Estimation of Annual Average Daily Traffic for Nonstate Roads in a Florida County

Abstract

A study was undertaken to develop a methodology to estimate annual average daily traffic (AADT) for nonstate roads in urbanized areas in Florida. The current practice related to the estimation of AADT for nonstate roads has been of great concern to the Florida Department of Transportation because of the potential lack of accuracy in the estimated data. For this study, a multiple regression model was developed for estimating AADT on nonstate roads. The model utilized a large sample size (data from 450 count stations in Broward County) and involved the investigation of up to 12 initial variables. Various methods, including geographic information systems (GIS), were explored to convert the current digital data and aggregate them into suitable forms for statistical analysis. Statistical tests were performed and the results showed that the most important contributing predictors are roadway characteristics, such as the number of lanes, functional classification, and area type. Socioeconomic variables, including population, dwelling units, automobile ownership, employment numbers, and school enrollment in the surrounding area, have an insignificant impact on AADT. Further analyses revealed the deficiency of traditional roadway functional classification, the need to improve the method of road classification, and the need for alternative methods to account for the impact of economic activities on AADT.

A Multiple Regression Approach for Traffic Flow Estimation

“…Cheng [4] proposed a regression model based on road functional classification -road width, surface type and population in geographical areas to estimate traffic flow. Xia et al [32] studied a model to estimate AADT for non-state roads in urbanized areas in Florida, which involved both road geometry (e.g. the number of lanes, road functional classification) and socioeconomic variables (e.g.…”

Mapping of truck traffic in New Jersey using weigh‐in‐motion data

“…For example, Mohamad et al [12] estimated this model for estimation of AADT for county roads using population, highway mileage, income, and the presence of interstate highways. Xia et al [13] developed a multiple regression model for AADT of non‐state roads in Florida using roadway characteristics, socio‐economic variables. Later, Zhao and Park [14] recognised the importance of spatial effects and used geographically weighted regression models to estimate AADT or truck AADT.…”

New Efficient Regression Method for Local AADT Estimation via SCAD Variable Selection

“…For example, Mohammad et al incorporated relevant demographic variables for county roads into a traffic prediction model in [16]. Xia et al [29] found roadway characteristics, such as the number of lanes, functional classification, and area type, which are contributing predictors to the AADT estimation of nonstate roads in urbanized areas in Florida, USA. Zhao and Chung [32] developed and compared four multiple linear regression models using geographic information system (GIS) technology.…”