Search Results (379)

Optimizing high-speed railway (HSR) timetables requires coordinated decisions on train stopping patterns and local feasibility constraints, such as headway separation and service ordering, particularly at stations where consecutive trains share infrastructure. As network size and service density increase, station-level interactions can induce strategic behavior among trains competing for overlapping passenger markets. This study introduces a delayed-feedback evolutionary game framework to model interactions between two trains under incomplete information. Here, “delayed feedback” represents the lag in information transmission and periodic strategy updates, rather than operational train delays. We first formulate replicator dynamics for the non-delayed scenario, deriving equilibrium points and local stability conditions. The model is then extended to include delayed feedback in payoff evaluation, and the impact of delay parameters on stability and convergence is analyzed. To account for operational heterogeneity, two station layouts are considered: (i) two tracks per direction, representing small stations with limited overtaking and stop–pass combinations, and (ii) four tracks per direction, representing large stations that allow simultaneous stopping and richer operational patterns. Numerical simulations examine convergence, oscillatory behavior, and parameter sensitivity. Results indicate that delayed feedback significantly influences system dynamics: small delays maintain convergence to evolutionarily stable strategies, whereas larger delays induce persistent oscillations and complex transient trajectories. Station layout further affects stability regions and long-term strategy profiles. This study is based on analytical derivation and numerical simulation rather than sample-based statistical inference; therefore, non-parametric hypothesis testing is not applicable in the present framework. This framework provides a game-theoretic and stability-oriented tool for station-level timetable analysis, offering methodological guidance for timetable design under delayed decision feedback in HSR operations. Full article

Urban innovation spaces are crucial to stimulate innovative thinking and facilitate the integration of science, technology, and humanities. On the one hand, existing research on urban innovation spaces focuses on spatial patterns, associated networks, and spillover effects. They are limited to the macro scale and lack of innovation subject perspective. On the other hand, few studies have explored factors influencing the distribution by examining the needs of innovative talent. This study aimed to identify the evolution mechanism of urban innovation spaces. In total, 36,519 high-tech enterprises in Guangzhou from 2008 to 2023 were selected to represent urban innovation spaces. Spatial analysis methods and statistical methods were employed to investigate the spatiotemporal dynamic characteristics. Furthermore, employing multiscale geographically weighted regression, the study identifies multiple factors influencing the development of innovation spaces from the dual perspectives of the innovation environment and services. The results indicated that characterized by a southeast-northwest orientation, the urban innovation spaces of Guangzhou have displayed an apparent point–axis–face structural evolution, expanding from the central district into sparsely distributed in the suburbs. The factors influencing the distribution of urban innovation spaces, ranked by their degree of impact, were as follows: vehicle carrying, research institutions, public park, living convenience, university resources, business hotel, industrial structure height, and metro station. These findings facilitated the understanding of urban innovation space development and grasped the influencing factors and their functioning mechanisms. They provided references for innovation space planning amidst urban stock development. Full article

Climate variability poses major challenges to agriculture worldwide amid an increasing world population and growing food demand. This study evaluates the impact of climate variability on rice production in Liberia. Rice yields and production data (1990–2023) were attained from the Food and Agriculture Organization Statistics (FAOSTAT), while temperature and precipitation were sourced from ERA5 Agrometeorological Indicators and the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS). Trends and relationships were analyzed using Mann–Kendall, Sen’s slope tests, and Spearman’s rank correlation. Multiple linear regression estimates climate variables’ impact on rice productivity. The results show that mean, minimum, and maximum temperatures increased by 0.57 °C, 0.55 °C, and 0.55 °C, respectively, with precipitation variability at 180.31 mm. Climate variables showed diverse correlations with rice production. Regression results revealed a significant negative impact of minimum temperature (p-value = 0.015) on production and a positive effect of precipitation on yields (p-value = 0.036). Farmers in Liberia recognized climate impacts and adopted adaptation strategies, but resilience is hindered by limited credit access, low technology adoption, reliance on traditional practices, and inadequate extension services. Overall, the findings highlight the sensitivity of rice production in Liberia to climate variability and underscore the need for guided adaptation and institutional support to augment farmer resilience. Full article

This study used Global Navigation Satellite System (GNSS) observations from the China Crustal Movement Observation Network (CMONOC) and the Kunming Continuously Operating Reference Station (KMCORS) network to investigate ionospheric response characteristics over China during the geomagnetic storm of 4–6 November 2023, and to assess their impacts on CORS-based real-time kinematic (RTK) positioning performance in the low-latitude Kunming region. A quantitative assessment was conducted by integrating regional two-dimensional dTEC (%) maps over China, BeiDou Navigation Satellite System (BDS) Geostationary Earth Orbit (GEO) total electron content (TEC), the rate of TEC index (ROTI), and RTK positioning solutions to evaluate ionospheric disturbances, irregularity activity, and associated degradation in positioning performance. Results indicate that, during geomagnetic storms, ionospheric responses over China exhibit pronounced phase-dependent and latitudinal variations. During the second geomagnetic storm on 5–6 November, positive responses were dominant at mid-to-high latitudes, whereas alternating positive and negative responses were observed at low latitudes. During the recovery phase, the Kunming region successively experienced a positive ionospheric storm lasting approximately 10 h, followed by a negative ionospheric storm lasting about 7 h, with relative TEC variations reaching a maximum of approximately 90%. The GEO TEC time series was consistent with the temporal evolution of the two-dimensional dTEC (%), while ROTI increased markedly during the disturbance enhancement period (21:00 UT on 5 November to 07:00 UT on 6 November 2023). During periods of enhanced ionospheric response and irregularities, RTK positioning performance was observed to deteriorate markedly. The fixed-solution rate at medium-to-long baseline stations decreased from nearly 100% to close to 0%, accompanied by an increase in vertical positioning errors to approximately 20 cm, whereas short-baseline stations were only minimally affected. These results indicate that ionospheric disturbances during geomagnetic storms exert a pronounced impact on CORS-based RTK positioning services in the Kunming region, with the magnitude of this impact being closely related to baseline length. Full article

Informal public transportation remains the backbone of urban mobility in many developing cities. While these systems offer flexible and affordable services, they are often associated with safety issues, unreliability, congestion, and environmental impacts. Consequently, transitioning travelers toward formal public transportation is a key objective for sustainable transport planning. This study investigates travelers’ willingness to shift from their current travel modes to a proposed Metro system in Alexandria, Egypt. The analysis uses stated preference data collected through interviews that presented respondents with multiple service scenarios. A correlated random parameters bivariate probit model with heterogeneity in means is estimated to capture interdependence between responses. The results reveal strong and statistically significant cross-equation error correlations, confirming that decisions are not independent and supporting the use of a joint modeling approach. Empirical results indicate that willingness to shift is influenced by socio-demographic characteristics, trip attributes, and current travel conditions. Female travelers are more sensitive to waiting time, while low-income and older individuals are less likely to shift across scenarios. Physical accessibility, especially walkability to and from stations, emerges as the most influential factor in encouraging adoption. These findings provide policymakers with actionable insights for designing inclusive, accessible, and sustainable public transportation systems. Full article

Accurate soil moisture assessment is essential for effective agricultural management in the southern US, where water availability has a significant impact on crop productivity. This study evaluates the Soil Moisture Active Passive (SMAP) Level-4 daily soil moisture product using in situ measurements from Natural Resources Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) stations and the US. Climate Reference Network (USCRN) across diverse agroecosystems in Texas from 2016 to 2024. SMAP’s performance was examined across ten climate zones and six major land cover types, including urban regions, pastureland, grassland, rangeland, shrubland, and deciduous forests. Statistical metrics, including the coefficient of determination (R²), Root Mean Square Error (RMSE), Bias, and unbiased RMSE (ubRMSE) were used to evaluate the agreement between SMAP-derived and in situ soil moisture measurements. Results show that SMAP effectively captures seasonal soil moisture dynamics but exhibits spatially variable accuracy. The highest agreement was observed at Panther Junction (R² = 0.57, RMSE = 2.29%), followed by Austin (R² = 0.57, RMSE = 9.95%). While a weaker coefficient of determination was observed at PVAMU (R² = 0.28, RMSE = 11.28%) and Kingsville (R² = 0.11, RMSE = 7.33%), likely due to heterogeneity in land cover, and urbanized landscapes in these stations. Applying the quantile mapping bias correction methods significantly reduced RMSE and improved the accuracy of SMAP soil moisture data at some in situ measurement stations. The results highlight the importance of station-specific calibration and the integration of satellite and ground-based measurements to improve soil moisture monitoring for agriculture and drought management in Texas and similar regions. Full article

Spatial efficiency drives the adoption of integrated station–bridge structures in maglev transit, yet the rigid coupling between track and station poses inherent challenges to vibration serviceability. This study isolates the impact of support constraints, specifically contrasting rigid connections with pinned supports, on the dynamic performance of a five-story maglev station. Using a unified, high-fidelity 3D coupled model that incorporates electromagnetic suspension nonlinearity, we evaluated structural responses under train speeds of 60–120 km/h. Simulations identify a critical operational threshold: while the waiting hall remains compliant with standard comfort criteria (DIN 4150-3), the platform floor exceeds the 1.5% g acceleration limit during dual-track operations at speeds ≥ 100 km/h. Beyond standard safety checks, the main scientific innovation of this study is revealing the mechanical transmission paths of structure-borne vibrations at the track-frame interface. The results demonstrate that rigid connections create full mechanical coupling, directly passing train-induced bending moments into the station frame. Conversely, pinned supports release the rotational degrees of freedom, which physically cuts off the primary energy transmission route. By explaining this structural decoupling mechanism, this work moves beyond a specific engineering case study to provide a fundamental theoretical framework for vibration control in complex maglev hubs. Full article

This study investigates the Wider Economic Impacts (WEIs) of railway infrastructure in Italy by analysing how station characteristics and surrounding urban contexts are capitalized into residential property values. A nationwide cross-sectional dataset covering 985 railway stations is used to estimate a Hedonic Price Model (HPM) combining observed variables and latent constructs derived from Confirmatory Factor Analysis (CFA). Results show that railway centrality, long-distance service provision, and multimodal integration are positively associated with housing prices. In particular, shared mobility services generate significant value uplift effects, especially around Local and Local Plus stations. Conversely, car-oriented accessibility is negatively associated with residential values, reflecting the capitalization of traffic-related externalities. Socioeconomic and tourism-related characteristics further contribute to heterogeneous capitalization patterns across the national territory. The findings provide systemic empirical evidence to support investment prioritization, multimodal integration, and value uplift of station areas within the Italian railway network. Full article

Under intensifying climate change and anthropogenic pressures, extreme low-flow events increasingly jeopardize water security in the Yellow River water supply region. This study develops the Inter-basin Multi-source Water Joint Allocation and Interconnected Routes Regulation System (IMWA-IRRS) to optimize spatiotemporal allocation of multi-source water and simulate topological relationships in complex water networks. The model integrates system dynamics simulation with multi-objective optimization, validated through multi-criteria calibration using three performance indicators: correlation coefficient (R), Nash-Sutcliffe Efficiency (E_ns), and percent bias (PBIAS). Application results demonstrated exceptional predictive performance in the study area: Monthly runoff simulations at four hydrological stations yielded R > 0.98 and E_ns > 0.98 between simulated and observed data during both calibration and validation periods, with |PBIAS| < 10%; human-impacted runoff simulations at four hydrological stations achieved R > 0.8 between simulated and observed values, accompanied by PBIAS within ±10%; sectoral water consumption across the Yellow River Basin exhibited PBIAS < 5%, while source-specific water supply simulations maintained PBIAS generally within 10%. Comparative analysis revealed the IMWA-IRRS model achieves simulation performance comparable to the WEAP model for natural runoff, human-impacted runoff, water consumption, and water supply dynamics in the Yellow River Basin. The 2035 water allocation scheme for Yellow River water supply region projects total water supply of 59.691 billion m³ with an unmet water demand of 3.462 billion m³ under 75% low-flow conditions and 58.746 billion m³ with 4.407 billion m³ unmet demand under 95% low-flow conditions. Limited coverage of the South-to-North Water Diversion Project’s Middle and Eastern Routes constrains water supply security, necessitating future expansion of their service areas to leverage inter-route complementarity while implementing demand-side management strategies. Collectively, the IMWA-IRRS model provides a robust decision-support tool for refined water resources management in complex inter-basin diversion systems. Full article

This paper presents an exploration of the electromagnetic field characteristics and parameters in the area of coverage of a particular base station, as well as the radio signal strength and the data speed values for optimal service. Although there are many investigations concerning the influence of the electromagnetic field on the reactions of the people positioned in such areas and general impact on humans’ health, it is worth exploring some specific aspects of that problem. One of them is to focus the investigation on some particular radio frequency ranges. The second is to propose some practical consequences of the steps and ways to perform the measurements, with fast opportunity to collect the results and to compare them with identical measurements performed by different equipment or different algorithms for measurements. As a consequence, approaches should be developed to allow relatively accurate measurements of the electromagnetic background with devices more accessible to ordinary people instead of the expensive specialized measuring instruments used by specialists in this field. Such alternative methods of control of the electromagnetic flux radiation could help reliably to update the permissible parameters set in the existing regulations. Full article

The performance of the MADOCA-PPP (Multi-GNSS Orbit and Clock Augmentation-Precise Point Positioning) wide-range ionospheric correction requires further investigation during periods of high ionospheric activity, particularly regarding its impact on the convergence time and positioning accuracy of both PPP and PPP with Ambiguity Resolution (PPP-AR). Thus, the present study selects the month with the highest average Kp index between January 2023 and May 2025 and conducts positioning analyses at nine stations. Results indicate that applying wide-range ionospheric corrections reduces PPP convergence time by 47% in static mode and 54% in kinematic mode. When these corrections are integrated into PPP-AR, they shorten the convergence time by 69% in static mode and 72% in kinematic mode. Moreover, PPP-AR enhanced with wide-range ionospheric corrections achieves the highest positioning accuracy across both modes: in static mode, the horizontal and vertical root mean square errors (RMSEs) are approximately 5.2 cm and 6.9 cm, respectively, while in kinematic mode, these values are 5.6 cm and 8.0 cm. These findings demonstrate that the wide-range ionospheric corrections provided by the MADOCA-PPP service effectively enhance PPP performance during periods of heightened ionospheric activity. Full article

The last kilometer connection problem of metro transit stations is the core factor to measure the connection efficiency and service quality. Establishing the spatiotemporal distribution pattern of the connection distance is conducive to clarifying the interaction mechanism between bike-sharing connections and urban space. This study focuses on the travel behavior of shared bicycle users accessing metro stations, aiming to reveal the access distance decay patterns and their relationship with influence factors. Finally, the random forest algorithm was used to explore the nonlinear relationship between the influencing factors and the connection decay distance, and to clarify the importance of the factors. Multiple linear regression was applied to examine the linear correlation between the distance decay coefficient and the factors influence. The geographically weighted regression was further employed to explore spatial variations in their effects. Finally, the random forest algorithm was used to rank the importance of the impact factors. The results indicate that proximity distance to metro stations, proximity distance to bus stops, and the number of bus routes serving the station area have significant negative correlations with the distance decay coefficient. Significant spatial heterogeneity was observed in the influence of each factor on the distance decay coefficient, based on the geographically weighted regression analysis. With a high goodness-of-fit (R² = 0.8032), the Random Forest regression model furthermore quantified the relative importance of each factor influencing the distance decay coefficient. The findings can be directly applied to optimize the layout of shared bicycle parking, metro access facilities planning, and multi-modal transportation system design. Full article

Urban waterfront spaces are key venues for residents’ physical activity, and their spatial environment significantly impacts usage efficiency. Existing studies predominantly employ linear models and focus on single waterfront types, making it difficult to reveal differences across various types and the nonlinear mechanisms of influencing factors. To address this, this study investigates three types of waterfront spaces in Shenzhen—riverfront, lakeshore, and seafront spaces—integrating multi-source data and machine learning techniques to systematically analyze the differential impacts of the same elements on physical activity. The results indicate: (1) In terms of transportation accessibility, public transport is the most important factor for riverfront and lakeshore spaces, while road network accessibility is most critical for seafront spaces. (2) Regarding natural landscapes, the dominant factors are normalized difference vegetation index (NDVI) for riverfront spaces, green view index for lakeshore spaces, and distance to the shoreline for seafront spaces. (3) For facility services, the core factors are building density (riverfront), number of sports facilities (lakeshore), and number of leisure facilities (seafront). (4) The study further reveals nonlinear relationships and threshold effects of multiple elements. For instance, a turning point in physical activity intensity occurs when the distance to a subway station reaches 2–2.5 km. The green view index shows a threshold of 30% in the overall model, while dual-threshold phenomena are observed in the lakeshore and seafront models. (5) Synergistic effects between elements vary by waterfront type: in riverfront and seafront spaces, activity is more vibrant when areas are close to subway stations and have a low sky view index, whereas the opposite pattern is observed in lakeshore spaces. A combination of a high green view index and greater distance to the shoreline promotes activity in lakeshore spaces, while a high green view index combined with proximity to the shoreline has the most significant promotional effect in riverfront and seafront spaces. This study provides a scientific basis for health-oriented, precise planning and design of urban waterfront spaces. Full article

During peak hours, urban rail transit systems often face imbalanced spatial–temporal demands. Due to the limited transportation capacity, passengers departing from downstream stations often experience longer waiting times. Mostly traditional timetable and skip-stop strategies overlook passengers’ transfer behavior, which may impact the implementation of optimization strategies. This paper aims to take passengers’ transfer behavior into account and construct a coordinated optimization model of timetable and skip-stop patterns. We regulate passengers’ transfer strategies and design a genetic algorithm for solving the optimization model. In order to characterize feasible passenger travel patterns, strict FCFS rules and capacity constraints are incorporated into the model. Our result demonstrates that considering passengers’ transfer behavior, the coordinated optimization of timetable and skip-stop strategy can not only mitigate the unfairness of acquiring rail service among passengers but also reduce the average waiting time of the entire system. We validate the effectiveness of our algorithm using the dataset from Line 1 of Singapore’s urban rail transit system as a case study. Full article

Global Navigation Satellite Systems (GNSS) have undergone more than half a century of development and construction, with more than a hundred navigation satellites currently providing precise and reliable positioning, navigation, and timing (PNT) services for various users. Meanwhile, efficient utilization of these satellites has become a topic of interest. Selecting an appropriate satellite set in a proper manner can reduce computational burden while ensuring positioning accuracy. Geometric Dilution of Precision (GDOP) is commonly used in satellite selection as it quantifies the impact of satellite geometry on positioning accuracy. Due to its computational simplicity, GDOP has been widely applied in satellite selection, but it only considers the satellite geometric configuration while ignoring the quality of satellite observations. As a result, the selected satellite set may lead to poor positioning accuracy. To address this issue, we use a satellite selection criterion based on the combination of near-real-time accuracy of satellite observations and geometric configuration. This criterion utilizes the combination of Geometry-Free Ionosphere-Free (GFIF) and Melbourne–Wübbena (MW) linear combinations of observations. Through a sliding window, we estimate the near-real-time accuracy of observations and use it to calculate the Weighted Geometric Dilution of Precision (WGDOP) for satellite selection. In a global International GNSS Service (IGS) station validation experiment, the satellite set selected based on WGDOP using near-real-time accuracy of GFIF and MW observations improved overall positioning accuracy by 11.6% and 12% when compared with the GDOP-based selection, and by 6% and 6.4% when compared with the Signal-to-Noise Ratio (SNR) weighting method. In a low-cost device validation experiment, the satellite selection method based on near-real-time accuracy of GFIF and MW improved positioning accuracy by 22.5% and 19.7% when compared with the GDOP-based method, and by 23.3% and 20.5% when compared with the SNR-based method. A set of dynamic observation experiments further demonstrates that the satellite selection method based on the near-real-time accuracy of GFIF and MW combinations outperforms the other two selection criteria in dynamic scenarios. Full article