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"Log driving."
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The log driver's waltz
\"Based on the perennially popular Canadian folk song and animated short film of the same name, The Log Driver's Waltz is a modern, contemporary retelling of a young woman whose parents are keen for her to marry. The town's well-to-do doctors, merchants, and lawyers all pursue her, but it's the humble log driver-with his style, grace, and joie de vivre-who captures her attention. When she and the log driver finally meet on the dance floor, their joy leaps off the page. The Log Driver's Waltz brings a hallmark of Canadian childhood to life.\"-- Provided by publisher.
Book
Long-term evolution of fish communities in European mountainous rivers: past log driving effects, river management and species introduction (Salzach River, Danube)
Using historical sources from the turn of the 19th to the 20th century, we investigated the long-term evolution of the fish community in a mountainous river network and the influence of different human uses and management measures. Within the alpine Salzach catchment, historical presence was reconstructed for 26 fish species, abundance classes for 19 species. Due to channelization, flood protection and dam erections, the spatial distribution of fish species was reduced during the 20th century. Many rheophilic and eurytopic fish species historically inhabited river reaches along a wide longitudinal profile and were present in more upstream river reaches than nowadays. The decrease of species diversity in the headwater sections is a consequence of lost lateral connectivity. Strongest effects are reported for sensitive species requiring different habitat types during their life cycles (especially pike, nase, Danube salmon). One of the most important shifts from the historical fish community to the present one reflects the deliberate introduction of fish species for fisheries. Rainbow trout and brook trout, absent from the historical fish assemblage, today represent up to 29 % of the total number of fish occurrences. In contrast, log driving, one of the most common historical pressures in European mountainous rivers, did not show significant negative effects on the past fish ecological situation. This result strongly differs from the impacts of log driving and deforestation demonstrated for recent times, and could be related to the change in log driving practices during the 20th century and to the high societal value of fish before the industrialization period along with other historical pressures affecting fish in rivers without log driving. In general, our results can be valid for a large number of European mountainous rivers. They highlight the usefulness of such detailed historical studies for our understanding of the long-term evolution of fish communities and their present functioning, and point the way for future river management strategies to restore fish biodiversity.
Journal Article
Estructura factorial del Driving Log en una muestra española
El presente estudio tuvo como objetivo la adaptación del Driving Log, un cuestionario que valora los comportamientos agresivos y arriesgados al volante, en una muestra española de 395 personas. El análisis factorial confirmatorio mostró que el cuestionario ajustaba satisfactoriamente en dos factores, etiquetados como Conducción Arriesgada y Conducción Agresiva. Los análisis posteriores mostraron que el número de trayectos realizados se asoció significativamente a la Conducción Arriesgada, mientras que el número de veces en que se experimentó ira lo hizo tanto con la Conducción Arriesgada como con la Conducción Agresiva. Igualmente, se vio que los hombres se comportaban de forma más arriesgada y agresiva que las mujeres, y que los jóvenes lo hacían en mayor grado que los mayores.
Journal Article
EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks
Driver fatigue detection is one of the essential tools to reduce accidents and improve traffic safety. Its main challenge lies in the problem of how to identify the driver's fatigue state accurately. Existing detection methods include yawning and blinking based on facial expressions and physiological signals. Still, lighting and the environment affect the detection results based on facial expressions. In contrast, the electroencephalographic (EEG) signal is a physiological signal that directly responds to the human mental state, thus reducing the impact on the detection results. This paper proposes a log-Mel spectrogram and Convolution Recurrent Neural Network (CRNN) model based on EEG to implement driver fatigue detection. This structure allows the advantages of the different networks to be exploited to overcome the disadvantages of using them individually. The process is as follows: first, the original EEG signal is subjected to a one-dimensional convolution method to achieve a Short Time Fourier Transform (STFT) and passed through a Mel filter bank to obtain a logarithmic Mel spectrogram, and then the resulting logarithmic Mel spectrogram is fed into a fatigue detection model to complete the fatigue detection task for the EEG signals. The fatigue detection model consists of a 6-layer convolutional neural network (CNN), bi-directional recurrent neural networks (Bi-RNNs), and a classifier. In the modeling phase, spectrogram features are transported to the 6-layer CNN to automatically learn high-level features, thereby extracting temporal features in the bi-directional RNN to obtain spectrogram-temporal information. Finally, the alert or fatigue state is obtained by a classifier consisting of a fully connected layer, a ReLU activation function, and a softmax function. Experiments were conducted on publicly available datasets in this study. The results show that the method can accurately distinguish between alert and fatigue states with high stability. In addition, the performance of four existing methods was compared with the results of the proposed method, all of which showed that the proposed method could achieve the best results so far.
Journal Article
Advanced Modeling of Fuel Efficiency in Light-Duty Vehicles Using Gamma Regression with Log-Link Under Real Driving Conditions at High Altitude: Quito, Ecuador Case Study
Fuel efficiency (FE) modeling under real-world conditions remains limited in Andean cities, where topographical and traffic conditions affect vehicle performance. Vehicles powered by spark-ignition engines are the most popular in Latin America, but few studies integrate dynamic conditions with geographic features. This study addresses this gap by developing an explanatory model to predict FE for light-duty vehicles (LDVs) in the Metropolitan District of Quito (DMQ), which is one of the most congested cities in Latin America. Data were collected from eight vehicles circulating under real conditions across 35 zones in the DMQ. Predictors such as vehicle speed (VSS), acceleration (A), speed per acceleration in its 95th percentile (VA[95]), road slope, and Vehicle-Specific Power (VSP) were included in the analysis. As a first attempt, linear models were tested, but the assumptions were not satisfied. Therefore, a Gamma regression model with a logarithmic link was selected. The final model achieved a Root Mean Square Error (RMSE) of 0.939, a Relative RMSE (RRMSE) of 0.155, a Mean Absolute Error (MAE) of 0.754, and an approximate coefficient of determination (R2) of 0.956. This methodology combines continuous and categorical variables and offers a replicable framework for FE estimation in other urban contexts.
Journal Article
Person–Vehicle–Environment Interactions Predicting Crash-Related Injury Among Older Drivers
OBJECTIVE. The object of this research was to identify interactions among person, vehicle, and environment factors associated with crashes and injuries among older drivers.
METHOD. We quantified risk factors and interactions for 5,744 drivers.
RESULTS. Women had a high crash risk during mornings (8:00 a.m.–1:00 p.m.; odds ratio [OR] = 1.73, confidence interval [CI] = 1.40–2.14) or afternoons (2:00 p.m.–8:00 p.m.; OR = 1.74, CI = 1.41–2.15); alcohol-related crashes were the least likely to occur during mornings (OR = 0.19, CI = 0.12–0.31). The greatest crash risk with another vehicle occurred during afternoons (OR = 3.89, CI = 2.41–5.05). Injury had interactions with fixed-object crashes (OR = 427, CI = 182.9–998.24), no seatbelt (OR = 5.69, CI = 3.90–8.29), female gender (OR = 1.54, CI = 1.67–1.92), and mornings (OR = 1.40, CI = 1.01–1.94).
CONCLUSION. An opportunity for crash and injury prevention research and shaping longer-range evaluation policies emerged.
Journal Article
Analysis on Reckless Driving Behavior by Log-Linear Model
This study analyzes the factors affecting reckless driving behavior. By using techniques of categorical analysis on drivers involved in traffic accidents or regulation violations, a log-linear model is established to find the relationship between reckless driving behavior and the affecting variables such as age, marital status, and education level. Traffic accidents or regulation violatio ns may occur as a result of unhabitual reckless driving behavior by chance including unexpected conditions of roadways, or as a result of habitual reckless driving behavior. The principal component analysis in factor analysis is used to determine the habitual reckless driving characteristics of drivers. In order to compare the differences in response between the reckless driving behavior and drivers' characteristics, odds multipliers from logit modeling are computed.
Journal Article
Suspected DUI crash closes Porter Creek Road
April 26--A suspected drunken driver on Porter Creek Road hit a power pole early Tuesday, knocking the pole and lines onto the rural commute route, forcing emergency officials to close the road for hours, said the CHP.
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