Advanced Quantitative Methods for Imminent Detection of Crash Prone Conditions and Safety Evaluation

Crashes can be accurately predicted through reliable data sources and rigorous statistical models; and prevented through data-driven, evidence-based traffic control strategies. Both predictive analysis and analysis to estimate the causal effect of traffic variables of real-time crashes are instrumental to crash prediction and a better understanding of the mechanism of crash occurrence. However, the research on the second analysis type is very limited for real-time crash prediction; and the conventional predictive analysis using inductive loop detector data has accuracy issues related to inconsistently and distantly spaced loop detectors. The effectiveness of traffic control strategies for improving safety performance cannot be measured and compared without an appropriate traffic simulation application. This dissertation is an attempt to address these research gaps. First, it conducts the propensity score based analysis to assess the causal effect of speed variation on crash occurrence using the crash data and ILD data. As a casual analysis method, the propensity score based model is applied to generate samples with similar covariate distributions in both high- and low-speed variation groups of all cases. Under this setting, the confounding effects are removed and the causal effect of speed variation can be obtained. Second, it conducts a predictive analysis on lane-change related crashes using lane-specific traffic data collected from three ILD stations near a crash location. The real-time traffic data for the two lanes – the vehicle’s lane (subject lane) and the lane to which that a vehicle intends to change (target lane) – are more closely related with lane-change related crashes, as opposed to congregated traffic data for all lanes. It is found that lane-specific variables are appropriate to study the lane-change frequency and the resulting lane-change related crashes. Third, it conducts a predictive analysis on real-time crashes using simulated traffic data. The purpose of using simulated traffic data rather than real data is to mitigate the temporal and spatial issues of detector data. The cell transmission model (CTM), a macroscopic simulation model, is employed to instrument the corridor with a uniform and close layout of virtual detector stations that measure traffic data when physical stations are not available. Traffic flow characteristics at the crash site are simulated by CTM 0-5 minutes prior to a crash. It shows that the simulated traffic data can improve the prediction performance by accounting for the spatial-tempo issue of ILD data. Fourth, it presents a novel approach to modeling freeway crashes using lane-specific simulated traffic data. The new model can not only account for the spatial-tempo issues of detector data but also account for heterogeneous traffic conditions across lanes using a lane-specific cell transmission model (LSCTM). The LSCTM illustrates both discretionary lane-changing (DLC) and mandatory lane-changing (MLC) activities. This new approach presents a viable alternative for utilizing traffic simulation models for safety analysis and evaluation. Last, it develops a crash prediction and prevention application (CPPA) based on simulated traffic data to detect crash-prone conditions and to help select the desirable traffic control strategies for crash prevention. The proposed application is tested in a case study with VSL strategies, and results show that the proposed crash prediction and prevention method could effectively detect crash-prone conditions and evaluate the safety and mobility impacts of various VSL alternatives before their deployment. In the future, the application will be more user-friendly and can provide both online traffic operations support as well as offline evaluation of various traffic control operations and methods

Understanding Micro-Level Lane Change and Lane Keeping Driving Decisions: Harnessing Big Data Streams from Instrumented Vehicles

It is important to get a deeper understanding of instantaneous driving behaviors, especially aggressive and extreme driving behaviors such as hard acceleration, as they endanger traffic efficiency and safety by creating unstable flows and dangerous situations. The aim of the dissertation is to understand micro-level instantaneous driving decisions related to lateral movements such as lane change or lane keeping events on various roadway types. The impacts of these movements are fundamental to microscopic traffic flow and safety. Sufficient geo-referenced data collected from connected vehicles enables analysis of these driving decisions. The “Big Data” cover vehicle trajectories, reported at 10 Hz frequency, and driving situations, which make it possible to establish a framework.The dissertation conducts several key analyses by applying advanced statistical modeling and data mining techniques. First, the dissertation proposes an innovative methodology for identifying normal and extreme lane change events by analyzing the lane-based vehicle positions, e.g., sharp changes in distance of vehicle centerline relative to the lane boundaries, and vehicle motions captured by the distributions of instantaneous lateral acceleration and speed. Second, since surrounding driving behavior influences instantaneous lane keeping behaviors, the dissertation investigates correlations between different driving situations and lateral shifting volatility, which quantifies the variability in instantaneous lateral displacements. Third, the dissertation analyzes the “Gossip effect” which captures the peer influence of surrounding vehicles on the instantaneous driving decisions of subject vehicles at micro-level. Lastly, the dissertation explores correlations between lane change crash propensity or injury severity and driving volatility, which quantifies the fluctuation variability in instantaneous driving decisions.The research findings contribute to the ongoing theoretical and policy debates regarding the effects of instantaneous driving movements. The main contributions of this dissertation are: 1) Quantification of instantaneous driving decisions with regard to two aspects: vehicle motions (e.g., lateral and longitudinal acceleration, and vehicle speed) and lateral displacement; 2) Extraction of critical information embedded in large-scale trajectory data; and 3) An understanding of the correlations between lane change outcomes and instantaneous lateral driving decisions

Identifying and Incorporating Driver Behavior Variables into Crash Prediction Models

All travelers are exposed to the risk for crashes on the road, as none of the roadways are entirely safe. Under Vision Zero, improving traffic safety on our nation’s highways is and will continue to be one of the most pivotal tasks on the national transportation agenda. For decades, researchers and transportation professionals have strived to identify causal relationships between crash occurrence and roadway geometry, and traffic-related variables on the mission of creating a safe environment for the traveling public. Although great achievements have been witnessed such as the publication of the Highway Safety Manual (HSM), research is rather limited in the area of incorporating driver behavior variables into safety modeling. As driver errors are responsible for more than 90 percent of crashes occurred, excluding such important information could cause ineffective, inaccurate, and incorrect prediction results and parameter inferences. The primary reasons for this research void are the lack of driver information and methods for integrating driver data with roadway and traffic characteristics. Standard procedures for collecting and archiving driver behavior data do not exist, as highway agencies are not obligated to collect them. The most relevant source for driver behavior information is perhaps the crash report where police officers may record driver conditions and the possible driver factors contributing to the crash. However, such information is not available to near misses, traffic conflicts and non-crash traffic events where good behaviors prevail. As a result, unobserved data heterogeneity will induce data overdispersion issues which are a significant limiting factor to safety modeling. Furthermore, the conventional approach to treating crashes as originated from a single risk source also induces heterogeneity in crash data and yields biased parameter estimates. Thus, a statistically rigorous methodology is in urgent need to consider the consequence of missing critical driver information in a crash model as well as to distinguish between distinct risk generating sources of a crash event when the driver information is available. This dissertation contributes to the prediction of crash frequency and severity by explicitly considering human factors and driver behaviors in the modeling process. This endeavor began with a comprehensive literature review that identified and addressed data needs, technical issues, and latest development on the incorporation of human factors in safety analysis; and concluded with analytical framework and modeling alternatives to quantify driver behavior being proposed, developed and evaluated. Given myriads of data elements to be explored, availability of contributing factors and crash data issues, a three-pronged modeling approach was adopted to accommodate a broad spectrum of data aggregated over areas, sites and crash events. This approach was informed by the complex nature of crashes involving highway geometry, traffic exposure, contextual factors, driver characteristics, vehicle factors, as well as the interactions among them. The availability of direct or surrogate measures of crash contributing factors varies by spatial unit. To give an example, socioeconomic and demographic features of the driving population are available at census tract; roadway geometry and traffic variables are available for segments and intersections; while specific driver conditions are only collected when a crash took place. With the flexibility in spatial context and risk generating sources, the three-pronged approach provides direct benefits to guide different safety applications such as planning, design, and operations; and informs different programs such as engineering and enforcement. The area-based crash models were developed to incorporate human factors and driver behavior in the form of socioeconomic and demographic data. In particular, behavior-based crash prediction models for speed and alcohol-related crashes were developed, respectively. Results showed that driver behavior-related crashes were more correlated with socioeconomic and demographic variables than traffic and trip-related explanatory variables. The site-specific crash models were exploited to address the effect of human factors and driver behaviors in two fronts: 1) developing rigorous statistical models to account for unobserved heterogeneity induced overdispersion when driver behavior information is not available, 2) treating behavior variables as a separate risk source in a prediction model. The first pursuit leads to the development of a mixed distribution random parameter model to explicitly account for unobserved heterogeneity. The second pursuit results in the development of a multivariate multiple risk source regression model to simultaneously predict crash count and severity. Modeling results show better model performance and valid model inferences for quantifying the effect of driver factors on crash occurrence can be achieved with proposed multiple risk source models. The event-oriented models were utilized to evaluate the interaction between human factors and engineering variables in a crash event. Driver errors were categorized by the driver’s action during a crash on a roadway segment. The modeling results identified many highway geometric features, traffic conditions, and driver characteristics as statistically correlated to different types of driver mistakes. An exploratory analysis was followed to evaluate the effect of driver mistakes on the crash injury outcomes. The dissertation demonstrates the strength of using diverse methods and models under various circumstances to incorporate human factors and driver behavior in crash prediction. The safety professionals can choose appropriate models based on their own data availability, unit of analysis, and design effective treatments or training programs. This research shares new insights to reinforce informed decision support for cumulative safety improvement of roadway network, recognizes the opportunities to address high priority safety issue areas, and determines the appropriate countermeasures