A multinomial logit model: Safety risk analysis of interchange area based on aggregate driving behavior data.

INTRODUCTION: Urban expressway interchanges have become accident-prone sites owing to the accelerated increase in motor-vehicle ownership. This study explored the impact of factors, including day of the week, time of day, congestion level, traffic control devices, and road conditions, on road safety risk levels in the interchange area of an urban expressway based on aggregate driving behavior data. METHOD: A large amount of aggregate driving behavior data were obtained from AutoNavi navigation software. The database was built by matching various types of data and observing their characteristics. Day of the week, time of day, congestion level, road conditions (number of lanes, traffic disturbance, and traffic control devices [the type of advance guide sign system, number of warning signs, and the complexity of the diagrammatic guide sign]) were identified as the explanatory variables. The traffic order index (TOI), based on driving behavior and speed variation, was used to evaluate the road safety risk levels, including risky roads, general roads, and safe roads, which served as the response variables. The multinomial logit model (MNL) was developed to explore the impact of various factors, including traffic control devices and road conditions, on road safety risk levels. RESULTS: The results showed that the factors that significantly influence risky roads include day of the week, number of lanes, congestion level (slow moving), traffic disturbance (with the merge or diverge within 500 m), type of advance guide sign system (three-level advance guide sign system), and complexity of diagrammatic guide signs (low or medium complexity). Practical Applications: This study could offer plausible suggestions for traffic management departments for the rehabilitation of road conditions and traffic control devices in urban expressway interchange areas.

A study of freeway crash risk prediction and interpretation based on risky driving behavior and traffic flow data.

The prediction of traffic crashes is an essential topic in traffic safety research. Most of the previous studies conducted experiments on real-time crash prediction of expressways or freeways, based on traffic flow data. However, the influence of risky driving behavior on traffic crash risk prediction has rarely been considered. Thus, a traffic crash risk prediction model based on risky driving behavior and traffic flow has been developed. The data employed in this research were captured using the in-vehicle AutoNavigator software. A random forest to select variables with strong impacts on crashes and the synthetic minority oversampling technique (SMOTE) to adjust the imbalanced dataset were included in the research. A logistic regression model was developed to predict the risk of traffic crash and to interpret its relationship with traffic flow and risky driving behavior characteristics. This model accurately predicted 84.48% of the crashes, while its false alarm rate remained as low as 9.75%, which indicated that this traffic crash risk prediction model had high accuracy. By analyzing the relationship between traffic flow, risky driving behavior, and crashes through partial dependency plots (PDPs), the impact of traffic flow and risky driving behavior variables on certain traffic crashes in the prediction model were determined. Through this study, the data of traffic flow and risky driving behavior could be used to assess the traffic crash risk on freeways and lay a foundation for traffic safety management.

Influence of prompt timing and messages of an audio navigation system on driver behavior on an urban expressway with five exits.

An audio navigation system is a very useful tool for driving path guidance with less distraction. However, the influence of the audio navigation system on driver behavior, especially in complicated road environments, is still not entirely clear. This study aims to investigate navigation prompt timing (NPT), navigation prompt message (NPM), and their combination in an audio navigation system on driving behavior on an urban expressway with five exits. Driving simulator technology was used to reproduce the experimental environment and obtain driver behavior data. Four indicators-speed, standard deviation of speed, absolute values of acceleration, and depth of accelerator-were selected to examine the main and interactive effects of NPT, NPM, and their combination based on repeated measures analysis of variance. The results show that the driver's psychological state and operation of the vehicle on the urban expressway were affected by the prompt timing and messages of the audio navigation system. An interaction effect existed between prompt timing and prompt messages of the system, and this effect adjusted the effect on the driver's psychological state and vehicle operation caused by these two important factors. This study contributes to our understanding of the influence of audio navigation systems on driver behavior in complex road environments and thus lays a foundation for developing standard audio navigation broadcast guidelines to improve drivers' acceptance of navigation systems, reduce drivers' cognitive workload, and improve the level of vehicle operation safety.

Identification of amino acids in highly pathogenic avian influenza H5N1 virus hemagglutinin that determine avian influenza species specificity.

To test the role of neutralizing antibodies (nAbs) and receptor adaptation in interspecies transmission of influenza virus, two H5N1 strains, isolated from human and avian hosts, with four amino acid differences in hemagglutinin (HA) and seven HA mutations were studied. We found that a mutation at amino acid position 90 in the H5N1 HA, outside the receptor-binding domain (RBD), could simultaneously induce changes in the RBD conformation to escape from nAb binding and alter the receptor preference through long-range regulation. This mutation was deemed a "key event" for interspecies transmission. It is likely a result of positive selection caused by antibodies, allowing the original invasion by new species-specific variants. A mutation at amino acid position 160 in the RBD only induced a change in receptor preference. This mutation was deemed a "maintaining adaptation", which ensured that influenza virus variants would be able to infect new organisms of a different species successfully. The mutation is the result of adaptation caused by the receptor. Our results suggest that continuing occurrence of these two types of mutations made the variants persist in the new host species.

Rapid and sensitive detection of severe acute respiratory syndrome coronavirus by rolling circle amplification.

The severe acute respiratory syndrome (SARS) epidemic of 2003 was responsible for 774 deaths and caused significant economic damage worldwide. Since July 2003, a number of SARS cases have occurred in China, raising the possibility of future epidemics. We describe here a rapid, sensitive, and highly efficient assay for the detection of SARS coronavirus (SARS-CoV) in cultured material and a small number (n = 7) of clinical samples. Using rolling circle amplification (RCA), we were able to achieve sensitive detection levels of SARS-CoV RNA in both solid and liquid phases. The main advantage of RCA is that it can be performed under isothermal conditions with minimal reagents and avoids the generation of false-positive results, a problem that is frequently encountered in PCR-based assays. Furthermore, the RCA technology provides a faster, more sensitive, and economical option to currently available PCR-based methods.