Studies on the economic impact of air cargo traffic have been gaining traction in recent years. The slowed growth of air cargo traffic at California’s airports, however, has raised more pressing questions amongst airport planners and policy makers regarding the determinants of air cargo traffic. Specifically, it would be useful to know howCalifornia’s air cargo traffic is affected by urban economic characteristics surrounding airports. Accordingly, this study estimates the socioeconomic determinants of air cargo traffic across cities in California. We construct a 7-year panel (2003-2009) using quarterly employment, wage, population, and traffic data for metro areas in the state. Our results reveal that the concentration of service and manufacturing employment impacts the volume of outbound air cargo. Total air cargo traffic is found to grow faster than population, while the corresponding domestic traffic grows less than proportionally to city size. Wages play a significant role in determining both total and domestic air cargo movement. We provide point estimates for the traffic diversion between cities, showing that 80 percent of air cargo traffic is diverted away from a small city located within 100 miles of a large one. Using socioeconomic and demographic forecasts prepared for California’s Department of Transportation, we also forecast metro-level total and domestic air cargo tonnage for the years 2010-2040. Our forecasts for this period indicate that California’s total (domestic) air cargo traffic will increase at an average rate of 5.9 percent (4.4 percent) per year.

This report presents the results of a multi-year research effort on the development of a real-time section-based traffic performance measurement system using inductive vehicle signatures obtained from single conventional loop sensors along a six-mile freeway corridor in the City of Irvine, California and a separate effort to investigate the potential of a new type of inductive sensor called the Blade™ for the purpose of commercial vehicle surveillance at the San Onofre Truck Weigh and Inspection Facility in Southern California. The real-time performance measurement system (RTPMS) is based on a new vehicle reidentification algorithm called RTREID-2 and vehicle classification model, both of which are based on a new data extraction method that extracts an equal number of Piecewise Slope Rate (PSR) values from each vehicle signature. As a part of this study, a framework based on CORBA was developed to enable communication between field computers and the RTPMS server. A database system was also developed to store the output from the RTPMS server and present it in a prototype RTPMS Testbed Website that presents advanced real-time traffic performance measures. In the separate investigation of Blade™ inductive sensors, a new commercial vehicle classification model was developed to profile commercial vehicles by their body type and axle configuration. A new commercial vehicle vector classification framework is introduced to describe the depth of information available from this developed model. The results obtained from both studies have yielded very promising results, and warrants the need for further investigation.