Existing public transport (PT) planning methods use a trip-based approach, rather than a user-based approach, leading to neglecting equity. In other words, the impacts of regular users—i.e., users with higher trip rates—are overrepresented during analysis and modelling because of higher trip rates. In contrast to the existing studies, this study aims to show the actual demand characteristic and users’ share are different in daily and monthly data. For this, 1-month of smart card data from the Kocaeli, Turkey, was evaluated by means of specific variables, such as boarding frequency, cardholder types, and the number of users, as well as a breakdown of the number of days traveled by each user set. Results show that the proportion of regular PT users to total users in 1 workday, is higher than the monthly proportion of regular PT users to total users. Accordingly, users who have 16–21 days boarding frequency are 16% of the total users, and yet they have been overrepresented by 39% in the 1-day analysis. Moreover, users who have 1–6 days boarding frequency, have a share of 66% in the 1-month dataset and are underrepresented with a share of 22% in the 1-day analysis. Results indicated that the daily travel data without information related to the day-to-day frequency of trips and PT use caused incorrect estimation of real PT demand. Moreover, user-based analyzing approach over a month prepares the more realistic basis for transportation planning, design, and prioritization of transport investments.

Heavy vehicles, considering their size and low rate of acceleration and/or deceleration in traffic stream, are responsible for numerous negative effects on other vehicles. As a result, the capacity of signalized intersections declines significantly as the ratio of heavy vehicles in traffic flow is increased. In literature, there are different methods that are used with the goal of determining those negative effects. HR (Headway Ratio) is one of the most frequently used methods amongst all other methods. In this study, HR method was extended by substituting independent probability combinations set of vehicle types for percentage (%) of heavy vehicles in heavy vehicles adjustment factor F_HV equation. Also, four independent car-following combinations probability were considered: heavy vehicle following passenger car (H-C), passenger car following heavy vehicle (C-H), passenger car following passenger car (C-C), and heavy vehicle following another heavy vehicle (H-H). Probabilities of occurrence of each combination for percentage (%) of heavy vehicles were calculated by using the Bayes' theorem. The comparisons of results with different studies indicate that there is a significant difference between suggested new model and other studies on high heavy vehicle ratio condition. Moreover, the results showed that the proposed new model may give more accurate results being used in the intersections with high ratio of heavy vehicles.