Comparison of Traffic Volume Estimates from Greenshield Modeling Using Google Traffic Data and Field Survey Results in Palembang City, Indonesia

Traffic volume serves as a critical metric for assessing the efficiency and planning of urban road infrastructure. Traditionally, traffic volume data is collected through field surveys, a process that demands substantial time, resources, and financial investment. As an alternative, the utilization of secondary data sources, such as Google Traffic data, offers a more efficient and cost-effective method for estimating traffic volume with improved accuracy. This study applies Greenshield's modelling to estimate traffic volume. The primary objective of this research is to validate the traffic volume estimation derived from Greenshield's model, using speed data obtained via an API, by comparing it with field-observed traffic survey data in Palembang City, Indonesia. The data retrieved from the Google Traffic server via the Application Programming Interface (API) consists of real-time travel times recorded at 15-minute intervals for a fixed-distance road segment. The real-time speed of a given road segment is computed directly based on the ratio of distance to travel time when data retrieval. The free-flow speed (<img src=image/14841715_01.gif>) is determined under uncongested traffic conditions and is represented by the average speed recorded between 22:00 and 05:00. Meanwhile, the average speed (<img src=image/14841715_02.gif>) corresponds to the traffic conditions observed during specific time periods that align with field survey observations, namely morning (06:00–08:00), afternoon (12:30–14:30), and evening (16:00–18:00). Additionally, the maximum vehicle density (<img src=image/14841715_03.gif>) during congestion is assumed to be 200 pcu/km for each lane. Subsequently, the model integrates the accumulated values of traffic density (<img src=image/14841715_04.gif>) and average vehicle speed (<img src=image/14841715_02.gif>) to determine the traffic volume using an hourly traffic flow (<img src=image/14841715_05.gif>) approach on the road. This study showed a strong correlation existed between traffic volume estimated using Greenshield modeling with Google Traffic speed data and traffic volume data collected from the field survey. The coefficient of determination (<img src=image/14841715_06.gif>) was recorded at 0.87, while the Pearson correlation coefficient reached 0.93, reflecting a high degree of agreement between the modeled and observed traffic volumes. These results showed that Greenshield modeling provided a reliable estimation of traffic volume, as evidenced by how the estimated value closely approximated direct field observations.