DEVELOPMENT OF A FREIGHT DATABASE FOR USE IN ALLOCATING FREIGHT TRAFFIC TO SUB-STATE TRAFFIC ZONES
Gregory Harris, Niles C. Schoening, Michael Anderson, Jeffrey S. Thompson, Robert J Jilla, Emmanuel C Oranika
Abstract
Gregory Harris, Niles C. Schoening, Michael Anderson, Jeffrey S. Thompson, Robert J Jilla, Emmanuel C Oranika
Abstract
This paper will discuss how the use of national freight data at the local level is challenging due to the high level of aggregation and the fact that all freight data is proprietary. Many national freight databases aggregate information to the individual states or major communities. Most methods of utilizing freight data depend on applying proxy factors in allocating freight to the system. The planning factors used in freight system analysis must be capable of describing the freight generation and attraction characteristics of the region. The use of employment as a planning factor has come under scrutiny mainly because of the inability of the factor to accurately estimate the effect of productivity improvements made by a company to increase production without increasing employment. This research has shown that local economic data from many different sources can successfully be used to allocate freight volume into smaller zones from the future freight traffic volumes provided by highly aggregated national databases. The output of this effort is used as input to the modeling of freight, and the integration of that freight into existing transportation planning and modeling activities at the state and local level. This has been accomplished in Alabama at the statewide and metropolitan planning organization level, resulting in validated transportation models that integrate freight into the planning activity. The methodology described in this paper can easily be replicated by other states and metropolitan planning organizations. The use of national freight data at the local level is challenging due to the high level of aggregation and it is proprietary. This research presents a framework for data development and integration into transportation models.
A significance statement is not available in the OpenAlex record.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
This paper will discuss how the use of national freight data at the local level is challenging due to the high level of aggregation and the fact that all freight data is proprietary. Many national freight databases aggregate information to the individual states or major communities. Most methods of utilizing freight data depend on applying proxy factors in allocating freight to the system. The planning factors used in freight system analysis must be capable of describing the freight generation and attraction characteristics of the region. The use of employment as a planning factor has come under scrutiny mainly because of the inability of the factor to accurately estimate the effect of productivity improvements made by a company to increase production without increasing employment. This research has shown that local economic data from many different sources can successfully be used to allocate freight volume into smaller zones from the future freight traffic volumes provided by highly aggregated national databases. The output of this effort is used as input to the modeling of freight, and the integration of that freight into existing transportation planning and modeling activities at the state and local level. This has been accomplished in Alabama at the statewide and metropolitan planning organization level, resulting in validated transportation models that integrate freight into the planning activity. The methodology described in this paper can easily be replicated by other states and metropolitan planning organizations. The use of national freight data at the local level is challenging due to the high level of aggregation and it is proprietary. This research presents a framework for data development and integration into transportation models.
Key concepts: Metropolitan area, Traffic management, Transport engineering, Aggregate (composite), Business, Operations research, Engineering, Geography