Collecting Commercial Vehicle Tour Data with Passive Global Positioning System Technology Issues and Potential Applications

total VKT (2). In developed countries, freight road transport continues to grow at a faster rate than the gross domestic product (3). This growth in commercial vehicle activity is increasing preexisting concerns over the associated negative externalities of urban freight movements. These externalities include greenhouse gases, air pollution, safety, and congestion and have been estimated to be as much as four times greater in terms of costs per kilometer than the corresponding costs of a private car (4). Despite the critical role played by freight transportation, urban freight movements have not received the same level of attention as that given to the movements of people. Urban freight transportation modeling remains a relatively immature field in which most models and applications are of a highly aggregate nature. Models of this type are ill suited to explain and predict the implications of behavioral policy changes such as a change in allowable operating hours, the introduction of congestion toll charges, or the introduction of time restrictions on deliveries. Commercial vehicle tours also have been largely ignored in most urban freight models around the world (5), with notable exceptions (6). Tours can be classified according to their routing constraints and supply chain characteristics such as cargo time sensitivity; these tour characteristics have a significant impact on the efficiency and VKT generated by freight movements in urban areas (7, 8). A primary reason for this limited understanding of freight movements is the paucity of disaggregate data on commercial vehicle movements (9). Although most trucking companies keep run sheets of their truck activities for their own business and benchmarking purposes, these detailed records are rarely made available to others because they contain sensitive customer information. When disaggregated data are available, the analysis of commercial vehicle route and trip chain structure can provide insightful information about urban commercial vehicle tours, travel patterns, and congestion levels. For example, a study based in Sydney, Australia, analyzed several months of detailed truck activity records (10). Route patterns were identified, and their relationship to trip and tour length distribution was analyzed. The analysis also highlighted how travel between different industrial suburbs explained the shape of multimodal trip length distributions and how congestion affects the duration of tours. Over the last 15 years, the ability to collect detailed travel information has been expanded by developments in global positioning system (GPS) technology. With GPS, it is now possible through integration with geographic information systems (GIS) to determine origins, destinations, travel times, distances, routes, and vehicle speeds at highly disaggregate levels of spatial and temporal resolution withCollecting Commercial Vehicle Tour Data with Passive Global Positioning System Technology Issues and Potential Applications