Perception and Interpretation of Advance Warning Signs on County Roads, HR-256, 1984

This research consisted of five laboratory experiments designed to address the following two objectives in an integrated analysis: (1) To discriminate between the symbol Stop Ahead warning sign and a small set of other signs (which included the word-legend Stop Ahead sign); and (2) To analyze sign detection, recognizability, and processing characteristics by drivers. A set of 16 signs was used in each of three experiments. A tachistoscope was used to display each sign image to a respondent for a brief interval in a controlled viewing experiment. The first experiment was designed to test detection of a sign in the driver's visual field; the second experiment was designed to test the driver's ability to recognize a given sign in the visual field; and the third experiment was designed to test the speed and accuracy of a driver's response to each sign as a command to perform a driving action. A fourth experiment tested the meanings drivers associated with an eight-sign subset of the 16 signs used in the first three experiments. A fifth experiment required all persons to select which (if any) signs they considered to be appropriate for use on two scale model county road intersections. The conclusions are that word-legend Stop Ahead signs are more effective driver communication devices than symbol stop-ahead signs; that it is helpful to drivers to have a word plate supplementing the symbol sign if a symbol sign is used; and that the guidance in the Manual on Uniform Traffic Control Devices on the placement of advance warning signs should not supplant engineering judgment in providing proper sign communication at an intersection.

Addendum to Perception and Interpretation of Advance Warning Signs on County Roads, HR-256, 1985

This contract extension was granted to analyze data obtained in the original contract period at a level of detail not called for in the original contract nor permitted by the time constraints of the original contract schedule. These further analyses focused on two primary questions: I. What sources of variation can be isolated within the overall pattern of driver recognition errors reported previously for the 16 signs tested in Project HR-256? 2. Were there systematic relations among data on the placement of signs in a simulated signing exercise and data on the respondents' ability to detect the presence of a sign in a visual field or their ability to recognize quickly and correctly a sign shown them or the speed with which these same persons can respond to a sign for a driver decision?

Spatial Scale of Clustering of Motor Vehicle Crash Types and Appropriate Countermeasures, 2009

This project analyzes the characteristics and spatial distributions of motor vehicle crash types in order to evaluate the degree and scale of their spatial clustering. Crashes occur as the result of a variety of vehicle, roadway, and human factors and thus vary in their clustering behavior. Clustering can occur at a variety of scales, from the intersection level, to the corridor level, to the area level. Conversely, other crash types are less linked to geographic factors and are more spatially “random.” The degree and scale of clustering have implications for the use of strategies to promote transportation safety. In this project, Iowa's crash database, geographic information systems, and recent advances in spatial statistics methodologies and software tools were used to analyze the degree and spatial scale of clustering for several crash types within the counties of the Iowa Northland Regional Council of Governments. A statistical measure called the K function was used to analyze the clustering behavior of crashes. Several methodological issues, related to the application of this spatial statistical technique in the context of motor vehicle crashes on a road network, were identified and addressed. These methods facilitated the identification of crash clusters at appropriate scales of analysis for each crash type. This clustering information is useful for improving transportation safety through focused countermeasures directly linked to crash causes and the spatial extent of identified problem locations, as well as through the identification of less location-based crash types better suited to non-spatial countermeasures. The results of the K function analysis point to the usefulness of the procedure in identifying the degree and scale at which crashes cluster, or do not cluster, relative to each other. Moreover, for many individual crash types, different patterns and processes and potentially different countermeasures appeared at different scales of analysis. This finding highlights the importance of scale considerations in problem identification and countermeasure formulation.

GPS to LRM: Integration of Spatial Point Features with Linear Referencing Methods, 2001

Global positioning systems (GPS) offer a cost-effective and efficient method to input and update transportation data. The spatial location of objects provided by GPS is easily integrated into geographic information systems (GIS). The storage, manipulation, and analysis of spatial data are also relatively simple in a GIS. However, many data storage and reporting methods at transportation agencies rely on linear referencing methods (LRMs); consequently, GPS data must be able to link with linear referencing. Unfortunately, the two systems are fundamentally incompatible in the way data are collected, integrated, and manipulated. In order for the spatial data collected using GPS to be integrated into a linear referencing system or shared among LRMs, a number of issues need to be addressed. This report documents and evaluates several of those issues and offers recommendations. In order to evaluate the issues associated with integrating GPS data with a LRM, a pilot study was created. To perform the pilot study, point features, a linear datum, and a spatial representation of a LRM were created for six test roadway segments that were located within the boundaries of the pilot study conducted by the Iowa Department of Transportation linear referencing system project team. Various issues in integrating point features with a LRM or between LRMs are discussed and recommendations provided. The accuracy of the GPS is discussed, including issues such as point features mapping to the wrong segment. Another topic is the loss of spatial information that occurs when a three-dimensional or two-dimensional spatial point feature is converted to a one-dimensional representation on a LRM. Recommendations such as storing point features as spatial objects if necessary or preserving information such as coordinates and elevation are suggested. The lack of spatial accuracy characteristic of most cartography, on which LRM are often based, is another topic discussed. The associated issues include linear and horizontal offset error. The final topic discussed is some of the issues in transferring point feature data between LRMs.