI

VII

VIII
0

I

99

0

0

0

0

0

0

II

1

100

2

0

0

0

0

0

III

0

0

91

0

0

0

0

10

IV

0

0

1

100

0

0

0

0

V

0

0

0

0

100

0

0

0

VI

0

0

0

0

0

100

0

0

VII

0

0

0

0

0

0

100

0

VIII

0

0

6

0

0

0

0

90

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Results indicate that traffic conditions in urban arterials
are typically affected by the movements of taxis and motorcycles and less affected by trucks and passenger cars.
Buses appear to have small contribution to traffic conditions; this result is probably due to the small-scale transit
system in the city of Patras.
In Tables 5-7 the above relative significance is further examined separately for regions describing stable(Classes) and the remaining variables are evaluated on average, but also for each class separately. Table 4 showseach node’s relative significance (mutual information i ,max{mutual information i}i = 1, 2, …, n, where n is the number of variables describing traffic conditions) on average with respect to the information gain brought by each node to the knowledge ofthe target node (Classes) (sorted by descending order).

Table 3 Relations between the traffic variables with respect to theirimportance, in a decreasing order. The mutual informationis computed for each link of the network between the parent(start of link) and the child (end of link).

Table 4 Information gain, on average,
brought by each variable to theknowledge of the speed-volume
relationship

Mutual Information

Node

Relative SignificanceClassesTravel Speed

2.133

Travel Speed (km/h)

1.000ClassesVolume1.322

Volume (pcu/h)

0.620

Motorcycles (%)

Trucks (%)

1.082

Taxis (%)

0.252

Trucks (%)

Taxis (%)

1.078

Motorcycles (%)

0.243

Trucks (%)

Passenger Cars (%)

0.848

Trucks (%)

0.216

Passenger Cars (%)

Buses (%)

0.548

Passenger Cars (%)

0.162

Classes

Motorcycles (%)

0.518

Buses (%)

0.121

Classes

Buses (%)

0.198

Classes

Passenger Cars (%)

0.171

Classes

Taxis (%)

0.136

Classes

Trucks (%)

0.086

ParentChild

Table 5 Information gain brought by each variable to the knowledge of the speed-volume regions for stablespeed traffic conditions
Region I

Region II

NodeRelative
Significance

Modal Value

Node

Relative
SignificanceTravel Speed (km/h)

1.00

<=32

Travel Speed (km/h)

1.00

<=44

Volume (pcu/h)

0.49

<=74

Volume (pcu/h)

0.36

<=218

Motorcycles (%)

0.32<=22.32%buses0.23<=1

Taxis (%)

0.31<=11.55

Taxis (%)

0.22<=11.525

Trucks (%)

0.28

<=3.5

Motorcycles (%)

0.17<=21.44

Passenger Cars (%)

0.21<=62.63

Trucks (%)

0.13

<=4.6

Buses (%)

0.12

<=1

Passenger Cars (%)

0.06<=63.94

Region III

Modal Value

Region IV

Node

Relative
Significance

Modal Value

Node

Relative
SignificanceTravel Speed (km/h)

1.00

<=59

Travel Speed (km/h)

1.00

>59

Volume (pcu/h)

0.32

>700

Taxis (%)

0.22<=8.52Motorcycles (%)

0.10<=20.53

Trucks (%)

0.21

>4.6

Taxis (%)

0.09<=8.52Motorcycles (%)

0.20<=20.53

Trucks (%)

0.09

>4.6

Volume (pcu/h)

0.17

>700

Passenger Cars (%)

0.09<=62.42

Passenger Cars (%)

0.11>65.46

Buses (%)

0.07

>2.915

Buses (%)

0.09<=2.915

116

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Modal ValueSOME EMPIRICAL RELATIONS BETWEEN TRAVEL SPEED, TRAFFIC VOLUME AND TRAFFIC COMPOSITION IN URBAN ARTERIALS

E. I. VLAHOGIANNI

Table 6 Information gain brought by each variable to the knowledge of the speed-volume regions of speed
change occurrence
Region V
Node

Relative
SignificanceRegion VI
Modal Value

Node

Relative
Significance

Modal Value

Volume (pcu/h)

1.00

<=438

Volume (pcu/h)

1.00

<=551

Travel Speed (km/h)

0.62

<=29

Travel Speed (km/h)

0.43

<=29

Taxis (%)

0.10<=11.525

Taxis (%)

0.14<=8.52Passenger Cars (%)

0.10<=63.94

Passenger Cars (%)

0.13<=63.94

Motorcycles (%)

0.09<=21.44

Motorcycles (%)

0.13<=20.53

Trucks (%)

0.07

<=4.6

Trucks (%)

0.09

>4.6

Buses (%)

0.03

<=1

Buses (%)

0.07

>2.915Modal Value

Table 7 Information gain brought by each variable to
the knowledge of the speed-volume regions
at congestion

Region VII
Relative
SignificanceVolume (pcu/h)

1.00

<=700

Travel Speed (km/h)

0.35

<=44

Taxis (%)

0.22<=8.52Motorcycles (%)

0.22

Trucks (%)

0.17

Passenger Cars (%)
Buses (%)

0.16
0.05

Region VIII
Node

Relative
Significance<=20.53

Travel Speed (km/h)

1.00

<=13

>4.6

Volume (pcu/h)

0.31

<=74

<=62.42

Motorcycles (%)

0.16<=22.32<=1

Taxis (%)

0.16<=11.55

Trucks (%)

0.14

<=3.5

Passenger Cars (%)

0.09<=62.63

Buses (%)

0.08

<=1

speed conditions (Regions I to IV), for those describing
speed changes (Regions V to VII), as well as for low values of speed and volume (Region VIII). Moreover, themodal value (most frequent) of the variables under study
is also depicted in tables. Some interesting findings
emerge from these results: in stable speed conditions,traffic is strongly related to speed and is highly affectedby taxis and motorcycles, and less affected by passenger
cars and buses. This does not apply to speeds of 40 km/h
or above (Region II), where buses seem to be the primary
affecting parameter, as well as in the case of 70 km/h
(Regions IV) where the effect of trucks is critical. In thecase of speed changes, traffic conditions are strongly related to volume; when speed changes occur in low volumes (Region V), motorcycles are the primary affecting
parameter with second affecting parameter being the passenger cars. In medium and higher volumes (Regions VI
and VII), the effect of taxis is greater than the one of motorcycles. Moreover, speed changes are less affected bybuses. Finally, in low values of volume and speed (Region VII), taxis and motorcycles are the primary affecting
parameters. Finally, an in depth look at the relations canbe obtained by Figures 5 and 6. Figure 5 shows the variability of the relative significance of travel speed and vol-120%

Relative Mutual Information (%)

NodeModal Value

Travel Speed (km/h)
Volume (pcu/h)

100%

80%

60%

40%

20%

0%

Region I Region II Region III Region IV Region V Region VI Region VII Region VIII

RegionsFig. 5 The graphical representation of the relative
significance of travel speed and volume withrespect to regions

ume with respect to the regions. Moreover, Figure 6 gives
the graphical representation of the relative significance ofthe percent of each motorized vehicle to the regions. As
can be observed, the evolution of the effect of passenger
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35%
Buses (%)
Taxis (%)

Relative Mutual Information (%)

30%

Motorcycles (%)
Trucks (%)

Passanger Cars (%)

Region V

Region VII Region VIII

25%
20%
15%
10%
5%
0%

Region I

Region II Region III

Region IV

Region VI

RegionsFig. 6 The graphical representation of the relative significance of the percent of motorized
vehicles with respect to regions

cars and buses is different from the one of the effect oftaxis, motorcycles and trucks to the knowledge of regions.

4. Conclusions
Developing speed-volume relationships in urban
arterials is a topic of considerable interest and importancein practice. Most approaches for evaluating the functional
form of the speed-volume relationship are based on modeling and simulation and are applicable to uninterrupted
homogeneous traffic flow and cannot accommodate traffic conditions imposed by the interaction of fast and slow
moving vehicles in urban signalized arterials. The present paper treats the effect of traffic composition on theform of the speed-volume relationship; instead of proposing a specific functional form (be it linear or nonlinear and so on), the present paper adopts an advanced
data-oriented methodological framework for revealing
regions in the speed-volume relationship and assessing
on the contribution of different types of motorized vehicles on each region separately.
Results indicate that in the basic three patterns of
the speed-volume relationship (free-flow, speed changes
and congestion) there exist eight different regions of speedvolume behavior that can be characterized by either stability in travel speed (at different value levels) or stability
in volume (at different value levels). It was also determined that the contribution of traffic composition varies
in each pattern and in each region. The role of taxis and118

IATSS Research Vol.31 No.1, 2007

motorcycles is found to be critical in the operation of mostarterials examined and in most regions of speed-volume
relationship, with the exception of high speed values
where trucks affect significantly the speed-volume relationship.
The proposed methodology evaluates the arterial
operation based on the consideration of the effect of traffic composition to the speed-volume relationship without
providing an a priori functional assumption. This provides a flexible framework for integrating empirical knowledge for traffic conditions based on real-time traffic
information into intelligent transportation systems. Moreover, it provides a data-driven and traffic-oriented framework that can be extended to the study of other relationships
between traffic variables (such as the volume-density relationship) that can be adaptable and applicable to urban
arterials regardless of geometric and control issues.

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