Abstract
Developing an understanding of the socio-economic factors that can be used to generate truck trip productions and attractions in small and medium sized communities can be used to improve travel models and provide better information upon which infrastructure decisions are made. Unfortunately, it is difficult to collect this data in a timely, cost-effective manner. This paper presents a methodology that uses matrix estimation techniques from existing traffic counts to develop origin/destination pairs that can be used to statistically develop truck trip generation models. A case study is presented and a model is presented for one smaller urban community.
Highlights
Freight transportation has become an increasingly important issue for small and medium sized communities [1-3]
This paper presents a methodology that uses matrix estimation techniques from existing traffic counts to develop origin/destination pairs that can be used to statistically develop truck trip generation models
This paper examined the use of a matrix estimation technique to develop a trip generation model for truck trips in a medium sized community
Summary
Freight transportation has become an increasingly important issue for small and medium sized communities [1-3]. Understanding which factors influence truck productions and attractions, and the magnitude of the influence within the modeling environment, could improve the accuracy of the models and potentially lead to better decisions regarding transportation infrastructure investment These factors, if understood, could be developed into a truck trip generation model similar to other models that have been proposed [3], but developed for a local area. The paper discusses the application of the methodology to convert the truck counts into an origin/destination matrix for a case study community and applies statistical techniques to determine the appropriate factors for truck trip generation using the number of truck produced and attracted to areas of the network and the corresponding socio-economic data for those areas. The contribution of the paper is the application of the methodology and a model that potentially can be utilized by other communities
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