Search Results (127)

The increasing density and complexity of airport surface operations have intensified the risk of crossing conflicts between ground service vehicles and taxiing aircraft. Such interactions are characterized by strong spatiotemporal coupling, asymmetric right-of-way relationships, and stringent safety requirements, making conventional human-driven conflict avoidance highly dependent on drivers’ perception and judgment. To address this problem, this study proposes a spatiotemporal prediction-driven model predictive control (MPC) framework for autonomous ground vehicles on airport surfaces. First, the spatial interaction between the aircraft safety boundary and the vehicle service road is modeled to define the vehicle–aircraft conflict zone. Aircraft motion information is then used to predict the temporal occupancy of the conflict zone, based on which a dynamic time-window constraint is constructed to characterize the time-varying safe passage conditions for autonomous vehicles. The predicted spatiotemporal constraints are embedded into a rolling MPC framework that continuously optimizes vehicle motion while jointly considering safety, traffic efficiency, and energy consumption. Simulation results show that, compared with human-driven vehicles, the proposed method reduces average energy consumption from 1200.16 kJ to 866.67 kJ and shortens average arrival time from 43.63 s to 41.27 s. In addition, the method demonstrates effective disturbance compensation under aircraft-state uncertainty and adaptability to sequential multi-aircraft crossing scenarios. Full article

Taxi drivers are a group with high driving exposure and are involved in a significant number of urban traffic casualties. Using two modelling approaches, this study examines whether the intention to speed, as measured by the Theory of Planned Behaviour (TPB), can better fit a crash frequency model than errors or lapses as measured by the Driving Behaviour Questionnaire (DBQ). Data from 1000 drivers in Tehran was collected through questionnaires. The crash prediction model included a cross-sectional model using negative binomial (NB) regression methods and a tree regression model from a previous study. In the last three years, the drivers had been involved in 544 road crashes, and of those, 42 resulted in serious injuries. Due to the rare and random nature of crashes, the empirical Bayesian (EB) method was used for model testing. Comparing AIC and BIC showed that zero-inflated NB (ZINB) models performed better. The final selected model was the intention-based ZINB model without the age variable. The coefficients for intention, exposure, and driver experience were 0.205, 0.103, and −0.443, respectively. The high EB coefficients indicated strong reliance on predicted crash values. The conclusion is that road crashes are closely related to taxi drivers’ intention to speed rather than errors and lapses. This indicates that it can be described as a traffic violation, rather than a mistake. Therefore, significant efforts are required to increase compliance with speed limits and reduce road crashes. Further education and high-quality campaigns are essential elements to achieve this goal. Full article

Brominated flame retardants (BFRs) are widely used in automotive polymers and electronic components, yet vehicles remain an under-characterized and potentially high-exposure microenvironment, particularly in hot climates. This study provides the first comprehensive assessment of BFR occurrence, sources, and exposure risks in vehicle dust from Saudi Arabia, addressing a critical regional data gap. This study systematically investigates the occurrence, compositional patterns, sources, and human exposure risks of polybrominated diphenyl ethers (PBDEs) and selected alternative BFRs in dust from 80 vehicles (domestic cars and taxis; model years 2015–2022) operating in Jeddah, Saudi Arabia. Dust samples were collected using a standardized vacuuming protocol, extracted and cleaned using solvent extraction and silica SPE, and analyzed via GC–NCI–MS. Both legacy PBDE congeners and emerging alternatives (including DBDPE and TBB) were consistently detected, with BDE-209 dominating the overall BFR burden with mean concentrations of 6560 ng/g in domestic vehicles and 5454 ng/g in taxis, with maximum values reaching 220,860 ng/g. Lower-brominated PBDEs occurred at substantially lower concentrations, reflecting the ongoing global transition away from Penta- and Octa-BDE formulations. Taxis exhibited generally higher concentrations than domestic vehicles, likely due to prolonged occupancy, increased usage intensity, and enhanced dust resuspension dynamics. Multivariate analysis (PCA and correlation) revealed two distinct source categories: (i) legacy Penta-BDE-related congeners associated with polyurethane foam and textile materials and (ii) high-brominated PBDEs and DBDPE linked to hard plastics and electronic components. Human exposure assessment demonstrated that dust ingestion is the dominant exposure pathway, while dermal and inhalation routes contribute minimally. Non-carcinogenic hazard indices (HI) were well below unity for all compounds (HI < 1.67 × 10⁻⁶), and incremental lifetime cancer risks (ILCR) for BDE-209 remained within or near accepted risk thresholds (7.52 × 10⁻⁶–1.04 × 10⁻⁵), although occupational exposure among taxi drivers was consistently higher. Overall, the results demonstrate that modern vehicle cabins act as significant microenvironments for chronic BFR exposure, particularly under high-temperature conditions. Despite generally low estimated risks, the combined effects of chemical persistence, bioaccumulation potential, and mixture toxicity—amplified by extreme in-cabin temperatures—highlight vehicles as overlooked yet significant exposure environments. These findings provide the first comprehensive dataset for the Arabian Peninsula and emphasize the need for climate-sensitive exposure assessment, safer material design, and targeted mitigation strategies in vehicle interiors. Full article

Prior studies have typically analyzed the injury severity of bus or taxi passengers at the crash level or single-vehicle level, neglecting vehicle-level interdependence between them. To address the gap, this research sets out to analyze the factors contributing to the vehicle-level injury severities of transit bus–taxi crashes, with consideration of their interdependence and heterogeneities. The random-parameters bivariate probit model, which can capture both unobserved heterogeneity and within-crash correlation between bus and taxi injury outcomes, was advocated for the joint analysis. In the model, the factors related to the two vehicles and their drivers, together with other factors (e.g., roadway, environment, and crash configuration), were used as the explanatory variables. A total of 3404 two-vehicle bus–taxi crash records in Hong Kong, China, from 2009 to 2019 were used for model estimation. The results indicate that taxi driver age, taxi age, crash location, and collision manner resulted in heterogeneous effects on bus injury severity, and the time of day yielded a heterogeneous effect on taxi injury severity. In addition, bus driver error and street light resulted in fixed yet moderate (less than 6%) effects on bus injury severity, while taxi driver gender, speed limit, rainfall, and collision manner resulted in fixed effects on taxi injury severity, where female drivers and front collisions significantly increased the likelihood of fatality and severe injury with their marginal effects more than 20%. Based on the findings, tailored strategies pertaining to safety education, law enforcement, vehicle safety devices, and traffic management and control were proposed to mitigate crash outcomes involving public buses and taxis. Full article

Decarbonizing the transport sector is crucial for achieving global carbon peaking and carbon neutrality goals. Electric taxis (e-taxis), which play a vital role in urban public transportation, are central to this transition. However, their operational performance deteriorates significantly under extremely cold conditions. Existing planning models for charging infrastructure often overlook the impact of low temperatures, creating a critical research gap. To address this issue, we propose a novel planning framework using Urumqi, China (43.8° N, 87.6° E) as a case study. Urumqi is a major cold-region metropolis, where January temperatures regularly drop below $-20$ °C. Our methodology includes two key steps: integrating 412 driver questionnaires and 1.2 million high-resolution GPS trajectories to extract temperature-sensitive charging demand profiles; and incorporating these profiles into an integer linear programming (ILP) model to minimize lifecycle costs, considering climatic constraints, taxi operation patterns, and grid limitations. A key innovation is a temperature-correction coefficient, which dynamically adjusts vehicle energy consumption and driving range based on ambient temperature. Results show superiority over conventional (temperature-ignoring) and random plans: 14-fold lower annualized cost, 23-fold shorter average queuing time, 96.2% high-frequency demand coverage (+16.6%), and 78% charging station utilization (+50.0%). It achieves 29.8–32.3% cost savings at $-5$ °C (over 25.9% even at $-35$ °C) and scales stably for 5–50% e-taxi penetration, offering a transferable framework for cold-region e-taxi charging optimization. Full article

Urban air mobility solutions such as drone taxi services are increasingly viewed as a promising response to congestion, sustainability, and smart-city mobility challenges. However, the large-scale adoption of such services depends on users’ perceptions of service experience, trust, and readiness to engage with emerging technologies. This study investigates the determinants of sustainable adoption of drone taxi services in the United Arab Emirates (UAE) by examining technology readiness and service experience factors, interpreted through conceptual alignment with the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). A structured questionnaire was administered to potential users, capturing perceptions related to optimism, innovation readiness, efficiency, control, privacy, insecurity, discomfort, inefficiency, and perceived operational risk, along with behavioral intention to adopt drone taxi services. Measurement reliability and validity were rigorously assessed using Cronbach’s alpha, composite reliability, average variance extracted (AVE), and the heterotrait–monotrait (HTMT) criterion. The validated latent construct scores were subsequently used to estimate a structural regression model examining the relative influence of each factor on adoption intention. The results indicate that privacy assurance and perceived control exert the strongest influence on behavioral intention, followed by optimism and innovation readiness, while negative readiness factors such as discomfort, insecurity, inefficiency, and perceived chaos demonstrate negligible effects. These findings suggest that in technologically progressive contexts such as the UAE, adoption intentions are primarily shaped by trust-building and empowerment-oriented perceptions rather than deterrence-based concerns. By positioning technology readiness and service experience constructs within established TAM and UTAUT theoretical perspectives, this study contributes a context-sensitive understanding of adoption drivers for emerging urban air mobility services. The findings offer practical insights for policy makers and service providers seeking to design user-centric, trustworthy, and sustainable drone taxi systems. Full article

Rural transport remains a critical factor of social inclusion in South Africa, particularly for people with disabilities who rely on public transport. This study explores the experiences of minibus taxi drivers in transporting passengers with disabilities in Mt Elias, a rural community in the KwaZulu-Natal province. A qualitative research design was adopted, involving semi-structured interviews with 15 drivers operating between Dalton and Mt Elias route. Thematic analysis was conducted using ATLAS.ti to identify key patterns and relationships across the dataset. The four key themes that emerged from the dataset are: infrastructure and environmental challenges, accessibility and support for passengers, operational and economic constraints, and human interactions and attitudes. Findings reveal that drivers face multiple barriers, including poor road conditions, limited vehicle space, and a lack of formal training, yet many demonstrate empathy and commitment to assisting passengers with disabilities. The study highlights the need for targeted policy interventions to improve road infrastructure, provide disability awareness training for drivers, and redesign vehicles for accessibility. Promoting inclusive rural transport requires coordinated action among government spheres, taxi associations, and disability advocacy groups. This research contributes new insights into the lived realities of rural drivers and promotes the importance of inclusive mobility as a component of social justice. Full article

We investigated the effects of Continuous Positive Airway Pressure (CPAP) on blood pressure (BP) and vigilance in taxi drivers with obstructive sleep apnea (OSA). This pilot study recruited taxi drivers aged ≥60 years to undergo polysomnography. Those diagnosed with OSA underwent 6 months of CPAP therapy. Baseline and follow-up assessments included 24 h ambulatory blood pressure monitoring (ABPM) and the psychomotor vigilance test (PVT). Among the 32 participants, 22 (68.8%) were diagnosed with OSA (median age 63.0 [62.0–65.0] years; 21 males). The average CPAP adherence was 3.1 ± 2.3 h per night, with 23.5% using CPAP for more than 4 h per night. There were no significant changes in 24 h mean systolic ABPM (125.9 [116.8–134.9] mmHg to 126.0 [118.3–133.7] mmHg; p = 0.93) or reaction times measured by PVT (2.0 [0.0–3.0] lapses to 2.0 [1.0–3.0] lapses; p = 0.82) after CPAP therapy. A high prevalence of OSA was observed among taxi drivers. CPAP adherence was suboptimal and did not result in significant improvements in BP or vigilance. Full article

Currently, the short-term accurate prediction of multi-region taxi pick-up demand often adopts methods that integrate graph neural networks with temporal modeling. However, most models focus solely on node features during the learning process, neglecting or simplifying edge features. This study adopts a hybrid prediction framework, Edge-GATv2-LSTM, which integrates an edge-aware attention-based graph neural network (Edge-GATv2) with a temporal modeling component (LSTM). The framework not only models spatial interactions among regions via GATv2 and temporal evolution via LSTM but also incorporates edge features into the attention computation structure, jointly representing them with node features. This enables the model to perceive both node attributes and the strength of inter-regional relationships during attention weight calculation. Experiments are conducted based on real-world taxi order data from Ningbo City, and the results demonstrate that the adopted Edge-GATv2-LSTM model exhibits favorable performance in terms of pick-up demand prediction accuracy. Specifically, the model achieves the lowest RMSE and MAE of 3.85 and 2.86, respectively, outperforming all baseline methods and confirming its effectiveness in capturing spatiotemporal demand patterns. This research can provide decision-making support for taxi drivers, platform operators, and traffic management departments—for example, by offering a reference basis for optimizing taxi pick-up route planning when vehicles are unoccupied. Full article

Motorbike taxis are widely used in Yaoundé and Douala, despite their association with heightened accident risks and relatively high fares. This research combines qualitative methods, including 38 semi-structured interviews and direct field observations, with a quantitative survey of 280 motorbike taxi passengers (customers). It employs a dynamic risk approach to analyse both the factors motivating individuals to choose motorbike taxis and the strategies adopted by drivers and passengers to mitigate and prevent accidents. The findings reveal that speed, cost-effectiveness, and the limited accessibility of certain neighbourhoods to other transport options are key factors driving regular motorbike taxi use. Moreover, strategies for managing accident risks include regulating passenger positions based on gender, perceived age, or physical stature; invoking deities for protection; and passengers’ verbal interactions with drivers to ensure safer behaviour. This research also explores how overloading, a collectively tolerated deviance, is managed to avoid or minimize the impact of accidents. By addressing both risk acceptance and prevention strategies, this study provides new insights into passengers’ social perceptions, which are often overlooked in motorbike taxi research. It expands the understanding of motorbike taxi use in urban Global South transport contexts, particularly in terms of users’ risk management behaviours. Full article

With the rapid development of intelligent transportation systems, voice assistants are increasingly integrated into driving environments, providing an effective means to mitigate the risks of fatigued driving. This study explored drivers’ interaction preferences with voice assistants under different fatigue states and proposed a fatigue-state-based dialogue-awakening mechanism. Using Grounded Theory and the Stimulus–Organism–Response (SOR) framework, in-depth interviews were conducted with 25 drivers from diverse occupational backgrounds. To validate the qualitative findings, a driving simulation experiment was carried out to examine the effects of different voice interaction styles on driver fatigue arousal across various fatigue levels. Results indicated that heavily fatigued drivers preferred highly stimulating and interactive voice communication; mildly fatigued drivers tended toward gentle and socially supportive dialogue; while drivers in a non-fatigued state preferred minimal voice interference, activating voice assistance only when necessary. Significant occupational differences were also observed: long-haul truck drivers emphasized practicality and safety in voice assistants, taxi drivers favored voice interactions combining navigation and social content, and private car owners preferred personalized and emotional support. This study enriches the theoretical understanding of fatigue-sensitive voice interactions and provides practical guidance for the adaptive design of intelligent voice assistants, promoting their application in driving safety. Full article

Progressive advancements in the global economy and technology have propelled human civilization forward; however, they have also inflicted significant harm on the global ecological environment. In the present era, electric vehicle (EV) technology is playing a vital role due to its environmentally friendly technological advances. However, widespread adoption of EVs has been hindered by their limited travel range, inadequate charging infrastructure, and high costs. This can be closely observed when we assess the adoption of electric vehicles (EVs) among motorcycle taxi drivers, commonly called ‘pilots,’ in Goa, India. Motorcycle taxis are crucial in Goa’s transportation network, providing affordable, efficient, and door-to-door services, especially in regions with limited public transport options. However, the rising costs of petrol and vehicle maintenance have adversely affected the income of these pilots, prompting concerns about their willingness to adopt EVs. This study aims to analyze the factors prompting the behavioral intention to adopt EVs by motorcycle taxi pilots in Goa, India, focusing on six key determinants: charging infrastructure, effort expectancy, performance expectancy, price value, social influence, and satisfaction with incentive policies. A quantitative approach was employed, utilizing stratified proportionate random sampling techniques to collect data from 242 motorcycle taxi pilots registered with the Goa State Government Transport Department. It was analyzed using partial least squares-structural equation modeling (PLS-SEM) through Smart-PLS 4.0 software. The research highlights that performance expectancy and price value are the potential motivators for the adoption of electric vehicles. These findings suggest that pilots are more likely to embrace EVs when they perceive tangible benefits in performance and find the cost reasonable in relation to the value offered. The results offer actionable insights for policymakers, manufacturers, and other stakeholders. These insights can guide strategic decisions and policy frameworks aimed at fostering a sustainable and user-centric transportation ecosystem. Full article

High battery swapping demand from electric taxis and drivers’ subjective station selection often leads to congestion and the uneven utilization of battery swapping stations (BSSs). Efficient vehicle guidance is essential for improving the operational performance of electric taxis. In this study, we have developed a vehicle-to-station guidance model that considers dynamic demand and diverse driver response-time preferences. We have proposed two decision-making strategies for BSS recommendations. The first is a real-time optimization method that uses a greedy algorithm to provide immediate guidance. The second is a delayed optimization framework that performs batch scheduling under high demand. It integrates a genetic algorithm with KD-tree search to handle dynamic demand insertion. A case study based on Beijing’s Fourth Ring Road network was conducted to evaluate the strategies under four driver preference scenarios. The results show clear differences in vehicle waiting times. A balanced consideration of travel distance, waiting time, and cost can effectively reduce delays for drivers and improve station utilization. This research provides a practical optimization approach for real-time vehicle guidance in battery swapping systems. Full article

The rise of ride-hailing services has brought new riding experiences for passengers and exerted a profound impact on the traditional taxi market. To enhance patrol efficiency, increase revenue, and promote sustainable development in the taxi industry, traditional taxis have actively undergone transformation and adopted an integrated “online-offline” operating model, combining online order acceptance with offline order-taking. Meanwhile, a considerable number of orders are canceled by passengers after being accepted, leading to a waste of platform capacity, reduced order dispatch efficiency, and additional empty-running costs for drivers. This issue is particularly prominent during peak hours with order overflow. Based on the changes in taxi order acceptance during order overflow, this paper constructs a model for passenger order cancellation probability during peak hours, examines the relationship between regional order density and the proportion of offline taxi order acceptance, discusses the impact of regional order density changes on the passenger order cancellation probability and stakeholder returns, and proposes optimal order dispatch strategies for ride-hailing platforms with different order densities. Additionally, it analyzes more optimal taxi operating models under varying arrival states. The research findings provide more scientific and efficient operational recommendations for ride-hailing platforms and taxis, promoting sustainable development in the entire travel market and thereby contributing to a greener and more efficient travel environment. Full article

The taxiing stage of an aircraft is characterized by its long duration, low operating thrust, and low combustion efficiency, resulting in substantial emissions of CO, CO₂, and VOCs, which adversely affect air quality near airports. This study has developed an open-path Fourier transform infrared spectroscopy (OP-FTIR) monitor with second-level time resolution to enable the optical remote monitoring of pollutants during taxiing. Measurements of CO, CO₂, and VOCs were conducted over one month at Hefei Xinqiao International Airport (HXIA). The generalized additive model (GAM) is used for data analysis to reveal complex nonlinear relationships between aircraft emission concentrations and meteorological factors, aircraft models, and their corresponding registration numbers. The GAM analysis shows that among meteorological factors, humidity, and atmospheric pressure have the most significant impact on aircraft exhaust monitoring, with a relative average contribution value as high as approximately six. The explanatory power of aircraft models for emissions is low (R² < 0.18), whereas that of registration numbers is high (R² > 0.6), suggesting that individual differences between aircrafts play a crucial role in emission concentration variations. Furthermore, a noticeable correlation was found between the CO/CO₂ ratio and volatile organic compound (VOC) concentrations (R² > 0.63), indicating that combustion efficiency significantly affects VOC emissions. This study not only advances the real-time remote sensing monitoring of pollutants during aircraft taxiing but also underscores the crucial role of the GAM in identifying the key drivers of emissions, providing a scientific basis for precise environmental protection management and policy-making. Full article