Intervention in the social population space of Cultural Algorithm

Cultural Algorithms (CA) offers a better way to simulate social and culture driven agents by introducing the notion of culture into the artificial population. When it comes to mimic intelligent social beings such as humans, the search for a better fit or global optima becomes multi dimensional because of the complexity produced by the relevant system parameters and intricate social behaviour. In this research an extended CA framework has been presented. The architecture provides extensions to the basic CA framework. The major extensions include the mechanism of influencing selected individuals into the population space by means of existing social network and consequently alter the cultural belief favourably. Another extension of the framework was done in the population space by introducing the concept of social network. The agents in the population are put into one (or more) network through which they can communicate and propagate knowledge. Identification and exploitation of such network is necessary sinceit may lead to a quicker shift of the cultural norm

Validated Agent-Based Model using Predictive Data Mining and Intervention Policy Testing Framework: A Case Study in Child Vehicle Safety

Much work has been done on making and perfecting agent-based simulations on child safety measures in cars. These simulations, using algorithms based on social networks, cultural algorithms etc. try and predict what factors are responsible for the propagation of knowledge about child safety measures in a given society. One of the biggest factors being over-looked in these simulations is the validity of the model. In absence of validation against real data, these models may not be a true representation of a real world scenario, and the trends predicted though these simulations are questionable. This paper proposes a system design using regression analysis and predictive data mining on a survey done in the field of child safety. Using the result of this data mining process in the form of a decision tree, we can initialize our agent-based model with data from the survey and later validate the model comparing the results to the survey data. Consequently a framework is formed to test different agent profile based intervention techniques, so that a decision about selecting an intervention technique with a given cost can be demonstrated

Modeling the Evolution of Artifact Capabilities in Multi-Agent Based Simulations

Cognitive scientists agree that the exploitation of objects as tools or artifacts has played a significant role in the evolution of human societies. In the realm of autonomous agents and multi-agent systems, a recent artifact theory proposes the artifact concept as an abstraction for representing functional system components that proactive agents may exploit towards realizing their goals. As a complement, the cognition of rational agents has been extended to accommodate the notion of artifact capabilities denoting the reasoning and planning capacities of agents with respect to artifacts. Multi-Agent Based Simulation (MABS) a well established discipline for modeling complex social systems, has been identified as an area that should benefit from these theories. In MABS the evolution of artifact exploitation can play an important role in the overall performance of the system. The primary contribution of this dissertation is a computational model for integrating artifacts into MABS. The emphasis of the model is on an evolutionary approach that facilitates understanding the effects of artifacts and their exploitation in artificial social systems over time. The artifact theories are extended to support agents designed to evolve artifact exploitation through a variety of learning and adaptation strategies. The model accents strategies that benefit from the social dimensions of MABS. Realized with evolutionary computation methods specifically genetic algorithms, cultural algorithms and multi-population cultural algorithms, artifact capability evolution is supported at individual, population and multi-population levels. A generic MABS and case studies are provided to demonstrate the use of the model in new and existing MABS systems. The accommodation of artifact capability evolution in artificial social systems is applicable in many domains, particularly when the modeled system is one where artifact exploitation is relevant to the evolution of the society and its overall behavior. With artifacts acknowledged as major contributors to societal evolution the impact of our model is significant, providing advanced tools that enable social scientists to analyze their findings. The model can inform archaeologists, economists, evolution theorists, sociologists and anthropologists among others

Traffic Crash Characteristics in Shenzhen, China from 2014 to 2016

Road traffic crashes cause fatalities and injuries of both drivers/passengers in vehicles and pedestrians outside, thus challenge public health especially in big cities in developing countries like China. Previous efforts mainly focus on a specific crash type or causation to examine the crash characteristics in China while lacking the characteristics of various crash types, factors, and the interplay between them. This study investigated the crash characteristics in Shenzhen, one of the biggest four cities in China, based on the police-reported crashes from 2014 to 2016. The descriptive characteristics were reported in detail with respect to each of the crash attributes. Based on the recorded crash locations, the land-use pattern was obtained as one of the attributes for each crash. Then, the relationship between the attributes in motor-vehicle-involved crashes was examined using the Bayesian network analysis. We revealed the distinct crash characteristics observed between the examined levels of each attribute, as well the interplay between the attributes. This study provides an insight into the crash characteristics in Shenzhen, which would help understand the driving behavior of Chinese drivers, identify the traffic safety problems, guide the research focuses on advanced driver assistance systems (ADASs) and traffic management countermeasures in China

The Impact of injury control research centers : advancing the field of injury and violence prevention--an update

cdc:48485Injury Control Research Centers (ICRCs) put research into action to prevent injuries and violence. Underlying all other ICRCs\u2019 core functions is their ability to bring together multiple stakeholders from disparate disciplines, perspectives, and agencies to tackle critical public health problems. They advance the field of injury and violence prevention through leadership that combines their injury topic and core areas of expertise\u2014research, outreach, and training.The National Center for Injury Prevention and Control (NCIPC) within the Centers for Disease Control and Prevention (CDC) funded seven ICRCs from 2012\u20132019 (2012 funding cycle) and three ICRCs from 2014\u20132019 (2014 funding cycle). These 10 ICRCs had a major impact on the field of injury and violence prevention. They conducted groundbreaking research, contributed to local and state policies, and trained hundreds of future researchers and practitioners. This report describes key achievements from their annual and interim progress reports from 2012\u20132019.Publication date from document properties.the-impact-of-injury-control-research-centers_final_508.pd

LINCS : Linking Information for Nonfatal Crash Surveillance : a guide for integrating motor vehicle crash data to help keep Americans safe on the road

The Linking Information for Nonfatal Crash Surveillance (LINCS) Guide is intended to help states start a data linkage program or expand their current program to help prevent motor vehicle crash-related injuries and deaths. The guide discusses the key components of successful linkage programs and details each step in the data linkage process.Motor vehicle crashes (MVCs) are a leading cause of death for people aged 1-54 years in the United States (U.S.). More than 100 people die in MVCs each day and thousaOne method to better understand MVCs is to effectively use existing data sources, such as police, hospital, and emergency medical services (EMS) records. These data sources contain different information and the data sets are generally collected and stored separately. Therefore, linking the data sets together can create a more comprehensive understanding of MVCs by pulling all of the data together into one linked data set. A linked data set will include information about what happened before (e.g., impaired driving), during (e.g., seat belt was being used), and after a crash (e.g., medical outcomes and costs).nds more are injured. Understanding the risk factors and ways to address them can help prevent MVC-related injuries and deaths and reduce costs.The CDC\u2019s National Center for Injury Prevention and Control (NCIPC) enlisted the Centers for Medicare & Medicaid Services (CMS) Alliance to Modernize Healthcare (CAMH)\u2014a federally funded research and development center operated by The MITRE Corporation\u2014to create a guide to help states start or enhance data linkage programs. Linking MVC data sets creates a more comprehensive set of linked data for each MVC incident and for each individual involved in the MVC. Comprehensive MVC linked data can enable analysis of the relationships among contributing factors, interventions, outcomes, and impacts. For example, one advantage of linking police MVC records to hospital records is to assess the magnitude of nonfatal MVC injuries and associated healthcare costs.CS 302338-APublication date from document properties.CDC_LINCS_GUIDE_2019-F.pdfExecutive Summary -- Motor Vehicle Crashes and LINCS -- Introduction -- The LINCS Guide -- Section 1. Establishing a Motor Vehicle Crash Data Linkage Program -- Section 2. Building Partnerships -- Section 3. Developing a Business Model -- Section 4. Establishing the Data Linkage Process -- Conclusion -- Appendix A. National Systems for Motor Vehicle Crash Data -- Appendix B. Literature Review of Published Motor Vehicle Crash Research Using Linked Data -- Appendix C. Crash Outcome -- Data Evaluation System (CODES) -- Appendix D. Stakeholder Listening Sessions -- Appendix E. Select Data Linkage Method(s) -- Appendix F. Select Data Linkage Tools. -- Appendix G. State Motor Vehicle Crash Data Linkage Programs -- Appendix H. Motor Vehicle Crash Data Linkage Program Resources -- Appendix I. Department of Transportation Traffic Records Coordinating Committee Technical Assistance Resources -- Appendix J. Security Program Activities -- Appendix K. Privacy Program Activities. -- Appendix L. Sample Data Use Agreement -- Appendix M. Reduce Computational Requirements. -- Appendix N. Multiple Imputation and Missing Data -- Appendix O. Assessing Data Quality: Variation -- Appendix P. Evaluating Data Linkage Processes -- Appendix Q. Examples of MVC Data Content Standards -- Appendix R. Explanation of Figures for Accessibility -- Acknowledgments -- Acronyms. -- Glossary \u2013 References.2019674

Comprehensive Safety Analysis of Vulnerable Road User Involved Motor Vehicle Crashes

This dissertation explores, identifies, and evaluates a multitude of factors significantly affecting motor vehicle crashes involving pedestrians and bicyclists, commonly defined as vulnerable road users (VRUs). The methodologies are guided by the concept of safe behavior of different parties that are primary responsible for a crash, either a pedestrian, a bicyclist or a driver, pertaining to roadway design, traffic conditions, land use and built environment variables; and the findings are beneficial for recommending targeted and effective safety interventions. The topic is motivated by the fact that human factors contribute to over ninety percent of the crashes, especially the ones involving VRUs. Studying the effect of road users’ behavior, their responses to the dynamics of traveling environment, and compliance rate to traffic rules is instrumental to precisely measure and evaluate how each of the investigated variables changes the crash risk. To achieve this goal, an extensive database is established based on data collected from sources such as the linework from topologically integrated geographic encoding and referencing, Google maps, motor vehicle accident reports, Wisconsin Information System for Local Roads, and Smart Location Dataset from Environmental Protection Agency. The crosscutting datasets represent various aspects of motorist and non-motorists travel decisions and behaviors, as well as their safety status. With this comprehensive database, intrinsic relationships between pedestrian-vehicle crashes and a broad range of socioeconomic and demographic factors, land use and built environment, crime rate and traffic violations, road design, traffic control, and pedestrian-oriented design features are identified, analyzed, and evaluated. The comprehensive safety analysis begins with the structural equation model (SEM) that is employed to discover possible underlying factor structure connecting exogenous variables and crashes involving pedestrians. Informed by the SEM output, the analysis continues with the development of crash count models and responsible party choice models to respectively address factors relating to roles in a crash by pedestrians and drivers. As a result, factors contributing to crashes where a pedestrian is responsible, a driver is responsible, or both parties are responsible can be specified, categorized, and quantified. Moreover, targeted and appropriate safety countermeasures can be designed, recommended, and prioritized by engineers, planners, or enforcement agencies to jointly create a pedestrian-friendly environment. The second aspect of the analysis is to specify the crash party at-fault, which provides evidence about whether pedestrians, bicyclists or drivers are more likely to be involved in severe crashes and to identify the contributing factors that affect the fault of a specific road user group. An extensive investigation of the available information regarding the crash (i.e., issued citations, actions/circumstances that may have played a role in the crash occurrence, and crash scenario completed by the police officer) are considered. The goal is to recognize and measure the factors affecting a specific party at-fault. This provides information that is vital for proactive crisis management: to decrease and to prevent future crashes. As a part of the result, a guideline is proposed to assign the party at-fault through crash data fields and narratives. Statistical methods such as the extreme gradient boosting (XGboost) decision tree and the multinomial logit (MNL) model are used. Appealing conclusions have been found and suggestions are made for law enforcement, education, and roadway management to enhance the safety countermeasures. The third aspect is to evaluate the enhancements of crash report form for its effectiveness of reporting VRU involved motor vehicle crashes. One of the State of Wisconsin projects aiming to develop crash report forms was to redesign the old MV4000 crash report form into the new DT4000 crash report form. The modification was applied from January 1, 2017, statewide. The reason behind this switch is to resolve some matters with the old MV4000 crash report form, including insufficient reporting in roadway-related data fields, lack of data fields describing driver distraction, intersection type, no specification of the exact traffic barrier, insufficient information regarding safety equipment usage by motorists and non-motorists, unclear information about the crash location, and inadequate evidence concerning non-motorists actions, circumstances and condition prior to the crash. Hence, the new DT4000 crash form modified some existing data fields incorporated new crash elements and more detailed attributes. The modified and new data fields, their associated attribute values have been thoroughly studied and the effectiveness of improved data collection in terms of a better understanding of factors associated with and contributing to VRU crashes has been comprehensively evaluated. The evaluation has confirmed that the DT4000 crash form provided more specific, details, and useful about the crash circumstances

Identification and safety effects of road user related measures. Deliverable 4.2 of the H2020 project SafetyCube

Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported Horizon 2020 project with the objective of developing an innovative road safety Decision Support System (DSS). The DSS will enable policy-makers and stakeholders to select and implement the most appropriate strategies, measures, and cost-effective approaches to reduce casualties of all road user types and all severities. This document is the second deliverable (4.2) of work package 4, which is dedicated to identifying and assessing road safety measures related to road users in terms of their effectiveness. The focus of deliverable 4.2 is on the identification and assessment of countermeasures and describes the corresponding operational procedure and outcomes. Measures which intend to increase road safety of all kind of road user groups have been considered [...continues]

A comprehensive survey on cultural algorithms

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