GIS-based risk management database integration and implementation framework for transportation agencies

Risk management analysis is one of the new requirements under MAP-21 and subsequently the FAST Act that separates transportation asset management programs (TAMP) from business as usual for the State departments of transportation (DOTs). Based on this requirement, each agency will discuss the concept of risk and how it should be incorporated into its transportation asset management program as well as how it informs maintenance practices, asset replacement or rehabilitation, and emergency management and response planning. This will require an agency to provide a list of risk exposures and document its system-wide risk management strategy. As a result, this research investigates the state of the practice of how agencies are developing their risk-based asset management plan and discusses recommendations for future research. The survey results show that state highway agencies are increasingly adapting the way they do business to include explicit considerations of risks. At the moment, this consideration of risk is not linked to data, and as a result most agencies do not have a data driven way of tracking risks and updating their risk exposures. Accordingly, this research proposed a data integration framework utilizing Geographic Information Systems (GIS) and Application Programming Interface (API) to implement a risk management database of all the relevant variables an agency needs for risk modeling to drive risk mitigation, risk monitoring, risk updates, and decision making. In addition, this study proposed modifications to the risk register workshop that leverages the collaborative aspects of risk management to quantify risk in monetary terms. This study leverages available data and analysis tools to help agencies visualize and articulate, in both qualitative and quantitative terms, how the combination of various risks and strategies would influence performance targets. The significance of the results highlights the need for further research on data driven risk management and to synthesize methodologies for integrating risk assessment into the agency’s decision-making process

Knowledge discovery interface in environmental science applications

Environmental problems such as global climate change and unsustainable developments in many parts of the world are evolving as major issues for the future of the planet and of mankind. Acidification of lakes and rivers, destruction of vital natural wetlands, loss of biotic integrity and habitat fragmentation, eutrophication of surface waters, bioaccumulation of toxic pollutants in the food web, and degradation of air quality contribute some of the many examples of how human-induced changes have impacted the Earth system. Also, anthropogenic stresses such as those associated with population growth, dwindling resources, chemical and biological pollution of water resources are expected to become more acute and costly. The approach to dealing with these environmental issues requires a balanced response in the form of an environmental management strategy. Such a response must utilize the best available scientific understanding and data in addition to an infrastructure that combines both in order to deliver sound science-based solutions to the myriad of environmental problems. This thesis proposes and implements a knowledge discovery interface (KDI) that provides an integrated platform for tackling the current environmental challenges by making use of existing applications. The existing applications that were employed in implementing this KDI include ESRI\u27s ArcGIS Engine

Pavement Markings and Safety

Previous research on pavement markings from a safety perspective tackled various issues such as pavement marking retroreflectivity variability, relationship between pavement marking retroreflectivity and driver visibility, or pavement marking improvements and safety. A recent research interest in this area has been to find a correlation between retroreflectivity and crashes, but a significant statistical relationship has not yet been found. This study investigates such a possible statistical relationship by analyzing five years of pavement marking retroreflectivity data collected by the Iowa Department of Transportation (DOT) on all state primary roads and corresponding crash and traffic data. This study developed a spatial-temporal database using measured retroreflectivity data to account for the deterioration of pavement markings over time along with statewide crash data to attempt to quantify a relationship between crash occurrence probability and pavement marking retroreflectivity. First, logistic regression analyses were done for the whole data set to find a statistical relationship between crash occurrence probability and identified variables, which are road type, line type, retroreflectivity, and traffic (vehicle miles traveled). The analysis looked into subsets of the data set such as road type, retroreflectivity measurement source, high crash routes, retroreflectivity range, and line types. Retroreflectivity was found to have a significant effect in crash occurrence probability for four data subsets—interstate, white edge line, yellow edge line, and yellow center line data. For white edge line and yellow center line data, crash occurrence probability was found to increase by decreasing values of retroreflectivity

Development of Railroad Highway Grade Crossing Consolidation Rating Formula

The goal of this project was to provide an objective methodology to support public agencies and railroads in making decisions related to consolidation of at-grade rail-highway crossings. The project team developed a weighted-index method and accompanying Microsoft Excel spreadsheet based tool to help evaluate and prioritize all public highway-rail grade crossings systematically from a possible consolidation impact perspective. Factors identified by stakeholders as critical were traffic volume, heavy-truck traffic volume, proximity to emergency medical services, proximity to schools, road system, and out-of-distance travel. Given the inherent differences between urban and rural locations, factors were considered, and weighted, differently, based on crossing location. Application of a weighted-index method allowed for all factors of interest to be included and for these factors to be ranked independently, as well as weighted according to stakeholder priorities, to create a single index. If priorities change, this approach also allows for factors and weights to be adjusted. The prioritization generated by this approach may be used to convey the need and opportunity for crossing consolidation to decision makers and stakeholders. It may also be used to quickly investigate the feasibility of a possible consolidation. Independently computed crossing risk and relative impact of consolidation may be integrated and compared to develop the most appropriate treatment strategies or alternatives for a highway-rail grade crossing. A crossing with limited- or low-consolidation impact but a high safety risk may be a prime candidate for consolidation. Similarly, a crossing with potentially high-consolidation impact as well as high risk may be an excellent candidate for crossing improvements or grade separation. The results of the highway-rail grade crossing prioritization represent a consistent and quantitative, yet preliminary, assessment. The results may serve as the foundation for more rigorous or detailed analysis and feasibility studies. Other pertinent site-specific factors, such as safety, maintenance costs, economic impacts, and location-specific access and characteristics should be considered

Impact of Pavement Surface Condition on Roadway Departure Crash Risk in Iowa

Safety performance is a crucial component of highway network performance evaluation. Besides their devastating impact on roadway users, traffic crashes lead to substantial economic losses on both personal and societal levels. Due to the complexity of crash events and the unique conditions in each country and state, empirical local calibration for the correlation between attributes of interest and the safety performance is always recommended. Limited studies have established a procedure to analyze the impact of pavement condition on traffic safety in a risk analysis scheme. This study presents a thorough analysis of some roadway departure crashes which occurred in Iowa between 2006 and 2016. All crash records were mapped onto one-mile segments with known traffic volume (i.e., AADT), posted speed limits (SL), skid numbers (SN), ride qualities (IRI), and rut depths (RD) in a geographic information system (GIS) database. The crash records were correlated to the pavement surface condition (i.e., SN, IRI, and RD) using negative binomial regression models. Moreover, a novel risk analysis framework is introduced to perform crash risk assessment and evaluate the possible consequences for a given combination of events. The analysis shows a significant impact of pavement skid resistance on roadway-departure crashes under all accident conditions and severities. Risk analysis will facilitate coordination between the pavement management system and safety management system in the future, which will help with optimizing the overall highway network performance

Statewide Heavy-Truck Crash Assessment

In 2010, 16.5 percent of all fatal vehicle crashes in Iowa involved large trucks compared to the national average of 7.8 percent. Only about 16 percent of these fatalities involved the occupants of the heavy vehicles, meaning that a majority of the fatalities in fatal crashes involve non-heavy-truck occupants. These statistics demonstrate the severe nature of heavy-truck crashes and underscore the serious impact that these crashes can have on the traveling public. These statistics also indicate Iowa may have a disproportionately higher safety risk compared to the nation with respect to heavy-truck safety. Several national studies, and a few statewide studies, have investigated large-truck crashes; however, no rigorous analysis of heavy-truck crashes has been conducted for Iowa. The objective of this study was to investigate and identify the causes, locations, and other factors related to heavy-truck crashes in Iowa with the goal of reducing crashes and promoting safety. To achieve this objective, this study used the most current statewide data of heavy-truck crashes in Iowa. This study also attempted to assess crash experience with respect to length of commercial driver’s license (CDL) licensure using the most recent five years of CDL data linked to the before mentioned crash data. In addition, this study used inspection and citation data from the Iowa Department of Transportation (DOT) Motor Vehicle Division and Iowa State Patrol to investigate the relationship between enforcement activities and crash experience

Safety and Mobility Impacts of Winter Weather --Phase 3

Highway agencies spend millions of dollars to ensure safe and efficient winter travel. However, the effectiveness of winter-weather maintenance practices on safety and mobility are somewhat difficult to quantify. Safety and Mobility Impacts of Winter Weather - Phase 1 investigated opportunities for improving traffic safety on state-maintained roads in Iowa during winter-weather conditions. In Phase 2, three Iowa Department of Transportation (DOT) high-priority sites were evaluated and realistic maintenance and operations mitigation strategies were also identified. In this project, site prioritization techniques for identifying roadway segments with the potential for safety improvements related to winter-weather crashes, were developed through traditional naïve statistical methods by using raw crash data for seven winter seasons and previously developed metrics. Additionally, crash frequency models were developed using integrated crash data for four winter seasons, with the objective of identifying factors that affect crash frequency during winter seasons and screening roadway segments using the empirical Bayes technique. Based on these prioritization techniques, 11 sites were identified and analyzed in conjunction with input from Iowa DOT district maintenance managers and snowplow operators and the Iowa DOT Road Weather Information System (RWIS) coordinator

Asset Management and Safety: A Performance Perspective

Incorporating safety performance measures into asset management can assist transportation agencies in managing their aging assets efficiently and improve system-wide safety. Past research has revealed the relationship between individual asset performance and safety, but the relationship between combined measures of operational asset condition and safety performance has not been explored. This project investigates the effect of pavement marking retroreflectivity and pavement condition on safety in a multi-objective manner. Data on one-mile segments for all Iowa primary roads from 2004 through 2009 were collected from the Iowa Department of Transportation and integrated using linear referencing. An asset condition index (ACI) was estimated for the road segments by scoring and weighting individual components. Statistical models were then developed to estimate the relationship between ACI and expected number of crashes, while accounting for exposure (average daily traffic). Finally, the researchers evaluated alternative treatment strategies for pavements and pavement markings using benefit-cost ratio analysis, taking into account corresponding treatment costs and safety benefits in terms of crash reduction (number of crashes proportionate to crash severity)

Safety and Mobility Impacts of Winter Weather --Phase 3 Tech Transfer Summary

Highway agencies spend millions of dollars to ensure safe and efficient winter travel. However, the effectiveness of winter-weather maintenance practices on safety and mobility are somewhat difficult to quantify. Safety and Mobility Impacts of Winter Weather - Phase 1 investigated opportunities for improving traffic safety on state-maintained roads in Iowa during winter-weather conditions. In Phase 2, three Iowa Department of Transportation (DOT) high-priority sites were evaluated and realistic maintenance and operations mitigation strategies were also identified. In this project, site prioritization techniques for identifying roadway segments with the potential for safety improvements related to winter-weather crashes, were developed through traditional naïve statistical methods by using raw crash data for seven winter seasons and previously developed metrics. Additionally, crash frequency models were developed using integrated crash data for four winter seasons, with the objective of identifying factors that affect crash frequency during winter seasons and screening roadway segments using the empirical Bayes technique. Based on these prioritization techniques, 11 sites were identified and analyzed in conjunction with input from Iowa DOT district maintenance managers and snowplow operators and the Iowa DOT Road Weather Information System (RWIS) coordinator

GIS-based risk management database integration and implementation framework for transportation agencies

Risk management analysis is one of the new requirements under MAP-21 and subsequently the FAST Act that separates transportation asset management programs (TAMP) from business as usual for the State departments of transportation (DOTs). Based on this requirement, each agency will discuss the concept of risk and how it should be incorporated into its transportation asset management program as well as how it informs maintenance practices, asset replacement or rehabilitation, and emergency management and response planning. This will require an agency to provide a list of risk exposures and document its system-wide risk management strategy. As a result, this research investigates the state of the practice of how agencies are developing their risk-based asset management plan and discusses recommendations for future research. The survey results show that state highway agencies are increasingly adapting the way they do business to include explicit considerations of risks. At the moment, this consideration of risk is not linked to data, and as a result most agencies do not have a data driven way of tracking risks and updating their risk exposures. Accordingly, this research proposed a data integration framework utilizing Geographic Information Systems (GIS) and Application Programming Interface (API) to implement a risk management database of all the relevant variables an agency needs for risk modeling to drive risk mitigation, risk monitoring, risk updates, and decision making. In addition, this study proposed modifications to the risk register workshop that leverages the collaborative aspects of risk management to quantify risk in monetary terms. This study leverages available data and analysis tools to help agencies visualize and articulate, in both qualitative and quantitative terms, how the combination of various risks and strategies would influence performance targets. The significance of the results highlights the need for further research on data driven risk management and to synthesize methodologies for integrating risk assessment into the agency’s decision-making process.</p