Improving the Annual Average Daily Road Traffic Estimation by using Multitemporal High Resolution Aerial Imagery

Abstract

The annual average daily traffic (AADT) is a key indicator for describing transport development and an essential parameter for traffic engineering and transportation planning. Commonly, the AADT is collected by permanent counting stations on selected sections of the major road network and supplemented by temporary counts at multi-annual intervals. Because of the high expenditures for construction and maintenance, dense networks of counting stations are rarely established which leads to uncertainties about the spatial and temporal distribution of vehicle volumes. Remote sensing data can be part of the solution as it can be acquired flexibly and over large areas. This paper utilizes aerial image sequences to capture traffic density as well as vehicle speeds to determine the traffic flow on motorway and federal road segments. The instantaneous observations at different days and times are extrapolated to the AADT using time series from reference counting stations in the study area of Braunschweig (Germany). Discrepancies of up to 11 % between estimated and measured AADT are found at three of five metering sections. For the remaining sections, this paper elaborates why limits of the approach are reached here. The presented method is particularly suitable for motorway sections where no or only insufficient data are available.