Modeling highway traffic crash frequency is an important approach for identifying high crash risk areas that can help transportation agencies allocate limited resources more efficiently, and find preventive measures. ...Modeling highway traffic crash frequency is an important approach for identifying high crash risk areas that can help transportation agencies allocate limited resources more efficiently, and find preventive measures. This paper applies a Poisson regression model, Negative Binomial regression model and then proposes an Artificial Neural Network model to analyze the 2008-2012 crash data for the Interstate I-90 in the State of Minnesota in the US. By comparing the prediction performance between these three models, this study demonstrates that the Neural Network is an effective alternative method for predicting highway crash frequency.展开更多
Road traffic crash data are useful tools to support the development, implementation, and assessment of highway safety programs that tend to reduce road traffic crashes. Collecting road traffic crash data aims at gaini...Road traffic crash data are useful tools to support the development, implementation, and assessment of highway safety programs that tend to reduce road traffic crashes. Collecting road traffic crash data aims at gaining a better understanding of road traffic operational problems, locating hazardous road sections, identifying risk factors, developing accurate diagnosis and remedial measures, and evaluating the effectiveness of road safety programs. Furthermore, they can be used by many agencies and businesses such as: law enforcements to identify persons at fault in road traffic crashes;insurers seeking facts about traffic crash claims;road safety researchers to access traffic crash reliable database;decision makers to develop long-term, statewide strategic plans for traffic and highway safety;and highway safety administrators to help educate the public. Given the practical importance of vehicle crash data, this paper presents an overview of the sources, trends and problems associated with road traffic crash data.展开更多
Identifying vehicular crash high risk locations along highways is important for understanding the causes of vehicle crashes and to determine effective countermeasures based on the analysis. This paper presents a GIS a...Identifying vehicular crash high risk locations along highways is important for understanding the causes of vehicle crashes and to determine effective countermeasures based on the analysis. This paper presents a GIS approach to examine the spatial patterns of vehicle crashes and determines if they are spatially clustered, dispersed, or random. Moran’s I and Getis-Ord Gi* statistic are employed to examine spatial patterns, clusters mapping of vehicle crash data, and to generate high risk locations along highways. Kernel Density Estimation (KDE) is used to generate crash concentration maps that show the road density of crashes. The proposed approach is evaluated using the 2013 vehicle crash data in the state of Indiana. Results show that the approach is efficient and reliable in identifying vehicle crash hot spots and unsafe road locations.展开更多
Spatial autocorrelation is a measure of the correlation of an observation with other observations through space. Most statistical analyses are based on the assumption that the values of observations are independent of...Spatial autocorrelation is a measure of the correlation of an observation with other observations through space. Most statistical analyses are based on the assumption that the values of observations are independent of one another. Spatial autocorrelation violates this assumption, because observations at near-by locations are related to each other, and hence, the consideration of spatial autocorrelations has been gaining attention in crash data modeling in recent years, and research have shown that ignoring this factor may lead to a biased estimation of the modeling parameters. This paper examines two spatial autocorrelation indices: Moran’s Index;and Getis-Ord Gi* statistic to measure the spatial autocorrelation of vehicle crashes occurred in Boone County roads in the state of Missouri, USA for the years 2013-2015. Since each index can identify different clustering patterns of crashes, therefore this paper introduces a new hybrid method to identify the crash clustering patterns by combining both Moran’s Index and Gi*?statistic. Results show that the new method can effectively improve the number, extent, and type of crash clustering along roadways.展开更多
Multinomial logistic regression (MNL) is an attractive statistical approach in modeling the vehicle crash severity as it does not require the assumption of normality, linearity, or homoscedasticity compared to other a...Multinomial logistic regression (MNL) is an attractive statistical approach in modeling the vehicle crash severity as it does not require the assumption of normality, linearity, or homoscedasticity compared to other approaches, such as the discriminant analysis which requires these assumptions to be met. Moreover, it produces sound estimates by changing the probability range between 0.0 and 1.0 to log odds ranging from negative infinity to positive infinity, as it applies transformation of the dependent variable to a continuous variable. The estimates are asymptotically consistent with the requirements of the nonlinear regression process. The results of MNL can be interpreted by both the regression coefficient estimates and/or the odd ratios (the exponentiated coefficients) as well. In addition, the MNL can be used to improve the fitted model by comparing the full model that includes all predictors to a chosen restricted model by excluding the non-significant predictors. As such, this paper presents a detailed step by step overview of incorporating the MNL in crash severity modeling, using vehicle crash data of the Interstate I70 in the State of Missouri, USA for the years (2013-2015).展开更多
Road crash prediction models are very useful tools in highway safety, given their potential for determining both the crash frequency occurrence and the degree severity of crashes. Crash frequency refers to the predict...Road crash prediction models are very useful tools in highway safety, given their potential for determining both the crash frequency occurrence and the degree severity of crashes. Crash frequency refers to the prediction of the number of crashes that would occur on a specific road segment or intersection in a time period, while crash severity models generally explore the relationship between crash severity injury and the contributing factors such as driver behavior, vehicle characteristics, roadway geometry, and road-environment conditions. Effective interventions to reduce crash toll include design of safer infrastructure and incorporation of road safety features into land-use and transportation planning;improvement of vehicle safety features;improvement of post-crash care for victims of road crashes;and improvement of driver behavior, such as setting and enforcing laws relating to key risk factors, and raising public awareness. Despite the great efforts that transportation agencies put into preventive measures, the annual number of traffic crashes has not yet significantly decreased. For in-stance, 35,092 traffic fatalities were recorded in the US in 2015, an increase of 7.2% as compared to the previous year. With such a trend, this paper presents an overview of road crash prediction models used by transportation agencies and researchers to gain a better understanding of the techniques used in predicting road accidents and the risk factors that contribute to crash occurrence.展开更多
Temporal autocorrelation (also called serial correlation) refers to the relationship between successive values (i.e. lags) of the same variable. Although it has long been a major concern in time series models, however...Temporal autocorrelation (also called serial correlation) refers to the relationship between successive values (i.e. lags) of the same variable. Although it has long been a major concern in time series models, however, in-depth treatments of temporal autocorrelation in modeling vehicle crash data are lacking. This paper presents several test statistics to detect the amount of temporal autocorrelation and its level of significance in crash data. The tests employed are: 1) the Durbin-Watson (DW);2) the Breusch-Godfrey (LM);and 3) the Ljung-Box Q (LBQ). When temporal autocorrelation is statistically significant in crash data, it could adversely bias the parameter estimates. As such, if present, temporal autocorrelation should be removed prior to use the data in crash modeling. Two procedures are presented in this paper to remove the temporal autocorrelation: 1) Differencing;and 2) the Cochrane-Orcutt method.展开更多
The Road Surface Management System (RSMS) is a powerful tool that can provide an overview and rough estimate of a roadway system’s condition at the network level and the approximate costs for future improvements in t...The Road Surface Management System (RSMS) is a powerful tool that can provide an overview and rough estimate of a roadway system’s condition at the network level and the approximate costs for future improvements in towns and small cities. This helps municipalities and local agencies to apply limited budget resources and provide the greatest road quality benefits. To control the cost of roadway surface deterioration, local agencies and municipalities need to make cost-effective decisions regarding the maintenance, rehabilitation, and reconstruction of the roadway network. RSMS can help in assessing the condition of the network, weighing alternatives, and establishing long-term treatment plans and budgets. In this paper, RSMS is used to evaluate a university campus road network in the state of Idaho and to establish the necessary repair methods for 10 selected sections in the campus network.展开更多
文摘Modeling highway traffic crash frequency is an important approach for identifying high crash risk areas that can help transportation agencies allocate limited resources more efficiently, and find preventive measures. This paper applies a Poisson regression model, Negative Binomial regression model and then proposes an Artificial Neural Network model to analyze the 2008-2012 crash data for the Interstate I-90 in the State of Minnesota in the US. By comparing the prediction performance between these three models, this study demonstrates that the Neural Network is an effective alternative method for predicting highway crash frequency.
文摘Road traffic crash data are useful tools to support the development, implementation, and assessment of highway safety programs that tend to reduce road traffic crashes. Collecting road traffic crash data aims at gaining a better understanding of road traffic operational problems, locating hazardous road sections, identifying risk factors, developing accurate diagnosis and remedial measures, and evaluating the effectiveness of road safety programs. Furthermore, they can be used by many agencies and businesses such as: law enforcements to identify persons at fault in road traffic crashes;insurers seeking facts about traffic crash claims;road safety researchers to access traffic crash reliable database;decision makers to develop long-term, statewide strategic plans for traffic and highway safety;and highway safety administrators to help educate the public. Given the practical importance of vehicle crash data, this paper presents an overview of the sources, trends and problems associated with road traffic crash data.
文摘Identifying vehicular crash high risk locations along highways is important for understanding the causes of vehicle crashes and to determine effective countermeasures based on the analysis. This paper presents a GIS approach to examine the spatial patterns of vehicle crashes and determines if they are spatially clustered, dispersed, or random. Moran’s I and Getis-Ord Gi* statistic are employed to examine spatial patterns, clusters mapping of vehicle crash data, and to generate high risk locations along highways. Kernel Density Estimation (KDE) is used to generate crash concentration maps that show the road density of crashes. The proposed approach is evaluated using the 2013 vehicle crash data in the state of Indiana. Results show that the approach is efficient and reliable in identifying vehicle crash hot spots and unsafe road locations.
文摘Spatial autocorrelation is a measure of the correlation of an observation with other observations through space. Most statistical analyses are based on the assumption that the values of observations are independent of one another. Spatial autocorrelation violates this assumption, because observations at near-by locations are related to each other, and hence, the consideration of spatial autocorrelations has been gaining attention in crash data modeling in recent years, and research have shown that ignoring this factor may lead to a biased estimation of the modeling parameters. This paper examines two spatial autocorrelation indices: Moran’s Index;and Getis-Ord Gi* statistic to measure the spatial autocorrelation of vehicle crashes occurred in Boone County roads in the state of Missouri, USA for the years 2013-2015. Since each index can identify different clustering patterns of crashes, therefore this paper introduces a new hybrid method to identify the crash clustering patterns by combining both Moran’s Index and Gi*?statistic. Results show that the new method can effectively improve the number, extent, and type of crash clustering along roadways.
文摘Multinomial logistic regression (MNL) is an attractive statistical approach in modeling the vehicle crash severity as it does not require the assumption of normality, linearity, or homoscedasticity compared to other approaches, such as the discriminant analysis which requires these assumptions to be met. Moreover, it produces sound estimates by changing the probability range between 0.0 and 1.0 to log odds ranging from negative infinity to positive infinity, as it applies transformation of the dependent variable to a continuous variable. The estimates are asymptotically consistent with the requirements of the nonlinear regression process. The results of MNL can be interpreted by both the regression coefficient estimates and/or the odd ratios (the exponentiated coefficients) as well. In addition, the MNL can be used to improve the fitted model by comparing the full model that includes all predictors to a chosen restricted model by excluding the non-significant predictors. As such, this paper presents a detailed step by step overview of incorporating the MNL in crash severity modeling, using vehicle crash data of the Interstate I70 in the State of Missouri, USA for the years (2013-2015).
文摘Road crash prediction models are very useful tools in highway safety, given their potential for determining both the crash frequency occurrence and the degree severity of crashes. Crash frequency refers to the prediction of the number of crashes that would occur on a specific road segment or intersection in a time period, while crash severity models generally explore the relationship between crash severity injury and the contributing factors such as driver behavior, vehicle characteristics, roadway geometry, and road-environment conditions. Effective interventions to reduce crash toll include design of safer infrastructure and incorporation of road safety features into land-use and transportation planning;improvement of vehicle safety features;improvement of post-crash care for victims of road crashes;and improvement of driver behavior, such as setting and enforcing laws relating to key risk factors, and raising public awareness. Despite the great efforts that transportation agencies put into preventive measures, the annual number of traffic crashes has not yet significantly decreased. For in-stance, 35,092 traffic fatalities were recorded in the US in 2015, an increase of 7.2% as compared to the previous year. With such a trend, this paper presents an overview of road crash prediction models used by transportation agencies and researchers to gain a better understanding of the techniques used in predicting road accidents and the risk factors that contribute to crash occurrence.
文摘Temporal autocorrelation (also called serial correlation) refers to the relationship between successive values (i.e. lags) of the same variable. Although it has long been a major concern in time series models, however, in-depth treatments of temporal autocorrelation in modeling vehicle crash data are lacking. This paper presents several test statistics to detect the amount of temporal autocorrelation and its level of significance in crash data. The tests employed are: 1) the Durbin-Watson (DW);2) the Breusch-Godfrey (LM);and 3) the Ljung-Box Q (LBQ). When temporal autocorrelation is statistically significant in crash data, it could adversely bias the parameter estimates. As such, if present, temporal autocorrelation should be removed prior to use the data in crash modeling. Two procedures are presented in this paper to remove the temporal autocorrelation: 1) Differencing;and 2) the Cochrane-Orcutt method.
文摘The Road Surface Management System (RSMS) is a powerful tool that can provide an overview and rough estimate of a roadway system’s condition at the network level and the approximate costs for future improvements in towns and small cities. This helps municipalities and local agencies to apply limited budget resources and provide the greatest road quality benefits. To control the cost of roadway surface deterioration, local agencies and municipalities need to make cost-effective decisions regarding the maintenance, rehabilitation, and reconstruction of the roadway network. RSMS can help in assessing the condition of the network, weighing alternatives, and establishing long-term treatment plans and budgets. In this paper, RSMS is used to evaluate a university campus road network in the state of Idaho and to establish the necessary repair methods for 10 selected sections in the campus network.
