Physics-informed multi-step real-time conflict-based vehicle safety prediction.

Real-time vehicle safety prediction is critical in roadway safety management as drivers or vehicles can be altered beforehand to take corresponding evasive actions and avoid possible collisions. This study proposes a physics-informed multi-step real-time conflict-based vehicle safety prediction model to enhance roadway safety. Physics insights (i.e., traffic shockwave properties) are combined with data-driven features extracted from deep-learning techniques to improve prediction accuracy. A time series of future vehicle safety indicators are predicted such that vehicles/drivers have enough time to take precautions. The safety indicator at each time stamp is a continuous value that the sign reflects the presence of conflict risks, and the absolute value indicates the conflict risk level to advise different magnitudes of evasive actions. A customized loss function is developed for the proposed prediction model to give more attention to risky events, which are the focus of safety management. The prediction superiority of the proposed model is proven through numerical experiments by comparing it with three benchmarks constructed based on the literature. Further, sensitivity analysis on key model parameters is carried out to advise parameter selections in developing real-world conflict-based vehicle safety prediction applications.

Synthesis and bioimaging of a BODIPY-based fluorescence quenching probe for Fe3.

Iron is the main substance for maintaining life. Real-time determination of ferric ion (Fe3+) in living cells is of great significance for understanding the relationship of Fe3+ concentration changes with various physiological and pathological processes. Fluorescent probes are suitable for the detection of trace metal ions in cells due to their low toxicity and high sensitivity. In this work, a boron-dipyrromethene-based fluorescent probe (BODIPY-CL) for selective detection of Fe3+ was synthesized. The fluorescence emission of BODIPY-CL was determined at 516 nm. In a pH range of 1 to 10, the probe BODIPY-CL exhibits a quenching response to Fe3+. Meanwhile, BODIPY-CL showed a highly selective response to Fe3+ compared with 16 kinds of metal ions. The stoichiometry ratio of BODIPY-CL bound to Fe3+ was nearly 2 : 1. The fluorescence quenching response obtained by the sensor was linear with the Fe3+ concentration in the range of 0-400 µM, and the detection limit was 2.9 µM. BODIPY-CL was successfully applied to image Fe3+ in cells. This study provides a promising fluorescent imaging probe for further research on the physiological and pathological effects of Fe3+.

A mixed logit model with mean-variance heterogeneity to investigate factors of crash occurrence.

This study thoroughly investigated factors affecting crash occurrence using detailed data of crash, traffic condition and freeway geometries. To fully account for heterogeneity induced by unobserved characteristics of crash factors, a mixed logit model with mean-variance heterogeneity was estimated as an alternative to the commonly used mixed logit model and the fixed parameters logit model. Results indicate that the mixed logit model with mean-variance heterogeneity could improve the goodness-of-fit and was more flexible in accounting for unobserved heterogeneity compared with its counterparts. Additionally, by allowing means and variances of random parameters to be estimable functions of explanatory variables, the safety effect of interactions among multiple factors was concluded, for example: (1) sharp curves resulted in an increasing risk of crash and the rate of increase was positively correlated with the distance travelled by vehicles along a steep downgrade; (2) the adverse safety effect of steep downgrade increased with the distance covered by vehicles, especially for segments with high proportion of heavy trucks; (3) downhill segments with steep slopes were particularly dangerous. Findings from this study are expected to provide an insightful knowledge to the mechanism of crash occurrence and should be beneficial to design and manage safer freeways.

A correlated random parameters tobit model to analyze the safety effects and temporal instability of factors affecting crash rates.

Numerous studies have previously used a variety of count-data models to investigate factors that affect the number of crashes over a certain period of time on roadway segments. Unlike past studies which deal with crash frequency, this study views the crash rates directly as a continuous variable left-censored at zero and explores the application of an alternate approach based on tobit regression. To thoroughly investigate the factors affecting freeway crash rates and the potentially temporal instability in the effects of crash factors involving traffic volume, freeway geometries and pavement conditions, a classic uncorrelated random parameters tobit (URPT) model and a correlated random parameters tobit (CRPT) model were estimated, along with a conventional fixed parameters tobit (FPT) model. The analysis revealed a large number of safety factors, including several appealing and interesting factors rarely studied in the past, such as the safety effects of climbing lanes and distance along composite descending grade. The results also showed that the CRPT model was not only able to reflect the heterogeneous effects of various factors, but also able to estimate the underlying interactions among unobserved characteristics, and therefore provide better statistical fit and offer more insights into factors contributing to freeway crashes than its model counterparts. Additionally, the results showed significant temporal instability in CRPT models across the studied time periods indicating that crash factors (including unobserved characteristics and the underlying interactions among them) and their effects on crash rates varied over time, and more attentions should be paid when interpreting crash data-analysis findings and making safety policies. The modeling technique in this study demonstrates the potential of CRPT model as an effective approach to gain new insights into safety factors, particularly when the heterogeneous effects of factors on safety are interactive. Additionally, findings from this study are also expected to assist in developing more effective countermeasures by better understanding the safety effects of factors associated with freeway design characteristics and pavement conditions.