Search Results (177)

Conventional web analytics treats the human user as its fundamental unit of analysis, assuming stable preferences, identifiable intentions, and behavioral patterns that unfold over time. That assumption is under strain. Crawlers and traditional bots already account for a substantial fraction of online interactions, and autonomous AI agents are emerging as a further class of actors layered on top of this automated traffic. Unlike either, these agents do not possess persistent identities or psychologically grounded motivations. They are task-specific, dynamically instantiated processes whose behaviors are contingent and often orchestrated by external systems. Their presence weakens the interpretive value of core metrics, including sessions, engagement, conversion, and retention. A click may reflect an optimization routine, a proxy objective, or a recursive agent-to-agent exchange rather than meaningful human intent, and traditional inference frameworks cannot reliably distinguish among these possibilities. This is a position paper. It synthesizes literature across bot and agent detection, agent architecture, web measurement validity, governance of automated systems in adjacent sectors, and the epistemology of digital trace data, and it argues that web analytics should supplement, and in places replace, its human-centered model with an agent-aware model focused on interaction dynamics within hybrid ecosystems of human and non-human actors. The paper develops a working taxonomy of crawlers, traditional bots, AI agents, LLM-powered agents, and autonomous agents; identifies three properties of LLM agents (identity discontinuity by design, task-based instantiation, agent-to-agent loops) that distinguish the present challenge from prior bot-detection problems; examines opaque agent objectives, synthetic traffic loops, and the indistinguishability between human-originated and agent-mediated signals; and proposes five candidate measurement primitives (task chain, actor class, interaction provenance, objective alignment, signal authenticity) with explicit operational definitions. Governance machinery from energy systems and critical infrastructure offers a partial template, and we delimit which dimensions transfer and which do not. The contribution is conceptual and programmatic, presenting a vocabulary, set of candidate primitives, and research agenda for a field whose foundational unit of analysis is becoming unreliable. Full article

In the Internet of Vehicles, vehicular crowdsensing is crucial for alleviating traffic congestion and ensuring the safety of autonomous driving. However, practical vehicular crowdsensing processes face dual challenges of skewed spatial distributions of vehicles and inadequate data quality guidance. These issues cause sensing redundancy in high-participation areas (HPAs) and coverage deficits in low-participation areas (LPAs), while also leading to unstable data quality. Given that participants’ decisions are profoundly influenced by bounded rationality and psychological preferences, this paper proposes a collaborative incentive mechanism integrating behavioral economics and psychology (BEP-IM) to drive sustained spatial coverage and proactive sensing shaping. First, to mitigate coverage deficits in LPA, a reference-dependent two-sided selection and bidding strategy (RD-TSB) is designed to guide participants toward LPA via a reference-driven utility evaluation. Concurrently, a loss-aversion-based sustained incentive strategy (LA-RPI) is introduced to enhance their sustained participation within LPAs by amplifying loss perception. Furthermore, to overcome weak data quality constraints, an operant conditioning-based proactive sensing shaping strategy (OC-SFQ) is constructed, utilizing a closed-loop mechanism of relative improvement, variable-ratio reinforcement, and association updating to drive participants to output high-quality data. Simulation results demonstrate that the proposed mechanism effectively increases participation frequency in LPAs and optimizes sensing data quality. Full article

Global transition toward sustainable micro-mobility is an essential aspect of Saudi Vision 2030; however, high car dependency remains a significant barrier to public health and safety targets. In this context, this study explores the factors determining the adoption of electric bicycles (e-bikes) in Al-Qunfudhah, Saudi Arabia. The present research used a convenience sampling strategy through an online survey conducted via social media and texting, utilizing a designed questionnaire of 10 sections delivered to 171 participants, alongside a 5-point Likert scale. Additionally, the scientific validation and analysis were conducted utilizing internal consistency, validity and scale reliability via statistical analysis. The findings indicated a significant intention–action disparity; while respondents demonstrate a strong psychological intention to adopt e-bikes within 12 months (an average of 3.51), real household ownership was relatively low at 11.1%. In addition, a significant 71.9% of participants use private vehicles for short-distance travel (<5 km), influenced by an average bus stop distance of 21.22 km. The hierarchy of barriers indicates infrastructure and security as the main barrier, particularly the absence of dedicated bike lanes, and concerns regarding traffic safety. In contrast, a perception of physical fitness, and interpersonal interaction behave as significant facilitators. Public health data reveals an average weekly activity of 109.77 min, significantly lower than worldwide recommendations; however, 66.7% of individuals believe e-bikes may address the difference. The statistical evaluation acknowledged the questionnaire’s robustness, with significant Pearson correlation coefficients (p < 0.01) demonstrating internal consistency validity and Cronbach’s alpha values between 0.71 and 0.88 indicating high scale reliability, demonstrating a scientifically stable framework for assessing the measured behavioral determinants. The research recommends the establishment of shaded, dedicated micro-mobility networks and the enforcement of safety regulations to promote a healthy, multi-modal urban ecosystem. Full article

The large-scale deployment of autonomous vehicles (AVs) in mixed-traffic environments raises an important question: how do human drivers evaluate safety when interacting with AVs under real-world uncertainty? This study aims to examine how drivers’ objective knowledge of AVs shapes their perceived safety when sharing the road with AVs in mixed-traffic environments. Using survey data from 905 licensed drivers in Wuhan, China, this study treats perceived road-sharing safety as an interaction-level evaluative outcome rather than merely a precursor of adoption intention. Latent class analysis was first used to identify knowledge-based driver segments, structural equation modeling was then applied to estimate Theory of Planned Behavior (TPB)-related psychological constructs, and ordered logit regression was finally employed to examine the determinants of perceived safety across segments. The results indicate that behavioral intention consistently shows a positive association with perceived safety; however, attitude toward AVs exhibits a significant negative association among high-knowledge drivers. This attitudinal reversal challenges the implicit homogeneity assumption embedded in conventional TPB applications and suggests that cognitive familiarity may recalibrate, rather than amplify, technological optimism. Overall, the findings show that knowledge-based heterogeneity changes the psychological mechanisms underlying safety appraisal in mixed traffic. These insights carry important implications for differentiated communication strategies and trust calibration in transitional automated mobility systems. Full article

Urban air pollution is increasingly recognised as a determinant of mental wellbeing, yet most existing studies rely on static exposure estimates and lack spatial granularity. This limits understanding of how pollutant-specific patterns influence psychological states in real-world settings. To address this gap, we integrate real-time environmental and physiological data from 40 participants using the DigitalExposome dataset, applying multivariate and spatial analysis techniques. Our findings confirm that Particulate Matter (PM_2.5) exerts the strongest negative association with mental wellbeing while extending prior work by establishing a preliminary ranking of other pollutants Particulate Matter (PM₁₀), Particulate Matter (PM₁), Carbon Monoxide (CO), Nitrogen Dioxide (NO₂), Ammonia (NH₃). We applied statistical and spatial analysis methods, including heatmaps and Voronoi diagrams, to explore links between pollutants and wellbeing and compare the relative influence of air pollution and noise. This enabled identification of pollutant-specific hotspots and multi-level wellbeing patterns across individual, accumulated, and collective scales. These results demonstrate the value of spatial analysis for environmental health research and support targeted urban interventions, such as green space placement and traffic re-routing, to mitigate mental wellbeing risks. Full article

Urban living streets are core venues for promoting public health; however, existing studies often lack a multidimensional quantitative evaluation system that integrates physical, psychological, and social health dimensions. To address this gap, this study constructs a space quality evaluation model comprising 15 indicators across three health dimensions, integrating multi-source data (including Street View Imagery, POI data, and field measurements). Taking six typical living streets in the central urban area of Changsha as a case study, we applied the Analytic Hierarchy Process to determine indicator weights and evaluate space quality. The results reveal significant spatial heterogeneity: (1) The comprehensive quality scores vary markedly, with Cai’e South Road ranking highest (66.62) and Zengjiawan Lane lowest (28.37); (2) key factor analysis indicates that seven indicators—including Street Width, Motorization Level, and POI Functional Diversity—are significantly associated with space quality, among which Sidewalk Width and Relative Sidewalk Width are identified as critical determinants; (3) addressing identified deficits in slow-traffic spaces and service amenities, this study proposes health-oriented micro-renewal strategies. This study provides a transferable analytical framework and practical decision support for the assessment and improvement of urban living street space quality. Full article

This paper addresses the optimization of electric vehicle (EV) charging facility configuration on highways by proposing a collaborative planning method that integrates driver anxiety psychology, mixed traffic flow dynamics, and service area queuing characteristics. By abstracting the road travel and service area replenishment processes into an integrated queuing network, a system analysis framework is constructed to characterize the coupling relationship of “facility supply, traffic assignment, and state feedback.” On this basis, a bi-level optimization model is established with the objective of minimizing the generalized total social cost. The upper level makes decisions on the coordinated quantities of fixed charging piles and mobile charging vehicles, while the lower level describes the stochastic user equilibrium behavior of drivers under the influence of real-time congestion and anxiety. To tackle the high-dimensional nonlinear nature of the model, an efficient solution algorithm based on simultaneous perturbation stochastic approximation (SPSA) is designed. A case study of the Nei-Yi Expressway demonstrates that compared with the traditional peak demand proportional allocation method, the proposed approach can better balance construction costs, operation and dispatching costs, and user travel experience under limited investment, significantly reducing waiting times and psychological anxiety costs. It provides theoretical methods and decision support for planning a resilient energy replenishment network that achieves “fixed facilities ensuring base load and mobile resources responding to peak demands.” Full article

According to in-depth research on the perception ability of dangerous omens of excellent drivers, references can be provided for the development of brain-like intelligence and its transplantation, as well as applications in the field of autonomous driving, which will improve the active safety and intelligence level of vehicles. Previous studies have shown that there is indeed a dangerous omen before an accident occurs. However, current studies are still unclear about the bio-psychophysiological characteristics exhibited by drivers with high levels of sensory agility when they anticipate potential warning signs, and there is no method for screening such drivers who can perceive dangerous omens proposed by any research. To address the above issues, this paper conducts in-depth research. Firstly, through designing dangerous scenarios and conducting hazard perception tests, we collect physiological, psychological, and physical data, such as drivers’ bioelectrical signals (electroencephalogram and electrocardiogram) and eye movements. Secondly, through playing back experimental videos, actively questioning drivers, and analyzing local changes in their electroencephalogram data, the driver’s ability to identify a dangerous omen and the moment of perception are determined. Thirdly, based on techniques such as the Kolmogorov–Smirnov test and the Mann–Whitney U test, the differences in bioelectrical and eye movement characteristics between drivers who can perceive a dangerous omen and others can be further revealed. Finally, the driver’s bioelectrical and eye movement characteristics are used as latent variables, and their corresponding data are utilized as observation indicators. We construct a structural equation model for screening drivers capable of perceiving a dangerous omen and conduct calibration and validation. This study provides inspirational ideas for empowering vehicles to identify potential hazards, advancing end-to-end and other higher-level autonomous driving technologies, and further enhancing road traffic safety. Full article

Expressway traffic noise poses a critical threat to public health in developed high-density cities, causing chronic environmental stress in adjacent residential areas. While physical noise barriers are commonly used, the potential of audiovisual interactions in mitigating the adverse effects of traffic noise remains under-explored. Using immersive virtual reality (VR), this study examined the efficacy of visual greenery and auditory masking (birdsong) in promoting stress recovery, and tested whether audiovisual perception mediates the environment–restoration link. Following an acute stressor, 100 participants were randomly assigned to a 2 × 2 between-subjects experiment manipulating Green View Index (high vs. low) and soundscape composition (traffic noise vs. traffic noise plus birdsong), with 25 participants in each group. Restorative outcomes were assessed using self-reported measures and continuous physiological monitoring (heart rate variability [HRV] and electrodermal activity [EDA]). Results demonstrated that high-intensity visual greenery and natural sounds effectively enhance psychological restoration in noise-affected environments. Structural equation modeling revealed that audiovisual perception fully mediated the relationship between environmental features and restorative outcomes. The physiological outcome showed a distinct tiered restoration pattern, indicating that immediate psychological buffering can be achieved through natural sounds, while consistent visual reinforcement remained essential for deep physiological recovery. Consequently, soundscape planning in expressway-adjacent zones should integrate visual greening strategies to optimize the perceptual masking of traffic noise and enhance the environmental quality. Full article

Traffic police officers represent a critical occupational group with high vulnerability to vehicular air pollution, a severe environmental health threat in rapidly urbanizing metropolises such as Addis Ababa. This cross-sectional study explored occupational exposure, protective practices, health risks, perceptions, and awareness of air-quality-associated health risks among 120 traffic police officers in Addis Ababa. The officers were mostly male (80%) and married (93.3%), with the majority (62.6%) having served for more than ten years. While vehicle emissions were consistently recognized as the main source of air pollution, critical knowledge gaps were identified, i.e., only 24.2% had received pollution-related training, fewer than half (45.8%) were aware of government policies, and just 9.2% reported collaboration with environmental authorities. Awareness of the Air Quality Index (AQI) was generally low, and regular monitoring of AQI was limited. Self-reported health symptoms were highly prevalent among participants, with cough (75.0%), eye irritation (61.7%), sneezing (58.3%), and runny nose (55.8%) being the most frequently reported. Notably, sneezing, runny nose, eye irritation, and psychological stress showed significant association with perceived pollution levels at the workplace (p < 0.05), while blood pressure, cough, difficulty concentrating, and sleep loss were not significantly associated (p > 0.05). A higher prevalence of symptoms was generally observed in groups experiencing moderate-to-very high levels of pollution. Protective measures were applied inconsistently; while 63.3% of participants reported using masks, their beliefs about the effectiveness of using masks varied. Relocation (60%) and use of face covers/glasses (13.3%) were less commonly practiced. Overall, traffic police officers are exposed to occupational air pollution, which is associated with various health symptoms. These findings highlight the need for enhanced training, clearer communication of policies, stronger institutional engagement, the provision of standardized protective masks, and the promotion of AQI utilization to reduce occupational health risks and safeguard the wellbeing of traffic police officers in Addis Ababa. Full article

This document deals with the implementation of virtual tracks as an innovative element for autonomous vehicle navigation. A virtual track improves the driving accuracy, safety, and efficiency of autonomous vehicle operation in various environments. The methodology provides a theoretical framework; analyzes legislative (Czech and EU legal framework) and technical aspects, as well as traffic psychological aspects; defines infrastructure requirements; and describes implementation procedures. It also assesses the impact of technology on the existing transport infrastructure. The outputs of the methodology serve autonomous vehicle operators, municipalities, and legislative authorities as a key tool for planning and implementing autonomous systems. The document contributes to the development of intelligent mobility and the future integration of autonomous vehicles into mainstream traffic. Full article

A growing body of evidence highlights the beneficial role of physical activity in supporting cognitive functions and learning outcomes. Yet, recent studies indicate that these effects may be shaped by environmental conditions, conceptualized within the framework of the urban exposome. The present study explores the interaction between physical activity, cognitive enhancement, and environmental exposures such as air pollution, noise, sensory overstimulation, and access to green spaces. A multi-method experimental design was implemented with 60 participants randomly assigned to either an experimental or a control group. The experimental group engaged in moderate-intensity physical activity across diverse urban settings, including green parks, high-traffic streets, and indoor facilities, while the control group performed the same activity in a stable indoor environment without environmental variability. Cognitive performance was assessed before and after physical activity through standardized measures of attention, memory, and executive function. Psychological and physiological stress responses were also monitored using the Perceived Stress Scale (PSS) and heart rate variability (HRV). Results suggest that the cognitive benefits of physical activity are not exclusively attributable to internal physiological mechanisms but are significantly moderated by environmental exposures. These findings underscore the relevance of considering contextual factors when examining the links between physical activity, cognition, and academic performance. Full article

Subways play an irreplaceable role in alleviating urban traffic congestion and showcasing a city’s historical and cultural heritage. Their speed and environmental benefits make them a vital component of sustainable urban development. Historical and cultural expression has become a focal point of subway spatial design and a core component of station planning. Building on this, the present study develops an evaluation system for metro station space that integrates history and culture and is grounded in the theory of genius loci (spirit of place). The Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE) are used to derive indicator weights and conduct quantitative assessment. AHP results indicate that visual design, auditory elements, and cultural identity are the core priorities within the Xi’an metro station evaluation system. Design strategies integrate visual elements with historical and cultural contexts to create multisensory experiences encompassing form, color, sound, and touch. FCE further analyzes the indicators and shows that the overall design quality of the sampled Xi’an metro stations is generally high: auditory and visual elements are dominant, spiritual (psychological) experience and cultural identity approach excellence, and tactile elements show somewhat weaker performance. These findings suggest that metro space design requires deeper consideration across multiple dimensions. The proposed methodology can be applied to the design and evaluation of metro stations, providing practical guidance for culturally integrated metro spaces. Full article

Accurate traffic modeling is essential for effective urban mobility planning within Smart Cities. Conventional capacity assessment methods assume rule-based driver behavior and therefore neglect psychological priority, an informal interaction in which drivers negotiate right-of-way contrary to traffic regulations. This study investigates how the absence of this behavioral factor affects the accuracy of delay and capacity evaluation at unsignalized intersections. A 12 h field observation was conducted at an intersection in Prešov, Slovakia, and 28 driver interactions were analyzed using linear regression modeling. The derived model (R² = 0.83, p < 0.05) demonstrates that incorporating psychological priority significantly improves the agreement between calculated and observed waiting times. Unrealistic results occurring under oversaturated conditions in standard methodologies were eliminated. The findings confirm that behavioral variability has a measurable impact on traffic performance and should be reflected in analytical and simulation models. Integrating these behavioral parameters into Smart City traffic modeling contributes to more realistic and human-centered decision-making in intersection design and capacity management, supporting the development of safer and more efficient urban mobility systems. Full article

Current research and development in understanding road users’ driving behaviors play a key role in improving traffic safety. Recently, several driving simulators have been employed as a suitable approach to investigate several drivers’ responses in challenging traffic scenarios. Although professional drivers represent a particular category among driving populations, the body of literature about their comparative behavioral and psychological characteristics remains limited. This study examined the differences in driving performance and visual and physiological responses between civilian and professional drivers in a simulated environment. A total of 30 drivers, with an equal split between professional and civilian categories, took part in a series of driving simulations. The simulations incorporated various infrastructure types, including four cone avoidance tasks and a high-speed motorway task. This study collected comprehensive data on performance metrics, hand usage, heart rate, and eye movements. Eye-tracking technology was used to measure visual attention. The findings revealed that during cone avoidance scenarios, civilian drivers exhibited a similar performance, visual behavior, and physiological response, except for the speed, experiment duration, and throttle, to professional drivers. In the motorway scenario, all metrics showed no significant difference between the two driver groups. These results highlight the need for cautious interpretation, particularly given the limitations of the sample. Revalidation is needed in larger studies, especially for understanding the differences between drivers’ metrics, which is crucial to elevate drivers’ safety, and assessing training programs in Hungary. Full article