Estimating Calibration Factors and Developing Calibration Functions for the Prediction of Crashes at Urban Intersections in Kansas PDF Download
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Author: Rijesh Karmacharya
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Category :
Languages : en
Pages :
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Kansas experienced about 60,000 crashes annually from 2013 to 2016, 25% of which occurred at urban intersections. Hence, urban intersections in Kansas are one of the most critical locations in terms of frequency of crashes. Therefore, an accurate prediction of crashes at these locations would help identify critical intersections with a higher probability of an occurrence of crash, which would help in selecting appropriate countermeasures to reduce those crashes. The crash prediction models provided in the Highway Safety Manual (HSM) predict crashes using traffic and geometric data for various roadway facilities, which are incorporated through Safety Performance Functions (SPFs) and Crash Modification Factors. The primary objective of this study was to estimate calibration factors for different types of urban intersection in Kansas. This study followed the crash prediction method and calibration procedure provided in the HSM to estimate calibration factors for four different urban intersection types in Kansas: 3-leg unsignalized intersections with stop control on the minor approach (3ST), 3-leg signalized intersections (3SG), 4-leg unsignalized intersections with stop control on the minor approach (4ST), and 4-leg signalized intersections (4SG). Following the HSM methodology, the required data elements were collected from various sources. The Annual Average Daily Traffic (AADT) data were extracted from Kansas Crash Analysis & Reporting System (KCARS) database and GIS Shapefiles downloaded from Federal Highway Administration website. For some of 3ST and 3SG intersections, minor-street AADT was not available. Hence, multiple linear regression models were developed for the estimation of minor-street AADT. Crash data were extracted from the Kansas Crash Analysis and Reporting System database, and other geometric data were extracted using Google Earth. The HSM requirement for sample size is 30 to 50 sites, with at least 100 crashes per year for the study period for the combined set of sites. In this study, the study period for 3ST, 3SG, and 4SG intersections were taken as 2013 to 2015, and 2014 to 2016 for 4ST, based on the availability of recent crash data at the beginning of the calibration procedure for each facility type. The sample size considered for calibration was 234 for 3ST, 89 for 3SG, 167 for 4ST, and 198 for 4SG intersections. Out of the 234 3ST intersections, minor-street AADT was estimated using multiple linear regression models for 106 intersections. For 3SG intersections, minor-street AADT was estimated for 21 out of the 89 intersections. The calibration factors for these facility types were estimated to be 0.64 for 3SG, 0.51 for 3ST, 1.17 for 4SG, and 0.61 for 4ST when considering crashes of all severities. Considering only the fatal and injury crashes, the calibration factors were estimated as 0.52 for 3SG, 0.40 for 3ST, 2.00 for 4SG, and 0.73 for 4ST. The calibration factors show that the HSM methodology underpredicted crashes for 4SG, and overpredicted crashes for other three intersection types. The reliability of the calibration factors was assessed with the help of Cumulative Residual plots and coefficient of variation. The results from the goodness-of-fit tests showed that the calibration factors were not reliable and showed bias in the prediction of crashes. Hence, calibration functions were developed, and their reliability were examined. The results showed that calibration functions had better reliability as compared to calibration factors, with more accuracy in crash prediction. The findings from this study can be used to identify intersections with a higher probability of having crashes in the future. Suitable countermeasures can be applied at critical locations which would help reduce the number of crashes at urban intersections in Kansas; thus increasing the safety.
Author: Rijesh Karmacharya
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Kansas experienced about 60,000 crashes annually from 2013 to 2016, 25% of which occurred at urban intersections. Hence, urban intersections in Kansas are one of the most critical locations in terms of frequency of crashes. Therefore, an accurate prediction of crashes at these locations would help identify critical intersections with a higher probability of an occurrence of crash, which would help in selecting appropriate countermeasures to reduce those crashes. The crash prediction models provided in the Highway Safety Manual (HSM) predict crashes using traffic and geometric data for various roadway facilities, which are incorporated through Safety Performance Functions (SPFs) and Crash Modification Factors. The primary objective of this study was to estimate calibration factors for different types of urban intersection in Kansas. This study followed the crash prediction method and calibration procedure provided in the HSM to estimate calibration factors for four different urban intersection types in Kansas: 3-leg unsignalized intersections with stop control on the minor approach (3ST), 3-leg signalized intersections (3SG), 4-leg unsignalized intersections with stop control on the minor approach (4ST), and 4-leg signalized intersections (4SG). Following the HSM methodology, the required data elements were collected from various sources. The Annual Average Daily Traffic (AADT) data were extracted from Kansas Crash Analysis & Reporting System (KCARS) database and GIS Shapefiles downloaded from Federal Highway Administration website. For some of 3ST and 3SG intersections, minor-street AADT was not available. Hence, multiple linear regression models were developed for the estimation of minor-street AADT. Crash data were extracted from the Kansas Crash Analysis and Reporting System database, and other geometric data were extracted using Google Earth. The HSM requirement for sample size is 30 to 50 sites, with at least 100 crashes per year for the study period for the combined set of sites. In this study, the study period for 3ST, 3SG, and 4SG intersections were taken as 2013 to 2015, and 2014 to 2016 for 4ST, based on the availability of recent crash data at the beginning of the calibration procedure for each facility type. The sample size considered for calibration was 234 for 3ST, 89 for 3SG, 167 for 4ST, and 198 for 4SG intersections. Out of the 234 3ST intersections, minor-street AADT was estimated using multiple linear regression models for 106 intersections. For 3SG intersections, minor-street AADT was estimated for 21 out of the 89 intersections. The calibration factors for these facility types were estimated to be 0.64 for 3SG, 0.51 for 3ST, 1.17 for 4SG, and 0.61 for 4ST when considering crashes of all severities. Considering only the fatal and injury crashes, the calibration factors were estimated as 0.52 for 3SG, 0.40 for 3ST, 2.00 for 4SG, and 0.73 for 4ST. The calibration factors show that the HSM methodology underpredicted crashes for 4SG, and overpredicted crashes for other three intersection types. The reliability of the calibration factors was assessed with the help of Cumulative Residual plots and coefficient of variation. The results from the goodness-of-fit tests showed that the calibration factors were not reliable and showed bias in the prediction of crashes. Hence, calibration functions were developed, and their reliability were examined. The results showed that calibration functions had better reliability as compared to calibration factors, with more accuracy in crash prediction. The findings from this study can be used to identify intersections with a higher probability of having crashes in the future. Suitable countermeasures can be applied at critical locations which would help reduce the number of crashes at urban intersections in Kansas; thus increasing the safety.
Author: Sunanda Dissanayake
Publisher:
ISBN:
Category :
Languages : en
Pages : 113
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Kansas experienced about 60,000 crashes annually from 2013 to 2016, 25% of which occurred at urban intersections. Hence, urban intersections in Kansas are one of the critical locations in terms of frequency of crashes. Therefore, an accurate prediction of crashes at these locations would help identify critical intersections with a higher probability of an occurrence of crash, which would help in selecting appropriate countermeasures to reduce those crashes. The crash prediction models provided in the Highway Safety Manual (HSM) predict crashes using traffic and geometric data for various roadway facilities, which are incorporated through Safety Performance Functions (SPFs) and Crash Modification Factors. The primary objective of this study was to estimate calibration factors for different types of urban intersections in Kansas. This study followed the crash prediction method and calibration procedure provided in the HSM to estimate calibration factors for four different urban intersection types in Kansas: 3-leg unsignalized intersections with stop control on the minor approach (3ST), 3-leg signalized intersections (3SG), 4-leg unsignalized intersections with stop control on the minor approach (4ST), and 4-leg signalized intersections (4SG). Following the HSM methodology, the required data elements were collected from various sources. The Annual Average Daily Traffic (AADT) data were extracted from the Kansas Crash Analysis & Reporting System (KCARS) database and GIS Shapefiles were downloaded from the Federal Highway Administration website. For some of the 3ST and 3SG intersections, minor-street AADT was not available. Hence, multiple linear regression models were developed for the estimation of minor-street AADT. Crash data were extracted from the KCARS database, and other geometric data were extracted using Google Earth. The HSM requirement for sample size is 30 to 50 sites, with at least 100 crashes per year for the study period for the combined set of sites. In this study, 2013 to 2015 was chosen as the study period for 3ST, 3SG, and 4SG intersections, and 2014 to 2016 was chosen for 4ST intersections, based on the availability of recent crash data at the beginning of the calibration procedure for each facility type. The sample size considered for calibration was 234 for 3ST, 89 for 3SG, 167 for 4ST, and 198 for 4SG intersections. Out of the 234 3ST intersections, minor-street AADT was estimated using multiple linear regression models for 106 intersections. For 3SG intersections, minor-street AADT was estimated for 21 out of the 89 intersections. The calibration factors for these facility types were estimated to be 0.64 for 3SG, 0.51 for 3ST, 1.17 for 4SG, and 0.61 for 4ST when considering crashes of all severities. Considering only the fatal and injury crashes, the calibration factors were estimated as 0.52 for 3SG, 0.40 for 3ST, 2.00 for 4SG, and 0.73 for 4ST. The calibration factors show that the HSM methodology underpredicted crashes for 4SG intersections, and overpredicted crashes for the other three intersection types. The reliability of the calibration factors was assessed with the help of Cumulative Residual plots and coefficient of variation. The results from the goodness-of-fit tests showed that the calibration factors were not reliable and showed bias in the prediction of crashes. Hence, calibration functions were developed, and their reliability was examined. The results showed that calibration functions had better reliability as compared to calibration factors, with more accuracy in crash prediction. The findings from this study can be used to identify intersections with a higher probability of having crashes in the future. Suitable countermeasures can be applied at critical locations which would help reduce the number of crashes at urban intersections in Kansas, thus increasing the safety.
Author: Imalka Chiranthi Matarage
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ISBN:
Category :
Languages : en
Pages :
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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.
Author: Michael Colety
Publisher:
ISBN:
Category : Traffic accidents
Languages : en
Pages : 197
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The predictive method in the Highway Safety Manual (HSM) includes a safety performance function (SPF), crash modification factors (CMFs), and a local calibration factor (C), if available. Two alternatives exist for applying the HSM prediction methodology to local conditions. They are either calibration of the SPFs found in the HSM or the development of jurisdiction-specific SPFs. The objective of this study was to develop a process to evaluate the SPFs contained in the HSM for road segments and intersections on the Arizona State Highway System and to determine if those SPFs should be calibrated or if Arizona-specific SPFs should be developed. The recommendations are that ADOT move forward with SPF calibration for all HSM safety performance functions as for project-level safety analysis in Arizona. A specific calibration function has been calculated for two-lane rural undivided highways. Safety analysis is progressing at a promising rate and can be used to attain significant reductions in fatal crashes and crash severity. To achieve this, ADOT will need to make a significant commitment to developing and maintaining a comprehensive database of roadway characteristics combined with crash data and average annual daily traffic volume data that are all linked through a common linear referencing system.
Author: Uditha Nandun Galgamuwa
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Category :
Languages : en
Pages :
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Lane-departure crashes are the most predominant crash type in Kansas which causes very high number of motor vehicle fatalities. Therefore, the Kansas Department of Transportation (KDOT) has implemented several different types of countermeasures to reduce the number of motor vehicle fatalities associated with lane-departure crashes. This research was conducted to estimate the safety effectiveness of commonly used lane-departure countermeasures in Kansas on all crashes and lane-departure crashes using Crash Modification Factors (CMFs). Paved shoulders, rumble strips, safety edge treatments and median cable barriers were identified as the commonly used lane-departure countermeasures on both tangent and curved road segments while chevrons and post-mounted delineators were identified as the most commonly used lane-departure countermeasures on curved road segments. This research proposes a state-of-art method of estimating CMFs using cross-sectional data for chevrons and post-mounted delineators. Furthermore, another state-of-art method is proposed in this research to estimate CMFs for safety edge treatments using before-and-after data. Considering the difficulties of finding the exact date of implementation of each countermeasure, both cross-sectional and before-and-after studies were employed to estimate the CMFs. Cross-sectional and case-control methods, which are the two major methods in cross-sectional studies were employed to estimate CMFs for paved shoulders, rumble strips, and median cable barriers. The conventional cross-sectional and case-control methods were modified when estimating CMFs for chevrons and post-mounted delineators by incorporating environmental and human behaviors in addition to geometric and traffic-related explanatory variables. The proposed method is novel and has not been used in the previous cross-sectional models available in the literature. Generalized linear regression models assuming negative binomial error structure were used to develop models for cross-sectional method to estimate CMFs while logistic regression models were used to estimate CMFs using case-control method. Results showed that incorporating environmental and human-related variables into cross-sectional models provide better model fit than in conventional cross-sectional models. To validate the developed models for cross-sectional method, mean of the residuals and the Root Mean Square Error (RMSE) were used. For the case-control method, Receiver Operational Characteristic (ROC) was used to evaluate the predictive power of models for a binary outcome using classification tables. However, it was seen that the case-control method is not suitable for estimating CMFs for all crashes since the range of the crash frequency is wide in each road segment. A regression-based method of estimating CMFs using before-and-after data was proposed to estimate CMFs for safety edge treatments. This method allows researchers to identify the safety effectiveness of an individual CMFs on road segments where multiple treatments have been applied at the same time. Since this method uses road geometric and traffic-related characteristics in addition to countermeasure information as the explanatory variables, the model itself would be the Safety Performance Function (SPF). Therefore, developing new SPF is not necessary. Finally, the CMFs were estimated using before-and-after Empirical Bayes method to validate the results from the regression-based method. The results of this study can be used as a decision-making tool when implementing lane-departure countermeasures on similar roadways in Kansas. Even though there are readily available CMFs from the national level studies, having more localized CMFs will be beneficial due to differences in traffic-related and geometric characteristics on different roadways.
Author: Michael Y. K. Lau
Publisher:
ISBN:
Category : Roads
Languages : en
Pages : 600
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Author: Yiu-Kuen Michael Lau
Publisher:
ISBN:
Category :
Languages : en
Pages : 608
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Author: American Association of State Highway and Transportation Officials. Task Force for Roadside Safety
Publisher:
ISBN:
Category : Roads
Languages : en
Pages : 560
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Author: Mirza Ahammad Sharif
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Category :
Languages : en
Pages :
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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.
Author: Scott Matthew Ney
Publisher:
ISBN:
Category :
Languages : en
Pages : 322
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