About: Annual average daily traffic is a research topic. Over the lifetime, 608 publications have been published within this topic receiving 9114 citations. The topic is also known as: AADT & average annual daily traffic.
TL;DR: The model illustrated the significance of the Annual Average Daily Traffic (AADT), degree of horizontal curvature, lane, shoulder and median widths, urban/rural, and the section's length, on the frequency of accident occurrence.
TL;DR: The models developed in this paper for Italian motorways appear to be useful for many applications such as the detection of critical factors, the estimation of accident reduction due to infrastructure and pavement improvement, and the predictions of accidents counts when comparing different design options.
TL;DR: It is indicated that demand reduction strategies should be seriously considered when seeking solutions to urban freeway congestion problems and new and innovative improvements must be developed to address the portion of the problem that is currently difficult to treat in a cost-effective manner.
Abstract: A major research study of traffic congestion on urban freeways is reported. The study undertook an analysis of the problem, and the Highway Performance Monitoring System (HPMS) was used as the basic source of data. The data were for 1984 and were limited to freeways in urban areas with populations greater than 50,000. The data set consisted of 8,036 sample sections. The data were entered into a microcomputer for computation of travel and congestion statistics. Predictions of future urban freeway congestion statistics were calculated using state-supplied estimates of annual average daily traffic for each sample section in 2005. The analysis of remedial measures is described and includes effectiveness analysis, cost analysis, combined improvement analysis, and the implications of the analyses. The study indicated that demand reduction strategies should be seriously considered when seeking solutions to urban freeway congestion problems. It also indicated the scope and magnitude of existing and predicted urban freeway congestion. The study also provides a first cut at estimating the cost and congestion reduction potential of various options generally available. It is noted that new and innovative improvements must be developed to address the portion of the problem that is currently difficult to treat in a cost-effective manner, and existing solutions must be pursued.
TL;DR: Analysis of crash, traffic, and roadway inventory data from a rural county in Pennsylvania indicates the importance of including spatial correlation in road crash models and the potential of spatial correlation to reduce the bias associated with model misspecification, as shown by the change in the estimate of the AADT coefficient and other parameters.
Abstract: Despite the evident spatial character of road crashes, limited research has been conducted in road safety analysis to account for spatial correlation; further, the practical consequences of this omission are largely unknown. The purpose of this research is to explore the effect of spatial correlation in models of road crash frequency at the segment level. Different segment neighboring structures are tested to establish the most appropriate one in the context of modeling crash frequency in road networks. A full Bayes hierarchical approach is used with conditional autoregressive effects for the spatial correlation terms. Analysis of crash, traffic, and roadway inventory data from a rural county in Pennsylvania indicates the importance of including spatial correlation in road crash models. The models with spatial correlation show significantly better fit to the data than the Poisson lognormal model with only heterogeneity. Parameters significantly different from zero included annual average daily traffic (AA...
TL;DR: This study identifies and compares the significant factors affecting pedestrian crash injury severity at signalized and unsignalized intersections and recommends several countermeasures to reduce pedestrian injury severity.