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Traffic-related analyses are essential to implementing traffic management approaches to create mobility more convenient. In this context, Passenger Car Unit (PCU) values as a uniform measure of vehicles are instrumental in analysis such as traffic capacity estimation, traffic flow model development, level of service determination, etc. These studies always demand a recently calculated set of PCU values since the traffic conditions in arterials often change. PCU values for expressway and three-lane (one-way) road types have not been calculated in the Sri Lankan context, and the available set of values has become somewhat outdated. Hence, this study used Chandra's method to assess the PCU values of ten vehicle categories for four different mid-block road sections in the Colombo district, Sri Lanka. The data relating to the two main variables of this method, categorical speed, and area of vehicles, were collected from field surveys. The results of four-lane arterials were aggregated and compared with the existing PCU values, intending to propose an updated set for general transportation studies in Sri Lanka.

A technological revolution has emerged in the context of mobility surveys with the widescale usability of wearable and onboard global positioning system (GPS) devices. Sri Lanka is also at the edge of utilizing these, in replacing the traditional methods. The earnestness' for this transition is supported by the deficiencies such as higher cost, higher nonresponses rates, over and under-reporting, and small sample sizes of traditional surveys. The activity that a passenger performs after a trip, or the purpose of a trip is a vital concern in transportation research as it is the reason behind the generation of travel demands. Hence, trip purpose inference from GPS data has become an important study area in this context [1]. Gong et al. [2] reviewing the existing trip purpose inference studies categorized the methodologies that had been used into three as rule-based, probabilistic, and machine learning-based. In this study, we utilized the GPS records of taxi trips from a popular service provider in the country as shown in Figure 1 in developing a suitable trip purpose inference approach.

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