UC Irvine

Statewide travel forecasting models are developed by state agencies for different purposes such as forecasting network congestion, fuel consumption and air pollution. But in the end, they model the same travel activity from different procedures. Among those models and surveys, the California Statewide Freight Forecasting and Travel Demand Model (CSF2TDM), California Vehicle Inventory and Use Survey (CA-VIUS) from the California Department of Transportation (Caltrans), and the Emission Factor (EMFAC) model from the California Air Resources Board (CARB), are the most well-known ones in California. This thesis compared these models based on results such as Vehicle Miles Traveled (VMT) and vehicle inventory for heavy duty class 8 trucks. In addition, it connected the commodity-based activity of CSF2TDM to the CA-VIUS class 8 truck inventory and forecasted this population for future years. CSF2TDM and CA-VIUS forecasted 17, 19 and 27 percent less class 8 trucks for 2030, 2040 and 2050 target years compared to the EMFAC model. This difference is due to the different procedures and inputs these models have. EMFAC is good at capturing all truck activity while lacking detailed characteristics such as geographical resolution, while CSF2TDM provides a detailed profile of truck activity on the network with no truck inventory associated with truck activity. Moreover, new policies in California are raising questions about the infrastructure impact of zero emission vehicles and electrification of vehicles. The second part of this thesis investigated a framework for feasibility of electric class 8 trucks in California by analyzing the optimal locations of charging stations and their impact on grid infrastructure based on forecasted travel demand from CSF2TDM. The framework would determine the fraction of truck trips that are not feasible for electrification. Feasible trips would be analyzed under two scenarios: charge at origin and charge at destination. Charge at origin means truck gets charged for the trip at the origin and charge at destination means a truck is fully charged at the origin, makes the trip and then gets charged at the destination to get the battery full. Since the OD matrix is not symmetrical, there would be a difference in charging demand on the grid network under these two scenarios.