An Investigation of Vehicle Travel Characteristics on Rural Arterials of Pakistan

Abstract

With ever increasing transportation demand, highway agencies are struggling to provide reliable level of service (LOS) to the road users. Knowledge of capacity of existing highway system is an important input for capacity enhancement decision making. In order to determine capacity and level of service, existing methodologies make use of concept of passenger car equivalent (PCE). PCE is used to convert a mixed traffic stream into a base stream (passenger cars only) for the determination of traffic density and highway capacity/LOS. Most of the existing PCE values are based on research carried out in USA and other developed countries. Pakistan being a developing country has traffic composition and vehicles travel characteristics considerably different from those of developed countries. Consequently, standard traffic parameters practiced in developed countries may not be appropriate for Pakistan. In present research, an effort has been made to develop PCE values for different vehicle classes on the rural arterials of Pakistan using the concept of lagging headway ratios. Video data were collected from four different locations on Grand Trunk Road (N-5) and software package “Traffic Tracker” was used for the extraction of necessary information from video clips of 10- minute duration. Three-stage-least-squares (3SLS) regression model was developed to estimate the lagging headways for passenger car (PC), passenger van (PV), single unit truck (SUT) and combination truck (CT). The PCE values estimated for PV, SUT, and CT are 0.94, 0.87 and 1.03 respectively. Study results revealed that passenger van has similar travel characteristics as that of passenger car (almost similar speed and headway). Also, there is no conclusive evidence that SUT and CT have large headways under stable stream conditions. However, additional efforts are required to refine the estimated PCE values for different vehicle classes on rural arterials of Pakistan using data from different geographical locations/ highways.