Evaluation and comparison of parametric and non-parametric methods for driving behavior analysis

Car crashes are a scourge to society world-wide and a significant public health issue. Many studies have been conducted using different statistical techniques to understand the causal factors for these crashes, and to minimize injuries and deaths. This thesis aims to identify and analyze the techniques used in previous studies, propose better techniques and demonstrate that these techniques can be applied to build better models which yield us better prediction results.

To demonstrate the application of parametric methods drivers’ seat belt buckling data was used for analysis. Logistic and linear regression techniques were applied for driver’s seatbelt buckling behavior and buckling time analyses, respectively. With the models developed from these regression techniques seat belt reminder systems can be customized which can reduce the perception of nuisance and protects the drivers from higher speed unbuckled crashes. However, parametric methods are non-flexible and cannot produce better performing models when applied on complex data sets such as driving performance measures (DPMs) related data.

To compare parametric and non-parametric methods three benchmark methods were applied on DPM related data. Although interactions among different DPMs are studied and are widely reported in existing literature, these interactions were not fully considered in DPM modeling. The techniques used in previous literature considered the analysis of a single DPM at a time (each DPM is modeled individually), even though modeling multiple DPMs together by considering the interactions among DPMs is possible and beneficial. To expand previous works, a novel DPM modeling and prediction method i.e., multi-output convolutional Gaussian process (MCGP) which can incorporate the interactions among different DPMs was proposed in this chapter. The proposed method is compared with three benchmark methods, and the results demonstrate the superiority of the MCGP method.

These studies on the seat belt buckling data and the DPM data help us to identify the appropriate method based on the data being analyzed. The advantages and disadvantages of the parametric and non-parametric methods are discussed in each application scenario to demonstrate the applicability of each method. Moreover, insightful results are discovered after analyzing the seat belt buckling data and the DPM data. For example, the age and sex are identified as the important independent variables when modeling the seat belt buckling time. The results from DPM data analysis were used to demonstrate the superiority of modeling related DPMs together, which presents an interpretable characterization of the interactions among different DPMs. The proposed methods in this thesis are also applicable in many other problems of driving behaviors, e.g., driving anomaly detection and prediction.