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Efficient And Interpretable Crash Prediction Models
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Book Synopsis Efficient and Interpretable Crash Prediction Models by : Thomas Véran
Download or read book Efficient and Interpretable Crash Prediction Models written by Thomas Véran and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Worldwide, highway accidents have important social and financial impacts. To reduce their frequency and gravity, crash prediction models (CPM) are used to identify hazardous roadway segments and to provide actionable clues about the associated risk factors. CPM are either interpretable-by-design parametric statistical models, in particular generalized linear models (GLM), or machine learning models with a large number of parameters without associated uncertainty estimates (e.g., ensemble of decision trees, support vector machine ...). When pondering high stake decisions, such as in the context of highway safety, field experts expect predictive models to be both effective and glass-box interpretable to help them deploy preventive safety actions. As such, we contribute to enhancing the predictive performance of parametric models while maintaining their interpretability. Our main contributions aim to achieve this goal in two steps. First, we introduce a supervised method to discover a partition of the original observations and build a hierarchical model above it. Second, we introduce two algorithmic approaches (viz., a polynomial neural network, and an extension of multi-objective symbolic regression) to discover highly discriminant non-linear transforms of the original variables. The former can handle correlations among groups of observations which usually lead to improvements in the quality of the models' predictions and of their interpretation. The latter, while remaining simple (e.g. first-order interactions), allow the models to capture more of the variability in the dependent variable. Experiments have been conducted on a highway safety dataset and on more than ten public datasets covering classification and regression tasks. They show promising results with our contributions outperforming traditional glass-box interpretable models while getting close to the best non-parametric models. Finally, we illustrate the benefits of our approach by introducing, on a realistic case study, an application we designed for highway safety experts.
Book Synopsis Pedestrian Crash Prediction and Analyzing Contributing Factors Across Texas by : Mostaq Ahmed (M.S. in Community and Regional Planning)
Download or read book Pedestrian Crash Prediction and Analyzing Contributing Factors Across Texas written by Mostaq Ahmed (M.S. in Community and Regional Planning) and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study applied tree-based machine learning methods to investigate the contributing factors to both crash frequency and injury severity in vehicle-pedestrian crash events. Vehicle and roadway characteristics, driver and pedestrian attributes, traffic controls and land use conditions, transit provision and weather conditions are used as covariates to predict pedestrian crash frequencies (by roadway segment) and injury severity levels (for pedestrians struck by vehicles on public roadways). In both cases, tree-based models offered significantly more prediction accuracy than traditional statistical models (using negative binomial and ordered probit specifications, with the same covariates). The tree-based models also offer valuable interpretability through the regression tree graph itself (with clear branching based on variable cut-points), variable importance plots (for each covariate), and partial dependence plots to help analysts understand the relationship between contributing factors and the target variable (count or severity). Average daily vehicle-miles travelled (DVMT) on each road segment, population density, segment length, census tract-level job density, distance from nearest K-12 school, transit stop density, and segment speed limits were estimated to be the top contributing factors for increasing pedestrian crash counts. DVMT has been found as the single most responsible factor for vehicle-pedestrian crashes and in a way representing pedestrian exposure to such situations. In terms of predicting injury outcomes, intoxication of the pedestrian and/or driver, higher speed limits at the site, crash location not being in the traffic way, older pedestrian, interstate highway locations, and dark and unlit conditions were predictors for more severe outcomes. Importantly, if the surrounding urban area’s population is reasonably high (over 25,000 persons), the probability of the pedestrian dying falls significantly, which supports the ‘safety in numbers’ idea, for more people available to help save the crash victims, or drivers going more slowly due to crowded conditions, closer hospitals, and so on. While few crash studies have included land use variables and local demographics, including proximity to schools, hospitals, and transit stops, population and jobs density variables appeared to add to crash counts and severity for pedestrians, though this is moderated by the 25,000-population threshold and distance variables
Book Synopsis Cross-section Fatal Crash Type Prediction Models by : Hong Zhu
Download or read book Cross-section Fatal Crash Type Prediction Models written by Hong Zhu and published by . This book was released on 2010 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rural two-lane highway in the southeastern United States is frequently associated with a disproportionate number of serious and fatal crashes. The major research objectives are to investigate the relations between probabilities of fatal crash type occurrence and potential contributing factors from road geometric design characteristics and roadside, environmental features. This dissertation analyzes the regional fatal crash database and successfully develops statistical models to examine the relations and provided meaningful research findings. This dissertation contributes to current traffic safety analysis by directly examining the connection between major fatal crash type occurrence and roadway geometrics, roadside characteristics, and environmental conditions through a regional case study. This study effort addresses the less understood relationship between fatal crash types and road features compared to other crash measures, such as crash frequency, crash rate, and injury severity. The developed fatal crash type prediction models not only demonstrate strong connections between crash types and road characteristics, but also provide a quantitative assessment tool for countermeasures in terms of reduction of fatal crash type occurrence. Since most countermeasures are more effective at mitigating certain type of crashes, the information revealed from the crash type prediction models help clarify the relationship between candidate countermeasures and expected crash reductions.
Book Synopsis Real-time Crash Prediction of Urban Highways Using Machine Learning Algorithms by : Mirza Ahammad Sharif
Download or read book Real-time Crash Prediction of Urban Highways Using Machine Learning Algorithms written by Mirza Ahammad Sharif and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Motor vehicle crashes in the United States continue to be a serious safety concern for state highway agencies, with over 30,000 fatal crashes reported each year. The World Health Organization (WHO) reported in 2016 that vehicle crashes were the eighth leading cause of death globally. Crashes on roadways are rare and random events that occur due to the result of the complex relationship between the driver, vehicle, weather, and roadway. A significant breadth of research has been conducted to predict and understand why crashes occur through spatial and temporal analyses, understanding information about the driver and roadway, and identification of hazardous locations through geographic information system (GIS) applications. Also, previous research studies have investigated the effectiveness of safety devices designed to reduce the number and severity of crashes. Today, data-driven traffic safety studies are becoming an essential aspect of the planning, design, construction, and maintenance of the roadway network. This can only be done with the assistance of state highway agencies collecting and synthesizing historical crash data, roadway geometry data, and environmental data being collected every day at a resolution that will help researchers develop powerful crash prediction tools. The objective of this research study was to predict vehicle crashes in real-time. This exploratory analysis compared three well-known machine learning methods, including logistic regression, random forest, support vector machine. Additionally, another methodology was developed using variables selected from random forest models that were inserted into the support vector machine model. The study review of the literature noted that this study's selected methods were found to be more effective in terms of prediction power. A total of 475 crashes were identified from the selected urban highway network in Kansas City, Kansas. For each of the 475 identified crashes, six no-crash events were collected at the same location. This was necessary so that the predictive models could distinguish a crash-prone traffic operational condition from regular traffic flow conditions. Multiple data sources were fused to create a database including traffic operational data from the KC Scout traffic management center, crash and roadway geometry data from the Kanas Department of Transportation; and weather data from NOAA. Data were downloaded from five separate roadway radar sensors close to the crash location. This enable understanding of the traffic flow along the roadway segment (upstream and downstream) during the crash. Additionally, operational data from each radar sensor were collected in five minutes intervals up to 30 minutes prior to a crash occurring. Although six no-crash events were collected for each crash observation, the ratio of crash and no-crash were then reduced to 1:4 (four non-crash events), and 1:2 (two non-crash events) to investigate possible effects of class imbalance on crash prediction. Also, 60%, 70%, and 80% of the data were selected in training to develop each model. The remaining data were then used for model validation. The data used in training ratios were varied to identify possible effects of training data as it relates to prediction power. Additionally, a second database was developed in which variables were log-transformed to reduce possible skewness in the distribution. Model results showed that the size of the dataset increased the overall accuracy of crash prediction. The dataset with a higher observation count could classify more data accurately. The highest accuracies in all three models were observed using the dataset of a 1:6 ratio (one crash event for six no-crash events). The datasets with1:2 ratio predicted 13% to 18% lower than the 1:6 ratio dataset. However, the sensitivity (true positive prediction) was observed highest for the dataset of a 1:2 ratio. It was found that reducing the response class imbalance; the sensitivity could be increased with the disadvantage of a reduction in overall prediction accuracy. The effects of the split ratio were not significantly different in overall accuracy. However, the sensitivity was found to increase with an increase in training data. The logistic regression model found an average of 30.79% (with a standard deviation of 5.02) accurately. The random forest models predicted an average of 13.36% (with a standard deviation of 9.50) accurately. The support vector machine models predicted an average of 29.35% (with a standard deviation of 7.34) accurately. The hybrid approach of random forest and support vector machine models predicted an average of 29.86% (with a standard deviation of 7.33) accurately. The significant variables found from this study included the variation in speed between the posted speed limit and average roadway traffic speed around the crash location. The variations in speed and vehicle per hour between upstream and downstream traffic of a crash location in the previous five minutes before a crash occurred were found to be significant as well. This study provided an important step in real-time crash prediction and complemented many previous research studies found in the literature review. Although the models investigate were somewhat inconclusive, this study provided an investigation of data, variables, and combinations of variables that have not been investigated previously. Real-time crash prediction is expected to assist with the on-going development of connected and autonomous vehicles as the fleet mix begins to change, and new variables can be collected, and data resolution becomes greater. Real-time crash prediction models will also continue to advance highway safety as metropolitan areas continue to grow, and congestion continues to increase.
Book Synopsis Interpretable Machine Learning by : Christoph Molnar
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Book Synopsis Reliability of Crash Prediction Models by : Raghavan Srinivasan (Transportation engineer)
Download or read book Reliability of Crash Prediction Models written by Raghavan Srinivasan (Transportation engineer) and published by . This book was released on 2021 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Understanding and Communicating Reliability of Crash Prediction Models by :
Download or read book Understanding and Communicating Reliability of Crash Prediction Models written by and published by . This book was released on 2021 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding and communicating consistently reliable crash prediction results are critical to credible analysis and to overcome barriers for some transportation agencies or professionals utilizing these models. The TRB National Cooperative Highway Research Program's NCHRP Web-Only Document 303: Understanding and Communicating Reliability of Crash Prediction Models provides guidance on being able to assess and understand the reliability of Crash Prediction Models. This document is supplemental to NCHRP Research Report 983: Reliability of Crash Prediction Models: A Guide for Quantifying and Improving the Reliability of Model Results.
Book Synopsis Accident Prediction Models by : Shane Turner
Download or read book Accident Prediction Models written by Shane Turner and published by . This book was released on 2001 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Roundabout Crash Prediction Models by :
Download or read book Roundabout Crash Prediction Models written by and published by . This book was released on 2009 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Improving Freeway Crash Prediction Models Using Disaggregate Flow State Information by : Nancy Dutta
Download or read book Improving Freeway Crash Prediction Models Using Disaggregate Flow State Information written by Nancy Dutta and published by . This book was released on 2020 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt: Crash analysis methods typically use annual average daily traffic as an exposure measure, which can be too aggregate to capture the safety effects of variations in traffic flow and operations that occur throughout the day. Flow characteristics such as variation in speed and level of congestion play a significant role in crash occurrence and are not currently accounted for in the American Association of State Highway and Transportation Officials’ Highway Safety Manual. This study developed a methodology for creating crash prediction models using traffic, geometric, and control information that is provided at sub-daily aggregation intervals. Data from 110 rural four-lane segments and 80 urban six-lane segments were used. The volume data used in this study came from detectors that collect data ranging from continuous counts throughout the year to counts from only a couple of weeks every other year (short counts). Speed data were collected from both point sensors and probe data provided by INRIX. The results showed that models that used data aggregated to an average hourly level reflected the variation in volume and speed throughout the day without compromising model quality. Crash predictions for urban segments underwent a 20% improvement in mean absolute deviation for total crashes and a 9% improvement for injury crashes when models using average hourly volume, geometry, and flow variables were compared to the model based on annual average daily traffic. Corresponding improvements over annual average daily traffic models for rural segments were 11% and 9%. Average hourly speed, standard deviation of hourly speed, and differences between speed limit and average speed had statistically significant relationships with crash frequency. For all models, prediction accuracy was improved across all validation measures of effectiveness when the speed components were added. The positive effect of flow variables was true irrespective of the speed data source. Further investigation revealed that the improvement achieved in model prediction by using a more inclusive and bigger dataset was larger than the effect of accounting for spatial/temporal data correlation. For rural hourly models, mean absolute deviation improved by 52% when short counts were added in comparison to the continuous count station only models. The respective value for urban segments was 58%. This means that using short count stations as a data source does not diminish the quality of the developed models. Thus, a combination of different volume data sources with good quality speed data can lessen the dependency on volume data quality without compromising performance. Although accounting for spatial and temporal correlation improved model performance, it provided smaller benefits than inclusion of the short count data in the models. This study showed that it is possible to develop a broadly transferable crash prediction methodology using hourly level volume and flow data that are currently widely available to transportation agencies. These models have a broad spectrum of potential applications that involve assessing safety effects of events and countermeasures that create recurring and non-recurring short-term fluctuations in traffic characteristics.
Book Synopsis Defining New Exposure Measures for Crash Prediction Models by Type of Collision by : Chen Zhang
Download or read book Defining New Exposure Measures for Crash Prediction Models by Type of Collision written by Chen Zhang and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Discovery Science by : Poncelet Pascal
Download or read book Discovery Science written by Poncelet Pascal and published by Springer Nature. This book was released on 2022-11-05 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 25th International Conference on Discovery Science, DS 2022, which took place virtually during October 10-12, 2022. The 27 full papers and 12 short papers presented in this volume were carefully reviewed and selected from 59 submissions.
Book Synopsis Improved Prediction Models for Crash Types and Crash Severities by :
Download or read book Improved Prediction Models for Crash Types and Crash Severities written by and published by . This book was released on 2021 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: The release of the Highway Safety Manual (HSM) by the American Association of State Highway and Transportation Officials (AASHTO) in 2010 was a landmark event in the practice of road safety analysis. Before it, the United States had no central repository for information about quantitative road safety analysis methodology. The TRB National Cooperative Highway Research Program's NCHRP Web-Only Document 295: Improved Prediction Models for Crash Types and Crash Severities describes efforts to develop improved crash prediction methods for crash type and severity for the three facility types covered in the HSM—specifically, two‐lane rural highways, multilane rural highways, and urban/suburban arterials. Supplemental materials to the Web-Only Document include Appendices A, B, and C (Average Condition Models, Crash Severities – Ordered Probit Fractional Split Modeling Approach, and Draft Content for Highway Safety Manual, 2nd Edition).
Book Synopsis Development of Crash Prediction Models for Transportation Planning Analysis by :
Download or read book Development of Crash Prediction Models for Transportation Planning Analysis written by and published by . This book was released on 2015 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Linear Regression Crash Prediction Models by : Montasir Abbas
Download or read book Linear Regression Crash Prediction Models written by Montasir Abbas and published by . This book was released on 2010 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The paper develops a linear regression model approach that can be applied to crash data to predict vehicle crashes. The proposed approach involves novice data aggregation to satisfy linear regression assumptions; namely error structure normality and homoscedasticity. The proposed approach is tested and validated using data from 186 access road sections in the state of Virginia. The approach is demonstrated to produce crash predictions consistent with traditional negative binomial and zero inflated negative binomial general linear models. It should be noted however that further testing of the approach on other crash datasets is required to further validate the approach."--P. [1].
Book Synopsis Macro-Level Analysis of Safety Planning and Crash Prediction Models by :
Download or read book Macro-Level Analysis of Safety Planning and Crash Prediction Models written by and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Highway Safety Manual (HSM) is a tool that helps transportation agencies make data-driven decisions about safety. It includes methods for quantifying safety performance and predicting crash frequencies. The HSM is currently being updated to include macro-level crash prediction models, which can be used to assess safety trends at a regional or national level. NCHRP Web-Only Document 348: Macro-Level Analysis of Safety Planning and Crash Prediction Models: A Guide, from TRB's National Cooperative Highway Research Program, provides guidance on how to use a spreadsheet tool developed during this project. The document is supplemental to NCHRP Research Report 1044: Development and Application of Quantitative Macro-Level Safety Prediction Models.
Book Synopsis Developing Transferable Real-time Crash Prediction Models for Highly Imbalanced Data by : Cheuk Ki Man
Download or read book Developing Transferable Real-time Crash Prediction Models for Highly Imbalanced Data written by Cheuk Ki Man and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
