Application of big data in transportation safety analysis using statistical and deep learning methods

The emergence of new sensors and data sources provides large scale high-resolution big data from instantaneous vehicular movements, driver decision and states, surrounding environment, roadway characteristics, weather condition, etc. Such a big data can be served to expand our understanding regarding the current state of the transportation and help us to proactively evaluate and monitor the system performance. The key idea behind this dissertation is to identify the moments and locations where drivers are exhibiting different behavior comparing to the normal behavior. The concept of driving volatility is utilized which quantifies deviation from normal driving in terms of variations in speed, acceleration/deceleration, and vehicular jerk. This idea is utilized to explore the association of volatility in different hierarchies of transportation system, i.e.: 1) Instance level; 2) Event level; 3) Driver level; 4) Intersection level; and 5) Network level. In summary, the main contribution of this dissertation is exploring the association of variations in driving behavior in terms of driving volatility at different levels by harnessing big data generated from emerging data sources under real-world condition, which is applicable to the intelligent transportation systems and smart cities. By analyzing real-world crashes/near-crashes and predicting occurrence of extreme event, proactive warnings and feedback can be generated to warn drivers and adjacent vehicles regarding potential hazard. Furthermore, the results of this study help agencies to proactively monitor and evaluate safety performance of the network and identify locations where crashes are waiting to happen. The main objective of this dissertation is to integrate big data generated from emerging sources into safety analysis by considering different levels in the system. To this end, several data sources including Connected Vehicles data (with more than 2.2 billion seconds of observations), naturalistic driving data (with more than 2 million seconds of observations from vehicular kinematics and driver behavior), conventional data on roadway factors and crash data are integrated

Crash/Near-Crash: Impact of Secondary Tasks and Real-Time Detection of Distracted Driving

The main goal of this dissertation is to investigate the problem of distracted driving from two different perspectives. First, the identification of possible sources of distraction and their associated crash/near-crash risk. That can assist government officials toward more informed decision-making process, allowing for optimized allocation of available resources to reduce roadway crashes and improve traffic safety. Second, actively counteracting the distracted driving phenomenon by quantitative evaluation of eye glance patterns. This dissertation research consists of two different parts. The first part provides an in-depth analysis for the increased crash/near-crash risk associated with different secondary task activities using the largest real-world naturalistic driving dataset (SHRP2 Naturalistic Driving Study). Several statistical and data mining techniques are developed to analyze the distracted driving and crash risk. More specifically, two different models were employed to quantify the increased risk associated with each secondary task: a baseline-category logit model, and a rule mining association model. The baseline-category logit model identified the increased risk in terms of odds ratios, while the A-priori association algorithm detected the associated risks in terms of rules. Each rule was then evaluated based on the lift index. The two models succeeded in ranking all the secondary task activities according to the associated increased crash/near-crash risk efficiently. To actively counteract to the distracted driving phenomenon, a new approach was developed to analyze eye glance patterns and quantify distracted driving behavior under safety and non-Safety Critical Events (SCEs). This approach is then applied to the Naturalistic Engagement in Secondary Tasks (NEST) dataset to investigate how drivers allocate their attention while driving, especially while distracted. The analysis revealed that distracted driving behavior can be well characterized using two new distraction risk indicators. Additional statistical analyses showed that the two indicators increase significantly for SCE compared to normal driving events. Consequently, an artificial neural network (ANN) model was developed to test the SCEs predictability power when accounting for the two new indicators. The ANN model was able to predict the SCEs with an overall accuracy of 96.1%. This outcome can help build reliable algorithms for in-vehicle driving assistance systems to alert drivers before SCEs

Microscopic Modeling of Driver Behavior Based on Modifying Field Theory for Work Zone Application

Because many freeways in the U.S. and abroad are being reconstructed or rehabilitated, it becomes increasingly important to plan and design freeway work zones with the utmost in safety and efficiency. Central to the effective design of work zones is being able to understand how drivers behave as they approach and enter a work zone area. While simple and complex microscopic models have been used over the years to analyze driver behavior, most models were not designed for application in work zones and thus do not capture the interdependencies between lane-changing and car-following vehicle movements along with the drivers’ cognitive and physical characteristics. With the use of psychology’s field theory, this dissertation develops a framework for creating vector-based, explanatory, deterministic microscopic models, to enhance our understanding of driver behavior in work zones and better aid freeway planners and designers. In field theory, an agent (i.e. the driver) views a field (i.e. the area surrounding the vehicle) filled with stimuli and perceives forces associated with each stimuli once these stimuli are internalized. Based on this theory, the new modeling framework, Modified Field Theory (MFT), is designed to directly incorporate drivers’ perceptions to roadway stimuli along with vehicle movements for drivers of different cognitive and physical abilities. From this framework, specific microscopic models, such as a simple freeway work zone car following model, can be created. It is postulated that models derived from this framework would more accurately reflect the driver decision-making process, naturally modeling the effects of external stimuli such as innovative geometric configurations, lane closures, and technology applications such as variable message boards. A simple freeway work zone car following model was created using the MFT framework. Two MFT car-following agents were created and calibrated. The second agent (Agent 2) followed the first agent (Agent 1) through a one-lane segment of freeway. Car-following data for Agent 2 was plotted on a graph of relative speed vs. distance to the lead vehicle, showing car-following behavior. Car-following behavior for Agent 2 was validated against Federal Highway Administration (FHWA) Turner Fairbank Highway Research Center (TFHRC) Living Laboratory data for simple freeway work zone car-following (Driver 15). The car-following behavior of Agent 2 replicated the “spiraling” trend observed in Driver 15. Unlike other models (such as Wiedemann), this model does not ‘force’ these trends to occur; these trends occur naturally, as a result of the perception-reaction time delay and the nature of the forces involved. Additionally, unusual car following trends reported for Driver 15 were replicated in Modified Field Theory when conditions surrounding each event were synthetically recreated. Results demonstrated that the Modified Field Theory framework can successfully replicate the process by which a driver scans the driving environment and reacts to their surroundings. Microscopic models can successfully be created using this framework. Results demonstrated that models created from this framework naturally recreate behavioral trends observed in empirical data, and that these models are capable of replicating driving behavior in unusual scenarios, such as the car following behavior of a subject vehicle when the lead vehicle has a strong sudden acceleration event. Before this model can be applied to work zones, other calibration and validation efforts are required

Prevalence and Self-regulation of Drivers’ Secondary Task Engagement: An Investigation of Behaviour at Intersections Based on Naturalistic Driving Data

Using data from the large-scale European Naturalistic Driving project (UDRIVE), this thesis explored the prevalence of engagement in secondary tasks whilst driving through intersections and investigated whether drivers self-regulate such behaviour in response to variations in roadway and environmental conditions. The thesis also examined the possible influence of secondary task engagement on turn signal usage at intersections. To these ends, 1630 intersection cases were randomly sampled from the UDRIVE dataset for coding and in-depth analysis. In-vehicle video recordings and recordings of external scenes in the selected sample were coded for precisely defined categories of secondary tasks and related contextual variables. The findings indicated that nearly one-quarter of the total driving time at intersections was spent on secondary activities and that such engagement decreased with increasing age. The drivers were less likely to occupy themselves with secondary tasks as they passed through an intersection itself, as opposed to the approach (upstream) and exit (downstream) phases. The drivers also tended to perform secondary tasks less frequently when their vehicles were moving than whilst they were stationary, when they did not have priority to pass through intersections compared with when they had priority and in bad weather conditions than in fine weather situations. Lastly, the drivers showed less inclination to use turn signals when they were engaged in secondary tasks than when they were driving under normal baseline conditions. In conclusion, the drivers appeared to self-regulate secondary task engagement according to road and driving situations, specifically when the primary task of driving becomes progressively challenging. This self-regulation behaviour was particularly strong for more complex and, therefore, more demanding secondary activities. The outcomes provide initial evidence that can serve as reference in targeting countermeasures and policies related to safe driving and managing distractions

Transport Systems: Safety Modeling, Visions and Strategies

This reprint includes papers describing the synthesis of current theory and practice of planning, design, operation, and safety of modern transport, with special focus on future visions and strategies of transport sustainability, which will be of interest to scientists dealing with transport problems and generally involved in traffic engineering as well as design, traffic networks, and maintenance engineers

AN INVESTIGATION OF MOTOR VEHICLE DRIVER INATTENTION AND ITS EFFECTS AT HIGHWAY-RAIL GRADE CROSSINGS

The relationship between accident injury severity and drivers’ inattentive behavior requires an in-depth investigation – this is especially needed in the case of motor vehicle drivers at highway-rail grade crossings (HRGCs). The relationship between drivers’ personality/ socioeconomic characteristics and inattentive behavior at HRGCs is another topic requiring research. Past educational programs about safe driving at HRGCs have often not been designed to target people who may be in urgent need of such information, which may limit the effectiveness of those programs. This dissertation thus focuses on the following four objectives: to investigate the association between motor vehicle inattentive driving and the severity of drivers’ injuries sustained in crashes reported at or near HRGCs; to investigate the association between drivers’ self-reported inattentive driving experience and a series of factors such as drivers’ knowledge of safe driving, attitudes towards safe driving, etc.; to identify driver groups that have lower or higher levels of knowledge of correct rail crossing negotiation; and to investigate the direct and indirect effects between drivers’ characteristics and their knowledge level as well as their involvement with inattentive driving behavior at HRGCs. The research obtained 12 years of police-reported crash data from the Nebraska Department of Roads and collected data in a statewide random-sample mail questionnaire survey. Statistical analysis methods, including random parameters binary logit model, confirmatory factor analysis, robust linear regression, multinomial logit model, and structural equation models were utilized in this research. Conclusions are that inattentive driving plays a significant role in contributing to more severe injuries in accidents reported in proximity of HRGCs in Nebraska; Nebraska motor vehicle drivers’ personality traits, knowledge levels of negotiating HRGCs and driving experience are associated with inattentive driving; drivers with lower levels of knowledge of correct HRGC negotiation are: drivers who drive vehicles other than passenger cars, have received less safety information, have a shorter driving history, are older, have lower household income, and have higher intent to violate rules at rail crossings; inattentive driving behavior at HRGCs is directly and indirectly affected by their personality traits while drivers’ knowledge of correct HRGC negotiation appears to only have an indirect effect. Advisor: Aemal J.Khatta

Hum Factors

Objective:This study reports current status of knowledge and challenges associated with the emergency vehicle (police car, fire truck, and ambulance) crashes, with respect to the major contributing risk factors.Background:Emergency vehicle crashes are a serious nationwide problem, causing injury and death to emergency responders and citizens. Understanding the underlying causes of these crashes is critical for establishing effective strategies for reducing the occurrence of similar incidents.Method:We reviewed the broader literature associated with the contributing factors for emergency vehicle crashes: peer-reviewed journal papers; and reports, policies, and manuals published by government agencies, universities, and research institutes.Results:Major risk factors for emergency vehicle crashes identified in this study were organized into four categories: driver, task, vehicle, and environmental factors. Also, current countermeasures and interventions to mitigate the hazards of emergency vehicle crashes were discussed, and new ideas for future studies were suggested.Conclusion:Risk factors, control measures, and knowledge gaps relevant to emergency vehicle crashes were presented. Six research concepts are offered for the human factors community to address. Among the topics are emergency vehicle driver risky behavior carryover between emergency response and return from a call, distraction in emergency vehicle driving, in-vehicle driver assistance technologies, vehicle red light running, and pedestrian crash control.Application:This information is helpful for emergency vehicle drivers, safety practitioners, public safety agencies, and research communities to mitigate crash risks. It also offers ideas for researchers to advance technologies and strategies to further emergency vehicle safety on the road.CC999999/ImCDC/Intramural CDC HHS/United States2020-11-24T00:00:00Z29965790PMC76855298713vault:3620

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