Technical Content
Investigation of Heavier-Than-Expected Vehicle Weights at Non-Interstate WIM Site in Georgia
The Port of Savannah, among the busiest maritime hubs in the U.S., has witnessed an escalating container capacity, significantly straining local infrastructure. This stress manifests as potential damage to pavements and bridges. This research focuses on quantitatively measuring the impact of heavy vehicle traffic on these structures and gauging the extent of damage. Additionally, there’s growing concern over vehicles in Georgia exceeding weight limitations beyond the Port’s traffic. This study seeks to quantify the frequency and weight of such over-limit vehicles. To achieve this, Weigh-in-Motion (WIM) technology will be employed at select Georgia sites.
Primary Objectives:
1. Provide insights into the expected rate of transportation infrastructure degradation in Georgia, enabling GDOT to comprehend both the immediate and anticipated implications of heavy vehicle weights.
2. Bolster the reliability of infrastructure supporting the transport of goods near port facilities, thereby enhancing statewide economic development. This will be achieved through on-site examinations of pavements and bridge structures and an in-depth analysis of the assets informed by dynamic WIM data.
Detailed Objectives:
1. Examine the distinct characteristics of various non-interstate WIM sites in Georgia, leveraging data from GDOT’s Traffic Analysis & Data Application (GDOT-TADA).
2. Gather data concerning gross vehicle weight and its frequency of occurrence.
Weigh-in-Motion (WIM) system is specialized technologies designed to capture axle and gross vehicle weights as vehicles pass over sensors. This system can provide valuable data sets that include gross vehicle weight, axle loads, vehicle speed, and type.
In this research, WIM data from non-interstate sites in Georgia will be analyzed to assess the impact of heavy vehicle traffic on local infrastructure. Using data from the GDOT-TADA, the research aims to classify vehicles, calculate frequency based on gross vehicle weight, and determine ESALs(Equivalent Single Axle Load). This will help quantifying the stress on pavements and bridges, informing infrastructure planning and policy enforcement.
In Georgia, there are 30 active Weigh-in-Motion (WIM) sites. Of these, seven are situated on non-interstate roads, as illustrated in Figure 2. For this study, publicly available monthly WIM statistical data from 2021 to 2023 were collected from the GDOT-TADA website. This dataset comprises both directional and weight-based traffic volumes. Additionally, raw data for these seven non-interstate WIM sites were directly obtained from GDOT.
Using data collected from GDOT-TADA, the traffic characteristics of each WIM station were analyzed. Figure 3 represents one of the seven selected non-interstate WIM stations, specifically 115-0052. The graph is color-coded to differentiate between the years 2021, 2022, and 2023. The x-axis denotes the months, while the y-axis represents the average daily traffic volume. Given that the existing weight limit for vehicles in Georgia is 80kips and the weight limit for vehicles to and from the Port is 100kips, separate graphs were created to represent traffic volumes exceeding 80kips, between 80-100kips, and above 100kips. Additionally, both bidirectional and unidirectional (eastbound and westbound) traffic volumes were graphed. The key findings from the analysis are as follows:
• Consistent heavy vehicle traffic trends from 2021 to 2023.
• Eastbound traffic was more than twice as high as westbound.
• A higher proportion of vehicles exceeded 100kips.
The same graphical format was applied to analyze the traffic characteristics of the other six non-interstate WIM stations.
Using GDOT’s WIM raw data, we analyzed heavy vehicle traffic, focusing on vehicle classes 4 to 13 as categorized in Figure 4. An example analysis for WIM station 003-0132, based on 2021 data, is displayed in Figure 5. The left set of graphs shows the distribution of vehicle weight, single axle weight, and tandem axle weight for all target vehicle classes. For single-unit vehicles (Classes 4-7), the middle set of graphs displays similar distributions, while the right set of graphs covers multi-unit vehicles (Classes 8-13).
The vehicle weight distribution graph is color-coded based on load limits: the traditional 80 kips, 10% above standard (88 kips), and the new 100 kips limit. Single axle weight is color-coded according to reference points set at 18 kips for pavement and 20 kips for bridges. Tandem axle weight uses 34 kips as the standard reference point for both pavement and bridge, with additional markers at 25% above standard (42.5 kips) and 50% above standard (51 kips) also indicated.
Utilizing the dataset spanning from 2021 to 2023, the analysis incorporates all seven non-interstate WIM stations. The findings aim to illuminate the traffic characteristics unique to each station. An upcoming focus will be to quantify the degradation impact on both pavement and bridge structures due to heavy vehicles that exceed standard weight limits. Furthermore, plans are in place to correlate the weight violation frequencies with maintenance records to establish if higher rates of infractions lead to quicker degradation of infrastructure. Ultimately, this research intends to provide foundational data that can be used to refine transportation infrastructure policies, potentially influencing future road maintenance scheduling and budget allocation strategies.
