CAPACITY AND DYNAMIC PASSENGER CAR UNIT …docs.trb.org/prp/17-00273.pdf · 53 passenger cars and...

CAPACITY AND DYNAMIC PASSENGER CAR UNIT ESTIMATION FOR 1 HETEROGENEOUS TRAFFIC STREAM OF URBAN ARTERIALS: A CASE STUDY OF 2

INDIAN METROPOLIS 3 4 5 6

Satyajit Mondal, Corresponding Author 7 Research Scholar, 8

Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi – 221005, U.P. 9

India, Mob No: 09804874257; Email: [email protected] 10

11

Sandip Chakraborty 12 Assistant Professor 13 Department of Civil Engineering, IIEST, Shibpur, Howrah, 711103, India 14

Mob No: 09830733143; Email: [email protected] 15 16

Sudip Kumar Roy 17 Professor 18 Department of Civil Engineering, IIEST, Shibpur, Howrah, 711103, India 19

Mob No: 09830233172; Email: [email protected] 20 21

Ankit Gupta 22 Assistant Professor 23 Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi – 221005, U.P. 24

India, Mob No: 07839114642; Email: [email protected] 25

26 27

Word count: 3,189 words text + 17 tables/figures x 250 words (each) = 7,439 words 28 29

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32 33 34

Submission Date: 01.07.2016 35 36

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mailto:[email protected]




2 Mondal, Chakraborty, Roy, Gupta

48 Abstract: Rapid urbanization increases the number of vehicles on a road section significantly 49

throughout the country. Indian traffic is purely heterogeneous consisting of variety of vehicles 50 which comprise of wide range of static and dynamic characteristics. To estimate volume of such 51

heterogeneous traffic it is essential to convert the different types of vehicles into equivalent 52 passenger cars and express the volume in terms of Passenger Car Unit (PCU) per hour. The 53

equivalency unit is universally adopted for measurement of traffic volume and the value is 54 obtained by taking the passenger car as the ‘Standard Vehicle’. The present study has been 55

concentrated on four and six lane divided urban arterials in Kolkata. The PCU of vehicles have 56 been presented for both categories of urban roads and the values are found to be higher than the 57

values given in IRC: 106-1990. It has also been observed that, PCU values of different types of 58 vehicles have been found to be different for different ranges of traffic volume due to its dynamic 59

characteristics. Capacity of four lane and six lane divided urban arterials have been found to be 60 4465 PCU/hr and 6264 PCU/hr using the dynamic PCU values fitted by Greenshield’s model. A 61

mathematical model has been developed on the basis of the variation of PCU with traffic volume. 62 The model is developed to forecast the PCU for several vehicular categories that has been 63

statistically validated at different ranges of traffic volume. 64

65 66

67 68 Keywords: Capacity, Heterogeneous Traffic, Passenger Car Unit, Traffic Volume, Urban Arterials. 69 70

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INTRODUCTION 94 95

India is a developing country with a great network that makes it the second largest road network 96 in the world. Urban arterials serve the major traffic carrying corridor in a metropolitan city. Rapid 97

urbanization increases the number of vehicles on a road section significantly throughout the 98 country. But the traffic in India is purely heterogeneous. The traffic stream in India has variety of 99

vehicles like cars, heavy vehicles such as buses, trucks, light commercial vehicles, motorized two 100 wheelers and three wheelers and non-motorized vehicles which comprise of wide range of static 101

and dynamic characteristics. The analysis of such heterogeneous traffic stream is simple if the 102 relative effect of each vehicle type can be expressed in terms of some common units. The PCU or 103

PCE is the universally adopted unit of measurement of traffic volume or capacity and the value is 104 derived by taking the passenger car as the ‘Standard Vehicle’. The estimation of PCU of different 105

categories of vehicles is also necessary for design of different traffic facilities, operational analysis 106 of roadway facilities, management of traffic regulation and control of traffic. 107

108

OVERVIEW OF PREVIOUS LITERATURES 109 110 There are many studies available in literature to estimate the PCU of different categories of 111

vehicles under heterogeneous traffic conditions in India and other countries. The term Passenger 112 Car Unit (PCU) was first introduced in the 1965 US HCM (TRB) (1) and reported for grades of 113

specific length and percent, proportion of trucks, and LOS (A-E). In the HCM 2000 (2), passenger 114 car equivalent (PCE) is defined as “the number of passenger cars displaced by a single heavy 115

vehicle of a particular type under specified roadway, traffic, and control conditions.” 116 Chandra and Kumar (2003) (3) studied the effect of lane width on PCU values and also on the 117

capacity of a two-lane road under mixed traffic conditions. PCUs were estimated at ten road 118 sections for nine categories of vehicles. They found that PCU for a vehicle type increases linearly 119

with the width of carriageway. Al-Kaisy et al. (2005) (4) worked on developing PCE factor for 120 heavy vehicles during congestion. A set of PCE factors for oversaturated traffic conditions was 121

developed for use in traffic analyses. Basu et al. (2006) (5) worked on PCE at an urban midblock 122 using stream speed They studied the impact of traffic volume and its composition on PCE of 123

different categories of vehicle in a mixed traffic stream of an urban midblock section. PCE values 124 were found to increase with increase in traffic volume. However, the effect of traffic volume on 125

PCE was predominant for heavy vehicles. It was found for heavy vehicles and new technology 126 cars that PCE values increased with an increase in compositional share of respective vehicle types 127

in the traffic stream. Zhang et al. (2006) (6) proposed PCE for different categories of vehicles 128 using vehicle moving space as a measure to derive PCEs. The data on two lane and four lane 129

highways in china were used to estimate PCE under different roadway and level of service 130 conditions. The authors suggested that PCE values increase with number of lanes and LOS A to 131

E. Rakha et al. (2007) (7) estimated the truck equivalency factor for freeway sections at different 132 grades. PCEs are developed for broader range of vehicle weight to power ratio in the 133

INTEGRATION software using HCM procedure. The authors estimated PCE for truck at different 134 LOS and 2 to 5 percent grades, when their proportion in the mix is more than 25 percent which 135

was beyond the limit of HCM 2000. Cao and Sano (2012) (8) worked on estimating capacity and 136 motorcycle equivalent units on urban roads in Hanoi, Vietnam. Nonlinear regression analysis was 137

employed to calculate the mean effective space for particular type of vehicle from the correlation 138 between the effective spaces of subject vehicle and the speed of motorcycle in the front of the 139

subject vehicle. 140


Satyanarayana et al. (2012) (9) worked on development of PCU factors and capacity norms at mid-141 blocks of rural highways in Visakhapatnam. Dhamaniya and Chandra (2013) (10) worked on the 142

concept of stream equivalency factors for heterogeneous traffic on urban arterial roads. They 143 converted heterogeneous traffic volume in vehicle per hour to homogeneous PCU per hour without 144

determining PCU factors for each and every individual vehicles type by making use of stream 145 equivalency factors (SEF). Paul and Sarkar (2013) (11) determined dynamic PCU of different 146

types of vehicles on urban roads. The effect of proportion of Non-Motorized Traffic (NMT) and 147 heavy vehicles on PCU of different categories of vehicles were studied and the effect of stream 148

speed on PCU was presented in form of mathematical equations. 149 Khode et al. (2014) (12) studied on impact of lane width of road on passenger car unit capacity 150

under mix traffic condition in cities on congested highways. It was found that the PCU for a vehicle 151 type increases with increasing lane width. Muhammad Adnan (2014) (13) studied on passenger 152

car equivalent in heterogeneous traffic environment. Four different methods were used to estimate 153 the PCU of vehicles. They found that method that incorporate vehicles speed along with projected 154

area of vehicles were provide appropriate estimate of PCE values. Dhamaniya and Chandra (2014) 155 (14) worked on midblock capacity of urban arterial roads in India. They considered the speed and 156

size of the vehicle as the prime variables for determination of PCU. The variation in PCU for 157 different types of vehicles was established graphically. 158

159

OBJECTIVE AND STUDY AREA 160 161 This study has been concentrated on finding the roadway capacity and PCU values for different 162

categories of vehicles under heterogeneous traffic conditions on mid-block section in urban 163 arterials and also to analyse the variation of PCU values with respect to different ranges of traffic 164

volume for urban arterials. 165 166

Two study sections have been selected on four lane divided urban arterial and one study section 167 on six lane divided urban arterial based on various criterion such as, the section should have wide 168

variation in proportion of different categories of vehicles, free from the effects of road side friction, 169 intersection, parking facilities, bus stop, pedestrian movements, curvature, gradient and median 170

opening etc. the selected study sections have been shown in Figure 1. A straight mid – block 171 section of the selected urban roads has been selected for the data collection purpose. The details 172

of study sections are shown in Table 1. 173 174

TABLE 1 Details of Study Sections 175 176

Study Sections Carriageway Width (m) Road Geometry

VIP Road 10.8 Six lane divided C/W

Kona Arterial Road 7.0 - 8.5 Four lane divided C/W

E. M. Bypass 7 Four lane divided C/W

177


178 179

FIGURE 1 Location of the selected study sections 180 181

RESEARCH METHODOLOGY 182 183 The present study has adopted Chandra’s method (REF) to estimate the PCU values of vehicles. 184

In a heterogeneous traffic stream, speed of the vehicles is mostly affected among the other traffic 185 stream parameters. In Chandra’s method speed is considered as the basic parameter for 186

determination of PCU. Hence, Chandra’s method has been adopted as proposed methodology. In 187 this study Standard Car (CS) is considered as the standard design vehicle. According to Chandra’s 188

method PCU of any vehicle type can be obtained by using the following relationship. 189 190

) )/(Ac/Ai (Vc/Vi PCUi 191

192

In equation 1, the variables Vc and Vi denotes the mean speed of standard car and vehicle type i 193 respectively and Ac and Ai denotes their respective projected rectangular area. The numerator in 194

the above equation is the function of volume of traffic stream as the speed of any vehicle type 195 depends upon its category, own volume and volume of other vehicles. Therefore, speed of any 196

vehicle type is true representation of overall interaction of a vehicle type due to presence of other 197 vehicle of its own category and of other types. The denominator represents the carriageway 198

occupancy with respect to standard car. The physical size of different types of vehicles have been 199 adopted from the Chandra and Kumar (2003). 200

201 202

203 204

205 206

(1)


TABLE 2 Vehicle Categories and Their Average Dimensions (Chandra and Kumar, 2003) 207 208

Category Average Dimension Projected rectangular

area on ground (m2) Length(m) Width (m)

Standard Car (Maruti800, Alto etc.) 3.72 1.44 5.36

Big Car (Honda city, Skoda etc.) 4.58 1.77 8.11

Truck 7.5 2.35 17.62

LCV 6.1 2.10 12.81

Bus 10.1 2.43 24.74

Two Wheeler 1.87 0.64 1.2

Three Wheeler 3.2 1.40 4.48

209

FIELD DATA COLLECTION 210 211 Field data have been collected on a weekday during 8 a.m. to 8 p.m. at Kona arterials and VIP road 212

and during 8 a.m. to 1 p.m. at E.M.Bypass in Kolkata to determine the traffic volume, speed of 213 different types of vehicles and composition of traffic stream. Video photography technique has 214

been used to record the movements of vehicular traffic in one direction of travel by considering a 215 trap length of 60 m for the determination of traffic volume and composition of the traffic stream. 216

Tru- speed laser gun has been used to collect the spot speed data of different categories of vehicles 217 travelling in the trap length for a sample size of 50%. 218

219

DATA EXTRACTION AND PROCESSING 220 221 The collected field data have been brought to a work station to extract different traffic stream 222

parameters such as: 223 224

Classified Traffic Volume and Composition 225 226

Classified traffic volume count has been carried out by playing the recorded video at the work 227 station. All vehicles in the traffic stream have been grouped and divided into seven categories. The 228

classified vehicle count has been done manually at every 5 minutes interval and it has been 229 converted into hourly traffic volume. 230

231


232 233

234

235

236 237 238

239

240 241

735

1558 1560 15041729

1528

0

500

1000

1500

2000

7:35 -8:35a.m.

9:40 -10:40a.m.

11:00 -12:00a.m.

1:56 -2:56p.m.

5:27 -6:27p.m.

6:32 -7:32p.m.

Hourl

y V

olu

me

(veh

/hr)

Time of Day

TW

25%

CS

20%CB

26%

Bus

6%

LCV

13%

Truck

6%

M3W

4%

Two Wheeler

Standard Car

Big Car

Bus

Light

Commercial

VehicleTruck

Motorized

Three Wheeler

3362

2498

0

1000

2000

3000

4000

10:30 - 11:30 a.m. 11:30 - 12:30 p.m.

Hou

rly

Volu

me

(veh

/hr)

Time of Day

TW

21%

CS

31%

CB

36%

Bus

4%

LCV

3%

Truck

2%M3W

3%

Two Wheeler

Standard Car

Big Car

Bus

Light

Commercial

VehicleTruck

Motorized

Three Wheeler

5299

4177

3246 3226 3283 3400

0

1000

2000

3000

4000

5000

6000

10:30 -11:30a.m.

11:30 -12:30p.m.

1:00 -2:00p.m.

2:00 -3:00p.m.

3:15 -4:15p.m.

4:15 -5:15p.m.

Hou

rly

Volu

me

(veh

/hr)

Time of Day

TW

24%

CS

36%

CB

23%

Bus

6%

LCV

4%

M3W

7%

Two Wheeler

Standard Car

Big Car

Bus

Light

Commercial

Vehicle

Motorized

Three Wheeler

FIGURE 2 Hourly variation of traffic volume and proportion of individual vehicle on Kona Arterial

FIGURE 4 Hourly variation of traffic volume and proportion of individual vehicle on VIP road

FIGURE 3 Hourly variation of traffic volume and proportion of individual vehicle on E. M. Bypass


Speed Data 242 243 The time mean speed of the individual vehicle has been obtained by using the laser gun technique. 244

The obtained time mean speed has been converted into space mean speed by using the relationship 245 between time mean speed and space mean speed. 246

247

DATA ANALYSIS 248 249

Variation in Speed with Traffic Volume 250 251 The variation in speed of individual vehicle type has been studied by determining the speed of 252

individual vehicle type at different volume levels shown in Figure 5 and Figure 6 respectively. 253 254

255 256

257 258

259 260

20

30

40

50

60

70

0 500 1000 1500 2000 2500 3000 3500 4000 4500

Veh

icula

r S

pee

d (

km

ph)

Volume (veh/hr)

Standard Car Big Car

Two Wheeler Motorized Three Wheeler

Bus Light Commercial Vehicle

Truck

30

40

50

60

70

0 1500 3000 4500 6000 7500 9000

Veh

icu

lar

Sp

eed (

km

/hr)

Volume (veh/hr)

Standard Car Big CarTwo Wheeler Motorized Three WheelerBus Light Commercial Vehicle

FIGURE 5 Variation in speed of individual vehicle with traffic volume on four lane divided road

FIGURE 6 Variation in speed of individual vehicle with traffic volume on six lane divided road


From the Figure 5 and 6, a flatter speed variation is observed for small size vehicle with traffic 261 volume due their higher maneuverability characteristics whereas a steep speed variation is 262

observed for large size vehicle with traffic volume due to their lesser maneuverability 263 characteristics with respect to standard car. 264

265

Estimation of PCU Values 266 267 In order to develop a proper speed – flow equation to estimate capacity and design of traffic 268

facilities for heterogeneous traffic, it is necessary to convert the heterogeneous traffic into 269 homogeneous by using a common unit, which is termed as Passenger Car Unit. Chandra’s model 270

has been used to determine the PCU of different vehicle categories. It has been observed that speed 271 of the individual vehicle class is different at different volume levels. So, a range of PCU values of 272

different categories of vehicles have been determined for limited range of traffic volume for both 273 urban roads and presented in the Table 3 and Table 4. 274

275

TABLE 3 PCU of Different Categories of Vehicles for Four Lane Divided Urban Arterial 276 277

Type of Vehicle

Traffic volume (veh/hr)

>0-500 500-1000 1000-1500 1500-2000

Min Max Min Max Min Max Min Max

Big Car (CB) 1.47 1.49 1.51 1.53 1.53 1.56 1.56 1.58

Two Wheeler (TW) 0.27 0.28 0.26 0.27 0.24 0.26 0.21 0.24

Motorized Three Wheeler

(M3W) 1.31 1.34 1.21 1.25 1.08 1.15 1.02 1.06

LCV 2.801 2.82 2.814 2.842 2.826 2.865 2.838 2.88

Bus 5.05 5.23 5.14 5.28 5.21 5.32 5.26 5.39

Truck 4.42 4.49 4.47 4.53 4.51 4.58 4.55 4.66

278

TABLE 4 PCU of Different Categories of Vehicles for Six Lane Divided Urban Arterial 279 280

Type of Vehicle

Traffic volume (veh/hr)

1000-2000 2000-3000 3000-4000 4000-5000

Min Max Min Max Min Max Min Max

Big Car (CB) 1.48 1.51 1.52 1.57 1.56 1.59 1.58 1.64

Two Wheeler (TW) 0.256 0.261 0.235 0.254 0.223 0.241 0.21 0.228

Motorized Three Wheeler

(M3W) 1.17 1.24 1.03 1.15 0.97 1.05 0.88 0.92

LCV 2.41 2.57 2.58 2.64 2.67 2.76 2.78 2.84

Bus 4.92 5.13 5.08 5.27 5.32 5.44 5.46 5.63

281 Estimation of Capacity 282 283 This study has been adopted the Greenshield’s model for estimation of capacity by considering the 284

average PCU value of individual vehicle type on four lane and six lane divided urban arterials. 285 Therefore, the speed – flow model which follows the parabolic relationship, developed by using 286


regression technique has been considered. A scatter diagram has been developed by plotting the 287 speed and flow of the urban arterials shown in Figure 7 and Figure 8 respectively. 288

289

290 291

292

293

294 295

296 297

It has been observed that the speed – flow curve fits well with the observed data, indicating the 298 validity of the field data for highly heterogeneous traffic flow. The capacity of four lane and six 299

lane divided urban arterial, under heterogeneous traffic conditions is estimated as about 4465 300 PCU/hour and 6264 PCU/hour respectively. 301

0

10

20

30

40

50

60

70

0 1000 2000 3000 4000 5000

Sp

eed (

km

/hr)

Flow (PCU/hr)

Greenshield's Model Field Data

0

10

20

30

40

50

60

70

2000 3000 4000 5000 6000 7000

Spee

d (

km

/hr)

Flow (PCU/hr)

Greenshield's Model Field Data

u = -0.1661k + 64.68, R² = 0.89

q= 389.404*u – 6.021*u2

u = -0.2056k + 60.604, R² = 0.8

q= 294.767*u – 8.864*u2

FIGURE 7 Speed-flow relationship for four lane divided urban arterial

FIGURE 8 Speed-flow relationship for six lane divided urban arterial


Variation of PCU at Different Traffic Volume on Four and Six Lane Divided Urban Arterials 302 303 The effect of traffic volume on PCU has been studied by determining the PCU of different types 304

of vehicles at different volume levels. It is observed from Figure 9 that PCUs of large size vehicles 305 such as CB, LCV, Bus and Truck are increasing and for small size vehicles such as TW and M3W, 306

it is decreasing with increase in traffic volume. In a road section, as the traffic volume increases 307 corresponding density will increase. So, the vehicles will move at a lower speed. Large vehicles 308

require more space to move in traffic stream and also have less maneuverability with respect to 309 CS while small size vehicles like 2W and M3W require less space and move to any lateral space 310

available between any large size vehicles in the traffic stream with better maneuverability respect 311 to CS. Therefore, the speeds of the small size vehicles are not affected by the increase in traffic 312

volume. So, the speed difference between CS and small size vehicle decreases and for larger 313 vehicles increases and corresponding PCU values for CB, LCV, Bus and Truck increases and for 314

2W and M3W it decreases. It is also observed from Figure 10 that variation of PCU values of 315 different categories of vehicles with traffic volume on six lane road follows the same trend as it is 316

on four lane road. PCUs of large size vehicles such as CB, LCV and Bus are increasing and for 317 small size vehicles such as TW and M3W, it is decreasing with increase in traffic volume. 318

319

320 321 322 323

324 325

1

2

3

4

5

6

0 500 1000 1500 2000 2500 3000 3500 4000 4500

PC

U v

alu

e

Traffic Volume (veh/hr)

CB LCV Bus Truck

0

0.3

0.6

0.9

1.2

1.5

0 500 1000 1500 2000 2500 3000 3500 4000 4500

PC

U v

alu

e


TW M3W

0.5

1.5

2.5

3.5

4.5

5.5

6.5

0 1000 2000 3000 4000 5000 6000 7000

PC

U v

alu

e


CB Bus LCV

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 1000 2000 3000 4000 5000 6000 7000

PC

U v

alu

e


TW M3W

FIGURE 9 Variation in PCU of CB, LCV, Truck, Bus, TW and M3W with traffic volume on 4 lane road

FIGURE 10 Variation in PCU of CB, LCV, Bus, TW and M3W with traffic volume on 6 lane road


The PCU of different categories of vehicles has been found to change linearly with increase in 326 traffic volume. A mathematical model has been developed based on variation of PCU values at 327

different volume levels for different categories of vehicles. The general form of the model is given 328 in the following equation. 329

330

cqaPCU b )10( 331

332

Where, 333 q = Traffic Volume in veh/hr 334

a, b, and c = Constant 335 336

The value of constants for different categories of vehicles for both urban arterial are given in the 337 Table 5. 338

339

TABLE 5 Values of Constants for Four and Six Lane Divided Urban Arterial 340 341

Vehicle Category Four Lane Road Six Lane Road

a b c a b c

Big Car 3 05 1.4694 4 05 1.4699

LCV 6 05 2.801 1 04 2.3218

Bus 1 04 5.0804 2 04 4.597

Truck 7 05 4.4358 - - -

TW -3 05 0.2951 -1 05 0.2743

M3W -2 04 1.4391 -9 05 1.3478

342

VALIDATION OF PCU VALUES AND MODEL 343 344

In order to check the accuracy of the PCU values and model for both the urban roads, field data 345 have been collected at one more four lane and six lane divided urban arterial. Required data have 346

been extracted from the collected field data. The obtained model has been used to estimate the 347 PCU values of the newly selected study sections. Therefore, the suggested (given in Table 3 and 348

4) and estimated PCU values of vehicles on four lane and six lane divided urban arterials have 349 been compared to observe the statistical significance between these two sets of PCU values using 350 statistical two tail t-test. Tables 6 and 7 shows the result of t-test performed for comparing mean 351

PCU of different categories of vehicles. The calculated value of t-statistics against the critical value 352 at 95% level of confidence is higher for every categories of vehicles. It implies that there is no 353

significant difference between suggested and estimated PCU values of vehicles which further 354 validated the obtained mathematical model. 355

356 357

358 359

(2)


TABLE 6 Results of t-test for Comparing PCU of Vehicles on Four Lane Divided Urban 360 Road with Traffic Volume 361 362

Vehicle Type Mean t0.05 tcritical (two-tailed) Remarks

Big Car Suggested Value 1.52

1.185 2.365

There is no

significant

difference

Estimated Value 1.50

Truck Suggested Value 4.55

1.223 2.365 Estimated Value 4.50

Bus Suggested Value 5.26


LCV Suggested Value 2.84


TW Suggested Value 0.25

-2.052 2.365 Estimated Value 0.26

M3W Suggested Value 1.16


363

TABLE 7 Results of t-test for Comparing PCU of Vehicles on Six Lane Divided Urban 364 Road with Traffic Volume 365

366

Vehicle Type Mean t0.05 tcritical (two-

tailed) Remarks

Big Car Suggested Value 1.56

-0.1999 2.57

There is no

significant

difference

Estimated Value 1.57

Bus Suggested Value 5.24


LCV Suggested Value 2.72


TW Suggested Value 0.235


M3W Suggested Value 1.01


367

CONCLUSION 368 369 In the present study the dynamic PCUs have been estimated for both the urban arterials. This study 370

considers speed and size of the vehicle as prime variables for estimation of PCU factors. The 371 calculated PCUs of vehicles are larger than the values given in IRC: 106-1990. A range of PCU 372

values have been estimated for each category of vehicles for limited range of traffic volume. 373 Capacity of four lane and six lane divided urban arterials have been found to be 4465 PCU/hr and 374

6264 PCU/hr using the Greenshield’s model. IRC 106:1990 suggests the capacity of four lane and 375 six lane divided urban road as 3600 PCU/hr and 5400 PCU/hr. This variation is obtained due to 376

higher operating speed and higher PCU values of vehicles. It has also been found that per lane 377 capacity decreases as the number of lane increases. The PCU value of Big Car, LCV, Bus and 378

Truck increases linearly with the increase in traffic volume but for Two Wheeler and Motorized 379 Three Wheeler it decreases linearly with increase in traffic volume. A mathematical model has 380


been developed based on the variation of PCU values. Two tail t-test has been used for the 381 validation of the suggested PCU values. It has been observed that the calculated value of t-statistics 382

against the critical value at 95% level of confidence is higher for every category of vehicles. It 383 implies that, there is no significant difference between suggested and estimated PCU values of 384

vehicles which further validated the obtained mathematical relationship. 385 386

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REFERENCES 428 429

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Research Council, 2000. 432 3. Chandra, S. and Kumar, U. Effect of Lane Width on Capacity under Mixed Traffic 433

Conditions in India”. Journal of Transportation Engineering, ASCE, Vol. 129, No. 2, 2003, 434 pp. 155-160. 435

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Mid-Block using Stream Speed as Measure of Equivalence. Journal of European 440 Transport/ Transporti Europei, Issue. 34, 2006, PP-(75-87). 441

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Washington, D.C., 2007, pp. 73-84. 448 8. Cao, N. Y. and Sano, K. Estimating Capacity and Motorcycle Equivalent Units on Urban 449

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CAPACITY AND DYNAMIC PASSENGER CAR UNIT …docs.trb.org/prp/17-00273.pdf · 53 passenger cars and...

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