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QUANTIFICATION OF LOS AT
UNCONTROLLED MEDIAN OPENING
USING APPROACH SPEED DELAY
Under the guidance of:
Dr . Jyoti Prakash Giri
Asst. Professor
Present By
M. MONIKA
(JNTU NO:18341A0160)
Department of Civil Engineering
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INTRODUCTION
Level of Service (LoS) is a term that designates a range of operating
conditions on a particular type of facility.
The traffic conditions at median openings in developing countries like
India are completely different as they consist of heterogeneous traffic, and
the rule of priority is hardly followed by road users. Due to this peculiar
characteristic of road users and vehicles, the approaching through vehicles
experience delay due to limiting or reversal of priority situations.
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LITERATURE REVIEW:
Mohanty & Dey (2018)
Traffic movement at uncontrolled median openings using ‘area
occupancy’ as a measure of effectiveness. In this study, the total area
occupancy at the possible conflict area of the median opening has been
used as the measure of effectiveness to define LOS ranges for the
uncontrolled median opening sections.
Mohapatra et al. (2015):
Defined the LOS criteria of uncontrolled median openings service
delay to minor priority movement. i.e.; delay to U turns is considered as
a measure of effectiveness.
The quality of operating conditions on a particular type of facility is
described by Level of service. The operating condition of a median
opening is described by the delay experienced by the low priority
movement i.e. U-turning vehicles. .
LITERATURE REVIEW
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Axer & Friedrich (2014)
A great advantage of the developed concept is the free and
therefor cost-neutral usage of digital map data from Open Street
Map. Further need for research could be finally seen in the
optimization of a fully-automatic TMC-segments generation when
working with Open Street Map data.
The paper demonstrates that the developed fourth stage concept
could be applied successfully for the road network of Hanover and
the surrounding area.
Dr. Tom V. Mathew (2014)
Non signalized remedies can be used to manage congestion by
providing more space in terms of extra lanes.
Signalized remedies are more efficient than any other measures
of street congestion management. It can be understood that urban
streets are integral part of transportation system. These are
classified on their function, design for various considerations
taking into account.
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Rousseeuw (1986)
Silhouettes: The entire clustering is displayed by combining the
silhouettes into a single plot, allowing an appreciation of the relative
quality of the clusters and an overview of the data configuration.
The average silhouette width provides an evaluation of clustering
validity, and might be used to select an ‘appropriate’ number of clusters.
HideyukiKita (2000)
Individual driver’s perception on the level-of-service.
The calibrated utility function based on a set of observation data
shows a fairly good reproduction capability on the behaviour of the
observed drivers.
Marwah & Singh (2000)
The level of service (LOS) is a composite of several operating
characteristics that are supposed to measure the quality of service as
perceived by the user at different flow levels.
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Pollard & van der Laan (2002)
Developed a clustering algorithm called PAMSIL that replaces the
criteria function in PAM with average silhouette. Since PAMSIL
optimizes average silhouette, it may be a more appropriate algorithm to
use with MSS.
Rahman & Nakamura (2005)
A study on passing over taking characteristics and level of service of
heterogeneous traffic flow.( gives a model of overtaking in terms of total
traffic volume and percentage of rickshaws).
Malikarjun & Rao (2006)
Developed a regression equation in paper (Modelling the area
occupancy of major stream traffic)
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Arasan & Dhivya (2008)
It was found that the relationships are logical and hence it is inferred
that the concept of area-occupancy is valid to measure accurately the
extent of usage of road space by vehicles.
Ghosh et al. (2013)
While the latest Highway Capacity Manuals(TRB, 2010) recommends
the use of ATS, PTSF, PFFS for different classes of roads as
performance measures, researchers in the United States and other
countries found large discrepancies between performance measures
obtained from HCM-defined analytical procedure and field data which
makes the evaluation of the existing operational conditions of two-lane
roads really challenging.
Patnaik et al. (2015)
Divisive Analysis Clustering (DIANA)is a very successful clustering
tool that be applied for all kinds of urban roads have varying traffic
flow. The applicability of GPS in collection of speed data with high
precision in short time is established.
9. METHODOLOGY
From the literature reviews we got to know the gaps, where
more work and research is needed.
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For example - Area occupancy method has not been used in non-
signalised intersections.
10. • A large amount of traffic data has been extracted from the
recorded video where the speed from start of slowdown section
to median opening and speed within the median opening has
been noted, consequently calculating the percentage of change
in speeds of individual vehicles from slowdown section to the
center of median opening area.
• It was observed that the speeds of the vehicles generally
decrease within the median opening area as reported by
Mohanty et al. (2017) earlier.
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METHODOLOGY
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Statistical Parameters
Speed up to the start of
median opening
Speed within the
median opening
Percentage reduction of
speed
Mean 40.8052 30.5752 25.3326
Std. Deviation 6.39173 8.08704 15.00765
Skewness .301 -.183 .510
TABLE-1
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The figures (1, 2, and 3) prove that the speeds of the vehicles while
approaching towards the median opening at the start of median opening
nearly matches a normal distribution which is little positively skewed.
Both the graphs depict that the vehicles are adversely affected by the
presence of median opening and U-turning vehicles which leads them to
decrease their speed non-uniformly.
Had the reduction in speed been according to their initial speeds, the
histogram for Figure 2 would have matched the histogram in Figure 1
which is not observed.
Figure 3 depicts the frequencies of percentage reduction in speed and as
can be seen from the figure and Table 1, majority of the vehicles have
reduced their speeds at a percentage of 10 to 30%.
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t-test Mean Std. Deviation
Std. Error
Mean
t Sig.
Speed upto
the start of
median
opening and
Speed within
the median
opening
10.23 5.96 0.163 62.81 0.00
TABLE-2
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Table 3 depicts that percentage reduction in speed has a negative
correlation with speed 1 and speed 2.
However, the correlation of percentage reduction in speed is statistically
significant only with speed 2 i.e., the speed within the median opening
area (R-value: -0.825).
This clearly indicates the initial speed of vehicles upto the start of
median opening doesn’t affect their reduction in speed within the median
opening area. Rather the undesirable rate of speed reduction depends
strongly on the speed of vehicles within the median opening.
Therefore, various mathematical relations (linear, logarithmic, quadratic,
exponential) are developed to estimate the percentage reduction in speed
considering speed 2 as independent variable.
The R-square values for all the models were checked along with p-
value/sig. value. The details of the statistics pertaining to various curve
estimations are provided in Table 4.
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Equation
Model Summary Parameter Estimates
R Square Sig. Constant b1 b2
Linear .680 .000 70.52 -1.45
Logarithmic .735 .000 167.42 -41.81
Quadratic .765 .000 119.36 -5.00 .06
Exponential .637 .000 117.12 -.05
TABLE-4
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It is observed that the p-value in case of all curve fittings have come less
than 0.05.
Therefore, the model with highest R-square value has been used for
determining the percentage reduction in speed.
In the present study, quadratic model has been found to estimate the
percentage reduction most accurately in speed from the speed values
within the median opening area with an R-square of 0.765 as shown in
Table 4.
Thus, the developed mathematical equation to determine the percentage
reduction in speed from the speed within the median opening area is as
follows.
PRS = 119.36 - (5 × SWMO) + (0.06 × SWMO^2)
Where,
PRS = Percentage reduction in speed
SWMO= Speed within the median opening area in kmph
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The equation works best for speeds ranging from 9 to 50 kmph within
the median opening area. To validate the equation, the difference between
field data and model result is compared for the data that has not been used
for model development. The mean absolute percentage error (MAPE) has
been calculated for the data. The formula used to calculate MAPE is as
follows.
Where, At is the actual value; and
Ft is the model value and n represents the number of data used for
validation.
The highest mean absolute percentage error (MAPE) for the present data
came to be in the order of 8%, which is an acceptable value. MAPE value
less than or equal to 10% is considered to be strong enough (Liu et al.,
2008). Therefore, by using speeds within the median opening area, the rate
of reduction in speed from the start of median opening to the center of the
median opening can be determined using developed equation (Eq. 1) with
good level of accuracy.
𝑀 = 1
𝑛
𝑡=1
𝑛
𝐴𝑡−𝐹𝑡
𝐴𝑡
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Thorough literature reviews were conducted after which speed at both
the positions were obtained then they were compared and it was found that
the calculated speeds were different from each other, therefore the
percentage reduction in speed has also been calculated, and using it we
have developed a quadratic equation with an accuracy of 92%.
This equation will help us to determine PRS with high level of accuracy,
after which we will use clustering technique to determine the LOS for the
median opening.
SUMMARY
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[1]Axer, Steffen, and Bernhard Friedrich. "Level of service estimation
based on low-frequency floating car data." Transportation Research
Procedia 3 (2014): 1051-1058.
[2] Castillo, J. E., and P. J. Roache. "0377-0427/87/$3.50 0 1987, Elsevier
Science Publishers BV (North-Holland)." Journal of Computational and
Applied Mathematics 20 (1987): 423-424.
[3] Kita, Hideyuki. "Level-of-service measure of road traffic based on the
driver’s perception." Transportation Research Circular EC 18 (2000): 4th.
[4] Marwah, B. R., and Bhuvanesh Singh. "Level of service classification
for urban heterogeneous traffic: A case study of Kanpur metropolis."
fourth international symposium on Highway Capacity, Hawaii. 2000.
[5] Pollard, Katherine S., and Mark J. Van Der Laan. "A method to
identify significant clusters in gene expression data." (2002).
REFERENCES
21. REFERENCES
[6] MIZANUR, Rahman Md, and Fumihiko NAKAMURA. "A study on
passing-overtaking characteristics and level of service of heterogeneous
traffic flow." Journal of the Eastern Asia Society for Transportation
Studies 6 (2005): 1471-1483.
[7] Mallikarjuna, Ch, and K. Ramachandra Rao. "Area occupancy
characteristics of heterogeneous traffic." Transport metrica 2.3 (2006):
223-236.
[8] Arasan, V. Thamizh, and G. Dhivya. "Measuring heterogeneous traffic
density." Proceedings of international conference on sustainable urbn
transport and enviroment. 2008.
[9] Becher, Thorsten. "A new procedure to determine a user-oriented level
of service of traffic light controlled crossroads." Procedia-Social and
Behavioral Sciences 16 (2011): 515-525.
[10] Axer, Steffen, Jannis Rohde, and Bernhard Friedrich. "Level of
service estimation at traffic signals based on innovative traffic data
services and collection techniques." Procedia-Social and Behavioral
Sciences 54 (2012): 159-168.
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22. 18-Jul-23 22
[11] Ghosh, Indrajit, Satish Chandra, and Amardeep Boora. "Operational
performance measures for two-lane roads: an assessment of
methodological alternatives." Procedia-Social and Behavioral Sciences 104
(2013): 440-448.
[12]Patnaik, Ashish Kumar, Prasanta Kumar Bhuyan, and KV Krishna
Rao. "Divisive Analysis (DIANA) of hierarchical clustering and GPS data
for level of service criteria of urban streets." Alexandria Engineering
Journal 55.1 (2016): 407-418.
[13] Pollard, Katherine S., and Mark J. Van Der Laan. "A method to
identify significant clusters in gene expression data." (2002).
[14] Jou, Rong-Chang, and Yi-Wen Chen. "Drivers’ acceptance of delay
time at different levels of service at signalised intersections."
Transportation research part A: policy and practice 58 (2013): 54-66.
[15] Yadav, Jyoti, and Monika Sharma. "A Review of K-mean Algorithm."
International journal of engineering trends and technology 4.7 (2013):
2972-2976.
23. 18-Jul-23 23
[16] Boora, Amardeep, Indrajit Ghosh, and Satish Chandra. "Clustering
technique: an analytical tool in traffic engineering to evaluate the
performance of two-lane highways." European Transport-Trasporti
Europei (2017).
[17] Martín, Sergio, Manuel G. Romana, and Matilde Santos. "Fuzzy
model of vehicle delay to determine the level of service of two-lane roads."
Expert Systems with Applications 54 (2016): 48-60.
[18]. Patnaik, A.K.; Bhuyan, P.K.; Rao, K.K.: Divisive analysis (DIANA) of
hierarchical clustering and GPS data for level of ser
vice criteria of urban streets. Alex. Eng. J. 55(1), 407–418 (2016)
[19].“Highway Capacity Manual”: Transportation Research Board.
National Research Council, Washington, DC (2010)
[20].Mohapatra, S.S.; Sil, G.; Dey, P.P.: Quantification of LOS at median
openings through cluster analysis. Indian Highway. 43(3), 25–31 (2015)
[21].. Patnaik, A.K.; Krishna, Y.; Rao, S.; Bhuyan, P.K.: Development of
roundabout entry capacity model using INAGA method for heterogeneous
traffic flow conditions. Arab. J. Sci. Eng. 42(9), 4181–4199 (2017)
24. 18-Jul-23 24
[22].Chandra, S.; Agrawal, A.; Rajamma, A.: Microscopic analysis of
service delay at uncontrolled intersections in mixed traffic conditions. J.
Transp. Eng. ASCE 135(6), 323–329 (2009)
[23].. Mohanty, M.; Dey, P. P: Modelling the major stream delay due to U-
turns. Transp. Lett. 1–8. https://doi.org/10.1080/19427867. 2017.1401701
(2017)
[24].Ma, D.F.; Ma, X.L.; Jin, S.; Sun, F.; Wang, D.H.: Estimation of major
stream delays with a limited priority merge. Canad. J. Civil Eng. 40(12),
1227–1233 (2013)
[25].. Mohapatra, S.S.; Dey, P.P.: Lateral placement of U-turns at median
openings on six-lane divided urban roads. Transp. Lett. 7(5), 252–263
(2015)
[26].Ashalatha, R.; Chandra, S.: Critical gap through clearing behavior of
drivers at unsignalised intersections. KSCE J. Civil Eng. 15(8), 1427–1434
(2011).
