Variability Analysis of Isolated Intersections Through Case Study

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International Journal of Engineering Trends and Technology Volume 70 Issue 8, 359-374, August 2022

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Original Article

Variability Analysis of Isolated Intersections Through

Case Study

Savithramma R M1, R Sumathi2, Sudhira H S3

1,2Department of CSE, Siddaganga Institute of Technology, Karnataka, India.

3Gubbi Labs LLP, Karnataka, India.

1savirmrl@sit.ac.in

Received: 10 June 2022 Revised: 10 August 2022 Accepted: 24 August 2022 Published: 27 August 2022

Abstract - Population and economic growth of urban areas have led to intensive use of private vehicles, thereby increasing

traffic volume and congestion on roads. The traffic management in the city is a challenge for concerned authorities, and the

signalized intersections are the primary interest of traffic management. Interpreting traffic patterns and current traffic signal

operations can provide thorough insights to take appropriate actions. In this view, a comprehensive study is conducted at

selected intersections from Tumakuru (tier-2 city), Karnataka, India. Data estimates traffic parameters such as saturation

flow, composition, volume, and volume-to-capacity ratio. The statistical results currently confirm the stable traffic condition

but do not ensure sustainability. The volume-to-capacity ratio is greater than 0.73 along three major arterial roads of study

intersections, indicating congestion in the future as the traffic volume is increasing gradually, as per the Directorate of

Urban Land Use and Transportation, Government of Karnataka. The statistical results obtained through the current study

uphold the report. The empirical results showed 40% of green time wastage at one of the study intersections, which results in

additional waiting delays, thereby increasing fuel consumption and emissions. The overall service level of the study

intersections is of class C based on computed delay and volume-to-capacity ratio. The study suggests possible treatments for

improving the service level at the intersection operations and sustaining the city's stable traffic condition. The study supports

city traffic management authorities in identifying suitable treatment and implementing accordingly.

Keywords - Heterogeneous traffic, Signalized intersections, Smart city, Traffic composition, Variability analysis.

1. Introduction

The role of transportation is vital in the development of

any country. Urban areas are expanding with the rise in

population. The economic and social affair department of

the United Nations (Anon 2018) reported that around 55%

of the population is city residents as of 2018, and it is

expected to be 68% by 2050. The revised World

Urbanization Prospectus (United Nations 2018) disclosed

that the world's urban population is expected to be largely

concentrated among a few countries. China, Nigeria and

India together will account for 35% of the projected growth

in urban areas between 2018 and 2050 across the world. The

top ten cities in India recorded a growth rate of more than

20% in 1991-2001, with Surat growing at 87%

(Balachandran, Adhvaryu, and Lokre 2006). It is expected

that most of these cities will continue to grow at the same

rate for the next decades. The study by (Duranton and

Turner 2012) describes the direct relationship between

population growth and the transportation system.

While the population has increased (Kohler 2012)

(Sudhira and Gururaja 2012), public transport usage has

deteriorated. As per a study (Group n.d.) conducted by the

Louis Berger Group in Ahmedabad, one of the major cities

in India, the passenger occupancy factor for the Ahmedabad

Municipal Transport Service (AMTS) declined from 71% in

the 1990s to 55% in 2000. One of the biggest problems of

urban transport has been its inability to keep pace with the

population growth in the city (Balachandran et al. 2006).

The urban expansion (Sprawl) (Sudhira, Ramachandra, and

Jagadish 2003) has adversely affected the sustainability of

the transportation system (Zhao 2010). With the economic

growth (Jones 2016), people gained the potential to own

personal vehicles, thereby increasing the traffic volume,

which is one of the common causes of congestion on

roadways. Concerned urban management authorities are

planning and allocating public funds to improve road

infrastructure and other operational facilities to avoid

congestion and ensure safety (Peixoto Neto et al. 2008).

The success of a good transportation system is in the

number of people it can move from one place to another at

an affordable cost, in reasonable comfort and at minimum

Savithramma R M et al. / IJETT, 70(8), 359-374, 2022

360

cost to the environment. Under these circumstances, the

traffic and transport studies support the development of

project proposals focussing on transportation management

in urban areas. Limited studies have been carried out for

traffic characterization at intersections in the urban area

(Pothula Sanyasi Naidu et al., 2015)(Shen and Wang, 2018).

At the same time, a review of existing studies on

intersection traffic modelling is presented (Pegu and Nath

2017).

Operations of signalized intersections are a part of the

transportation management system. Junctions are the critical

segments of the urban road network, while the delay is the

prime concern of operational strategies implemented at the

intersection. In this article, an attempt has been made to

study the signalized intersections to assess their level of

service. With this perspective, two junctions from

Tumakuru, namely the Shivakumara Swamiji circle and the

Town Hall circle, have been chosen for the current study.

Tumakuru is a tier-2 city in India's southern state

(Karnataka). The necessary data for the study is collected

through videography. The parameters such as ratios of

volume-to-capacity, traffic volume, traffic composition,

saturation movements, signal configuration, delay, fuel

consumption and emissions are analyzed to explore the

operation level at selected city intersections.

In North America, the HCM (Highway Capacity

Manual) TRB 2000 (Transportation Research Board,

National Research Council, Washington, DC 2000) is the

most widely used method for signalized intersections

analysis. The intersection performance is defined in terms of

mean delay. The delay is mapped against predefined service

levels ranging from A to F, where 'A' represents the best

Level of Service (LoS), and 'F' designates the worst service

level operations. And the delay is computed as a function of

many other factors, including signal configurations,

temporal traffic variations, driver behaviour and

environmental conditions. The analysis strategies defined by

HCM are focused on uniform traffic composition. But the

traffic conditions in India are highly heterogeneous. Hence,

the analysis guidelines described in Indian Road Congress

(IRC) (Indian Roads Congress 1990) and Indo-HCM (Year,

Project, and Delhi 2010) are adopted in Indian scenarios.

The forthcoming sections of the article are laid as

follows; the next section presents the available studies

concerned with the current study. The area selected for the

current study is introduced in section 3. The preliminary

statistical analysis conducted over signal operations and the

data collection concerning traffic at selected intersections

are presented in sections 4 and 5, respectively. The results

of variability analysis concerned with different parameters

are presented in section 6, and the corresponding

observations and probable solutions are discussed in section

7. Finally, the paper concludes with the report of the

proposed study.

2. Literature

By identifying the delay variability, more reliable

traffic signal configurations can be estimated to improve the

service level (Transportation Research Board, National

Research Council, Washington, DC 2000). Therefore, many

researchers worldwide have presented studies on

intersection operations and traffic conditions in specific

regions. The analysis of signalized intersections in Waterloo

and Kitchener cities of Canada is presented in (Hellinga and

Abdy 2008) with the objective of day-to-day peak-hour

traffic volume variability implications on delay. The authors

(Darma and Karim 2005) researched to determine the set of

components (variables) of the HCM delay model that

influences control delay using the capacity softwares

Transyt-7F and SIDRA.

The research is in progress to develop advanced models

for delay estimation. In this connection, many authors

worldwide have already presented the models. Different

analytical models (Fu and Hellinga 2000) (Akgungor and

Bullen 2007) (Chen et al. 2013) (Fawaz and El Khoury

2016) were presented to estimate delay. In contrast, the

studies including (Amrutsamanvar and Arkatkar 2018) and

(Yesufu et al. 2019), investigated the contribution of delay

at signalized intersections on overall travel time variability

along the route. The variance of overall delay at the

signalized intersection is estimated by (Fu and Hellinga

2000) based on delay evolution patterns in oversaturated

and undersaturated traffic situations. But, (Akgungor and

Bullen 2007) consider the traffic flow variations to compute

the delay. The model involves a delay parameter k,

expressed as a function of the degree of saturation. The

authors have proved that the proposed model performs well

in all expected traffic conditions.

Delay estimation at the pre-timed signal-operated

intersection is presented in (Chen et al. 2013) by

considering traffic arrival distributions using traditional

cumulative curves. A uniform control delay is modelled for

undersaturated intersections (Fawaz and El Khoury 2016).

The delay model (Webster 1958) proposed is appropriate for

homogeneous traffic with lane-adhered traffic situations.

Hence, its modified versions were presented by (Hoque and

Imran 2007) (Minh et al., 2010) (Preethi, Varghese, and

Ashalatha 2016) (and Saha, Chandra, and Ghosh 2017) to

suit the uniform traffic conditions in India.

A comparison of various delay models like

deterministic queuing, shock wave theory-based, Webster,

HCM, Australian Capacity Guide, etc. has been presented

(Dion, Rakha, and Kang 2004). The delay estimates were

analyzed under high and low traffic conditions and observed

that all models work better in case of low traffic demand

while showing differences in case of saturated conditions.

Performance of the traffic signal control system is expressed

in terms of delay encountered by each vehicle in a waiting

Savithramma R M et al. / IJETT, 70(8), 359-374, 2022

361

queue. Authors (Ghavami, Kar, and Ukkusuri 2012) have

compared the delays caused by three different traffic signal

control algorithms, namely, static (state-independent) Fixed-

Time Scheduling (FTS), Dynamic Maximum Weight

(backlog) Scheduling (DMWS) algorithm and Adaptive

length MWS (AMWS) algorithm. A study has been

presented by (Feng et al. 2014) focusing on the impact of

the actuated signal operations to manage intersection in

terms of waiting delay.

Reliable estimation of traffic saturation flow is crucial

for proper intersection design and operations. Hence, the

authors (Chodur, Ostrowski, and Tracz, 2016) presented a

comparative analysis of saturation flow estimates at urban

and rural intersections. The saturation flow estimation

model is proposed by (Saha, Chandra, and Ghosh 2018) for

signalized intersections under unstructured traffic scenarios.

Variability analysis of saturation flow estimation is given by

(Nguyen 2016) by considering cities with heterogeneous

traffic but mostly depend on two-wheelers as a case study.

The methodology has been described in (Chand, Gupta, and

Velmurugan 2017) (and Marfani and Dave 2016) for

computing saturation flow and Passenger Car Unit (PCU)

under heterogeneous traffic. The authors (Vasantha Kumar

et al., 2018) explored the impact of urbanization on traffic

flow rate at junctions through the study by considering a

three-legged intersection from Vellore, India, as a case

study.

The relationship between the volume-to-capacity ratio

and the service level at junctions under traffic heterogeneity

is discussed in (Othayoth and Rao 2020). In contrast, the

intersection operation level in South Africa is evaluated by

(Bester and Meyers 2007) based on saturation flow rate. A

study was carried out by (Prasanna Kumar and Dhinakaran

2013) to characterize the traffic patterns to assess the level

of service, while (Qu et al. 2013) (A J Mavani et al. 2016)

conducted a qualitative and quantitative analysis of traffic

discharge flow rate to know the dispersion characteristics of

traffic flow that helps in framing the reliable theoretical

basis for designing the traffic signal plans.

An appropriate mathematical model to express

commuters' behaviour helps minimize congestion, travel

time, and fuel consumption, thereby mitigating

environmental pollution. In this direction, (Macioszek and

Iwanowicz 2021) proposed a model to estimate the

maximum queue size at the intersections based on driver

behaviour. The traffic queuing behaviour has been analyzed

by (Verma et al. 2018), and they proposed a modified

Webster delay model to optimize the traffic signal plan. The

study presented by (Saw, Katti, and Joshi 2018) mainly

concentrated on various traffic movement patterns during

queuing and discharge periods which causes travel delay

and (Albrka Ali, Reşatoğlua, and Tozan 2018) analyzed the

traffic flow rate at roundabouts using SIDRA-5 software.

A study presented in (Darshan Patel, P. N. Patel 2018)

mainly focused on the effect of parameters such as traffic

arrival rate, discharge rate and composition on saturation

flow rate and road capacity at the signalized intersection. An

algorithm for traffic scheduling is proposed by (Roopa et al.

2020) using the Internet of Things (IoT) to avoid traffic

jams, thereby enhancing the traffic throughput at signalized

junctions and the dynamic use of left lanes to improve the

traffic flow rate along through lanes is proposed in (Zheng

et al. 2020).

3. Study area

The literature review discussed in the previous section

unveils that very few studies have been conducted on urban

signalized intersections in India. In particular, studies on

smart cities are rarely carried out concerning signalized

intersections. With this preliminary investigation,

Tumakuru, a tier-2 city in Karnataka state (Figure 1), has

been chosen for the study. A comprehensive analysis has

been conducted in Tumakuru, selected under Smart City

Mission (SCM) as an initial step. The SCM was launched by

the Indian government on 25 June 2015. The main objective

of this mission is to promote complete and sustainable cities

able to provide their civilians with a clean environment and

decent quality of life by applying smart solutions. In this

direction, the focus of the mission is to develop creative and

replicable models.

Currently, the Smart City Project (SCP) is under

progress in Tumakuru, and six junctions along the BH

(Bengaluru Honnavara) road are operated with an automated

signal system (Banerjee 1971). Among six, two isolated

signalized intersections are selected for the study:

Shivakumara Swamiji Circle (SSC) and Town Hall circle

(THC). The study circles are located 2.1 km away, with the

Bhadramma circle reclining. The approaching roads have

one to three lanes with one-way traffic movement with no

pressure from roadside parking, bus stops, or any other

resistance to the traffic flow.

Savithramma R M et al. / IJETT, 70(8), 359-374, 2022

362

Fig. 1 Location of the study area

3.1. Shivakumara Swamiji circle

It is a five-legged junction with two minor and three

major roads intersecting. In the context of this paper, major

road indicates heavy traffic flow, whereas minor road

indicates lower traffic flow. The roads are approaching from

Sira-gate (SR1), Bhadramma circle (SR2), Adithya hospital

(SR3), Batawadi (SR4) and Stadium (SR5). SR1, SR2 and

SR4 are major roads, while SR3 and SR5 are minor roads.

In a phase, the traffic can move in all possible directions

from an approaching road. SR1 and SR5 are permitted with

a free left. The top view of approaching roads of the SS

circle is presented in Figure 2.

Fig. 2 Shivakumara Swamiji circle

3.2. Town Hall circle

It is a cross (four-legged) intersection with one minor

and three major roads crossover. The intersecting roads are

approaching from Ashokaroad (TR1), Call-tax circle (TR2),

Municipal corporation (TR3) and Bhadramma circle (TR4).

TR1, TR2 and TR4 are major roads, while TR3 is a minor

road. The direction of traffic movement is allowed from one

road to all other roads in one phase. TR1 and TR2 are

permitted with a free left. The top view of intersecting roads

of the TH circle is presented in Figure 3.

Fig. 3 Town Hall circle

4. Preliminary analysis

The initial analysis has been carried out to recognize

the probable peak hour/s in a day and heavy-traffic-flow-

day/s in a week in the city by considering two intersections

introduced in the previous section. This initial analysis is

based on currently active signal operations. The statistical

process is applied over aggregates of cycle lengths utilized

throughout the day from morning 8:00 to evening 9:00. The

day-wise statistics are given in figures 4(a) and 5(a) for the

SSC and THC, respectively. The x-axis represents the time-

slices of 30 minutes, whereas average cycle lengths in

seconds are presented on the y-axis. The statistics

demonstrated that the average cycle lengths are longer

during weekdays than weekends (Saturday and Sunday).

However, the average cycle length on Saturdays lies

between weekdays and Sundays.

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InternationalJournalofEngineeringTrendsandTechnologyVolume70Issue8,359-374,August2022ISSN:2231–5381/https://doi.org/10.14445/22315381/IJETT-V70I8P237©2022SeventhSenseResearchGroup®ThisisanopenaccessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/)OriginalArticleVari...

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