Freight transportation is an indicator of the regional economy. In the United States, transportation by trucks accounted for 63.8% of total freight during the year 2015. The demand for freight transportation is increasing from time to time. It is anticipated that freight transportation will increase by 43% over the next 20 years. The increase in population, employment, and e-shopping (online shopping) along with the introduction of free shipping, 2-day fast shipping, etc. are some of the related factors that are expected to contribute to the increase in freight transportation over time.The movement of freight can be directly linked to the percentage of trucks on roads. These trucks differ in their physical and operational characteristics compared to passenger vehicles (in general, cars). The Highway Capacity Manual (HCM) recommends adopting the Passenger Car Equivalent (PCE) concept to account for the effect of trucks on road capacity and operational performance. The PCE varies from one location to another location and depends on geometric and traffic conditions.Many researchers have worked on computing and calibrating the PCE. However, it is not clear if the effect would be similar in terms of travel time (truck travel time compared to passenger car travel time). The area type, time-of-the-day, day-of-the-week, reference speed (free-flow speed), and the number of vehicles observed in a given time period (an indicator of data density) influence the travel time of a truck when compared to the travel time of a passenger car or other vehicle types. Therefore, the goal of this research is to examine the relationship between the travel time of trucks and the travel time of passenger cars or all vehicles to assess PCE of a truck from a travel time perspective. The objectives of this research are:1. to examine the relationship between the travel time of trucks and the travel time of passenger cars or all vehicles at the link-level,2. to examine to what extent the area type (the urban and rural) influences the relationship,3. to examine the relationship between the travel time of trucks and the travel time passenger cars or all vehicles by data density, day-of-the-week (DOW), time-of-the-day (TOD), and reference speed, and,4. to identify significant factors influencing the ratio of the travel time of trucks to the travel time of passenger cars or all vehicles.Travel time data for an urban area (Mecklenburg County) and a rural area (Iredell County), North Carolina was gathered from the National Performance Management Research Dataset (NPMRDS) under the Regional Integrated Transportation System (RITIS) website for the year 2017. In the urban area and the rural area, three travel time datasets were considered; the first dataset includes travel time data for trucks only, the second dataset includes travel time data for passenger cars only, and the third dataset includes travel time for all vehicles. In the urban area, travel time data for 894 links were considered for trucks only, 798 links were considered for passenger cars only, and 894 links were considered for all vehicles. Likewise, in the rural area, travel time data for 137 links were considered for trucks only, 133 links were considered for passenger cars only, and 137 links were considered for all vehicles.The data was processed to compute the average travel time of trucks, the average travel time of passenger cars, and the average travel time of all vehicles at link-level by area type, TOD, DOW, reference speed, and data density. Ordinary Least Square (OLS) regression models and Generalized Estimating Equations (GEE) models were developed to examine the relationships. The average travel time of trucks was considered as the dependent variable while the average travel time of passenger cars or all vehicles were considered as the independent variable when developing OLS regression models.The Pearson correlation coefficients were then computed to examine the relationship between travel time measures, area type, data density, DOW, TOD, and reference speed. A sample of 7654 records was used for this analysis and developing GEE models. The ratio of the average travel time of trucks to the average travel time of passenger cars or all vehicles was considered as the dependent variable while the area type, TOD, DOW, reference speed, and data density were considered as the independent variables when developing GEE models.Overall, one-hundred and seven models were developed using data for the urban and rural areas in this research. Of these, thirty-eight OLS regression models were developed after categorizing data by TOD (morning peak period, off-peak period, evening peak period, and night-time period). Forty-three OLS regression models were developed after categorizing data by the reference speed (<= 30mph, >30mph to <=40mph, >40mph to <=50mph, and >50mph). Twenty-two OLS regression models were developed by categorizing data by DOW. Four GEE models were developed considering area type, TOD, DOW, reference speed, and data density altogether. The GEE models were validated using the Root Mean Square Error (RMSE), and the Mean Absolute Percentage Error (MAPE).The average travel time of trucks was observed to be greater than the average travel time of passenger cars and all vehicles irrespective of the area type. Likewise, the average travel time of trucks was observed to be greater than the average travel time of passenger cars on links with a reference speed of less than 30 mph irrespective of the area type. In the rural area, the average travel time of trucks was more than the average travel time of passenger cars and all vehicles during the weekday when compared to the weekend. In the urban area, no trends in the relationship between the average travel time of trucks and the average travel time of passenger cars were observed by DOW. In the rural area, the average travel time of trucks was observed to be greater than the average travel time of passenger cars during the evening peak period when compared to other times of the day. However, the average travel time of trucks was observed to be greater than the average travel time of passenger cars during the off-peak period and the evening peak period for data density A, night-time period for data density B, and the morning peak period for data density C.Gamma log-link distribution-based models were observed to be a best-fitted model in this research. The results obtained indicate that the area type, data density, DOW, TOD, and reference speed have a significant influence on the ratio of the average travel time of trucks to the average travel time of passenger cars. The reference speed and DOW were observed to be negatively associated with the ratio of the average travel time of trucks to the average travel time of passenger cars. All the other variables were observed to be positively associated with the ratio of the average travel time of trucks to the average travel time of passenger cars. The DOW did not have a significant influence on the ratio of average travel time of truck and the average travel time of all vehicles.The findings from this research help practitioners and professionals to better plan, design, and operate the transportation infrastructure. They can also be used to assess the effect of the trucks on the travel time of other vehicles and the transportation system performance.