Estimation of traffic density to compare speed density models with moving observer data

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 04 Issue: 08 | August-2015, Available @ http://www.ijret.org 471
ESTIMATION OF TRAFFIC DENSITY TO COMPARE SPEED-
DENSITY MODELS WITH MOVING OBSERVER DATA
Saurav Barua1
, Anik Das2
, Md. Julfiker Hossain3
1
Lecturer, Department of Civil Engineering, University of Information Technology and Sciences, Dhaka, Bangladesh
2
Lecturer, Department of Civil Engineering, University of Information Technology and Sciences, Dhaka, Bangladesh
3
Research Assistant, Department of Civil and Architectural Engineering, University of Wyoming, Laramie, Wyoming,
United States
Abstract
An analytical study among vehicular density measured from moving observer method in field and density predicted from
theoretical speed-density models are presented in this paper. Kakoli intersection to Abdullahpur of Airport road was surveyed for
traffic data collection using test vehicle. Moving observer data were collected from nine test vehicle runs in a weekday. Vehicle
counts with and against test vehicle, vehicle passing and over taking the test vehicles, journey time and travel distance were
recorded. Parameters of traffic stream such as volume, speed and density were calculated from the collected data. Then
Greenshield’s model, Greenberg’s model and Underwood’s model were fitted in the graphical representation of speed-density
relationship and corresponding parameters were determined using SPSS. Later, sensitivity analysis and statistical significance
tests among these fitted models are presented in comparison with moving observer data.
Key Words: Moving Observer Method, Journey Time, Density, Test Vehicle, Volume.
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1. INTRODUCTION
Among various traffic parameters, density is the most
difficult to measure. Flow and speed can be measured using
stationary observer and moving observer method. Through
stationary observer, both traffic flow and speed cannot be
measured simultaneously. In this regard, moving
observation is better option. If flow and speed of a traffic
stream are measured, density can be calculated easily using
basic flow-density-speed relationship. Various speed-density
models have been developed, some of the most popular
models are- Greenshield’s model, Greenberg’s model and
Underwood’s model. [1] In this paper, moving observer data
for nine test vehicle runs were collected from Airport road
of Dhaka city (Kakoli to Abdullahpur intersection). Traffic
density was estimated using these models and were
compared with the density obtained from moving observer
data. An analytical study with field data were described and
it was found that Underwood’s model provide better
estimates comparing to other candidate models.
2. THE MOVING OBSERVER METHOD
Moving observer method is a procedure commonly used to
estimate the average flow and journey time of traffic on a
road link through collected data of moving vehicle. The
method was first described in a paper by Wardrop and
Charlesworth (1954) and developed by the road research
laboratory in UK. In this method, a series of runs of a test
vehicle made traveling ―with‖ and ―against‖ a one way
traffic system. The observers in the test vehicles record the
number of opposing vehicles met, number of vehicles the
test vehicle overtook, number of vehicles overtaking the
vehicle while it was traveling, average speed of the test
vehicle, the distance of the run and the journey times of the
observer in each run. [2] Using relevant equations, traffic
flow rate are calculated from the collected information. The
main advantage of moving observer method is that flow and
speed can be collected at the same time. In addition, it is
economical because less number of individuals needed to be
involved for measurement and less time consuming.
However, for low traffic flows a large number of test runs
are required to achieve accuracy and this may prove
impractical. Moreover, accuracy of this method is very
sensitive to fluctuations of the traffic stream. Various
researches have focused on comparing the moving observer
method with stationary counts, but stationary counts have
their own errors. [3]
3. CLASSICAL SPEED-DENSITY MODEL
There are many models to determine speed-density
relationship. Some of the most popular models include
Greenshield’s model, Greenberg’s Model and the
Underwood Model.
Greenshield assumes that under uninterrupted flow
conditions, speed and density are linearly related. However,
we can hardly find such relationship between speed and
density in field. [4]
k = kj (1-v/vf)
Where, v = speed of vehicle, Vf = free flow speed, k =
density and kj = jam density.
Greenberg assumes that speed-density relationship is
logarithmic rather than linear. [4]
It is unable to predict speed at lower densities, because as
density approaches zero, speed tends to increase to infinity.
k = kj exp (-v/vo)

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Where, v0= speed at maximum flow level.
Underwood’s assumption shows that speed-density relation
is exponential. The main drawback is speed becomes zero
only when density reaches infinity.[4]
k = k0 ln (vf/v)
Where, k0= density at maximum flow level.
4. FIELD DATA COLLECTION AND
CALCULATION
The field survey was carried out to collect the data on the
selected major road (Kakoli intersection to Abdullahpur). To
count the traffic volume, moving vehicle method or test car
method has been used. Our selected particular section of this
road was covered with traffic jam by all types of vehicle
including Bus, Mini Bus. Jeep, car, Lorry, Auto rickshaw
etc. Vehicle speed was low because density of vehicle on
this road was high. Vehicles enter in the road section from
three important roads such as Ashulia road, Tongi-Dhaka
roads and Farmgate.
Fig-1: Study Road Section
Fig-2: Vehicle Passing on the Road
Fig-3: Vehicle Counting through Test Vehicle
The method gives an unbiased estimate of the flow, random
errors can, however, occur due to observer’s errors and
random fluctuations in flow, but these are not serious under
normal condition. It gives values of flow and speed over a
section, rather than at a point thus it gives directly the space
mean speed, whereas spot speed studies gives the time mean
speed. Traffic counting using video camera recordings are
more reliable than mechanical and manual method, it can be
cross checked later as well. [5] The data assembled during
traffic volume studies was stored out and presented in
various forms depending upon the specific use of the data.
Data obtained from test car or floating car method were used
to calculate flow, speed, journey time and density using
following formula:
Where, q = volume of traffic, Ma = opposing traffic count of
vehicles meet when the test car was travelling, Ow =
Number if vehicles overtaking the test car while travelling,
Pw = Number of vehicles passed by the test car, v = Space
mean speed, Ta = travel time when travelling against traffic,
Tw = travel time when travelling with traffic. T = average
journey time.
If density k, then we can calculate density from q = k.v
relationship. [3]
5. MODEL ANALYSIS
Volume, speed and density of traffic stream are calculated
using formulas of moving observer method. Then speed-
density curve are plotted, where density is independent
variable and speed is dependent variable. Best fit linear,
logarithmic and exponential curve are drawn over the
observed data. In speed-density plotting linear, logarithmic
and exponential fit curve corresponding to Greenshield’s
model, Greenberg’s model and Underwood’s model
respectively. [1]
Fig-4: Speed vs Density Relationship

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In order to obtain the speed-density curves, regression
analysis are performed by using the Statistical software
SPSS 20.0.
Table-1: SPSS result output
The regression curves models were obtained by SPSS and
corresponding speed-density functions are as shown in the
following table.
Table-2: Speed-Density Function
Model
Speed-density
function
Transformed model
obtained from SPSS
Greensheild v=vf– vf/kj. k v = 35.601- 0.204 k
Greenberg v = v0. ln(kj/k) v = 109.558 – 20.542 ln(k)
Underwood v = vfexp(k0/k) v = 45.132 – exp(-0.011.k)
Speed of traffic stream for different models are obtained
using the above equations, Using the speed and
corresponding traffic volume, predicted density for each
case are calculated.
6. RESULTS AND DISCUSSIONS
From SPSS result, it is found that, all of the three models
have coefficient of determination R2
value within 0.5-0.6.
Among them, Greenberg’s model predicts slight better (R2
value is 0.561) comparing to other two models.
Greenshield’s model has the lowest R2
value (0.506) i.e. it
can describe variability of observed data less predictably.
R2
value in underwood model is almost same to
Greensheild’s model (0.517).
Study shows that, predicted Greenshield’s model and
Greenberg’s model are slightly overestimated whereas
Underwood’s model slightly underestimates the density. The
following figure describes the scenario graphically.
Fig-5: Predicted Traffic Density and Observed Traffic
Density
From sensitivity analysis, Greenshield’s model, Greenberg’s
model and Underwood’s model show 128%, 131% and
101.46% sensitive respectively. For example- one unit
change in observed density will change density estimated by
the Greenshield’s model will be 1.28 times. Among the
candidate models, Underwood’s model is least sensitive
while Greensberg’s model is most sensitive. Traffic stream
where flow fluctuate frequently, Underwood’s model will
not be feasible. Moreover, at very low density, prediction of
density is infeasible in Underwood’s model.
7. CONCLUSIONS
R2
value for Greensberg’s model is slightly high, which can
better predict variation of speed-density relationship
comparing with other two models. Density calculated by
Underwood’s model is underestimated, on the contrary,
density calculated by Greenshield’s model and Greenberg’s
model, are overestimated. Moreover, where flow fluctuation
of frequent, Greenshield’s model and Greenberg’s model
will predict more deviation with moving observer data. In
this case, Underwood’s model is more preferable.
REFERENCES
[1]. L. Zhaoyang, M. Qiang (2013). Analysis of Traffic
Speed-Density Regression Models—A Case Study of
Two Roadway Traffic Flows in China. Proceedings of
the Eastern Asia Society for Transportation Studies,
Vol.9.
[2]. M. Ann-Marie, N. Alan (2002). Uncertainty in traffic
flow estimation using the moving-observer method. In
IPENZ Transportation Group Conference Papers.
[3]. Nielsen, O.A., Jorgensen, R.M. (2008). Estimation of
speed-flow and flow-density relations on the motorway
network in the greater Copenhagen region. Intelligent
Transport Systems, IET 2(2), 120-131.
[4]. H. Wang, J. Li, Q.Y. Chen, D. NI (2009).Speed-
Density Relationship: Deterministic to stochastic,
Model Summary and Parameter Estimates
Dependent Variable: Speed
Equation
Model
Summary
Parameter Estimates
R
Square
Sig. Constant b1
Linear
(Greensheild's
model)
0.506 0.031 35.601 -0.204
Logarithmic
(Greenberg's
model)
0.561 0.020 109.558 -20.542
Exponential
(Underwood's
model)
0.516 0.029 45.132 -0.011
The independent variable is Density

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submitted to TRB 88th Annual Meeting, Washington
D.C., Jan. 2009.
[5]. Lum, K., Fan, H., Lam, S., Olszewski, P. (1998)
Speed-Flow Modeling of Arterial Roads in Singapore.
Journal of Transportation Engineering 124(3), 213-
222.
BIOGRAPHIES
Saurav Barua received his B.Sc. in Civil
Engineering in 2007 from Bangladesh
University of Engineering & Technology
and M.S. in 2012 from Rutgers
University, New Jersey, USA. He is
currently working as Lecturer in
University of Information Technology and
Sciences, Dhaka. His research interest are Traffic
Engineering, ITS and Transportation Economics
Anik Das completed his B.Sc. in Civil
Engineering in 2014 from Bangladesh
University of Engineering & Technology.
He is currently working as Lecturer in
University of Information Technology
and Sciences, Dhaka. His research
interests are Transportation Simulation,
Transportation Planning Accident Investigation and ITS.
Md. Julfiker Hossain completed B.Sc. in
Civil Engineering from Bangladesh
University of Engineering and Technology
(BUET), Dhaka. He is currently working
as a Research Assistant, Department of
Civil and Architectural Engineering at
University of Wyoming, United States. His
research interests are Road Safety Transportation Modeling,
Public Transportation, Traffic Survey Study etc.

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