Problem Statement: Recent advancements in statewide freight modeling and commercial vehicle activity data collection programs in California have led to the need for more advanced methods of truck data collection across the state. However, the commercial vehicle count data needed for the California Statewide Freight Forecasting Model (CSFFM) developed for Caltrans or the California Vehicle Activity Database (CalVAD) managed by the California Air Resources Board (CARB) are either missing or expensive to obtain from current data resources.  Moreover, the recent development of the statewide Truck Activity Monitoring System (TAMS) by ITS-Irvine researchers shows that dynamic truck activity data in a complex road network has great potential for freight modeling, truck monitoring and emissions estimation, because trucks possess distinct travel patterns that are affiliated with industries and facilities.  Paired with truck weight and detailed body classification data, truck activity data can further indicate the efficiency of various industries in minimizing resource-wasting empty truck movements. However, despite the need for comprehensive truck weight data, the current methods for collecting such data are only limited to existing weigh-in-motion (WIM) sites. Proposal: This study proposes the extension of existing link-based truck tracking developed at ITS-Irvine to network-wide truck tracking across multiple detector stations along different truck corridors to estimate the path flow of trucks, by utilizing the existing inductive loop detector (ILD) infrastructure.  Since ILDs collect temporally continuous real-time traffic data for the full population of trucks traveling a given route, the network-wide tracking framework will facilitate the understanding of spatial and temporal truck flow patterns.  This proposed system provides collateral benefits to an advanced truck classification model recently developed at ITS-Irvine using the fusion of ILD and WIM technologies.  The combination of these two systems has the potential to provide detailed tracking of commercial vehicles by their body configuration and industrial affiliation to yield a comprehensive data source of detailed truck activity.  In addition to network truck tracking, this study will also investigate the estimation of truck gross vehicle weight (GVW) distributions at non-WIM locations to facilitate more accurate emissions estimations.  Therefore, this study proposes a cost-effective approach to estimate weights through the use of tracked truck flows by spatially interpolating weight distributions obtained from neighboring WIM sites to detector sites that are used for truck tracking. Expected Impact and Benefits: The expected modeling outcomes will be valuable for estimating greenhouse gas (GHG) estimations and in guiding policies towards the reduction of GHGs (SB 32, SB 350).  Specifically, CARB and CEC will benefit from improved emissions estimation through the availability of a finer spatial resolution of GVW distributions for heavy-heavy duty trucks (HHDT).  The outcomes of the proposed network vehicle tracking model are expected to support pilot projects of the California Sustainable Freight Action Plan by monitoring detailed truck activity along major truck corridors with advanced measures such as travel time and average delays by truck vocation (AB32, AB 2170).