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PCU Estimation.pptx

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  1. 1. 1
  2. 2. Introduction Need for the study Objectives Works done Methodology Table of contents 2
  3. 3. • In developing countries like India the nature of the traffic conditions are heterogeneous - no lane discipline is followed and no segregation of different classes of vehicles. • In order to study the traffic characteristics, the different vehicular categories should be converted to one common standard vehicular unit. • It is the common practice to consider the passenger car as the standard vehicle unit to convert the other vehicle classes and this unit is called the Passenger Car Unit or PCU. • The PCU value varies based on the traffic characteristics, hence dynamic PCU values have to be adopted. INTRODUCTION 3
  4. 4. NEED FOR THE STUDY • The Highway Capacity Manual (HCM) has stated PCU values for the different vehicle categories and for the different road sections. • The stated values are static in nature but many researches has found out that the PCU values are dynamic in nature – it varies depends upon the nature of the vehicular dimensions and the traffic characteristics. • Many researches has found out many methods for the estimation of the dynamic PCU’s for different kind of vehicle categories but the PCU values found out using those methods varies from one another. • A slight variation in the PCU values may lead to the inappropriate effects in calculating the capacity of the roads. 4
  5. 5. • The present study is an attempt to discuss the suitability of the different methods for different circumstances so that one can make the right choice while adopting a PCU estimation method. • This study considers all the factors which may lead to the variations in the PCU values and comes up with a new methodology for the estimation of the PCU, so that the variations may be less when compared to the other estimation methods. Contd….. 5
  6. 6. OBJECTIVES Following are objective of this study,  To Identify the various factors which affects the PCU values in the urban arterial roads.  To study the suitability of different PCU estimation methods for the urban arterial roads.  To come up with a new methodology for estimating the PCU based on heterogeneous traffic conditions. 6
  7. 7. LITERATURE REVIEW • The study of Literature Review is carried in relation to the objective of this study and identified the different methodologies and concepts adopted in estimating the PCU of a selected section of the road. • The following are the different methods adopted to find out the PCU values  PCU based on speed and area ratio  PCU based on Homogeneous Co-efficient Method  PCU based on Lagging headway Method  PCU based on Platoon Formation Method 7
  8. 8. INFERENCE FROM LITERATURE REVIEW  The lane width and the number of the lanes have the significant effect on the speed of the vehicles.  The provision of a service lane has higher benefit cost ratio as compared to adding an extra lane on the main carriageway without a service lane.  The physical size of a vehicle is an indicator of the pavement occupancy, which is crucial in operational characteristics of traffic stream.  The PCU for a vehicle type increases linearly with the carriageway width.  The influence of the roadway and traffic characteristics on vehicular movement could be easily studied from the model which simulates the traffic flow characteristics 8
  9. 9.  When volume of the traffic increases, the physical dimensions and low maneuverability of heavy vehicles become dominating and therefore heavy vehicles become more detrimental to the traffic stream as compared to all other vehicle types.  The PCE of the vehicle type varies with the traffic volume and its composition. It increases with an increase in compositional share of respective vehicle types in the traffic stream.  The parameters used in the estimation of PCU for homogeneous and mixed traffic are different for almost all the facility types.  Dynamic PCU values might better define the influence of a vehicle type in a traffic stream over different traffic flow conditions. Contd….. 9
  10. 10. Problem Identification Formulation of objective Review of literature Selection of study stretch Data collection Geometric data: Carriageway width Roadway type Presence of shoulders IR method: Classified volume count Speed Headway Data extraction PCU Estimation by the selected methods Result and Conclusion Methodology 10
  11. 11. Study location For this study, a 4-lane divided urban road and a 6- lane divided urban road are selected as study locations in Chennai. 6 lane divided roads Rajiv Gandhi Road or IT Corridor is a major road in suburban Chennai, India, beginning at the Madhya Kailash temple in Adyar in South Chennai and continuing south till Mahabalipuram, ultimately merging with the East Coast Road. This road is State highway-49A 4 lane divided roads Sardar Patel road which connects Guindy and Adyar. It is a Four Lane Divided Carriageway. 11
  12. 12. Study Stretch – IT corridor, Madhya Kailash 12
  13. 13. Study Stretch – Sardar Patel Road 13
  14. 14. PARAMETERS INFLUENCING PCU VALUES Speed Headway Delay Density Travel time Queue Discharge flow For homogeneous traffic conditions several factors are considered, some of them are listed below Besides these for mixed traffic some other parameters are also considered which are as follows Area occupancy Platoon Formation Time occupancy Vehicle hours Influence area V/C ratio Note : Road width, Presence of shoulders, roughness of road, traffic composition, Land use and type of facility are also some of the factors which influences the PCU values 14
  15. 15. Sardar patel road Characteristics of study area SH – 49 A Towards Perungudi Six lane divided 3.1 m No Shoulder Location Direction Type Lane width Shoulder type Study area IT-Corridor SH – 49 Towards Guindy Four lane divided 3.5 m Paved Shoulder 15
  16. 16. Vehicle Composition – IT Corridor 2 - Wheeler 57 % Auto 12 % Car 23 % LCV 4 % MCV 3% 16
  17. 17. Vehicle Composition – Sardar Patel Road 2 - Wheeler 53 % Auto 10 % MCV 3 % Car 30 % LCV 4 % 17
  18. 18. Lane usage – IT Corridor L1 L2 L1 – L2 L2 – L3 • Lane usage are detected by the instrument used for data collection known as TIRTL. • Based on the time interval between the infrared beams from transmitter to the receiver, the lane usage are detected. • The collected data shows that the 28% of the total vehicle used L1, 40% of the total vehicle used the lane L2, 22% of the total vehicle used L3, 6% of the total vehicle doesn’t follows the lane discipline and travels in between lane 1 and lane 2 and 9% of the total vehicle doesn’t follows the lane discipline and travels in between lane 2 and lane 3 L3 18
  19. 19. Lane usage – Sardar Patel road L1 L2 L1 – L2 SL • Lane usage are detected by the instrument used for data collection known as TIRTL. • Based on the time interval between the infrared beams from transmitter to the receiver, the lane usage are detected. • The collected data shows that the 56% of the total vehicle used L1, 33% of the total vehicle used the lane L2, 11% of the total vehicle travelled in the intermediate distance, 0% of the total vehicle used shoulder for maneuvering 19
  20. 20. Speed and area ratio 01 02 Platoon Formation 03 Based on Homogeneous Co-efficient method Lagging headway 04 PCU estimation methods 20
  21. 21. Speed and area ratio method • The PCU of different categories of vehicles are estimated based on the speed of the individual vehicles and the area occupied by them. PCU could be estimated from the relation shown below PCUi = (Vc /Vi ) / (Ac/Ai ) PCUi = Passenger Car Unit of vehicle type i. Vc, Vi = Average speed of small car and vehicle type i, respectively Ac, Ai = Projected area of small car and vehicle type i, respectively. 21
  22. 22. Speed and area ratio method Vehicle Category Minimum Value PCU Maximum Value 2-Wheeler 0.2 0.25 0.36 Auto 0.87 1.1 1.61 Car - 1 - LCV 1.65 2.2 3.79 MCV 5.11 6.51 10.29 Vehicle Category Minimum Value PCU Maximum Value 2-Wheeler 0.14 0.24 0.44 Auto 0.64 1.02 2.66 Car - 1 - LCV 1.17 2.04 4.16 MCV 4.23 6.11 9.74 Sardar Patel Road IT Corridor 22
  23. 23. PCU based on Homogeneous Co-efficient Method Permanent International Association of Road Congress (PIARC) proposed a model to determine Homogeneous Coefficient (or PCU) of a vehicle category present in a mixed traffic stream. The speed, as well as the length of a vehicle, were considered to formulate the Homogeneous Coefficient (HCi) as given below. PCUi = (Li/ Vi) / (Lc / Vc ) Where Li is the length of the subject vehicle (m) Lc is the length of the standard car Vi is the speed of the subject vehicle (Km/hr) Vc is the speed of the standard car (Km/hr) 23
  24. 24. PCU based on Homogeneous Coefficient Method Vehicle Category Minimum Value PCU Maximum Value 2-Wheeler 0.38 0.46 0.65 Auto 0.75 0.96 1.42 Car - 1 - LCV 0.82 1.18 2.43 MCV 1.66 2.4 3.67 Vehicle Category Minimum Value PCU Maximum Value 2-Wheeler 0.29 0.44 1.05 Auto 0.56 0.9 2.72 Car - 1 - LCV 0.65 1.15 2.25 MCV 1.13 5.25 9.37 Sardar Patel Road IT Corridor 24
  25. 25. PCU based on lagging headway Here, PCEs is defined as the ratio of the mean lagging headway of a subject vehicle divided by the mean lagging headway of the basic passenger car. Lagging headway is defined as the time or space from the rear of the leading vehicle to the rear of the vehicle of interest; it is composed of the length of the subject vehicle and the intravehicular gap. PCUi = (Hij) / (Hpcj ) Lagging headway of various categories of vehicles was calculated from the aggregated data and the PCUs of various categories of vehicles are calculated. 25
  26. 26. PCU based on lagging headway Vehicle Category Minimum Value PCU Maximum Value 2-Wheeler 0.23 0.78 3.27 Auto 0.10 0.87 4.36 Car - 1 - LCV 0.14 1.04 3.91 MCV 0.13 1.2 4.6 Vehicle Category Minimum Value PCU Maximum Value 2-Wheeler 0.23 0.89 3.27 Auto 0.10 0.85 4.36 Car - 1 - LCV 0.14 1.31 3.91 MCV 0.13 1.15 4.6 Sardar Patel Road IT Corridor 26
  27. 27. Platoon Formation Method The proper value of critical headway is used to decide whether vehicles included in platoons or not. The value of critical headway can be determined based on the mean relative speed method. RSPi = Si – Si-1 RSPi is the relative speed between vehicles i and i-1 in km/h Si is the speed of vehicles i in km/h. In the present study, to estimate the PCU values of car, two-wheeler, LCV, MCV and trucks, the Huber’s concept was used. In Huber method two streams, one containing only Passenger car (base stream) and the other containing Passenger cars and vehicle type for which PCU values is going to be estimated . 27
  28. 28. Sardar Patel Road IT Corridor Platoon Formation Method Vehicle Category Minimum Value PCU Maximum Value 2-Wheeler 0.53 0.55 0.55 Auto 0.55 0.71 0.75 Car - 1 - LCV 1.07 1.15 1.22 MCV 2.47 2.66 3.35 Vehicle Category Minimum Value PCU Maximum Value 2-Wheeler 0.44 0.52 0.55 Auto 0.52 0.7 0.78 Car - 1 - LCV 1.16 1.18 1.23 MCV 2.37 2.47 2.52 28
  29. 29. CAPACITY ESTIMATION - Greenshield model 0 10 20 30 40 50 60 70 0 1000 2000 3000 4000 5000 Speed Flow 0 10 20 30 40 50 60 70 0 5000 10000 15000 Speed Flow 0 10 20 30 40 50 60 70 0 5000 10000 15000 Speed Flow y = -0.1033x + 51.737 R² = 0.4764 0 10 20 30 40 50 60 70 0 100 200 300 400 Speed Density y = -0.1598x + 53.733 R² = 0.6351 0 10 20 30 40 50 60 70 0 50 100 150 Speed Density y = -0.1622x + 53.845 R² = 0.6449 0 10 20 30 40 50 60 70 0 50 100 150 Speed Density Speed and area ratio method Homogeneous Coefficient method Lagging Headway method Platoon formation method Sardar Patel Road 29
  30. 30. CAPACITY ESTIMATION - Greenshield model PCU estimation Methods Capacity (PCU/hr) R2 value Equation Speed and area ratio method. 4469 0.6449 Y= -0.1622x + 53.845 Homogeneous coefficient method 4517 0.6351 Y= -0.1598x + 53.733 Lagging headway method 6478 0.4764 Y= -0.1033x+ 51.737 Platoon formation method 3008 0.1948 Y= -0.3261x +53.866 Sardar Patel Road 30
  31. 31. CAPACITY ESTIMATION - Greenshield model Speed and area ratio method Homogeneous Coefficient method Lagging Headway method Platoon formation method IT Corridor y = -0.1455x + 52.824 R² = 0.3219 0 10 20 30 40 50 60 0 20 40 60 80 Speed Density 0 10 20 30 40 50 60 0 2000 4000 6000 Speed Flow y = -0.1372x + 52.905 R² = 0.3595 0 10 20 30 40 50 60 0 20 40 60 80 100 Speed Density 0 10 20 30 40 50 60 0 2000 4000 6000 Speed Flow y = -0.0778x + 50.778 R² = 0.3335 0 10 20 30 40 50 60 0 50 100 150 Speed Density 0 10 20 30 40 50 60 0 5000 10000 Speed Flow 31
  32. 32. CAPACITY ESTIMATION - Greenshield model IT Corridor PCU estimation Methods Capacity (PCU/hr) R2 value Equation Speed and area ratio method. 4295 0.3219 Y= -0.1455x + 52.824 Homogeneous coefficient method 5100 0.3595 Y= -0.1372x + 52.905 Lagging headway method 8285 0.3335 Y= -0.0778x + 50.778 Platoon formation method 7140 0.1462 Y= -0.0467x + 49.145 32
  33. 33. PCU Comparison 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 2 Wheeler Auto Bicycle Car HCV 0.25 1.10 1.00 2.20 6.51 0.46 0.96 1 1.18 2.4 0.78 0.87 1 1.04 1.2 0.52 0.7 1 1.18 2.47 IT Corridor Speed area ratio method Homogeneous Coefficient Method Lagging headway method Platoon PCU 33
  34. 34. PCU Comparison 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 2 Wheeler Auto Bicycle Car HCV 0.24 1.02 1.00 2.04 6.11 0.44 0.9 1 1.15 2.15 0.89 0.85 1 1.31 1.15 0.55 0.71 1 1.15 2.66 PCU Vehicle class Sardar Patel Road speed and area ratio Homogeneous coefficient method Lagging Headway Platoon PCU 34
  35. 35. CONCLUSION  Based on the analysis of the observed data it was found out that the Speed and area ratio method is the best suited for estimating the PCUs of the different vehicle categories in the Urban roads in which there is no lane discipline is being followed.  The platoon formation methods could also be used to identify the PCU values in a situation in which the Speed and area ratio method is found to be unreliable. Capacity (PCUs/hr) Sathish Chandra method Homogeneous Coefficient method Lagging headway method Platoon formation method Sardar Patel Road 4469 4517 6479 3008 IT Corridor 4795 5100 8285 7140 35
  36. 36. LIMITATIONS OF THIS STUDY  Because the data was collected on urban roads, large vehicles such as HCVs, MAVs, and tractors have a very minor role in the traffic flow. As a result, the PCU values for these vehicles could not be calculated in this study.  If the nature of the traffic flow changes, the PCU values found out during this study may alter.  Since the PCU values found out using the Platoon formation method are based on the vehicular combinations, if there are more combinations available then the PCU values may alter. 36
  37. 37. REFERENCE 1. Anand. S., Sekhar. S. V. C., & Karim. M. R. (1999). Development of passenger car unit values for Malaysia. Journal of the Eastern Asia Society for Transportation Studies, Vol.3, No.3, September, 73-80. 2. Ashish Dhamaniya and Satish Chandra (2013), Concept of Stream Equivalency Factor for Heterogeneous Traffic on Urban Arterial Roads, Journal of Transportation Engineering, Vol. 139, No. 11, November 1, 2013. 3. Chandra, S., and Sikdar, P. K. (2000). “Factors affecting PCE in mixed traffic situations on urban roads.” Road Transp. Res., 9(3), 40–50. 4. Debasis Basu, Swati Roy Maitra and Bhargab Maitra (2006), Modelling Passenger Car Equivalency at an urban midblock using stream speed as the measure of equivalence, European Transport Trasporti Europei n. 34 (2006): 75-87. 5. Geetam Tiwari, Joseph Fazio, Sri Pavitravas (2007), ‘Passenger car units for heterogenous traffic using modified density method’, National transportation library, US department of Transportation, Document 8612, pp. 246. 6. Pooja Raj, Kalaanidhi Sivagnanasundaram, Gowri Asaithambi and Ayyalasomayajula Udaya Ravi Shankar (2019), Review of Methods for Estimation of Passenger Car Unit Values of Vehicles, Journal of Transportation Engineering, Part A: Systems. 37
  38. 38. 7. Rahman and Nakamura (2005), Measuring Passenger Car Equivalents for nonmotorized vehicle (rickshaws) at mid-block sections, Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 119 - 126, 2005. 8. Sabayasachi Biswas, Ms. Somya Singh, Mr. Nihal Malik, Mr. Atul V.Bisen (2018), Estimation of Passenger Car Unit by Multi-objective optimization technique. 9. Satish Chandra and U. Kumar (2003), “Effect of Lane Width on Capacity under Mixed Traffic Conditions in India.” Journal of Transportation Engineering, ASCE 129 (2): 155–160. doi:10.1061/(ASCE)0733- 947X(2003)129:2(155). 10. Subhadip Biswas, Satish Chandra & Indrajit Ghosh (2018): An advanced approach for estimation of PCU values on undivided urban roads under heterogeneous traffic conditions, Transportation Letters, DOI: 10.1080/19427867.2018.1563268 11. V. Thamizh Arasan and Reebu Zachariah Koshy (2005), Methodology for Modeling Highly Heterogeneous Traffic Flow, the Journal of Transportation Engineering, Vol. 131, No. 7, July 1, 2005. 12. V. Thamizh Arasan and Shriniwas S. Arkatkar (2010), Microsimulation Study of Effect of Volume and Road Width on PCU of Vehicles under Heterogeneous Traffic, the Journal of Transportation Engineering, Vol. 136, No. 12, December 1, 2010 38
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