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.

VISSIM Calibration for Urban Freeways, 2015

In urban areas, interchange spacing and the adequacy of design for weaving, merge, and diverge areas can significantly influence available capacity. Traffic microsimulation tools allow detailed analyses of these critical areas in complex locations that often yield results that differ from the generalized approach of the Highway Capacity Manual. In order to obtain valid results, various inputs should be calibrated to local conditions. This project investigated basic calibration factors for the simulation of traffic conditions within an urban freeway merge/diverge environment. By collecting and analyzing urban freeway traffic data from multiple sources, specific Iowa-based calibration factors for use in VISSIM were developed. In particular, a repeatable methodology for collecting standstill distance and headway/time gap data on urban freeways was applied to locations throughout the state of Iowa. This collection process relies on the manual processing of video for standstill distances and individual vehicle data from radar detectors to measure the headways/time gaps. By comparing the data collected from different locations, it was found that standstill distances vary by location and lead-follow vehicle types. Headways and time gaps were found to be consistent within the same driver population and across different driver populations when the conditions were similar. Both standstill distance and headway/time gap were found to follow fairly dispersed and skewed distributions. Therefore, it is recommended that microsimulation models be modified to include the option for standstill distance and headway/time gap to follow distributions as well as be set separately for different vehicle classes. In addition, for the driving behavior parameters that cannot be easily collected, a sensitivity analysis was conducted to examine the impact of these parameters on the capacity of the facility. The sensitivity analysis results can be used as a reference to manually adjust parameters to match the simulation results to the observed traffic conditions. A well-calibrated microsimulation model can enable a higher level of fidelity in modeling traffic behavior and serve to improve decision making in balancing need with investment.