Download or read book Crash Prediction Model for Freeway Facilities with High Occupancy Vehicle HOV and High Occupancy Toll HOT Lanes written by Sivaramakrishnan Srinivasan and published by . This book was released on 2015 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Calibration of Highway Safety Manual Prediction Models for Freeway Segments Speed change Lanes Ramp Segments and Crossroad Ramp Terminals in Kansas written by Imalka Chiranthi Matarage and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Crash prediction models in the Highway Safety Manual (HSM) are used to quantify the safety experience of existing and new roadways. Safety performance functions (SPFs) or crash prediction models are statistical formulas developed on limited data from a few selected states, Kansas not being one of those states. Therefore, the HSM recommends calibration of HSM-default SPFs, or development of local SPFs, to enhance accuracy of predicted crash frequency. This dissertation demonstrates the HSM calibration procedure and its' quality assessment for freeway segments, speed-change lanes, ramp segments, and crossroad ramp terminals in Kansas. The study used three years of recent crash data, the most recent geometric data, and HSM-recommended sample sizes for all facilities considered for the calibration. The HSM methodology overpredicted all fatal and injury (FI) crashes and underpredicted all property damage only (PDO) crashes for freeway segments. The HSM methodology consistently underpredicted both FI and PDO crashes for both entrance- and exit-related speed-change lanes. The HSM methodology overpredicted all FI crashes, underpredicted multiple vehicle PDO crashes, and overpredicted single vehicle PDO crashes for entrance ramp segments. In the case of exit ramp segments, the HSM methodology underpredicted all multiple vehicle crashes and overpredicted all single vehicle crashes. The HSM methodology overpredicted all FI crashes and underpredicted all PDO crashes for both signal- and stop-controlled crossroad ramp terminals. Cumulative residual plots and coefficient of variation were used to evaluate the quality of calibrated HSM-default SPFs. Results of calibration quality assessment indicated that estimated calibration factors were satisfactory for all freeway and ramp facilities considered in this study. However, for further accuracy and comparison purposes, calibration functions were developed to improve the fit to local data. Calibration functions were better fitted compared to calibrated HSM-default SPFs for freeway and ramp facilities in Kansas. Challenges faced, how those challenges were addressed, and data collection techniques used in this study are discussed. In summary, estimated calibration factors and developed calibration functions of this study would greatly improve making accurate decisions related to freeway and ramp safety in Kansas.

Download or read book Crash Prediction Models on Truck related Crashes on Two lane Rural Highways with Vertical Curves written by Srutha Vavilikolanu and published by . This book was released on 2008 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: "According to Federal Motor Carrier Safety Administration (FMCSA), truck involvement in fatal crashes is more on rural areas than on urban areas. The Fatality Analysis Reporting System (FARS) encyclopedia also indicates that truck involvement in fatal crashes are approximately 12% of the total fatal crashes in the nation and 14 % in The State of Ohio. One area for potential concern is the role of vertical curves on truck crashes. In the design of vertical curves stopping distance, grade and length of the curve are important factors taken into consideration. Vehicle operations on vertical curves are influenced by the grade of the curve, stopping sight distance and vehicle speed. These factors may create operational issues for vehicles traveling on vertical curves and in turn increase the likelihood for crashes. Truck specific studies in the past have focused on geometric roadway factors associated with crashes on vertical curves. Most of the research studies are focused on crest curve truck crashes, and little research has been done on crashes on vertical sag curves. The main research goal of the study is to develop prediction models to evaluate the impact of geometry, traffic volumes and speed on truck-related crashes on two-lane rural vertical curves. The accomplishment of the research goal is achieved by setting five objectives. The first objective is to develop three crash prediction models using negative binomial regression model. These models are 1. Full model - for all vertical curves 2. Reduced model I - for crest curves only and 3. Reduced model II - for sag curves only. The dataset includes 1,935 vertical curve segments with 205 truck crashes from 2002-2006. In second and third objective, Full Bayes approach is used to enhance the results obtained in the Reduced Models I and II. These results are then compared to the initial models. The fourth objective is evaluating the vertical curve variables which are statistically significant with truck-related crashes. These results show that higher grade change for the length of the vertical curve, total width in the range of 24 to 26ft, more number of passenger cars and trucks, increases the truck-related crashes on both crest and sag curves. Low speed limit on crest curves and high speed limit on sag curves increases truck-related crashes which may seem counter intuitive. The fifth objective is to provide suggestions on effective methods to reduce truck related crashes and improve safety. Some potential areas for design improvement may include flattening of steep vertical curves, advisory speed signs and increasing the roadway width on rural vertical curves in Ohio."--Abstract.

Download or read book Highway Economic Requirements System HERS Safety Model Assessment and Two Lane Urban Crash Model written by John A. Volpe National Transportation Syste Center and published by Createspace Independent Publishing Platform. This book was released on 2014-01-21 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are many reasons to be concerned with estimating the frequency and social costs of highway accidents, but most reasons are motivated by a desire to minimize these costs to the extent feasible. Competition for scarce resources is a practical necessity, and society seeks to apply those resources where they will do the most good. With highway crashes, given the high costs of mis-prediction in fatalities and injuries, sound information for prioritizing projects with limited funds is essential.

Download or read book Development of Crash Imminent Test Scenarios for Integrated Vehicle Based Safety Systems IVBSS written by Wassim Najm and published by Createspace Independent Publishing Platform. This book was released on 2007 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report identifies crash imminent test scenarios based on common pre-crash scenarios for integrated vehicle-based safety systems that alert the driver of a light vehicle or a heavy truck to an impending rear-end, lane change, or run-off-road crash. Pre-crash scenarios describe vehicle movements and critical events immediately prior to the crash. The General Estimates System (GES) crash database was queried to distinguish common pre-crash scenarios for light vehicles (2003 GES) and heavy trucks (2000-2003 GES) in terms of their frequency of occurrence. Analysis of two-vehicle rear-end crashes revealed four dominant scenarios that accounted for 97 percent of light-vehicle crashes and 95 percent of heavy-truck crashes in which the subject vehicle was striking. Four scenarios were also identified from an analysis of two-vehicle lane change crashes, comprising 65 percent of light-vehicle crashes and 76 percent of heavy-truck crashes in which the subject vehicle was encroaching onto another vehicle in adjacent lanes. There were five single-vehicle, run-off-road scenarios representing 63 percent of light-vehicle crashes and 83 percent of heavy-truck crashes, excluding crashes caused by vehicle failure or evasive maneuver. An additional set of scenarios is proposed to address multiple threats from near simultaneous critical events. This report also provides a statistical description of individual scenarios in terms of their environmental factors, roadway geometry, and speed conditions.

Download or read book Safety Data Analysis and Modeling written by and published by . This book was released on 2008 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: TRB's Transportation Research Record: Journal of the Transportation Research Board, No. 2083 includes 22 papers that explore data-driven perspective on safety risk management, macrolevel annual safety performance measures, tool with road-level crash prediction for safety planning, congestion and number of lanes on urban freeways relationship to safety, accident modification factors, identifying hazardous road locations, identifying hot spots, and safety influence area for four-legged signalized intersections. This issue of the TRR also examines automated analysis of accident exposure, new simulation-based surrogate safety measure, hit-and-run crashes, speed limit increases' effect on injury severity, safety of curbs, proximity to intersections and injury severity of urban arterial crashes, nested logit model of traffic flow on freeway ramps, intelligent transportation system data for assessing freeway safety, vehicle time spent in following on two-lane rural roads, indirect associations in crash data, crash prediction models for rural highways, and methodology for identifying causal factors of accident severity.

Download or read book Crash Data Identify Safety Issues for High occupancy Vehicle Lanes in Selected Texas Corridors written by A. Scott Cothron and published by . This book was released on 2005 with total page 4 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Methods in Highway Safety Analysis written by Bhagwant Naraine Persaud and published by . This book was released on 2001 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: TRB's National Cooperative Highway Research Program (NCHRP) Synthesis 295: Statistical Methods in Highway Safety Analysis focus on the type of safety analysis required to support traditional engineering functions, such as the identification of hazardous locations and the development and evaluation of countermeasures. Analyses related specifically to driver and vehicle safety are not covered, but some statistical methods used in these areas are of relevance and are summarized where appropriate.

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.

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 0 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.

Download or read book Crash Causal Factors and Countermeasures for High risk Locations on Multilane Primary Highways in Virginia written by Nicholas J. Garber and published by . This book was released on 2009 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: In 2004, a total of 95,020 vehicle crashes occurred on highways under the jurisdiction of the Virginia Department of Transportation (VDOT). Of these, 39,847 crashes occurred on primary highways, and 345 of these were fatal crashes. VDOT's traffic engineers continue to place increasing emphasis on identifying causal factors for crashes to enhance the selection of appropriate and effective countermeasures. The purpose of this study was to identify causal factors and appropriate countermeasures for crashes occurring at high-risk locations on multilane primary highways from 2001 through 2006. These high-risk locations were identified by Fontaine and Reed (2006) in a VDOT safety corridor study. A total of 365 sites, 1 to 2 mi in length, were used in the study. The statewide sites were located on rural and urban highways with divided, undivided, and traversable medians, with about 40 sites per VDOT district. Crash data were extracted from police crash reports, and geometric data were collected through site visits. Operational data were collected using VDOT's resources. The analysis involved more than 34,000 crashes and was conducted using fault tree analysis and generalized linear modeling. The fault tree analysis was used to determine the critical fault path based on the probability of an event occurring. Individual fault trees were constructed for each collision type and for each highway classification. The generalized linear models were developed for different highway classifications: urban divided, urban undivided, urban traversable (central lanes that can be used for left turns in both directions), and rural divided highways. Models were developed for rear-end crashes and total crashes, and separate models were developed for injury crashes, property damage only (PDO) crashes, and injury + PDO crashes. Appropriate potential countermeasures were then identified based on the significant causal factors identified in the models. The results indicated that rear-end crashes were the predominant type of crash, representing 56% of all PDO crashes on urban divided highways and 37% of all PDO crashes on rural divided highways. Implementing the recommended countermeasures for total, rear-end, and angle crashes for different assumed levels of rehabilitation is expected to result in a crash reduction of up to about 40% depending on the site and level of rehabilitation undertaken. A benefit/cost analysis showed that the benefit/cost ratios were higher than 1 for all levels of countermeasure implementation.

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).

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 Network based Highway Crash Prediction Using Geographic Information Systems written by and published by . This book was released on 2007 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Crash Prediction Modeling for Curved Segments of Rural Two lane Two way Highways in Utah written by Casey Scott Knecht and published by . This book was released on 2014 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: The HSM predictive model for rural two-lane two-way highways consists of a safety performance function (SPF), crash modification factors (CMFs), and a jurisdiction-specific calibration factor. For this research, two sample periods were used: a three-year period from 2010 to 2012 and a five-year period from 2008 to 2012. The calibration factor for the HSM predictive model was determined to be 1.50 for the three-year period and 1.60 for the five-year period. These factors are to be used in conjunction with the HSM SPF and all applicable CMFs.

Download or read book A Two stage Model for Predicting Crash Rate by Severity Types written by S M A Bin al Islam and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Highway Economic Requirements System HERS written by Lee Biernbaum and published by . This book was released on 2008 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report presents a crash prediction model which takes into consideration the "combined effects of [a] roadway's geometric features and traffic levels," as opposed to the current Highway Economic Requirements System (HERS} model which only considers traffic levels (p. 29). The report begins with an overview of crash prediction, including crash causes (driver, vehicle, and roadway characteristics) and modeling strategies. It describes the current HERS model, reviews existing highway and crash data sources, and reviews research on the effects of roadway geometry. Finally, it presents an improved model for predicting crashes on urban two-lane streets.