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The effect of aggressive driving behavior on driver’s injury severity is analyzed by considering a comprehensive set of variables at highway-rail grade crossings in the US. In doing so, we are able to use a mixed logit modelling approach; the study explores the determinants of driver-injury severity with and without aggressive driving behaviors at highway-rail grade crossings. Significant differences exist between drivers’ injury severity with and without aggressive driving behaviors at highway-rail grade crossings. The level of injury for younger male drivers increases a lot if they are with aggressive driving behavior. In addition, driving during peak-hour is found to be a statistically significant predictor of high level injury severity with aggressive driving behavior. Moreover, environmental factors are also found to be statistically significant. The increased level of injury severity accidents happened for drivers with aggressive driving behavior in the morning peak (6-9 am), and the probability of fatality increases in both snow and fog condition. Driving in open space area is also found to be a significant factor of high level injury severity with aggressive driving behaviors. Bad weather conditions are found to increase the probability of drivers’ high level injury severity for drivers with aggressive driving behaviors.

This paper is devoted to the theory of perishable inventory systems. In such systems items arrive and stay ‘on the shelf’ until they are either taken by a demand or become outdated. Our aim is to survey, extend and enrich the probabilistic analysis of a large class of such systems. A unifying principle is to consider the so-called virtual outdating process V, where V(t) is the time that would pass from t until the next outdating if no new demands arrived after t. The steady-state density of V is determined for a wide range of perishable inventory systems. Key performance measures like the rate of outdatings, the rate of unsatisfied demands and the distribution of the number of items on the shelf are subsequently expressed in that density. Some of the main ingredients of our analysis are level crossing theory and the observation that the V process can be interpreted as the workload process of a specific single server queueing system.

Highway railway level crossings, also widely recognized as HRLCs, present a significant threat to the safety of everyone who uses a roadway, including pedestrians who are attempting to cross an HRLC. More studies with new, proposed solutions are needed due to the global rise in HRLC accidents. Research is required to comprehend driver behaviours, user perceptions, and potential conflicts at level crossings, as well as for the accomplishment of preventative measures. The purpose of this study is to conduct an in-depth investigation of the HRLCs involved in accidents that are located in the northern zone of the Indian railway system. The accident information maintained by the distinct divisional and zonal offices in the northern railways of India is used for this study. The accident data revealed that at least 225 crossings experienced at least one incident between 2006 and 2021. In this study, the logistic regression and multilayer perception (MLP) methods are used to develop an accident prediction model, with the assistance of various factors from the incidents at HRLCs. Both the models were compared with each other, and it was discovered that MLP supplied the best results for accident predictions compared to the logistic regression method. According to the sensitivity analysis, the relative importance of train speed is the most important, and weekday traffic is the least important.

Magnetoreception, the remarkable ability of organisms to perceive and respond to Earth’s magnetic field, has captivated scientists for decades, particularly within the field of quantum biology. In the plant science, the exploration of the complicated interplay between quantum phenomena and classical biology in the context of plant magnetoreception has emerged as an attractive area of research. This comprehensive review investigates into three prominent theoretical models: the Radical Pair Mechanism (RPM), the Level Crossing Mechanism (LCM), and the Magnetite-based MagR theory in plants. While examining the advantages, limitations, and challenges associated with each model, this review places a particular weight on the RPM, highlighting its well-established role of cryptochromes and in-vivo experiments on light-independent plant magnetoreception. However, alternative mechanisms such as the LCM and the MagR theory are objectively presented as convincing perspectives that permit further investigation. To shed light on these theoretical frameworks, this review proposes experimental approaches including cutting-edge experimental techniques. By integrating these approaches, a comprehensive understanding of the complex mechanisms driving plant magnetoreception can be achieved, lending support to the fundamental principle in the RPM. In conclusion, this review provides a panoramic overview of plant magnetoreception, highlighting the exciting potential of quantum biology in unraveling the mysteries of magnetoreception. As researchers embark on this captivating scientific journey, the doors to deciphering the diverse mechanisms of magnetoreception in plants stand wide open, offering a profound exploration of nature’s adaptations to environmental cues.

Multiple projects within the rail industry across different regions have been initiated to address the issue of over-population. These expansion plans and upgrade of technologies increases the number of intersections, junctions, and level crossings. A level crossing is where a railway line is crossed by a road or right of way on the level without the use of a tunnel or bridge. Level crossings still pose a significant risk to the public, which often leads to serious accidents between rail, road, and footpath users and the risk is dependent on their unpredictable behavior. For Great Britain, there were three fatalities and 385 near misses at level crossings in 2015–2016. Furthermore, in its annual safety report, the Rail Safety and Standards Board (RSSB) highlighted the risk of incidents at level crossings during 2016/17 with a further six fatalities at level crossings including four pedestrians and two road vehicles. The relevant authorities have suggested an upgrade of the existing sensing system and the integration of new novel technology at level crossings. The present work addresses this key issue and discusses the current sensing systems along with the relevant algorithms used for post-processing the information. The given information is adequate for a manual operator to make a decision or start an automated operational cycle. Traditional sensors have certain limitations and are often installed as a “single sensor”. The single sensor does not provide sufficient information; hence another sensor is required. The algorithms integrated with these sensing systems rely on the traditional approach, where background pixels are compared with new pixels. Such an approach is not effective in a dynamic and complex environment. The proposed model integrates deep learning technology with the current Vision system (e.g., CCTV to detect and localize an object at a level crossing). The proposed sensing system should be able to detect and localize particular objects (e.g., pedestrians, bicycles, and vehicles at level crossing areas.) The radar system is also discussed for a “two out of two” logic interlocking system in case of fail-mechanism. Different techniques to train a deep learning model are discussed along with their respective results. The model achieved an accuracy of about 88% from the MobileNet model for classification and a loss metric of 0.092 for object detection. Some related future work is also discussed.

This paper studies the impact of removing the level crossing, which constitutes traffic hazard to the society, on house prices by conducting a quasi-natural experiment using the Level Crossing Removal Project (LXRP) implemented by the Victoria state government in Australia since 2015. Using a difference-in-differences method, we analyzed the changes in housing prices due to the improvement of transportation infrastructure, gauging the LXRP’s impact on house and unit submarkets separately. We found that the prices for house and unit markets increased significantly after the removal of level crossings, with the value uplift decreasing with distance from the removal site. This paper contributes to the existing literature by adding an empirical study related to the enhancement of infrastructure aiming to improve the traffic safety in the urban context. Unlike previous studies, this study examines the effect of improvement projects for existing infrastructure and provides relevant implications to improve the efficiency of investing public resources in infrastructure improvement.

There are some problems that the bit error rate is high but generation rate is low in existing physical layer secret key generation schemes, so we propose a highly consistent and high-speed secret key generation scheme in this paper. This scheme not only can achieve high consistency and high generation rate, but also can ensure randomness of secret key. First, the scheme conducts channel pre-detection to calculate weighted vectors to smooth the noise. Then, a modified level crossing algorithm is proposed to improve key consistency greatly. Furthermore, we put forward cyclic interleaving quantization method that can linearly improve key generation rate and enhance randomness of secret key. Simulation results show that the scheme can achieve zero bit disagreement, and key generation rate can reach at least 495% when SNR is 20 dB.

Urban and suburban level crossings are critical intersection points between rail and pedestrian infrastructure, requiring careful monitoring and analysis of traffic patterns for safety and planning purposes. This paper presents a comprehensive methodology for measuring and estimating pedestrian and cycle traffic at urban and suburban level crossings. A dual-component system is introduced that considers separately regular crossing users and rail transport passengers, acknowledging their distinct temporal patterns. Three distinct temporal patterns were identified for both pedestrian movement and rail passenger flows through analysis of fixed counter data and passenger statistics, while a singular pattern was determined for cyclists. The methodology was validated at 14 railway crossings, establishing minimum requirements for measurement duration and optimal timing. The results indicate that counting periods of at least 24 hours are required, with optimal accuracy achieved during the spring and autumn months. This approach provides optimal resource usage for achieving adequate accuracy. Data collection and estimation supported by this framework will provide the grounds for evidence-based decision-making in railway crossing infrastructure planning and safety assessment.

In this article, we provide a comprehensive analyses of two continuous review lost sales inventory system based on different replenishment policies, namely $(s,S)$ and $(s,Q)$ . We assume that the arrival times of demands form a Poisson process and that the demand sizes have i.i.d. exponential distribution. We assume that the items in stock may obsolete after an exponential time. The lead time for replenishment is exponential. We also assume that the excess demands and the demands that occurred during stock out periods are lost. Using the system point method of level crossing and integral equation method, we derive the steady-state probability distribution of inventory level explicitly. After deriving some system performance measures, we computed the total expected cost rate. We also provide numerical examples of sensitivity analyses involving different parameters and costs. As a result of our numerical analysis, we provide several insights on the optimal $(s,S)$ and $(s,Q)$ policies for inventory systems of obsolescence items with positive lead times. The better policy for maintaining inventory can be quantified numerically.

Conventional statistical models, such as Poisson or negative binomial, have predefined underlying relationships between explanatory variables. However, artificial neural network (ANN), which overcomes the limitations of statistical prediction model, have gained popularity in practice and research for their ability to increase prediction accuracy. Thus, this study employs zero-truncated negative binomial (ZTNB) models and artificial neural network (ANN) models to analyze the distribution of railroad accident frequency and the corresponding number of casualties for 1995–2021 accident dataset of Korea’s national railroad. The study mainly focused on two most dominant accident types which are human-involved accidents (accounted for 89.2% of all accidents) and ground-level crossing accidents (9.6%) from the historic dataset. This is because not just data proportion, rather such accident types were received very little attention compared to fatal train accident in the accident prediction study. Further, these types of accident showed clearly tended to decrease over time, but time trend has been found very weak at the type of fatal train accident. The performance of the developed models was estimated by mean square error (MSE), the root mean square error (RMSE), and the coefficient of determination ( R 2 ). Results present that ANN models outperform ZTNB models in fitting and prediction, demonstrating once again ANN’s superiority over statistical models for predicting accident frequency and casualty count.