Short-term crash risk prediction considering proactive, reactive, and driver behavior factors

Providing a safe and efficient transportation system is the primary goal of transportation engineering and planning. Highway crashes are among the most significant challenges to achieving this goal. They result in significant societal toll reflected in numerous fatalities, personal injuries, property damage, and traffic congestion. To that end, much attention has been given to predictive models of crash occurrence and severity. Most of these models are reactive: they use the data about crashes that have occurred in the past to identify the significant crash factors, crash hot-spots and crash-prone roadway locations, analyze and select the most effective countermeasures for reducing the number and severity of crashes. More recently, the advancements have been made in developing proactive crash risk models to assess short-term crash risks in near-real time. Such models could be applied as part of traffic management strategies to prevent and mitigate the crashes. The driver behavior is found to be the leading cause of highway crashes. Nevertheless, due to data unavailability, limited studies have explored and quantified the role of driver behavior in crashes. The Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) offers an unprecedented opportunity to perform an in-depth analysis of the impacts of driver behavior on crashes events. The research presented in this dissertation is divided into three parts, corresponding to the research objectives. The first part investigates the application of advanced data modeling methods for proactive crash risk analysis. Several proactive models for segment level crash risk and severity assessment are developed and tested, considering the proactive data available to most transportation agencies in real time at a regional network scale. The data include roadway geometry characteristics, traffic flow characteristics, and weather condition data. The analysis methods include Random-effect Bayesian Logistics Regression, Random Forest, Gradient Boosting Machine, K-Nearest Neighbor, Gaussian Naive Bayes (GNB), and Multi-layer Feedforward Deep Neural Network (MLFDNN). The random oversampling technique is applied to deal with the problem of data imbalance associated with the injury severity analysis. The model training and testing are completed using a dataset containing records of 10,155 crashes that occurred on two interstate highways in New Jersey over a period of two years. The second part of the study analyzes the potential improvement in the prediction abilities of the proposed models by adding reactive data (such as vehicle characteristics and driver characteristics) to the analysis. Commonly, the reactive data is only available (known) after the crash occurs. In the proposed research, the crash analysis is performed by classifying crashes in multiple groupings (instead of a single group), constructed based on the age of drivers and vehicles to account for the impact of reactive data on driver injury severity outcomes. The results of the second part of the study show that while the simultaneous use of reactive and proactive data can improve the prediction performance of the models, the absolute crash probability values must be further improved for operational crash risk prediction. To this end, in the third part of the study, the Naturalistic Driving Study data is used to calibrate the crash risk models, including the driver behavior risk factors. The findings show significant improvement in crash prediction accuracy with the inclusion of driver behavior risk factors, which confirms the driver behavior to be the most critical risk factor affecting the crash likelihood and the associated injury severity

Analysis of the impacts of risk factors on teenage and older driver injury severity using random-parameter ordered probit

The study analyzed injury severity of teenage and older drivers using 2015–2016 crash data from New Mexico. The fitness of the random-parameter ordered probit models developed for each age group was tested using likelihood ratio, comparing them to a unified model that combines both age groups, as well as comparing the random-parameter to fixed-parameter ordered probit for each age group. In both cases separate random-parameter ordered probit provided better results. It was found that vehicle type and age, lighting condition, alcohol or drug use, speeding, and seatbelt use were significant both for the teenage and older driver injury severity. The weather condition and gender were significant only in the teenage driver model, while driver inattention was significant for older drivers. The impacts of crash factors on injury severity was analyzed using marginal effects. The results indicate notable differences in the effects of contributing factors on driver injury severity between teenage and older drivers, including the sensitivity to changes in the mutual predictor parameter values.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Short-Term Segment-Level Crash Risk Prediction Using Advanced Data Modeling with Proactive and Reactive Crash Data

Highway crashes, along with the property damage, personal injuries, and fatalities that they cause, continue to present one of the most significant and critical transportation problems. At the same time, provision of safe travel is one of the main goals of any transportation system. For this reason, both in transportation research and practice much attention has been given to the analysis and modeling of traffic crashes, including the development of models that can be applied to predict crash occurrence and crash severity. In general, such models assess short-term crash risks at a given highway facility, thus providing intelligence that can be used to identify and implement traffic operations strategies for crash mitigation and prevention. This paper presents several crash risk and injury severity assessment models applied at a highway segment level, considering the input data that is typically collected or readily available to most transportation agencies in real-time and at a regional network scale, which would render them readily applicable in practice. The input data included roadway geometry characteristics, traffic flow characteristics, and weather condition data. The paper develops, tests, and compares the performance of models that employ Random effects Bayesian Logistics Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine methods. The paper applies random oversampling examples (ROSE) method to deal with the problem of data imbalance associated with the injury severity analysis. The models were trained and tested using a dataset of 10,155 crashes that occurred on two interstate highways in New Jersey over a two-year period. The paper also analyzes the potential improvement in the prediction abilities of the tested models by adding reactive data to the analysis. To that end, traffic crashes were classified in multiple classes based on the driver age and the vehicle age to assess the impact of these attributes on driver injury severity outcomes. The results of this analysis are promising, showing that the simultaneous use of reactive and proactive data can improve the prediction performance of the presented models

Short-Term Segment-Level Crash Risk Prediction Using Advanced Data Modeling with Proactive and Reactive Crash Data

Highway crashes, along with the property damage, personal injuries, and fatalities that they cause, continue to present one of the most significant and critical transportation problems. At the same time, provision of safe travel is one of the main goals of any transportation system. For this reason, both in transportation research and practice much attention has been given to the analysis and modeling of traffic crashes, including the development of models that can be applied to predict crash occurrence and crash severity. In general, such models assess short-term crash risks at a given highway facility, thus providing intelligence that can be used to identify and implement traffic operations strategies for crash mitigation and prevention. This paper presents several crash risk and injury severity assessment models applied at a highway segment level, considering the input data that is typically collected or readily available to most transportation agencies in real-time and at a regional network scale, which would render them readily applicable in practice. The input data included roadway geometry characteristics, traffic flow characteristics, and weather condition data. The paper develops, tests, and compares the performance of models that employ Random effects Bayesian Logistics Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine methods. The paper applies random oversampling examples (ROSE) method to deal with the problem of data imbalance associated with the injury severity analysis. The models were trained and tested using a dataset of 10,155 crashes that occurred on two interstate highways in New Jersey over a two-year period. The paper also analyzes the potential improvement in the prediction abilities of the tested models by adding reactive data to the analysis. To that end, traffic crashes were classified in multiple classes based on the driver age and the vehicle age to assess the impact of these attributes on driver injury severity outcomes. The results of this analysis are promising, showing that the simultaneous use of reactive and proactive data can improve the prediction performance of the presented models