Assessment of Operational Performance of Unsignalized Intersection using Microsimulation: A Case Study of Intersection at Pepsicola, Kathmandu

Abstract

The intersection at Pepsicola, Kathmandu is a three legged unsignalized intersection. Three approach roads from Jadibuti, Kadaghari and Sanothimi merge at Pepsicola, Kathmandu to form the intersection. The study aims to assess the existing operational scenario of the intersection. The data collected is used to replicate the actual condition of the intersection in PTV VISSIM 2023 after sufficient calibration and validation. VISSIM was preferred over other simulation softwares due to its easiness in use and flexibility. The performance of the intersection is evaluated in terms of Level of Service and Queue length. During the peak hour i.e. 9:00 AM to 10:00 AM the intersection accommodates 3759 vehicular traffic and during the same hour the intersection serves 318 pedestrians. At present condition the Level of Service of the intersection is C with average total delay of 18.62 Sec.

Full text

International Journal on Engineering Technology and Infrastructure Development (InJET-InDev)
Volume 1, Issue No.1, Page 52-59
Received Date: 12/30/2023
Revised Date: 1/9/2024
Accepted Date: 2/7/2024
ISSN: 3021-999X (Print)
52
Assessment of Operational Performance of Unsignalized Intersection using
Microsimulation: A Case Study of Intersection at Pepsicola, Kathmandu
Sanjeev Budhathoki1, Dr. Pradeep Kumar Shrestha2
1Transportation Engineer, sanjeevbudahthoki@gmail.com
2Asst. Professor, Institute of Engineering (Pulchowk), Nepal, pradeep.shrestha@pcampus.edu.np
Abstract
The intersection at Pepsicola, Kathmandu is a three legged unsignalized intersection. Three approach roads
from Jadibuti, Kadaghari and Sanothimi merge at Pepsicola, Kathmandu to form the intersection. The study
aims to assess the existing operational scenario of the intersection. The data collected is used to replicate the
actual condition of the intersection in PTV VISSIM 2023 after sufficient calibration and validation. VISSIM
was preferred over other simulation softwares due to its easiness in use and flexibility. The performance of the
intersection is evaluated in terms of Level of Service and Queue length. During the peak hour i.e. 9:00 AM to
10:00 AM the intersection accommodates 3759 vehicular traffic and during the same hour the intersection
serves 318 pedestrians. At present condition the Level of Service of the intersection is C with average total
delay of 18.62 Sec.
Keywords: Operational Performance, VISSIM, Level of Service, Delay, Queue Length
1. Background
An intersection is the junction of two or more roads either merging or crossing. It plays a vital role in any road
network as it helps in providing the access to any road or getting off of the road sections. Intersections can either
be controlled by a traffic light or manually by the traffic police. If an intersection is controlled by a traffic light it
is known as signalized intersection and if not, or being controlled by a traffic police, it is known as unsignalized
intersection. [6]
Intersections in urban areas can often cause significant traffic delays due to the multiple directions of traffic flow
[17]. The centralization of the Kathmandu Valley has contributed to a gradual rise in population thus impacting the
efficiency of the traffic management system. There is a need for targeted measures to address the challenges posed
by congestion and enhance the efficiency of traffic management at the intersections for smooth traffic flow [8].
Pepsicola intersection is a unsignalized intersection formed by merging of roads from Kadaghari, Sanothimi and
Jadibuti. The intersection accommodates large volume of traffic as well as pedestrians daily. Speed and volume
are often considered as the major contributor of road accidents and conclusion can be drawn that crashes are
influence to quite reasonable extent by speed, traffic volume at the locality and percentage of two wheelers [16]. T
Traffic congestion can be observed during the morning and evening hours in Pepsicola intersection. Thus,
evaluation of operational performance of the intersection is necessary to implement necessary measures to
improve the performance.
2. Objective
The objective of this study was to evaluate the operational performance of the intersection at Pepsicola. The study
area was modelled in VISSIM after collection and analysis of primary as well as secondary data. The objective
was achieved after analysis of adequately calibrated and validated model.

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3. Literature Review
Different studies have been reviewed for the evaluation of the operational performance of the unsignalized
intersection. Tiwari and Marsani: [18] calibrated model can be used for predicting the future scenario of speed and
density with anticipated change in traffic volume and Level of Service. The research is an initiation for calibration
of traffic flow models for Nepalese roads which suggests use of various simulation models in context of Nepal
rather than conventional ones.
Pokhrel et al; [10] provided an overview of the current situation of signalized intersections in Jay Nepal Hall. The
performance of the intersection was assessed based on the LOS, Average Delay and Queue length and is followed
by the implementation of the performance enhancement strategies.
Prajapati et al; [11] determined that microscopic simulation such as VISSIM has more individualistic approach
such as interaction of the driver to the environment and other vehicles, individual link behavior or behavior of the
driver class, etc.
Pandey and Shrestha [9] analyzed the impact of lane use restrictions on traffic flow at New Baneshwor intersection.
Data were extracted from the video-graphic survey and fed to VISSIM to carry out the simulation. 4 Scenarios
were prepared based on lane use plan to compare the traffic performance measures.
Ragab and El Nagae [12] evaluated the impact of different improvement measures on traffic operations at
signalized intersections in urban areas based on field data and microsimulation models. The micro-simulation
models were developed for the selected intersections using VISSIM software. Then, they were calibrated and
validated using the collected data. The developed models were used to evaluate two different improvement
measures.
Shrestha and Marsani [13] identified that the main cause of the traffic congestion problems that occurred in the
area of New-Baneshwor was due to heavy traffic volumes which exceed the capacity of the intersection. The study
used VISSIM to simulate the traffic and signal timing of the intersection on present condition. The study suggests
alternatives for improvement of performance in the intersection.
Ishaque & Noland [5] provided a method for including pedestrians in a vehicle microsimulation model, specifically
the VISSIM model. VISSIM provides a default mechanism for simulating pedestrian movements; however, this
does not adequately replicate pedestrian behavior. Instead pedestrians can be defined as vehicles and calibrate
various parameters within VISSIM so that pedestrian behavior is calibrated with pedestrian speed-flow models.
Luitel et al; [7] studied to enhance the traffic flow at the Buspark intersection in Birgunj Metropolitan City. 72
hours traffic volume data and geometrical characteristics of the intersection were collected and an existing model
of the intersection was developed in software ‘SIDRA Intersection 8.0’ The validation of the model involved
assessing both observed and simulated queue lengths for each approach, and an assessment of the current
performance of the intersections was conducted.
A study by Tiwari et al; [17] showed that coordinating the signal systems between intersections at Kanti Children's
Hospital and Shital Niwas significantly reduced the average delay time and maximum queue length at both
intersections. The study included survey to collect morning peak hour traffic volume and geometrical features of
the two intersections and developed a signalized intersection model in SIDRA software. The model was validated
based on observed and modelled queue lengths for each approach, and the existing performance of the
intersections was evaluated.
Dhakal et al; [3] provided an overview of the current situation of signalized intersections in Satdobato. The
environment and traffic flow parameters were modeled in a microsimulation software ‘SIDRA Intersection 8.0’.
The calibration and validation of the intersection model in the software were performed using field data. The
performance of the intersection was evaluated based on the LOS, and delay, and back of the queue.
Zainuddin et al; [19] studied the application of VISSIM microsimulation model to assess and compare the
operational performance at the two T-Leg intersection of Pengkalan Weld Road, Malaysia. In this study T-leg
Intersection was upgraded into roundabout and comparative study was carried out between operational
performance of existing and improved roundabout.

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Sun et al; [14] found out that for the large intersection and high volume of traffic, VISSIM is more appropriate
simulation software than CORSIM. Furthermore, VISSIM also more appropriate than CORSIM to calculate the
average control delay at the intersection
4. Study Area
The unsignalized intersection at Pepsicola, Kathmandu was chosen for this study. The intersection has four legs
but the road on west part of the intersection has very small volume of traffic and its effect is minimum compared
to the other legs, thus is ignored in this study. Jadibuti and Sanothimi legs have total width of 14m and Kadaghari
leg has width of 12m. The width of footpath in the intersection varies from 1.7m to 3m. Old Sinamangal Mandir
is located at edge of the intersection in North leg towards Kadaghari.
Figure 1 Pepsicola Intersection
5. Methodology
The intersection is uncontrolled three-legged intersection without any traffic police control or signals. The
movement of vehicles occurs spontaneously with short queue length. The study area was selected after studying
and analyzing research articles related to topic. Traffic volume was extracted from videographic survey that was
carried out for three days in May 9,10 and 11 from 8:00 AM-10:30 AM and 4:00 PM- 6:30 PM. Peak hour was
determined after classified vehicle count of the vehicles. Pedestrian count was carried out for the determined
vehicular peak hour. Geometry of the intersection was determined after field measurements. Speed survey using
radar gun was done to determine speed of vehicles and pedestrians.
Based on the real-world traffic data, evaluation of result of SIDRA and VISSIM have been analyzed in terms of
simplicity and output error. Results conclude that VISSIM is more accurate and error free simulation software
than SIDRA [14]. The data were then used to develop traffic model in PTV VISSIM-2023. Furthermore, the model
was calibrated using two days traffic data of speed and volume. After the data was sufficiently calibrated,
validation was done using third day volume and speed data. GEH statistic was used to validate volume and
RMNSE value was used to validate speed. Finally, operational performance of the intersection was analyzed using
three-day data after adequate calibration and validation. The operational performance of the intersection after 5
years was also evaluated by forecasting traffic and pedestrian volume.

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Figure 2 Flowchart for evaluation of operational performance of the unsignalized intersection
6. Data Analysis and Results
6.1 Hourly Traffic Volume
Three-day traffic count was done to determine the peak hour of the traffic in the intersection. The peak hour of
the intersection was identified to be 9:00 AM to 10:00 AM in which 3758 vehicles (average of three-day count)
cleared the intersection. Similarly, during the same period 318 pedestrians crossed the intersection.
Change Calibration Parameters
GEH Value <5
RMNSE<0.15
No
Yes
GEH Value <5?
RMNSE<0.15?
Yes
No
Assess the
Existing
Operational
Performance
Validation of Base Model: Comparing Actual
Traffic parameters to Model Output
Parameters
New Data: Peak
Hour Traffic Flow
for One Day
Traffic Count
Calibration of Base Model: Comparing
Actual field volume to Model Output Traffic
Volume
Peak Hour Traffic
Flow for Two
Days Traffic Count
Development of Base Model

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Figure 3 Hourly traffic volume
6.2 Directional Volume
The directional traffic volume of each leg was counted using videographic survey. The count was carried out for
three days. During the peak hour there were 3713 vehicles in Day 1, 4005 vehicles in day 2 and 3555 vehicles in
day 3 in the intersection.
Table 1. Directional Volume
Sanothimi
Jadibuti
Kadaghari
Total
Day
Jadibuti
Kadaghari
Sanothimi
Kadaghari
Sanothimi
Jadibuti
Day 1
830
463
404
648
238
1130
3713
Day 2
721
465
395
623
286
1515
4005
Day 3
638
406
334
627
254
1296
3555
6.3 Speed Distribution
Speed of vehicles was determined using radar gun and speed of pedestrian was determined from videographic
survey. Fifty samples (Edwing, 1999) of each vehicle type and pedestrians was surveyed to determine the average,
minimum and maximum speed. Spot speed survey of the vehicles was also carried out. The 50th percentile speed
was found to be 22.6 Kmph and 85th percentile speed was 30.5 Kmph.
Figure 4 Speed distribution of vehicles
6.4 Development of Base Model
Base model is prepared in VISSIM to replicate the existing geometry of the intersection that comprises of different
elements such as links, connectors of various shapes and measurements. Using data from videographic Survey,
vehicle and pedestrian volume input of peak hour 9:00 AM to 10: 00 AM was fed to VISSIM. The composition
of the traffic was also used as input during this process. The directional split of the vehicles obtained from
Videographic survey was fed to the VISSIM model. The speed distribution of each types of vehicles and
pedestrians from spot sped survey was also used. The developed base model was then calibrated and validated.

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6.5 Calibration of Model
Calibration has been done for volume, average speed and queue length. Two-day data has been used for calibration
of the model. GEH statistics and RMNSE values has been checked for calibration of model. Trial and error method
have been adopted for calibration of the model.
Table 2. Calibration of volume
SN
From
To
Simulated Volume
Actual Volume in Field
GEH Statistics
1
Sanothimi
Jadibuti
785
776
0.32
2
Sanothimi
Kadaghari
478
464
0.64
3
Jadibuti
Sanothimi
379
400
0.83
4
Jadibuti
Kadaghari
622
636
0.55
5
Kadaghari
Sanothimi
255
262
0.43
6
Kadaghari
Jadibuti
1256
1323
1.86
Table 3. Calibration of speed
SN
Category
Average Speed in Field
(Kmph)
Average Speed in model
(Kmph)
RMNSE
1
Two-Wheeler (Motor Cycle)
27.64
27.44
0.03
2
Pedestrian
4.18
4.18
0
3
Four-Wheeler Light (Jeep, Car)
24.02
23.26
0.11
4
Four-Wheeler Heavy (Truck,
Bus)
20.68
20.18
0.1
Table 4. Calibration of Queue Length
SN
Leg of Intersection
Queue length in VISSIM
model
Actual queue length in
Field
RMNSE
1
Sanothimi Leg
15.28
15
0.07
2
Kadaghari Leg
12.36
12
0.1
3
Jadibuti Leg
16.32
16
0.08
6.6 Validation of model
After the model was adequately calibrated, validation of model was done by checking the model for Day 3 traffic
(Third day volume). The GEH statistics and RMNSE statistic was calculated for Day 3 traffic by following process
similar to calibration.
Table 5. Validation of volume
SN
Movement
Volume in VISSIM
model
Actual Volume in Field
GEH Statistics
1
Sanothimi to Jadibuti
617
638
0.83
2
Sanothimi to Kadaghari
412
406
0.28
3
Jadibuti to Sanothimi
314
334
1.1
4
Jadibuti to Kadaghari
600
627
1.08
5
Kadaghari to Sanothimi
245
254
0.56
6
Kadaghari to Jadibuti
1229
1296
1.88

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Table 6. Validation of speed
SN
Category
Average Speed in Field
(Kmph)
Average Speed in
VISSIM (Kmph)
RMNSE
1
Two-Wheeler (Motor Cycle)
27.64
27.44
0.03
2
Pedestrian
4.18
4.18
0
3
Four-Wheeler Light (Jeep, Car)
24.02
23.26
0.11
4
Four-Wheeler Heavy (Truck, Bus)
20.68
20.18
0.1
Table 7. Validation of speed
SN
Leg of Intersection
Queue length in VISSIM
model
Actual queue length in Field
RMNSE
1
Sanothimi Leg
15.21
15
0.05
2
Kadaghari Leg
13.14
13
0.04
3
Jadibuti Leg
15.42
15
0.1
6.7 Operational Performance
After the VISSIM model of the intersection was adequately calibrated and validated, the operational performance
of the intersection was determined using delay and level of service. The overall Level of service of the intersection
was C and average delay was measured to be 18.62 Seconds. The maximum queue length in the intersection was
16.55m.
Table 8. Operational Performance
SN
Movement
Level of
Service
Delay (Sec)
Maximum Queue Length(m)
1
Sanothimi to Jadibuti
C
17.95
15.33
2
Sanothimi to Kadaghari
C
18.78
15.33
3
Jadibuti to Sanothimi
B
12.93
16.55
4
Jadibuti to Kadaghari
C
17.31
16.55
5
Kadaghari to Sanothimi
C
19.1
12.76
6
Kadaghari to Jadibuti
C
21.12
12.76
7
Overall
C
18.62
16.55
7.Discussion
The study evaluated the operational performance of the intersection at Pepsicola by using simulation software
VISSIM. The model replicated the present scenario of operation of the intersection. The intersection doesn’t suffer
much congestion compared to other busier intersection in Kathmandu valley which is also evident from its LOS
C. Thus, no immediate measures for improvement of operation is required for the intersection.
8. Conclusion
The study focused on the evaluation of the operational performance of the intersection at Pepsicola. At present
condition the Level of Service of the intersection is C with average total delay of 18.62 Sec. The movement from
Jadibuti to Sanothimi has better level of service with LOS B among other movements in the intersection. The
maximum queue length in the intersection is 16.32m. Signal phase design or coordination with other intersection
nearby could be done to improve the performance of the intersection. [15]
9. Acknowledgement
The authors would like to acknowledge Society for Transport Engineers Nepal (SOTEN) for the providing
valuable research grant which enhanced the quality of research and the organizations leadership in transportation

International Journal on Engineering Technology and Infrastructure Development (InJET-InDev) Volume 1, Issue 1, April 2024
59
sector. The authors would like to thank Mr. Hemant Tiwari for support during data collection, literature review
and analysis phase and Mr. Deepak Bahadur Kunwar during simulation of model and its analysis.
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