Steven T Parker

Intelligent geocoding system to locate traffic crashes

State agencies continue to face many challenges associated with new federal crash safety and highway performance monitoring requirements that use data from multiple and disparate systems across different platforms and locations. On a national level, the federal government has a long-term vision for State Departments of Transportation (DOTs) to report state route and off-state route crash data in a single network. In general, crashes occurring on state-owned or state maintained highways are a priority at the Federal and State level; therefore, state-route crashes are being geocoded by state DOTs. On the other hand, crashes occurring on off-state highway system do not always get geocoded due to limited resources and techniques. Creating and maintaining a statewide crash geographic information systems (GIS) map with state route and non-state route crashes is a complicated and expensive task. This study introduces an automatic crash mapping process, Crash-Mapping Automation Tool (C-MAT), where an algorithm translates location information from a police report crash record to a geospatial map and creates a pinpoint map for all crashes. The algorithm has approximate 83 percent mapping rate. An important application of this work is the ability to associate the mapped crash records to underlying business data, such as roadway inventory and traffic volumes. The integrated crash map is the foundation for effective and efficient crash analyzes to prevent highway crashes.

Combining State Route and Local Road Linear Referencing System Information

The Wisconsin Department of Transportation maintains two separate geographic information system databases: one for state roads and one for local roads. Both databases employ linear referencing system (LRS) theory to manage and locate information. Combining data from state and local roads into one system is desirable for the purpose of data management and analysis. This papers approach combines information from the state route LRS (link ID and offset distance) with information from the local road LRS (node ID) to produce a table that can be employed to transfer data between the systems. A computer program was developed to use this table, along with existing tables in each LRS, to move information from the state route system to the local road system. Quality assurance and quality control techniques are presented along with a long-term implementation approach; both help to update the table bridging these two systems. The approach described does not interfere with the current operation of either system; therefore, no interruption of business practice will occur as this data transfer approach is deployed. Although this approach can transfer any LRS data, a case study of crash data for Dane County, Wisconsin, is used to demonstrate and test the approach. In the case study, crash points on the state routes are combined with local road crashes to produce a complete data set of crashes within the county.

System for Digitizing Information on Wisconsin's Crash Locations

One of the key hurdles in identifying unsafe intersections and roadways in Wisconsin is the lack of a complete crash location map, especially for crashes that occurred on local streets. Crash locations are reported in terms of relative offset from an intersection on the basis of on- and at-street name information, which identifies the intersection, and direction and distance information, which identifies the offset. For intersection crashes, the offset distance is typically set to zero. As described in this paper, the Traffic Operations and Safety Laboratory at the University of Wisconsin, Madison, has developed a system to automate the mapping of Wisconsin local road crash locations. The location mapping algorithm involves the integration of two separate Wisconsin Department of Transportation databases: the Wisconsin crash database of police traffic accident reports and the Wisconsin Information System of Local Roads (WISLR). The application of WISLR, which is an inventory of local roads with details such as traffic information, pavement condition, and roadway geometry, provides invaluable access to more comprehensive safety analysis. Although the methodology introduced is specific to these two databases, the general ideas can be applied to any similar sets of crash and geographic information system databases. The final result is a pinpoint map of all the intersection and segment crashes that occurred on local roads in Wisconsin, along with the complete crash information associated with each mapped crash. The algorithm developed with this methodology is able to map approximately 79% of the intended pool of available crashes. Quality evaluations indicate that the mapping is almost 98% accurate.

Enhanced Analysis of Work Zone Safety Through Integration of Statewide Crash and Lane Closure System Data

Highway work zones interrupt regular traffic flow and lead to more severe types of crashes, as shown by many studies. In 2009 alone, more than 600 fatalities nationally were work zone related. Analysis of work zone safety can help to identify the risk factors and improve safety; such an analysis requires the consideration of a variety of data sources, including the frequency of crashes in and around a work zone and specific work zone characteristics. The traditional approach, in Wisconsin and many other states, has relied on the presence of a construction zone flag in the crash report and information from targeted work zone studies. The crash report provides a macroscopic view of work zone crashes but does not provide details about the work zones, except when noted in the police officers narrative description. Targeted work zone studies provide a wealth of information for specific work zones but are limited in number and scope. The Wisconsin Lane Closure System (WisLCS), a centralized scheduling and reporting system for highway lane closures statewide, provides a new opportunity to match crashes to specific work zones on a systemwide level. This paper investigated the ability to match highway crash records from the Wisconsin Department of Transportation to WisLCS lane closure records. A preliminary analysis of work zone safety based on WisLCS closure attributes is presented and verifies the benefits of integrating work zone information. This knowledge can lead to safer work zone operations and planning decisions. The general ideas of this study can also be applied to any similar sets of crash and work zone data.

Freeway recurrent bottleneck identification algorithms considering detector data quality issues

Computer algorithms used to identify recurrent freeway bottlenecks have been studied since the deployment of loop detecting systems. Such algorithms automatically analyze the archived loop detector data and identify potential recurrent bottlenecks and their characteristics, such as location, time of day, and activation rate, for further investigation. In a highway congestion mitigation project, such algorithms can save time and resources for the initial screening of bottlenecks over a large freeway network. These algorithms include rule-based, contour-map-based, and simulation-based methods. However, existing methods require loop detector data with high accuracy and consistency, which is difficult to achieve in prevailing loop detecting systems. This paper proposes a new bottleneck identification algorithm with strong error and noise tolerance. Several simple denoising methods to improve the error resistance of existing algorithms are also proposed. Using statistical error analysis methods, the proposed algorithm and the denoising methods were calibrated and evaluated using field data collected from two distinct freeway corridors (US 12/14 and I-894) in the U.S. state of Wisconsin. Ground truth data for this study come from the manual inspection of 287,055 traffic video snapshots in the course of a month. In the evaluation tests, the proposed algorithm can produce quality congestion identification results with fewer false alarms than the existing algorithms, especially when identifying severe bottleneck congestion.

Large Scale Intelligent Transportation System Traffic Detector Data Archiving

Archived traffic data can be used in transportation planning, administration, and research by various entities and agencies. During the past two decades, considerable effort has been dedicated to developing and implementing large-scale traffic data archives. The Wisconsin Traffic Operation and Safety (TOPS) Laboratory at the University of Wisconsin–Madison maintains a statewide traffic detector data archiving and retrieving system, which is developed to enable centralized management of statewide ITS detector and configuration data, optimizing the utilization of massive data on a systematic level, and improving the interactivity and accessibility for integration with other transportation data sources such as lane closure data or incident data. This data archive is currently being enhanced to incorporate higher resolution traffic data by migrating from 5-minute to 1-minute and even 20-second sampling intervals. At the same time, there is a desire to generate aggregated datasets such as hourly, monthly, and annual average values from the raw data. As the traffic data requirements continue to grow, the management of the traffic data archive becomes a complex big data problem. This paper describes a proposed redesign of the TOPS Lab traffic detector archived data management system to improve storage, performance, access, and integration capabilities. Particular detail is given to the data archiving process, including data validation, and support for spatial attributes and GIS data integration.

Translating Transportation Data Between Linear Referencing Systems of Dissimilar Resolution

The Wisconsin Department of Transportation uses two linear referencing systems (LRSs) for complete mapping of crashes statewide: the State Trunk Network (STN), which represents only state routes, and the Wisconsin Information System for Local Roads (WISLR), which includes all roads but gives specific detail for local routes. A functional link between the two systems was developed; this link allows data to be translated from the STN, a higher-resolution representation, to the WISLR, a lower-resolution representation. Although data are easily translated from high to low resolution, ambiguities arise when data are moved from low resolution to high resolution. The research presented in this paper identifies common problems associated with translation of data from low to high resolution and provides some rules and guidelines to accommodate these issues.

Work Zone Crash Cost Prediction with a Least Median Squares Linear Regression Model

Traffic safety considerations are an important component of work zone design and implementation plans. This study aimed to develop a safety estimation approach on the basis of work zone scheduling and configuration parameters to help engineers and decision makers during the work zone traffic management design stage. This approach has its basis in the fusion of multiple years of statewide work zone records, work zone–related crash reports, and traffic detection data. First, a matching algorithm was used to integrate those data sets to provide detailed circumstances of work zone crashes. Second, the actual vehicle miles traveled in work zones were calculated and used as the exposure measure. The work zone crash costs also were calculated. Third, a least median squares linear regression model was used to build the crash cost prediction tool on the basis of the work zone configurations and vehicle miles traveled. This model was tested, and the results indicated that it could accurately predict work zone crash costs. Recommendations for work zone scheduling and configuration parameters therefore could be made. Although the Wisconsin Lane Closure System work zone configurations are used for operational purposes and differ from the ones in the Manual on Uniform Traffic Control Devices, other types of work zone configurations in similar systems can be incorporated easily into this approach. Thus this approach would be easy to adopt for use with the existing traffic management plan process as a proactive method to avoid high-risk work zone scenarios.