Most of the studies in the literature that considered traffic in locating facilities rely on simplified traffic models that assume uniform distribution of annual traffic counts over the whole network which limits their practicality due to inaccuracy.
Considering accurate traffic data in facility location can significantly affect on the performance of the located facilities. For example, emergency stations or distribution facilities for retailers with quick delivery services like ‘Amazon prime Now’ need to be located carefully.
In this research, a model is proposed to estimate hourly travel times for all links on the road network using publicly available traffic count data. Since traffic counts are only available for a small fraction of links on the network and on annual basis, the proposed model uses an inverse distance weighting interpolation based method to estimate unknown traffic counts. also, hourly traffic counts are estimated based on hourly traffic patterns.
The proposed method was tested on a case study involving traffic counts and pattern data for the city of Quincy, Illinois. The road network data was acquired from the OpenStreetMap project. The proposed method was implemented in a PostgreSQL/PostGIS open source spatial database and the results were analyzed and visualized in QGIS.