ESTIMATION OF CAPACITY AND MODEL DEVELOPMENT USING LINEAR REGRESSION
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This document presents a study that aimed to estimate the capacity of two-lane roads and develop a multiple linear regression model for capacity. Data on vehicle volumes, speeds and road geometry were collected from two sites in Kerala, India. The data was analyzed to determine vehicle proportions and speed profiles. A regression model for capacity was developed using SPSS with capacity as the dependent variable and factors like stream equivalency, shoulder width, and speed as independent variables. The model had good fit with an R2 value of 0.996. The study concluded that capacity is positively associated with shoulder width and speed, but negatively associated with stream equivalency factor.
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 10 | Oct 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 74
ESTIMATION OF CAPACITY AND MODEL DEVELOPMENT USING LINEAR
REGRESSION
Flora Philip1, Archana S2
1P G Scholar, Dept. Of Civil Engineering, Jyothi Engineering College, Kerala, India
2Assistant Professor, Dept. Of Civil Engineering, JyothiEngineering College,Kerala, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Two lane highway play a major role in
highway system in India. The assessment of performance of
two-lane roads is necessary for the future traffic
planning, design, operation and also maintenance. Indian
roads have mixed traffic flow which leads to traffic
congestion. It causes slower speeds, vehicular queuing, and
various safety issues. Assessment of two-lane road is
according to the methodology of Indo Highway Capacity
Manual. To achieve this objective the study area selected
are Kanjany-Olari and Pazhuvil Peringottukara in Thrissur
district of Kerala.Videographic data from which volume
count of vehicle and spot speed of vehicle is calculated. The
aim of the study is to model the capacity for the selected
road stretch using multiple linear regression.
Key Words: Stream equivalency, Capacity, Congestion,
Vehicular queuing, Multiple Linear regression
1. INTRODUCTION
Most of the National and state highway system are composed
of two-lane roads. More than 50% of State Highways and
two-thirds of National Highways still have two lanes in each
direction. Traffic operation on a two-lane two-way highway
is unique. Lane changing and overtaking are possible only in
the face of on- coming traffic in the opposing lane. The
overtaking demand increases rapidly as traffic volume
increases, while passing opportunities in the opposing lane
decline as volume increases. Therefore, flow in one direction
influences flow in the other direction. When there is a
significant speed difference among various vehicle
classifications in mixed traffic, the problem is more severe.
With limited chances to overtake, it substantially increases
the desired number of overtakes.
In the design, planning, operation, and layout of road
network sections, predictions and knowledge of capacity is
essential. Lane width, gradient, lateral clearance, width, and
type of shoulder are all elements that affect a two-lane road's
capacity. Traffic capacity can be significantly impacted by
lane and shoulder width. Vehicles in narrow lanes travel
closer to one another laterally by slowing down or by
observing wide longitudinal gaps at a particular speed.
Important traffic conditions which affect capacity are
composition of traffic stream, directional split and
presence of slow-moving vehicles in the stream.
The performance and consequent capacity of the driver are
impacted by environmental factors such as wet pavement,
snow and ice conditions, rain, darkness, fog, and parking
regulations. There are indications that slippery or wet
pavement can reduce capacity by 5% to 15%.
1.1 objectives
• To determine Vehicle proportion of the
selected road stretch
• To develop multiple linear regression model
to determine capacity of the selected road
stretch.
2. BACKGROUND OF THE RESEARCH
Manish. Jain, Ajinkya Mane, et.al. [1] conducted study on
capacity estimation on 2-lane hilly roads under
heterogeneous traffic condition in India.The present study is
made on the estimating capacity on hilly road under
heterogeneous traffic.Here they develop a relationship
between capacity v/s operating speed of car, capacity v/s
gradient magnitude. Finally, they conclude by saying that
undivided hilly roads decrease with increase in magnitude
gradient.
Vijay B. G, Dr. Rajendra Khatawkar [2] Data collected for seven
days at different sections of two-lane national Highways (NH-
206, NH-209 and NH-207). Manual and videographic method
and developed a linear regression equation using SPSS. The
result shows that capacity of the roadway will increase
proportionally as lane width and the horizontal curve radius
increase, whereas the capacity of the highway decreases as
gradient increases.
Gauri.S. Biraje, Prof A.S Thorbole [3] conducted study on
capacity assessment on two lane road on state highway. This
study used the capacity evaluation of a 2-lane road by the Indo-
Highway Capacity Manual to determine the causes of traffic
congestion in a case study location in Maharashtra and found that
capacity is reduced, and traffic congestion is more during peak
hours.](https://image.slidesharecdn.com/irjet-v10i1012-240109131118-0aefa1ad/85/ESTIMATION-OF-CAPACITY-AND-MODEL-DEVELOPMENT-USING-LINEAR-REGRESSION-1-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 10 | Oct 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 75
Pinakin Patel, Ashish Dhamaniyab [4] study is conducted at
five signalized intersections of Ahmedabad, Surat and Noida,
India. A stream equivalency model based on regression has
been created. By developing stream equivalency based on the
calculated PCUs during saturated green time, this study
provides an approach for estimating saturation flow. The
equations are validated by comparing the field and calculated
values of saturation flow.Model value is closer to the field
saturation flow, according to the results.
ChetanR.Patel et.al. [5] Author conducts a case study on six-lane
roads in Pune and Patna to examine the sudden impact of
roadside friction on a given traffic flow condition. The author
compares service volume and stream speed at various volume-
to-capacity ratios to assess the presence of roadside friction.
Study analysis reveals that roadside friction has a significant
impact on the capacity of urban arterials.
Thamizh Arasan et.al. [6] provides a computer simulation
model (heterosim) to assess PCU values in heterogeneous
traffic flow situations and states that PCU values considerably
changes with changes in traffic volume and road width.
V. Thamizh Arasan et.al. [7] The author deceives diversified
flow with vehicles having different steady and moving
parameters without taking lane discipline into account, and
the study's results accurately replicate heterogeneous traffic
flow conditions on roads where vehicles are moving while
taking the absence of lane control into account.
3. STUDY AREA
Study areas were identified as Kanjany-Olari and Pazhuvil
Peringottukara of Thrissur district in Kerala. State highway 75
is the state highway in Kerala which starts in Thrissur and ends
in Vadanapally. Kanjany-Olari is the part of SH 75.
4. METHODOLOGY
The study stretch was identified. Data collection includes
collection of videographic data and geometric data of the study
stretch. Spot speed and Volume count data were extracted from
the videography. Geometric data collected include Carriageway
width, Shoulder width, type of shoulder, parking
characteristics, presence of footpath and side roads which
affects the capacity of road. The model was developed with
dependent variable capacity of the selected stretch and
independent variables are speed, volume count, carriageway
width, shoulder width, type of shoulder and parking
characteristics of vehicle.
5. DATA COLLECTION
Primary data is collected for 2 stretches which include
geometric details and traffic data. Traffic data include spot
speed data and volume count for the stretch. For the data
extraction, video was extracted for a time period of 4hrs
(morning and evening). Spot speed was extracted for time
period of 5-minute interval. geometric data were collected
from the field.
6. DATA ANALYSIS
With the aid of Excel tool data analysis in which vehicle
proportion in which composition of vehicle in percentage for
the Kanjany-Olari and Pazhuvil-Peringottukara were
estimated.
Chart-1: Vehicle proportion for Kanjany-Olari
Chart-2: Vehicle proportion for Pazhuvil-Peringottukara
1% 4% 3% 3%
24%
BUS
CAR
54% 11% 2W
HCV
LCV
CYCLE
12%
50% 25%
9%
0%1%2%
1%
CAR
2W
3W
BUS
LCV
HCV
MAV
total](https://image.slidesharecdn.com/irjet-v10i1012-240109131118-0aefa1ad/85/ESTIMATION-OF-CAPACITY-AND-MODEL-DEVELOPMENT-USING-LINEAR-REGRESSION-2-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 10 | Oct 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 77
ACKNOWLEDGEMENT
I am thankful to the teaching staff and Department of Civil Engineering,
Jyothi Engineering College, for great support throughout this project.
REFERENCES
[1] Manish P. Jaina et.al ‘’Capacity estimation on two lane hilly roads under
heterogeneous traffic condition in India’’. World Conference on Transport
Research - WCTR 2019Mumbai 26- 31 May 2019
[2] Dr. Rajendra Khatawkar, V. B. G. R. C. K. Study on impact of Gradient on
capacity of two Lane UndividedNational Highways.International Journal of
Advanced Science and Technology, 29(3),5513 – 5520
[3] Ms. Kanchan B G, Mr. Umapathi, Dr. Palakshappa.K ‘‘PCU estimation for
two lane undivided state highway (sh- 57)’’International Research Journal
of Engineeringand Technology (IRJET) e-ISSN: 2395-0056
[4] Pinakin Patela, Ashish Dhamaniya “Stream equivalency factor for
mixed traffic at urban signalized intersections’’
Transportation Research Procedia 37 (2019) 362–3687
[5] Chetan R. Patel, G. J. Joshi ‘‘Mixed Traffic Speed–Flow Behavior under
Influence of Road Side Friction and Non-Motorized Vehicles: A
Comparative Study of Arterial Roads in India’’.
Transport Research Arena Europe 2012
[6] V. Thamizh Arasan et. al," Micro simulation Study of Effectof Volume
and Road Width on PCU of Vehicles under Heterogeneous Traffic". ASCE.
[7] V. T. Arsan et.al “Methodology for modelling highly
heterogeneous traffic flow” (ASCE)
[8] India’s Highway Capacity Manual(2017),Council of Scientific and
Industrial Research (CSIR)New Delhi
[9] IRC:106-1990 Guidelines for capacity of urban roads in plain areas
(First revision), Indian code of Practice, Indian Road Congress, New Delhi](https://image.slidesharecdn.com/irjet-v10i1012-240109131118-0aefa1ad/85/ESTIMATION-OF-CAPACITY-AND-MODEL-DEVELOPMENT-USING-LINEAR-REGRESSION-4-320.jpg)
ESTIMATION OF CAPACITY AND MODEL DEVELOPMENT USING LINEAR REGRESSION
- 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 10 | Oct 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 74 ESTIMATION OF CAPACITY AND MODEL DEVELOPMENT USING LINEAR REGRESSION Flora Philip1, Archana S2 1P G Scholar, Dept. Of Civil Engineering, Jyothi Engineering College, Kerala, India 2Assistant Professor, Dept. Of Civil Engineering, JyothiEngineering College,Kerala, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Two lane highway play a major role in highway system in India. The assessment of performance of two-lane roads is necessary for the future traffic planning, design, operation and also maintenance. Indian roads have mixed traffic flow which leads to traffic congestion. It causes slower speeds, vehicular queuing, and various safety issues. Assessment of two-lane road is according to the methodology of Indo Highway Capacity Manual. To achieve this objective the study area selected are Kanjany-Olari and Pazhuvil Peringottukara in Thrissur district of Kerala.Videographic data from which volume count of vehicle and spot speed of vehicle is calculated. The aim of the study is to model the capacity for the selected road stretch using multiple linear regression. Key Words: Stream equivalency, Capacity, Congestion, Vehicular queuing, Multiple Linear regression 1. INTRODUCTION Most of the National and state highway system are composed of two-lane roads. More than 50% of State Highways and two-thirds of National Highways still have two lanes in each direction. Traffic operation on a two-lane two-way highway is unique. Lane changing and overtaking are possible only in the face of on- coming traffic in the opposing lane. The overtaking demand increases rapidly as traffic volume increases, while passing opportunities in the opposing lane decline as volume increases. Therefore, flow in one direction influences flow in the other direction. When there is a significant speed difference among various vehicle classifications in mixed traffic, the problem is more severe. With limited chances to overtake, it substantially increases the desired number of overtakes. In the design, planning, operation, and layout of road network sections, predictions and knowledge of capacity is essential. Lane width, gradient, lateral clearance, width, and type of shoulder are all elements that affect a two-lane road's capacity. Traffic capacity can be significantly impacted by lane and shoulder width. Vehicles in narrow lanes travel closer to one another laterally by slowing down or by observing wide longitudinal gaps at a particular speed. Important traffic conditions which affect capacity are composition of traffic stream, directional split and presence of slow-moving vehicles in the stream. The performance and consequent capacity of the driver are impacted by environmental factors such as wet pavement, snow and ice conditions, rain, darkness, fog, and parking regulations. There are indications that slippery or wet pavement can reduce capacity by 5% to 15%. 1.1 objectives • To determine Vehicle proportion of the selected road stretch • To develop multiple linear regression model to determine capacity of the selected road stretch. 2. BACKGROUND OF THE RESEARCH Manish. Jain, Ajinkya Mane, et.al. [1] conducted study on capacity estimation on 2-lane hilly roads under heterogeneous traffic condition in India.The present study is made on the estimating capacity on hilly road under heterogeneous traffic.Here they develop a relationship between capacity v/s operating speed of car, capacity v/s gradient magnitude. Finally, they conclude by saying that undivided hilly roads decrease with increase in magnitude gradient. Vijay B. G, Dr. Rajendra Khatawkar [2] Data collected for seven days at different sections of two-lane national Highways (NH- 206, NH-209 and NH-207). Manual and videographic method and developed a linear regression equation using SPSS. The result shows that capacity of the roadway will increase proportionally as lane width and the horizontal curve radius increase, whereas the capacity of the highway decreases as gradient increases. Gauri.S. Biraje, Prof A.S Thorbole [3] conducted study on capacity assessment on two lane road on state highway. This study used the capacity evaluation of a 2-lane road by the Indo- Highway Capacity Manual to determine the causes of traffic congestion in a case study location in Maharashtra and found that capacity is reduced, and traffic congestion is more during peak hours.
- 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 10 | Oct 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 75 Pinakin Patel, Ashish Dhamaniyab [4] study is conducted at five signalized intersections of Ahmedabad, Surat and Noida, India. A stream equivalency model based on regression has been created. By developing stream equivalency based on the calculated PCUs during saturated green time, this study provides an approach for estimating saturation flow. The equations are validated by comparing the field and calculated values of saturation flow.Model value is closer to the field saturation flow, according to the results. ChetanR.Patel et.al. [5] Author conducts a case study on six-lane roads in Pune and Patna to examine the sudden impact of roadside friction on a given traffic flow condition. The author compares service volume and stream speed at various volume- to-capacity ratios to assess the presence of roadside friction. Study analysis reveals that roadside friction has a significant impact on the capacity of urban arterials. Thamizh Arasan et.al. [6] provides a computer simulation model (heterosim) to assess PCU values in heterogeneous traffic flow situations and states that PCU values considerably changes with changes in traffic volume and road width. V. Thamizh Arasan et.al. [7] The author deceives diversified flow with vehicles having different steady and moving parameters without taking lane discipline into account, and the study's results accurately replicate heterogeneous traffic flow conditions on roads where vehicles are moving while taking the absence of lane control into account. 3. STUDY AREA Study areas were identified as Kanjany-Olari and Pazhuvil Peringottukara of Thrissur district in Kerala. State highway 75 is the state highway in Kerala which starts in Thrissur and ends in Vadanapally. Kanjany-Olari is the part of SH 75. 4. METHODOLOGY The study stretch was identified. Data collection includes collection of videographic data and geometric data of the study stretch. Spot speed and Volume count data were extracted from the videography. Geometric data collected include Carriageway width, Shoulder width, type of shoulder, parking characteristics, presence of footpath and side roads which affects the capacity of road. The model was developed with dependent variable capacity of the selected stretch and independent variables are speed, volume count, carriageway width, shoulder width, type of shoulder and parking characteristics of vehicle. 5. DATA COLLECTION Primary data is collected for 2 stretches which include geometric details and traffic data. Traffic data include spot speed data and volume count for the stretch. For the data extraction, video was extracted for a time period of 4hrs (morning and evening). Spot speed was extracted for time period of 5-minute interval. geometric data were collected from the field. 6. DATA ANALYSIS With the aid of Excel tool data analysis in which vehicle proportion in which composition of vehicle in percentage for the Kanjany-Olari and Pazhuvil-Peringottukara were estimated. Chart-1: Vehicle proportion for Kanjany-Olari Chart-2: Vehicle proportion for Pazhuvil-Peringottukara 1% 4% 3% 3% 24% BUS CAR 54% 11% 2W HCV LCV CYCLE 12% 50% 25% 9% 0%1%2% 1% CAR 2W 3W BUS LCV HCV MAV total
- 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 10 | Oct 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 76 Table -1: ANOVA table modal Sum of squa re df Mean square F sig 1 Regression 90464 0.294 3 301546. 765 2276. 401 .000b Residual 3709. 060 28 132.46 6 Total 90834 31 9.354 Chart-3: Spot speed analysis of Kanjany-Olari Chart-4: Spotspeed analysis of Pazhuvil-Peringottukara 7. MODEL DEVELOPMENT OF CAPACITY USING SPSS Multiple linear regression for capacity was developed using SPSS Software. Dependent variable used is Capacity(C) and independent variable used were Stream equivalency factor (SEF), Shoulder width left (SWL), Speed (S) C=602.505-419.961 x SEF+108.459x SWL+42.831x S Model developed with R2 value 0.996 which indicates the best fit. Table -2: Coefficients Model Coefficient Remark Constant 602.505 significant Stream equivalency factor -419.961 significant Shoulder width (m) left 108.459 significant Speed (Km/hr) 42.831 significant Parking condition and Carriageway width show a negative relationship. When parking on the carriageway exists the capacity of road reduces. An increase in carriageway can lead to decrease in capacity because there may be chance of on street parking on road. 8. CONCLUSION The model was developed with R2 value 0.996 which show the best fitof model. Shoulder width, Stream Equivalency factor and Speed of vehicle show good correlation. When the width of shoulder increases, capacity can also be increased that can lead to a reduction in congestion. The value of stream equivalency factor indicates that a greater proportion of small vehicles are present in the road rather than heavy vehicles. Standard errorof regression is smaller value which indicates it is better because it indicates that the observations are closer to the fitted line. From the obtained linear regression equation, The coefficient of SEF shows that 1unit increase in value of SEF would result in 419.961 decrease in capacity and other variable being held constant. The coefficient of SWL shows that 1m increase in value of SWL would result in 108.459 increase in capacity other variable being held constant. The coefficient of S shows that 1Km/hr increase in value of speed would result in 42.831increase in capacity other variable being held constant. 90 80 70 60 50 40 30 20 10 86 79 63 68 55 30 48 41 61 54 50 44 34 39 61 48 35 28 CAR 2W 3W BUS LCV HCV VEHICLE LOWEST SPEED MEDIUM SPEED 90 80 70 60 50 40 30 20 10 80 80 62 53 53 58 43 44 59 42 25 25 25 31 49 37 24 26 CAR 2W 3W BUS LCV HCV MAV VEHICLE LOWEST MEDIUM HIGHEST SPEED(Km/hr) SPEED(Km/hr)
- 4. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 10 | Oct 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 77 ACKNOWLEDGEMENT I am thankful to the teaching staff and Department of Civil Engineering, Jyothi Engineering College, for great support throughout this project. REFERENCES [1] Manish P. Jaina et.al ‘’Capacity estimation on two lane hilly roads under heterogeneous traffic condition in India’’. World Conference on Transport Research - WCTR 2019Mumbai 26- 31 May 2019 [2] Dr. Rajendra Khatawkar, V. B. G. R. C. K. Study on impact of Gradient on capacity of two Lane UndividedNational Highways.International Journal of Advanced Science and Technology, 29(3),5513 – 5520 [3] Ms. Kanchan B G, Mr. Umapathi, Dr. Palakshappa.K ‘‘PCU estimation for two lane undivided state highway (sh- 57)’’International Research Journal of Engineeringand Technology (IRJET) e-ISSN: 2395-0056 [4] Pinakin Patela, Ashish Dhamaniya “Stream equivalency factor for mixed traffic at urban signalized intersections’’ Transportation Research Procedia 37 (2019) 362–3687 [5] Chetan R. Patel, G. J. Joshi ‘‘Mixed Traffic Speed–Flow Behavior under Influence of Road Side Friction and Non-Motorized Vehicles: A Comparative Study of Arterial Roads in India’’. Transport Research Arena Europe 2012 [6] V. Thamizh Arasan et. al," Micro simulation Study of Effectof Volume and Road Width on PCU of Vehicles under Heterogeneous Traffic". ASCE. [7] V. T. Arsan et.al “Methodology for modelling highly heterogeneous traffic flow” (ASCE) [8] India’s Highway Capacity Manual(2017),Council of Scientific and Industrial Research (CSIR)New Delhi [9] IRC:106-1990 Guidelines for capacity of urban roads in plain areas (First revision), Indian code of Practice, Indian Road Congress, New Delhi