Download or read book Road Traffic Crash Severity Prediction Using Multi State Data written by Thomas M. England and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The socioeconomic burden of road traffic crashes is immense. Safer roads and vehicular mechanisms to reduce distracted driving help reduce collisions. Additionally, computational models can be used to understand the reasons for crashes and devise interventions. We study models predicting the severity of a crash based on the data reported at the crash scene. Many U.S. states have developed traffic safety programs to make the anonymized crash data publicly available. These datasets aid researchers in the creation of predictive models for crashes. While many states make data from collisions publicly available, each state reports data differently. There is a lack of standardization. As a result, it is difficult for researchers to develop machine learning algorithms to process data from multiple states without adequate preprocessing. Currently, the vast majority of projects in this field of study utilize a dataset of a single city, road, or state. This limits the use of the developed model to a region. This project aims to create a large crash database that will allow researchers to develop algorithms that utilize data from across the country. Additionally, we want to examine if the use of data from multiple states is effective in increasing the accuracy of machine learning models. In order to achieve these goals, we develop software to find common data categories from state reports and combine them into one large dataset. The data categories were selected based on reports from previous projects that identified variables having a large impact on model accuracy. In order to test the effectiveness of the new multi-state dataset, we used two models (neural network-based and decision tree-based) to predict crash injury severity. We trained and tested these models on datasets from a single state, combined two-state datasets, and a combined multi-state dataset. The results of this research reveal that there is a drop in accuracy when data from multiple states are combined. This trend is present in both the models tested, with the trend being more pronounced in the decision tree. There are some cases in the neural network model where multi-state data lead to a higher accuracy compared to the single-state experiments. We also observe a downward trend between neural network accuracy and the distance between the states present in the dataset. This implies that the closer the states are together geographically, the better the accuracy will be using the neural network model. In the decision tree model, there is a positive correlation between overall accuracy and the number of features present in the dataset. This observation means that the more features states have in common, the better the accuracy will be for a decision tree classifier. The software artifacts from this project are open-sourced.
Download or read book Highway and Traffic Safety written by National Research Council (U.S.). Transportation Research Board and published by . This book was released on 2000 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transportation Research Record contains the following papers: Method for identifying factors contributing to driver-injury severity in traffic crashes (Chen, WH and Jovanis, PP); Crash- and injury-outcome multipliers (Kim, K); Guidelines for identification of hazardous highway curves (Persaud, B, Retting, RA and Lyon, C); Tools to identify safety issues for a corridor safety-improvement program (Breyer, JP); Prediction of risk of wet-pavement accidents : fuzzy logic model (Xiao, J, Kulakowski, BT and El-Gindy, M); Analysis of accident-reduction factors on California state highways (Hanley, KE, Gibby, AR and Ferrara, T); Injury effects of rollovers and events sequence in single-vehicle crashes (Krull, KA, Khattack, AJ and Council, FM); Analytical modeling of driver-guidance schemes with flow variability considerations (Kaysi, I and Ail, NH); Evaluating the effectiveness of Norway's speak out! road safety campaign : The logic of causal inference in road safety evaluation studies (Elvik, R); Effect of speed, flow, and geometric characteristics on crash frequency for two-lane highways (Garber, NJ and Ehrhart, AA); Development of a relational accident database management system for Mexican federal roads (Mendoza, A, Uribe, A, Gil, GZ and Mayoral, E); Estimating traffic accident rates while accounting for traffic-volume estimation error : a Gibbs sampling approach (Davis, GA); Accident prediction models with and without trend : application of the generalized estimating equations procedure (Lord, D and Persaud, BN); Examination of methods that adjust observed traffic volumes on a network (Kikuchi, S, Miljkovic, D and van Zuylen, HJ); Day-to-day travel-time trends and travel-time prediction form loop-detector data (Kwon, JK, Coifman, B and Bickel, P); Heuristic vehicle classification using inductive signatures on freeways (Sun, C and Ritchie, SG).
Download or read book State Data System written by and published by . This book was released on 1997 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Modeling of Transport Demand written by V.A Profillidis and published by Elsevier. This book was released on 2018-10-23 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling of Transport Demand explains the mechanisms of transport demand, from analysis to calculation and forecasting. Packed with strategies for forecasting future demand for all transport modes, the book helps readers assess the validity and accuracy of demand forecasts. Forecasting and evaluating transport demand is an essential task of transport professionals and researchers that affects the design, extension, operation, and maintenance of all transport infrastructures. Accurate demand forecasts are necessary for companies and government entities when planning future fleet size, human resource needs, revenues, expenses, and budgets. The operational and planning skills provided in Modeling of Transport Demand help readers solve the problems they face on a daily basis. Modeling of Transport Demand is written for researchers, professionals, undergraduate and graduate students at every stage in their careers, from novice to expert. The book assists those tasked with constructing qualitative models (based on executive judgment, Delphi, scenario writing, survey methods) or quantitative ones (based on statistical, time series, econometric, gravity, artificial neural network, and fuzzy methods) in choosing the most suitable solution for all types of transport applications. Presents the most recent and relevant findings and research - both at theoretical and practical levels - of transport demand Provides a theoretical analysis and formulations that are clearly presented for ease of understanding Covers analysis for all modes of transportation Includes case studies that present the most appropriate formulas and methods for finding solutions and evaluating results
Download or read book Modeling Multilevel Data in Traffic Safety written by Hoong Chor Chin and published by Nova Science Publishers. This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Background: In the study of traffic system safety, statistical models have been broadly applied to establish the relationships between the traffic crash occurrence and various risk factors. Most of the existing methods, such as the generalised linear regression models, assume that each observation (e.g. a crash or a vehicle involvement) in the estimation procedure corresponds to an individual situation. Hence, the residuals from the models exhibit independence. Problem: However, this "independence" assumption may often not hold true since multilevel data structures exist extensively because of the data collection and clustering process. Disregarding the possible within-group correlations may lead to production of models with unreliable parameter estimates and statistical inferences. Method: Following a literature review of crash prediction models, this book proposes a 5 T-level hierarchy, viz. (Geographic region level -- Traffic site level -- Traffic crash level -- Driver-vehicle unit level -- Vehicle-occupant level) Time level, to establish a general form of multilevel data structure in traffic safety analysis. To model properly the potential between-group heterogeneity due to the multilevel data structure, a framework of hierarchical models that explicitly specify multilevel structure and correctly yield parameter estimates is employed. Bayesian inference using Markov chain Monte Carlo algorithm is developed to calibrate the proposed hierarchical models. Two Bayesian measures, viz. the Deviance Information Criterion and Cross-Validation Predictive Densities, are adapted to establish the model suitability. Illustrations: The proposed method is illustrated using two case studies in Singapore: 1) a crash-frequency prediction model which takes into account Traffic site level and Time level; 2) a crash-severity prediction model which takes into account Traffic crash level and Driver-vehicle unit level. Conclusion: Comparing the predictive abilities of the proposed models against those of traditional methods, the study demonstrates the importance of accounting for the within-group correlations and illustrates the flexibilities and effectiveness of the Bayesian hierarchical approach in modelling multilevel structure of traffic safety data.
Download or read book Highway Safety Analytics and Modeling written by Dominique Lord and published by Elsevier. This book was released on 2021-02-27 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Highway Safety Analytics and Modeling comprehensively covers the key elements needed to make effective transportation engineering and policy decisions based on highway safety data analysis in a single. reference. The book includes all aspects of the decision-making process, from collecting and assembling data to developing models and evaluating analysis results. It discusses the challenges of working with crash and naturalistic data, identifies problems and proposes well-researched methods to solve them. Finally, the book examines the nuances associated with safety data analysis and shows how to best use the information to develop countermeasures, policies, and programs to reduce the frequency and severity of traffic crashes. Complements the Highway Safety Manual by the American Association of State Highway and Transportation Officials Provides examples and case studies for most models and methods Includes learning aids such as online data, examples and solutions to problems
Download or read book Severity Prediction and Time Series Analysis of Vehicle Accidents Using Statistical Models written by Lisa Kaunitz and published by . This book was released on 2022 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study explores factors that effect vehicle accidents, predicts the severity of accidents through logistic regression, and forecasts the number of future accidents to occur using time-series analysis. From insights gathered during exploration, a final dataset is prepared for the use of a logistic regression model. The final model predicts whether or not an accident will be severe with an accuracy of 82%, and reveals the three main features that statistically contribute to the odds of an accident having a severe impact on traffic. Finally, a time-series analysis is run in order to model the number of accidents that can occur on a given day using historical data. This paper evaluates the dataset in ways that have yet to be explored, and provides a great baseline understanding of what is possible for the future of transportation.
Download or read book General Estimates System written by and published by . This book was released on 1988 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Application of Data Analytics to Outcomes of Missouri Motor Vehicle Crashes written by and published by . This book was released on 2015 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motor vehicle crashes are a leading cause of death in the United States, cost Americans $277 billion annually, and generate serious psychological burdens. As a result, extensive vehicle safety research focusing on the explanatory factors of crash severity is undertaken using a wide array of methodological techniques including traditional statistical models and contemporary data mining approaches. This study advances the methodological frontier of crash severity research by completing an empirical investigation that compares the performance of popular, longstanding techniques of multinomial logit and ordinal probit models with more recent methods of decision tree and artificial neural network models. To further the investigation of the benefits of data analytics, individual models are combined into model ensembles using three popular combinatory techniques. The models are estimated using 2002 to 2012 crash data from the Missouri State Highway Patrol Traffic Division - Statewide Traffic Accident Records System database, and variables examined include various driver characteristics, temporal factors, weather conditions, road characteristics, crash type, crash location, and injury severity levels. The accuracy and discriminatory power of explaining crash severity outcomes among all methods are compared using classification tables, lift charts, ROC curves, and AUC values. The CHAID decision tree model is found to have the greatest accuracy and discriminatory power relative to all evaluated modeling approaches. The modeling reveals that the presence of alcohol, driving at speeds that exceed the limit, failing to yield, driving on the wrong side of the road, violating a stop sign or signal, and driving while physically impaired lead to a large number of fatalities each year. Yet, the effect of these factors on the probability of a severe outcome is dependent upon other variables, including number of occupants involved in the crash, speed limit, lighting condition, and age of the driver. The CHAID decision tree is used in conjunction with prior literature and the current Missouri rules of the road to provide better formulated driving policies. This study concludes that policy makers should consider the interaction of conditions and driver related contributing factors when crafting future legislation or proposing modifications in driving statues.
Download or read book Transportation Cyber Physical Systems written by Lipika Deka and published by Elsevier. This book was released on 2018-07-30 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transportation Cyber-Physical Systems provides current and future researchers, developers and practitioners with the latest thinking on the emerging interdisciplinary field of Transportation Cyber Physical Systems (TCPS). The book focuses on enhancing efficiency, reducing environmental stress, and meeting societal demands across the continually growing air, water and land transportation needs of both people and goods. Users will find a valuable resource that helps accelerate the research and development of transportation and mobility CPS-driven innovation for the security, reliability and stability of society at-large. The book integrates ideas from Transport and CPS experts and visionaries, consolidating the latest thinking on the topic. As cars, traffic lights and the built environment are becoming connected and augmented with embedded intelligence, it is important to understand how smart ecosystems that encompass hardware, software, and physical components can help sense the changing state of the real world. Bridges the gap between the transportation, CPS and civil engineering communities Includes numerous examples of practical applications that show how diverse technologies and topics are integrated in practice Examines timely, state-of-the-art topics, such as big data analytics, privacy, cybersecurity and smart cities Shows how TCPS can be developed and deployed, along with its associated challenges Includes pedagogical aids, such as Illustrations of application scenarios, architecture details, tables describing available methods and tools, chapter objectives, and a glossary Contains international contributions from academia, government and industry
Download or read book Crash Severity Modeling in Transportation Systems written by Azad Salim Abdulhafedh and published by . This book was released on 2016 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling crash severity is an important component of reasoning about the issues that may affect highway safety. A better understanding of the factors underlying crash severity can be used to reduce the degree of crash severity injury, locate road hazardous sites, and adopt suitable countermeasures. In order to provide insights on the mechanism and behavior of the crash severity injury, a variety of statistical approaches have been utilized to model the relationship between crash severity and potential risk factors. Many of the traditional approaches for analyzing crash severity are limited in that they are based on the assumption that all observations are independent of each other. However, given the reality of vehicle movement in networked systems, the assumption of independence of crash incidence is not likely valid. For instance, spatial and temporal autocorrelations are important sources of dependency among observations that may bias estimates if not considered in the modeling process. Moreover, there are other aspects of vehicular travel that may influence crash severity that have not been explored in traditional analysis approaches. One such aspect is the roadway visibility that is available to a driver at a given time that can impact their ability to react to changing traffic conditions, a characteristics known as sight distance. Accounting for characteristics such as sight distance in crash severity modeling involve moving beyond statistical analysis and modeling the complex geospatial relationships between the driver and the surrounding landscape. To address these limitations of traditional approaches to crash severity modeling, this dissertation first details a framework for detecting temporal and spatial autocorrelation in crash data. An approach for evaluating the sight distance available to drivers along roadways is then proposed. Finally, a crash severity model is developed based upon a multinomial logistic regression approach that incorporates the available sight distance and spatial autocorrelation as potential risk factors, in addition to a wide range of other factors related to road geometry, traffic volume, driver's behavior, environment, and vehicles. To demonstrate the characteristics of the proposed model, an analysis of vehicular crashes (years 2013-2015) along the I-70 corridor in the state of Missouri (MO) and on roadways in Boone County MO is conducted. To assess existing stopping sight distance and decision sight distance on multilane highways, a geographic information system (GIS)-based viewshed analysis is developed to identify the locations that do not conform to AASHTO (2011) criteria regarding stopping and decision sight distances, which could then be used as potential risk factors in crash prediction. Moreover, this method provides a new technique for estimating passing sight distance along two-lane highways, and locating the passing zones and no-passing zones. In order to detect the existence of temporal autocorrelation and whether it's significant in crash data, this dissertation employs the Durbin-Watson (DW) test, the Breusch-Godfrey (LM) test, and the Ljung-Box Q (LBQ) test, and then describes the removal of any significant amount of temporal autocorrelation from crash data using the differencing procedure, and the Cochrane-Orcutt method. To assess whether vehicle crashes are spatially clustered, dispersed, or random, the Moran's I and Getis-Ord Gi* statistics are used as measures of spatial autocorrelation among vehicle incidents. To incorporate spatial autocorrelation in crash severity modeling, the use of the Gi* statistic as a potential risk factor is also explored. The results provide firm evidence on the importance of accounting for spatial and temporal autocorrelation, and sight distance in modeling traffic crash data.
Download or read book Analyses of Crash Occurence sic and Inury sic Severities on Multi Lane Highways Using Machine Learning Algorithms written by Abhishek Das and published by . This book was released on 2009 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reduction of crash occurrence on the various roadway locations (mid-block segments; signalized intersections; un-signalized intersections) and the mitigation of injury severity in the event of a crash are the major concerns of transportation safety engineers. Multi lane arterial roadways (excluding freeways and expressways) account for forty-three percent of fatal crashes in the state of Florida. Significant contributing causes fall under the broad categories of aggressive driver behavior; adverse weather and environmental conditions; and roadway geometric and traffic factors. The objective of this research was the implementation of innovative, state-of-the-art analytical methods to identify the contributing factors for crashes and injury severity. Advances in computational methods render the use of modern statistical and machine learning algorithms. Even though most of the contributing factors are known a-priori, advanced methods unearth changing trends. Heuristic evolutionary processes such as genetic programming; sophisticated data mining methods like conditional inference tree; and mathematical treatments in the form of sensitivity analyses outline the major contributions in this research. Application of traditional statistical methods like simultaneous ordered probit models, identification and resolution of crash data problems are also key aspects of this study. In order to eliminate the use of unrealistic uniform intersection influence radius of 250 ft, heuristic rules were developed for assigning crashes to roadway segments, signalized intersection and access points using parameters, such as 'site location', 'traffic control' and node information. Use of Conditional Inference Forest instead of Classification and Regression Tree to identify variables of significance for injury severity analysis removed the bias towards the selection of continuous variable or variables with large number of categories. For the injury severity analysis of crashes on highways, the corridors were clustered into four optimum groups. The optimum number of clusters was found using Partitioning around Medoids algorithm. Concepts of evolutionary biology like crossover and mutation were implemented to develop models for classification and regression analyses based on the highest hit rate and minimum error rate, respectively. Low crossover rate and higher mutation reduces the chances of genetic drift and brings in novelty to the model development process. Annual daily traffic; friction coefficient of pavements; on-street parking; curbed medians; surface and shoulder widths; alcohol / drug usage are some of the significant factors that played a role in both crash occurrence and injury severities. Relative sensitivity analyses were used to identify the effect of continuous variables on the variation of crash counts. This study improved the understanding of the significant factors that could play an important role in designing better safety countermeasures on multi lane highways, and hence enhance their safety by reducing the frequency of crashes and severity of injuries. Educating young people about the abuses of alcohol and drugs specifically at high schools and colleges could potentially lead to lower driver aggression. Removal of on-street parking from high speed arterials unilaterally could result in likely drop in the number of crashes. Widening of shoulders could give greater maneuvering space for the drivers. Improving pavement conditions for better friction coefficient will lead to improved crash recovery. Addition of lanes to alleviate problems arising out of increased ADT and restriction of trucks to the slower right lanes on the highways would not only reduce the crash occurrences but also resulted in lower injury severity levels.
Download or read book Highway Safety written by and published by . This book was released on 2001 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transportation Research Record contains the following papers: Incorporating crash risk in selecting congestion-mitigation strategies : Hampton Roads area (Virginia) case study (Garber, NJ and Subramanyan, S); Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections (Abdelwahab, HT and Abdel-Aty, MA); Transferability of models that estimate crashes as a function of access management (Miller, JS, Hoel, LA, Kim, S and Drummond, KP); Sensor-friendly vehicle and roadway cooperative safety systems : benefits estimation (Misener, JA, Thorpe, C, Ferlis, R, Hearne, R, Siegal, M and Perkowski, J); Interstate highway crash injuries during winter snow and nonsnow events (Khattak, AJ and Knapp, KK); Simulation of road crashes by use of systems dynamics (Mehmood, A, Saccamanno, F and Hellinga, B); Longitudinal analysis of fatal run-off-road crashes, 1975 to 1997 (McGinnis, RG, Davis, MJ and Hathaway, EA); Injury severity in multivehicle rear-end crashes (Khattack, AJ); Computing and interpreting accident rates for vehicle types driver groups (Hauer, E); Geographics information system-based accident data management for Mexican federal roads (Mendoza, A, Mayoral, EF, Vicente, JL and Quintero, FL); Bayesian identification of high-risk intersections for older drivers via gibbs sampling (Davis, GA and Yang, S); Automated accident detection system (Harlow, C and Wang, Y); Evaluation of inexpensive global positioning system units to improve crash location data (Graettinger, AJ, Rushing, TW and McFadden, J).
Download or read book Road Accident Prediction Using LSTM GRU Neural Networks written by Prudvi Saisaran Ponduru and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traffic accidents are a significant source of mortality and economic damage on a global scale. If it is not possible to make the roads safer, there is a need for better algorithms to forecast the severity of incidents. It is possible to divide the outcomes of a car accident into three categories depending on the severity of the injuries sustained by the driver: property damage, probable injury, and death (Salanova Grau et al., 2018). In order to solve the problem of identifying patterns in the severity of accidents, researchers have turned to deep learning, statistics, and even physical modelling (Liu, Liu, Song, & Liu, 2017). In order to convert an input vector into an output vector, deep learning models often use a sequence of nonlinear functions. Input vectors for accident severity include driver conduct and road, vehicle, and environmental elements. The resulting table covers the spectrum of potential accident outcomes. It is possible to train computer models using deep learning methods.
Download or read book The Crash Outcome Data Evaluation System Codes and Applications to Improve Traffic Safety Decision Making written by United States. National Highway Traffic Safety Administration and published by Createspace Independent Pub. This book was released on 2013-09-21 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Crash Outcome Data Evaluation System (CODES) is a program facilitated by the National Highway Traffic Safety Administration as a component of its State Data Program. CODES uniquely uses probabilistic methodology to link crash records to injury outcome records collected at the scene and en route by emergency medical services, by hospital personnel after arrival at the emergency department or admission as an inpatient and/or, at the time of death, on the death certificate. CODES is designed to foster and cultivate crash-outcome data linkage for highway safety applications at the State level, supporting State Highway Safety Offices, State Public Health and Injury Prevention Departments, State Emergency Medical Services Agencies, State transportation departments, and other such agencies; and to facilitate participation in NHTSA coordinated multistate studies using linked data at the Federal level. This document is intended to inform traffic safety professionals, from those in CODES programs to those in the agencies they support, as well as all others interested in traffic safety, on best-practice applications available through linked CODES data.
Download or read book Explainable AI Interpreting Explaining and Visualizing Deep Learning written by Wojciech Samek and published by Springer Nature. This book was released on 2019-09-10 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
Download or read book Highway Safety written by and published by . This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transportation Research Record contains the following papers: Incorporating crash risk in selecting congestion-mitigation strategies : Hampton Roads area (Virginia) case study (Garber, NJ and Subramanyan, S); Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections (Abdelwahab, HT and Abdel-Aty, MA); Transferability of models that estimate crashes as a function of access management (Miller, JS, Hoel, LA, Kim, S and Drummond, KP); Sensor-friendly vehicle and roadway cooperative safety systems : benefits estimation (Misener, JA, Thorpe, C, Ferlis, R, Hearne, R, Siegal, M and Perkowski, J); Interstate highway crash injuries during winter snow and nonsnow events (Khattak, AJ and Knapp, KK); Simulation of road crashes by use of systems dynamics (Mehmood, A, Saccamanno, F and Hellinga, B); Longitudinal analysis of fatal run-off-road crashes, 1975 to 1997 (McGinnis, RG, Davis, MJ and Hathaway, EA); Injury severity in multivehicle rear-end crashes (Khattack, AJ); Computing and interpreting accident rates for vehicle types driver groups (Hauer, E); Geographics information system-based accident data management for Mexican federal roads (Mendoza, A, Mayoral, EF, Vicente, JL and Quintero, FL); Bayesian identification of high-risk intersections for older drivers via gibbs sampling (Davis, GA and Yang, S); Automated accident detection system (Harlow, C and Wang, Y); Evaluation of inexpensive global positioning system units to improve crash location data (Graettinger, AJ, Rushing, TW and McFadden, J).
