High Road Research Articles

A revised model of noise sensitivity

Abstract Earlier definitions of noise sensitivity implicitly presupposed that some sounds can objectively be defined as noise and that some people are intrinsically more sensitive to it. Since the experience of noise (in the sense of it being an annoying sound) is subjective, this cannot be true. To avoid this, we have previously proposed that noise sensitivity is a process whereby sounds are perceived as noise under certain circumstances. The current research expands on our earlier model by adding new factors such as masking and perceived control over the sound, developed based on the findings of interviews in which people were asked about their own experience of noise and noise sensitivity. The model was also modified by explicitly adopting the terminology “low road” and “high road” to describe the inputs from the limbic system and the cortex, respectively. The model elucidates the potential mechanisms underlying the experience of noise thereby providing avenues for reducing the level of harm to those who experience it.

Return to duty after non-surgical treatment of a non-neurological thoracic or lumbar spine fracture in French military patients: a retrospective analysis of 54 patients

IntroductionNon-surgical management of non-neurological thoracic or lumbar spine (TL) fractures seems to provide good results in the civilian population, leading to return to work in most cases. However, data on...

Long Term Performance of Sustainable Concrete Pavement Preservation Strategies in Canada

Rigid pavement preservation and rehabilitation strategies for high traffic volume roads are important sustainable methods of extending the life of our concrete roadways. In 1989, the Ministry of Transportation of Ontario (MTO) undertook the rehabilitation of an exposed concrete pavement exhibiting various distresses. The existing pavement, originally constructed in 1963, consisted of 230 mm mesh reinforced Portland Cement Concrete (PCC) pavement with dowelled joints at a spacing of 21.3 m. The preservation strategies used on the northbound lanes, which had experienced moderate deterioration, consisted of using sustainable concrete pavement restoration (CPR) techniques, material specifications and construction methods, which included full depth repair, partial depth repair, diamond grinding and joint sealant replacement. The rehabilitation strategy selected for southbound lanes was a 180 mm thick plain jointed unbonded PCC overlay to address the severe 'D' cracking and spalling at all the joints and cracks. This paper will identify the sustainable concrete pavement preservation strategies using the pavement sustainability rating system – GreenPave. It will also discuss the pavement preservation strategies and their long term performance evaluation in terms of roughness measurements, frictional resistance measured, and pavement condition ratings. Overall, the 22 year performance of these preserved and rehabilitated concrete pavements has been good with acceptable levels of ride quality, frictional resistance and distress propagation.

Evaluating migration hypotheses for the extinct Glyptotherium using ecological niche modeling

The formation of the Isthmus of Panama allowed for migrations between the once separated continents of North and South America. This led to one of the greatest documented interchanges of biota in Earth history, wherein an array of species across many groups migrated between the continents. Glyptotherium, a giant extinct armadillo‐like grazer, is an example of a taxon that likely originated in South America and migrated to North America. Here we use Ecological niche modeling to test the extent of suitable conditions for Glyptotherium in Central America and surrounding regions during the intervals when the taxon is thought to have dispersed, allowing for assessment of plausible migration routes and the hypothesis that the genus migrated from North America back to South America during the Rancholabrean (14 000–240 000 years ago). Our niche modeling results show suitable abiotic conditions for Glyptotherium in Central America and the surrounding area throughout the Plio‐Pleistocene, with western South America (the ‘high road') suggested as their ancestors' route northwards. Depending on the extent of suitable conditions, it may have been possible for Glyptotherium to return to South America during the Rancholabrean. The results support previous hypotheses that the range of Glyptotherium was constrained by the need for warm, wet environments.

BHC-YOLOV8 : improved YOLOv8-based BHC target detection model for tea leaf disease and defect in real-world scenarios.

The detection efficiency of tea diseases and defects ensures the quality and yield of tea. However, in actual production, on the one hand, the tea plantation has high mountains and long roads, and the safety of inspection personnel cannot be guaranteed; on the other hand, the inspection personnel have factors such as lack of experience and fatigue, resulting in incomplete and slow testing results. Introducing visual inspection technology can avoid the above problems. Firstly, a dynamic sparse attention mechanism (Bi Former) is introduced into the model backbone. It filters out irrelevant key value pairs at the coarse region level, utilizing sparsity to save computation and memory; jointly apply fine region token to token attention in the remaining candidate regions. Secondly, Haar wavelets are introduced to improve the down sampling module. By processing the input information flow horizontally, vertically, and diagonally, the original image is reconstructed. Finally, a new feature fusion network is designed using a multi-head attention mechanism to decompose the main network into several cascaded stages, each stage comprising a sub-backbone for parallel processing of different features. Simultaneously, skip connections are performed on features from the same layer, and unbounded fusion weight normalization is introduced to constrain the range of each weight value. After the above improvements, the confidence level of the current mainstream models increased by 7.1%, mAP0.5 increased by 8%, and reached 94.5%. After conducting ablation experiments and comparing with mainstream models, the feature fusion network proposed in this paper reduced computational complexity by 10.6 GFlops, increased confidence by 2.7%, and increased mAP0.5 by 3.2%. This paper developed a new network based on YOLOv8 to overcome the difficulties of tea diseases and defects such as small target, multiple occlusion and complex background.

Identity‐Based Motivation and the Motivational Consequences of Difficulty

ABSTRACTDifficulty is an under‐appreciated but powerful motivational force. As outlined by identity‐based motivation (IBM) theory, a social cognition theory of self, self‐regulation, and goal pursuit, people prefer to act (action‐readiness) and understand their experiences (procedural‐readiness) in ways that fit who they are (identity‐congruence). IBM also predicts that experienced identity congruence is context‐sensitive—though experienced as stable anchors, people's identities are dynamically constructed in context. Contexts shape which identities come to mind, what these on‐the‐mind identities imply for action, and what people infer when thinking about a task, goal, or life feels hard. People can draw two inferences (termed difficulty‐as‐importance and difficulty‐as‐impossibility) when a task or goal feels hard to think about and a third (termed difficulty‐as‐improvement) when their life feels hard to think about. IBM predicts, and studies support, a bidirectional relationship among these three components (action‐readiness, procedural‐readiness, and dynamic construction). Situations shape the identities that are on the mind and feel relevant (dynamic construction) and the inferences people draw from difficulty (procedural readiness). On‐the‐mind and context‐relevant identities shape inferences from difficulty. Inferences from difficulty affect identity and action—when applying a difficulty‐as‐importance lens, people feel more certain of attaining their self‐relevant goals. They perform better and find engaging a “me” thing to do—“no pain, no gain”. When applying a difficulty‐as‐impossibility lens, people find engaging a waste of their time and unlikely to yield self‐benefits unless an easy means to goal attainment exists—“cut your losses”. They focus on their virtuous character traits and prefer effortful means to attain self‐relevant goals when applying a difficulty‐as‐improvement lens—“the high road”. Difficulty can be a green light of importance signaling you to get going, a detour sign of impossibility signaling you to shift to something else, or angel wings pointing you to the effortful route.

The location routing problem with time windows and load-dependent travel times for cargo bikes

Last-mile delivery with traditional delivery trucks is ecologically unfriendly and leads to high road utilization. Thus, cities seek for different delivery options to solve these problems. One promising option is the use of cargo bikes in last-mile delivery. These bikes are typically released at micro hubs, which are small containers or facilities located at advantageous places in the city center. Since the bike’s travel speed depends on its remaining load and the street gradient, placing the hubs at valleys might cause additional work for rides. Therefore, the following question arises: How high is the impact of load-dependent travel times on micro hubs’ cost-optimal placements?To answer this question, we introduce the location routing problem with time windows and load-dependent travel times. We formulate the problem as a mixed-integer linear program and introduce an adaptive large neighborhood search with a problem-specific procedure for micro hub placements and problem-specific operators to solve larger instances. In a numerical study, we find that load-dependent travel times significantly influence the location of hubs, following that hubs with a higher elevation are preferably used. Moreover, customers are served from hubs with a similar elevation. This would not be the case if load-dependent travel times are ignored, resulting in an increase in costs by up to 2.7% or, instead, to up to 26% infeasible solutions as time windows are not adhered to.

An analysis of road accidents caused by the current road infrastructure in Romania

The scientific paper presents in a concrete and elegant way an analysis of how the poor quality and insufficiency of the current road infrastructure in Romania negatively influences road safety. The existing road infrastructure in our country is built and developed on the basis of roads built in the 1950s and 1960s, largely modernised, but still insufficient in terms of high speed roads. Specialists in the field consider that average traffic speeds on the existing road infrastructure at that time are largely influenced by a number of factors that are exclusively related to it. A high density of localities and the roads that cross them, the topography, the climate, traffic jams, the defective way of action of the authorities, sometimes the lack of horizontal and vertical signalization, the obstacles encountered at every turn, the non-conforming, undersized design or construction of traffic lanes and other elements of infrastructure, current legislation with many interpretations, heavy, heterogeneous and congested traffic, all of which make traffic participants unsafe and stressful and generate serious road events or accidents, resulting in human casualties and significant material damage. At national level, these are just a few of the most important issues negatively affecting infrastructure. In order to remove these negative, nonconforming aspects, which do not add value to the modern society it serves, it is imperative that major investments are undertaken in a relatively short time to develop a modern, sustainable and durable road infrastructure. In this way, those interested can find out the current state of research in the field, as well as data and figures on the number of road accident fatalities caused by the poor state of road infrastructure in Romania. The road safety management carried out on the current road infrastructure and the degree of sustainability provided by the authorities is then presented. At the end of the paper, conclusions in the field are presented

VALUATING THE IMPACT OF URBAN ROAD TRANSPORTATION ON LAND USE PATTERNS IN GBAGADA LAGOS STATE

This study investigates the relationship between urban road transportation and land use patterns in Gbagada Lagos state. Using a mixed-methods approach combining Geographic Information System (GIS) analysis, survey questionnaires, and regression modeling, this research examines how road network density, traffic volume, and transportation modes influence land use changes. The findings reveal significant correlations between road proximity, land value, and urban sprawl. Specifically, areas with high road density exhibit increased commercial and residential development, while areas with limited accessibility experience decreased land value and reduced development. Policy recommendations include optimizing transportation infrastructure, promoting mixed-use development, and implementing smart growth strategies to mitigate urban sprawl and enhance sustainable land use. In particular, certain transportation planning decisions tend to increase sprawl (dispersed, urban fringe, automobile-dependent development), while others support smart growth (more compact, infill, multimodal development). These development patterns have various economic, social, and environmental impacts. This report describes specific methods for evaluating these impacts in transport planning.

Hot asphalt recycling in cold climate: performance-based mixture design and a test section in the Swiss Alps

The use of high reclaimed asphalt pavement (RAP) content in the production of hot asphalt mixtures increases the risk of pavement cracking, especially in locations with cold climate and high temperature fluctuations. Here we used a performance-based mixture design approach to prepare three plant-produced hot asphalt mixtures for base, binder and foundation courses containing between 60% and 85% RAP and paved them at 1900 m altitude in the Swiss Alps. The mixture performance-based test and road core test results demonstrated that it is possible to ensure the required mixture performance even when using high RAP content so long as the homogeneity of RAP is ensured. The conventional mixture quality control requirements were fulfilled as well. As a result of the research, recommendations for designing mixtures containing high RAP content for high altitude roads are proposed.

Environmental and Atmospheric Influences on Academic Performance: The Role of Green Spaces, Roads, and Wildfires Around Schools and Homes in the Federal District, Brazil

Environmental characteristics, such as proximity to green spaces and exposure to roads, can significantly influence atmospheric factors like air quality. For instance, areas with abundant green spaces typically exhibit better air quality, while high road density often correlates with increased air pollution, both of which can affect students’ cognitive functioning and academic performance. This study aimed to evaluate the association between the environmental and atmospheric conditions—specifically green spaces (measured by the NDVI and green space area), roads (total road length), and wildfires—around students’ schools and homes in the Federal District (FD), Brazil, and their impact on academic performance. We analyzed data from 344,175 public school students across 256 schools in the FD, covering the years 2017 to 2020. Using a mixed-effects regression model, we investigated how neighborhood characteristics such as green spaces, road density, and wildfire frequency influence individual-level academic performance while controlling for temporal, socioeconomic, and school-specific factors. Our findings indicate that the environmental factors around schools, particularly green spaces and road density, have significant associations with academic outcomes. Specifically, a higher road density around schools was linked to lower academic performance, whereas green space presence had a generally positive impact, especially around schools. Wildfires, while negatively associated with performance around homes, had mixed effects around schools. These results underscore the importance of considering environmental and atmospheric factors in urban planning and education policy to enhance student performance.

Three-point adaptive preview steady-state compensation driver model based on articulated heavy vehicle

This article proposes a three-point adaptive preview steady-state compensation driver model based on articulated heavy vehicles (AHVs). Although AHVs have extremely high road transportation efficiency, they have poor driving stability and high accident risk during high-speed transportation due to their large weight, high center of mass and multi-body characteristics. However, there are few studies on intelligent driving control of AHVs. In order to improve the active safety performance of AHVs, a three-point adaptive preview driver model is designed in this article. Based on the driver model, a sliding mode steady-state compensation controller is added. The three-point adaptive preview driver is different from other driver models. Its characteristic is that the preview distance can be automatically adjusted according to the vehicle speed and road curvature. A driver model with three-point preview weighted deviation, preview time, current vehicle speed and vehicle state as input and front wheel angle as output is established. Considering the trailer state, the front wheel angle is compensated and corrected by sliding mode steady-state compensation controller. The simulation results show that the model can reasonably judge the relationship between the target path and the vehicle position, adapt to the dynamic adjustment mechanism of the preview distance, and has good tracking accuracy. The front wheel compensation angle provided by the sliding mode controller under high speed and extreme conditions can ensure that the AHVs can track the target trajectory while maintaining driving stability.

Organizing planning knowledge for automated vehicles and intelligent transportation systems

AbstractIntelligent Transportation Systems (ITS) are crucial for developing fully automated vehicles. While significant progress has been made with advanced driver assistance systems and automation technology, challenges remain, such as improving traffic information, enhancing planning and control systems, and developing better decision‐making capabilities. Despite these hurdles, the potential benefits of ITS are so many that its challenges have attracted substantial industrial investment and research groups interested in the automated driving field. In this work, a methodology based on state space search for planning knowledge integration is proposed. The main goal of the proposal is to provide a planning system with the necessary information to perform properly any task related to lateral and longitudinal control, path following, trajectory generation, arbitration and behavior execution by localizing the vehicle with respect to a high‐level road plan. To this end, this research compares cutting‐edge methods for rapidly finding the K nearest neighbor in relatively high dimensional road plans constructed from the traffic information stored in a high definition map. During the experimentation phase, promising real‐time results have been obtained for fast KNN algorithms, leading to a robust tree index‐based methodology for decision making in self‐driving vehicles combining path planning, trajectory tracking, trajectory creation, knowledge aggregation and precise vehicle control.

Washable oil-coated structured support for passive outdoor particulate matters trapping

Direct outdoor air depollution represents an interesting path for preventing indirect disease. In the present work, a simple and efficient PMs trapping media based on the use of an oil-coated structured polymer media was developed for passive trapping of various PMs, ranging from coarse (PM10), to fine (PM2.5) and ultra-fine (PM1) dimension in outdoor environment. The device can be easily regenerated by a simple washing with a mixture of water and detergent followed by a new oil coating cycle. The total PM loading mass of the passive trap and the recovered PMs are analyzed through different techniques and confirm the great efficiency of such filter to trap various PMs when exposed to a high traffic road. The spent filter can be regenerated through a simple washing step and can be repeatedly re-used with similar PM loading mass. The high and long-lasting total PM loading mass were also supported by numerical simulations based on computational fluid dynamics, also used to propose an optimization implementation of such system for future deployment at scale.

Recycling steel slag as aggregate in developing an ultra-thin friction course with high comprehensive road performance

The demand for skid-resistant and wear-resistant alternative aggregates in the preparation of ultra-thin friction course (UTFC) asphalt mixtures is a key direction for achieving high durability and low-carbon development in pavement maintenance. As a typical industrial waste, steel slag with high alkalinity and strong adhesion can facilitate the mechanical property of asphalt mixtures. However, few researches focused on the impact of steel slag on the comprehensive road performance of UTFC mixtures. Hence, the issue merits systematic exploration. In this study, the chemical components, microstructure and morphological characteristics of three steel slags (GXSS, HBSS and IMSS) obtained from different sources were surveyed. Then the stone matrix asphalt (SMA)-5 and open-grade friction courses (OGFC)-5 mixtures with different steel slags were fabricated. And their comprehensive road performances were quantitatively explored via the improved radar chart. The results indicate that steel slag with approximately 40 % Ca content, about 20 % Fe content, irregular shapes and tiny pores endows its wear resistance and strong adhesion. IMSS demonstrates the largest average angularity and form 2D values of 3850 and 7.28, respectively, representing its significant crowding function and interlocking structure. Incorporating steel slag remarkably reinforces the rutting resistance, skid resistance and anti-interlayer shear performance of UTFC. While the effect of water stability and crack resistance of UTFC depend on the category of steel slag. All three steel slags manifest the improvement function on the comprehensive assessment indexes (CAI) of OGFC-UTFC. IMSS-OGFC reveals the optimal comprehensive road performance, achieving the highest CAI of 0.9909. It is inferred that steel slag is more suitable for OGFC-UTFC than SMA-UTFC due to its relatively high CAI and consistent performance regardless of the steel slag type.

Fisher activity patterns show potential for behavioral adaptations to human modified landscapes

Animals alter their diel activity in response to physiological constraints and ecological conditions. Fisher (Pekania pennanti) activity is known to vary through the diel cycle and change in response to cold stress and generally through both the climatic and biological seasons. However, less is known whether thermoregulatory effects impact fisher activity in milder climates and in areas of high human disturbance. We focused on two distinct research objectives to understand the 1) physiological constraints, and 2) ecological components of fisher activity in a highly disturbed landscape with a relatively mild climate. We used accelerometer data from 34 individual live-captured fisher in Rhode Island, USA from 2021 to 2023. We found that fisher activity patterns were primarily driven by diel cycle with higher activity levels at night than during the day. We did not observe any physiological influence of ambient temperature on fisher activity; daily minimum temperatures did not constrain fisher activity in the colder months, nor did daily maximum temperatures in warmer months. We did find that female activity levels differed by breeding status with non-pregnant females having higher activity levels than pregnant females. Considering ecological components, we found fisher decreased activity levels in higher road density areas during warmer months that coincide with higher traffic volumes. For fisher living in areas with lower road densities, we saw higher activity in the breeding season and summer than in winter. In contrast, fisher living in areas with high road densities had lower activity in the breeding season and summer than in winter. We conclude that fisher largely do not shift their activity to mitigate thermoregulatory costs in areas where temperatures do not reach extremes for extended periods of time. However, our findings suggest that behavioral shifts in activity are impacted by human disturbance and fisher minimize activity in risky areas.

The Mediating Role of Traffic Safety Awareness on Road Safety Attitude and Behavior of Drivers

This research study aimed to determine the mediating role of traffic safety awareness on the relationship between road safety attitude and behavior of drivers. The researcher surveyed 378 licensed drivers selected using a stratified random sampling technique. The study utilized Mean, Pearson Product Moment Correlation, Medgraph using Sobel z-test and Regression. The study found a very high level of Traffic Safety Awareness, a moderate level of Driving behavior, and high Road Safety Attitudes among licensed drivers. A significant correlation was observed between road safety attitude and drivers’ behavior. There is also a high significance on the relationship between road safety attitude and traffic safety awareness, as well as between traffic safety awareness and drivers’ driving behavior. The mediation analysis examined the role of traffic safety awareness as a mediator in the relationship between road safety attitude and driver behavior. The results point towards partial mediation, where the mediator variable partially explains the relationship between the independent and dependent variables.

High traffic roads and adverse birth outcomes: comparing births upwind and downwind of the same road.

Traffic-related air pollution is a major concern for perinatal health. Determining causal associations, however, is difficult because high-traffic areas tend to correspond with lower socioeconomic neighborhoods and other environmental exposures. To overcome confounding, we compared pregnant individuals living downwind and upwind of the same high-traffic road. We leveraged vital statistics data for Texas from 2007 to 2016 (n=3 570 272 births) and computed hourly wind estimates for residential addresses within 500m of high-traffic roads (ie, annual average daily traffic >25 000 vehicles) (10.9% of births). We matched pregnant individuals predominantly upwind with pregnant neighbors downwind of the same road segment (n=37 631 pairs). Living downwind was associated with a decrease of 11.6 g (95% CI, -18.01 to -5.21) in term birth weight. No associations were observed with low term birth weight, preterm birth, or very preterm birth. In distance-stratified models, living downwind within 50m was associated with a decrease of 36.3 g (95% CI, -67.74 to -4.93) in term birth weight and living 51-100m downwind was associated with an odds ratio of 3.68 (95% CI, 1.71-7.90) for very preterm birth. These results suggest traffic air pollution is associated with adverse birth outcomes, with steep distance decay gradients around major roads. This article is part of a Special Collection on Environmental Epidemiology.

Drive-by bridge mode shape identification using novel reference-based component scaling method

This article proposes a novel method, entitled Reference-based Component Scaling (RCS) method, for estimation of bridge mode shapes using vehicle scanning methods. The proposed method uses two instrumented vehicles to measure the accelerations on the bridge. While one vehicle remains stationary on a pre-determined location, the other one travels over the bridge with a constant speed both exciting the bridge and recording its response. The accelerations recorded on both vehicles are then processed using variational mode decomposition method and Hilbert transform to obtain their modal components. Leveraging its comprehensive framework that incorporates modal components derived from both vehicles, the RCS method effectively tackles the primary challenges encountered by vehicle scanning methods. These challenges include: i) shifting and shrinking effects of bridge damping on mode shapes, ii) distortions in modal components due to incomplete elimination of the roughness effect and iii) distortions in modal components caused by acceleration peaks during the vehicle´s transition from a rigid platform to the bridge. To this end, the RCS method addresses adverse effects of bridge damping without a priori knowledge of the modal damping ratios. To assess the accuracy of the proposed method, numerical analyses were carried out on three different bridges with different number of spans and boundary conditions. The effect of crucial parameters such as road roughness, bridge damping, uncertainties in measurements, and existing traffic on the accuracy of the proposed method was investigated. The results of the numerical study show that the RCS method stands alone in providing accurate estimates of mode shapes of bridges that are seated on elastic bearings, that have realistic damping ratios and relatively high road roughness profiles.

Prediction of Radio Signal Failures of Communication Based Train Operating Systems by Machine Learning Methods

The use of rail systems in urban public transportation has become a necessity for reasons such as time saving, travel comfort and operating costs, especially in cities with high population and road traffic. Communication based train operating systems (CBTC) are used for the safe use of urban rail systems and the maximum capacity of the railway line. In this study, in line with the data collected from the trains on a railway line operated with CBTC, the status of the radio signals that enable the wireless communication of the trains with the trackside signaling equipment was evaluated by machine learning methods, and the situations that may have negative effects on the train operations of the problems at the signal level were evaluate. The problems on the antennas which receives signals from trackside above trains, the poor connection related with fiber optical and LAN cables, the trackside transmitter antenna orientation problems causes decrease on signal levels. It is aimed that to take actions about the problematic signal levels without any negative impact on the passenger comfort and the operation yet. The radio signal losses cause unexpected trains stops and delays. A decision support model has been developed that will offer early solution suggestions to system maintainers in order to intervene first. In conclusion, since it is the first study related with failure prediction by using radio signal levels data on railway signaling system, this study presents an important innovation in terms of literature.