High Road Research Articles

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.

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.

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.

Road Extraction from Remote Sensing Imagery with Spatial Attention Based on Swin Transformer

Road extraction is a crucial aspect of remote sensing imagery processing that plays a significant role in various remote sensing applications, including automatic driving, urban planning, and path navigation. However, accurate road extraction is a challenging task due to factors such as high road density, building occlusion, and complex traffic environments. In this study, a Spatial Attention Swin Transformer (SASwin Transformer) architecture is proposed to create a robust encoder capable of extracting roads from remote sensing imagery. In this architecture, we have developed a spatial self-attention (SSA) module that captures efficient and rich spatial information through spatial self-attention to reconstruct the feature map. Following this, the module performs residual connections with the input, which helps reduce interference from unrelated regions. Additionally, we designed a Spatial MLP (SMLP) module to aggregate spatial feature information from multiple branches while simultaneously reducing computational complexity. Two public road datasets, the Massachusetts dataset and the DeepGlobe dataset, were used for extensive experiments. The results show that our proposed model has an improved overall performance compared to several state-of-the-art algorithms. In particular, on the two datasets, our model outperforms D-LinkNet with an increase in Intersection over Union (IoU) metrics of 1.88% and 1.84%, respectively.

Unveiling transportation disparities: investigating accessibility gaps in metropolitan cities using GIS—a case study of Alexandria, Egypt

IntroductionPublic transportation is essential to reduce pollution and traffic congestion in urban areas. However, in densely populated regions like Alexandria, characterized by high population density and limited road infrastructure, accessibility to diverse transportation options is crucial.MethodsThis study investigates the accessibility of public transportation modes, including railways and buses, and their interaction with informal transit. Questioning if public transportation alone will be sufficient for pedestrians or if the presence of informal transit is crucial, and whether all residents of Alexandria have their rightful accessibility for different transport modes, the goal was to establish an efficient and interconnected transportation network. A survey was conducted in cooperation with Transport for Cairo to investigate all transport routes throughout Alexandria, further using ArcGIS Pro to identify gaps in public transportation coverage.ResultsFindings revealed that public transportation alone is insufficient due to limited bus and railway coverage because of their fixed linear routes. Informal transit, like microbuses, plays a vital role in filling these gaps.DiscussionThe study emphasizes the need for a comprehensive and sustainable transportation network that considers both formal and informal transit. These insights can guide policymakers and urban planners in improving public transportation in Alexandria and similar cities worldwide.

Manifestations of Criminal Professionalism in the Activities of Robber Gangs of Pre-Revolutionary Russia

Relevance. Professional crime is one of the most dangerous, relatively independent types of crime, which poses a serious threat to modern society and the state. Currently, there is no unified approach to assessing the state, dynamics and trends of this dangerous criminal phenomenon. Mechanisms to combat this type of criminal activity that are adequate to the level of danger have not been identified. In this regard, special studies of professional crime in all its complexity and ambiguity are becoming particularly relevant, including those affecting certain issues of the evolution of this dangerous criminal phenomenon in time and space.The purpose. He purpose of the study is a theoretical analysis and definition of the socio-legal essence of professional crime in pre-revolutionary Russia (using the example of the analysis of socially dangerous activities of robber gangs).Objectives: to analyze the domestic legislation of the pre-revolutionary period, providing for accountability for repeated robbery; to determine the specifics of committing crimes on a professional basis by robber gangs in prerevolutionary Russia; to identify elements of the criminal subculture supported by the community of professional robbers in the period under study in Russia.Methodology. To achieve this goal, such general scientific and private scientific research methods as the method of analysis, synthesis, generalization, comparative legal and chronological methods were used.Results. It was determined that many robbers who traded on the «high roads» during the studied period possessed all the signs of criminal professionalism: the presence of criminal specialization, qualifications, perception of criminal activity as a source of income, connections with the criminal world, including those expressed in the perception and multiplication of values and norms of the emerging criminal subculture.Conclusion. By the end of the nineteenth and early twentieth centuries, a monolithic, well–structured consolidated and criminal world had formed in Russia, in which the professional criminal «core» played a special role. Professional robbers occupied not the least place in the criminal hierarchy, causing significant harm to economic relations in society and the national security of the country.

Flood Management in the Babon River Watershed, Semarang City

Floods are natural disasters that often occur during the rainy season, Apart from that, there are also tidal floods which are caused by global warming. This problem occurs in the city of Semarang, especially northern Semarang, specifically the northern coast floods that occurred in 2021 in the city of Semarang including Genuk, Kaligawe, Tawang Station, Tanjungmas, and Karangayu. Flood disasters are caused by high rainfall, while tidal floods are caused by high tides and inundate the roads and houses of residents on the north coast of Semarang city. To handle this problem, the current government has built embankments in the Sringin River, which is equipped with a ponpa, Tenggang River, Babon River, Semarang River, Banger River, and others. The embankment is equipped with a pump so that when the water from upstream to downstream is high, the water is pumped into the sea. The aim is to determine one of the annual discharges of the Baboon river, as well as to determine the capacity of the Baboon river’s longstorage capacity. Apart from that, there is a method for dealing with tidal floods, namely using Baboon River Longstorage which is equipped with a pump. If high tide floods roads and houses, the pump is turned on, whereas if the water recedes and does not inundate residential areas, the pump is turned off. Flood control analysis was calculated using the HEC-RAS program simulation using a return period of 50 years. Based on calculations using the HEC-RAS program, the flood discharge is 2,529 m³/sec. The high tide elevation is + 1.67 m, while the normal water level is -0.5 m. The implication of the results of this research is to provide input to local governments when discussing flood management meetings in the city of Semarang. Keywords: babon, longstorage, hec-ras

Wildlife mortality risk posed by high and low traffic roads.

Wildlife mortality due to collisions with vehicles (roadkill) is one of the predominant negative effects exerted by roads on many wildlife species. Reducing roadkill is therefore a major component of wildlife conservation. Roadkill is affected by various factors, including road attributes and traffic volume. It is theorized that the effect of traffic volume on roadkill probability should be unimodal. However, empirical evidence for this theory is lacking. Using a large-scale roadkill database of 18 wildlife species in Israel, encompassing 2846km of roads over 10years, we explored the effects of traffic volume and road attributes (e.g., road lighting, verge vegetation) on roadkill probability with a multivariate generalized linear mixed model. A unimodal effect of traffic volume was identified for the striped hyena (Hyaena hyaena), whereas 5 species demonstrated a novel quadratic U-shaped effect (e.g., golden jackal [Canis aureus]). Four species showed a negative linear effect (e.g., wild boar [Sus scrofa]). We also identified varying effects of road attributes on roadkill. For instance, road lighting and roadside trees decreased roadkill for several species, whereas bus stops and concrete guardrails led to increased roadkill. The theorized unimodal effect of traffic volume may only apply to large, agile species, and the U-shaped effect could be related to intraspecies variability in traffic avoidance behavior. In general, we found that both high-traffic and low-traffic roads can pose a high mortality risk for wildlife. It is therefore important to monitor roadkill on low-traffic roads and adapt road attributes to mitigate roadkill. Road design for effective roadkill mitigation includes reducing the use of concrete guardrails and median barriers where possible and avoiding dense bushes in verge landscaping. These measures are complemented by employing wildlife detection systems, driver warnings, and seasonal speed reduction measures on low-traffic roads identified as roadkill hotspots.

I’ll take the high road: Paths to goal pursuit and identity-based interpretations of difficulty

ABSTRACT When imagining their futures, people can prioritize getting there the easy way, prefer more demanding paths, or be indifferent to means and focus only on making progress. Identity-based motivation theory predicts and mixed effect regressions reveal that what people infer about themselves when facing unchosen life difficulties and when thinking about or working on goals feels hard shapes action (N = 537 undergraduates, three studies). To varying degrees, they can infer that unchosen life difficulties build character (difficulty-as-improvement), that chosen goals are really not for them (difficulty-as-impossibility), and that chosen goals are valuable for them (difficulty-as-importance). The more people endorse difficulty-as-impossibility, the more they choose ease. The more they endorse difficulty-as-improvement the more they disdain ease and prefer the effortful way.

Vehicle routing problem with time windows and carbon emissions: a case study in logistics distribution.

Logistics and transportation industry is not only a major energy consumer, but also a major carbon emitter. Developing green logistics is the only way for the sustainable development of the logistics industry. One of the main factors of environmental pollution is caused by carbon emissions in the process of vehicle transportation, and carbon emissions of vehicle transportation are closely related to routing, road conditions, vehicle speed, and speed fluctuations. The low-carbon vehicle routing problem with high granularity time-dependent speeds, speed fluctuations, road conditions, and time windows is proposed and formally described. In order to finely evaluate the effects of vehicle speed and speed fluctuations on carbon emissions, a graph convolutional network (GCN) is used to predict the high granularity time-dependent traffic speeds. To solve this complicated low-carbon vehicle routing problem, a hybrid genetic algorithm with adaptive variable neighborhood search is proposed to obtain vehicle routing with low carbon emissions. Finally, this method is validated using a case study with the logistics and traffic data in Jingzhou, China, and also the results show the effectiveness of this proposed method.