Problems related to transportation and inspection of valuable or sensitive assets such as commercial products and materials, cultural items and works of art, and hazardous materials share similarities and can be modeled by a core set of abstract entities including a payload, a vehicle, a driver, and an inspector. To make the load handling capabilities of security monitoring and inspection systems more scalable, security can be increased by reducing the variability of transportation routes to a finite set of authorized routes between trip origin and destination locations. Then trip anomalies, which are unexpected trip variations, can be used in inspection targeting criteria. In addition, the effectiveness of the inspection sampling rate can be increased by always inspecting trips with unjustifiable anomalies and fulfilling the remainder of the sampling rate with other trips. While some anomalies can be automatically detected by vehicle tracking systems, other anomalies can be detected by inspectors based on information provided by drivers. This creates a need for an independent objective verification of the quality of the information provided by the driver, as this information can be used to detect transportation anomalies and, in some cases, justify the anomalies to avoid costly inspections. This thesis proposes the use of a geographic awareness modeling (GAM) system to determine whether route-following anomalies of monitored vehicles can be justified by driver detection of adverse road conditions. While current GIS applications provide geographically accurate representations of a road network, they do not provide mechanisms or information to corroborate or contradict scenarios describing trip conditions along a given route. This thesis also presents a design and one implementation of a GAM system which can provide independent, reliable verification of route-related information provided by vehicle drivers. A benefit of implementing such a system would be to complement the expertise and the experience of inspectors to increase the effectiveness of inspection targeting policies and sampling rates.