Estimating Traffic Volume Based on Sampling Expansion Technique and Geographically Weighted Poisson Regression

Abstract:

A method combining sampling expansion with geographically weighted Poisson regression (GWPR) was proposed to estimate the road network traffic volume with limited observation values. Firstly, a sampling expansion method based on the spatial similarity was employed to correct the imbalance missing data. Then, the GWPR was employed to estimate the hourly traffic volume of the lane considering the influence of the geometric characteristics of the road and the built environment. Results show that: compared with traditional linear models and GWPR with the original sample set, the proposed combination model has the best estimation performance. In addition, the local spatial heterogeneity of the relationship between independent variables and traffic volume is also well captured.