Traffic monitoring is one of the primary activities of state highway agencies. A reliable estimation of the traffic is vital for the management and future planning of the roadways, and as well as the apportionment of the federal funding. Traffic Monitoring Program in states is responsible for collecting, storing, processing, and disseminating the traffic data. Determination of volume and vehicle classification trends, utilization of appropriate MADT and AADT estimation methods, establishment of Traffic Pattern Groups (TPG) and use of the adjustment factors to expand the short duration counts are some of the primary activities within states' traffic monitoring program. ☐ DelDOT Traffic Monitoring Program has been evaluated and updated to establish the TPGs and derive the adjustment factors to represents the current traffic conditions in Delaware. Analysis of data revealed few problems that should be addressed (i.e. adjustment factors are sometimes not properly used, and TPGs are not regularly evaluated/updated). Additionally, a national level survey conducted to understand the issues and challenges that state highway agencies facing in collecting and processing of state traffic monitoring data, specifically continuous and shortduration data. Both survey responses and DelDOT analysis results have shown that a Knowledge-based Expert System (KBES) application can contribute to states' traffic monitoring program by informing and guiding the user to improve the traffic monitoring related decisions. ☐ The primary objective of this study was to develop a KBES application, called TMDEST, for providing assistance and decision support tool to the transportation agencies in states' traffic monitoring programs, specifically in TPG analysis. TMDEST asks focused and relevant questions to the user and provide situation-specific advice in six modules. In some modules, the user is asked to provide numerical input such as the number of stations and coefficient of variation value if available. ☐ Class/Weight Trend Module is designed to guide the user to identify the most important vehicle classes and the trucks that exert the most weight by using FHWA's VTRIS W-Tables. MADT/AADT Methods Module and TPG Methods Module are designed to inform the user regarding the major MADT/AADT estimation methods and TPG analysis methods to recommend the most appropriate methods based on the presence and amount of missing data and the inclusion of temporal variations. TPG Groups Module provides an approximate estimation of TPGs based on roadway functional classification and seasonal variation. Sample Size Estimation Module is designed to test the number of continuous count stations in each TPG for statistical significance. Lastly, Adjustment Factors Module incorporates all possible adjustment factors and evaluates the necessity of the use by asking multiple-choice questions to the end user regarding the extent of the collected short duration data. ☐ Overall evaluation of the TMDEST revealed that each module well satisfies the design specifications, and in general, the developed tool (1) informs and guides the user regarding the methods and procedures, (2) provides an approximate method for establishing TPGs. Additionally, verification, validation, and evaluation of the TMDEST showed that the expert system based tool was built right and does the job that it intends to do. Utilization of an expert system development tool (Exsys Corvid® Core) significantly expedited to the verification and validation process. The simple proof method was used to evaluate each module for completeness, consistency, and correctness. Although the majority of the content in the knowledge base was obtained from FHWA's traffic monitoring guide, simple true/false test was applied to the modules where the content was partially generated to validate the knowledge base. TMDEST and each module are considered as valid and applicable tool in states traffic monitoring program. Lastly, a discussion of further work is provided to improve the extent of the TMDEST in states' traffic monitoring program.