Examining the associations between urban bus drivers' rule violations and crash frequency using observational data.

Urban bus crashes have been an increasing concern in China thanks to the expansion of urban bus systems. Bus drivers' rule violation behavior appears to be an important factor of bus crashes. However, existing studies rely heavily on questionnaire survey of rule violation behaviors, which may suffer from self-reporting bias. Using observational data of 1,356 bus drivers from onboard video recordings, this paper develops a random parameter zero-inflated Poisson model (with heterogeneity in means and variances) to uncover the relationship between drivers' rule violation behaviors and bus crashes. Model results show that rule violation behaviors such as using a cell phone, driving outside the bus lane, and touching solid line with wheel while driving significantly correlate with crash frequencies with large marginal effects, whereas some other violation behaviors (e.g., running red lights, changing lanes suddenly) show a minor role. This finding highlights the importance of differentiating various rule violations when examining bus crashes instead of aggregating them altogether. Furthermore, the effects of rule violation behavior show substantial heterogeneity. For example, the association between not yielding to pedestrians and bus crashes is enhanced when drivers also show records of touching solid line with wheel while driving, driving outside the bus lane, or are internally trained. Findings of this study provide insights for countermeasures to improve bus safety by targeting critical bus driver groups with specific rule violations, and inform future research to collect observational data on drivers' rule violation behaviors.

Rapid measurement of a Mueller matrix for biological tissues.

We propose a novel model for the measurement of a Mueller matrix for biological tissues. Compared with earlier measurement methods, our method can reduce measurement times and can significantly improve measurement efficiency. Our model needs only six intensity measurements to derive all 16 Mueller matrix components of a biological sample in arbitrary pixels. We used the pellicle cell of magnolia as our sample, and the experimental results are identical with those obtained with other methods. We demonstrate that we can obtain Mueller matrix components to recognize different biological tissues in the same visual field rapidly and reliably.