Search Results (220)

The Vehicle Routing Problem (VRP) is central to last-mile logistics, yet a gap remains when products have late release times and vehicles can be reloaded en route via mobile satellites that rendezvous with reloading vehicles at customer locations. We propose the VRP with Release Times and Reloading at Mobile Satellites (VRP-RT-RMS) and develop two mixed-integer linear programming formulations: a three-index (MILP-3) and a two-index (MILP-2). The objective minimizes total distance subject to capacity, route duration, synchronization, and time constraints. We generated 40 instances from real data (10 per size $N\in \{10,15,20,25\}$). En-route reloads simultaneously reduce distance and fleet size and can restore feasibility when the classical VRP is infeasible. To contrast the classical VRP with our VRP-RT-RMS, we analyzed a particular instance with $N=10$ customers: total distance decreased by 7.26% and the number of vehicles fell from 5 to 3. As instance size grows, MILP-2 shows superior scalability and efficiency compared with MILP-3. Beyond the technical scope, coordinating reloads is pertinent to urban operations with late product releases, lowering kilometers traveled and delivery times. Full article

The multidimensional urban built environment (BE) in high-density cities has been shown to be closely related to the urban vitality (UV) of residents’ travelling. However, existing research lacks consideration of the differences in this relationship over a week, so this paper proposes an ensemble machine learning approach that simultaneously considers different time periods of the week. This study reveals the impacts of four dimensions of BE variables on UV at different time periods at the scale of the community life circle. The four well-performing base models are integrated to reveal the mechanism of differential effects of BE variables on UV under different time periods in the old city of Nanjing through Shapley addition explanation. The findings reveal that (1) the seven most important built environment variables existed in different time periods of the week: floor area ratio, service POI density, remote sensing ecological index, POI mixability, average building height, fractional vegetation cover, and maximum building area; (2) The nonlinear and threshold effects of the built environment factors differed across time periods of the week; (3) There is a dominant interaction between built environment variables at different time periods of the week. This study can provide guidance for the refined management of complex urban systems. Full article

Urban mobility plays a critical role in ensuring resilience during natural disasters such as floods, yet developing reliable traffic models remains challenging for medium-sized cities with limited monitoring infrastructure. This study developed a hybrid traffic modeling approach that integrates Google Traffic data with field measurements to address incomplete digital coverage in flood-prone areas of Loja, Ecuador. The methodology involved collecting 1501 field speed measurements and 235,690 Google Typical Traffic observations using exclusively open-source tools and freely available data sources. Adjustment factors ranging from 0.25 to 0.97 revealed systematic discrepancies between Google Traffic estimates and field observations, highlighting the need for local calibration. The resulting traffic network model encompassing 4966 nodes and 5425 edges accurately simulated flood impacts, with the most critical scenario (Thursday 17–19, 100% road impact) showing travel time increases of 1123% and congestion index deterioration from 1.79 to 21.69. Statistical validation confirmed significant increases in both travel times (p = 0.0231) and distances (p = 0.0207) under flood conditions across five representative routes. This research demonstrates that accurate traffic models can be developed through intelligent integration of heterogeneous data sources, providing a scalable solution for enhancing urban mobility analysis and emergency preparedness in resource-constrained cities facing climate-related transportation challenges. Full article

Understanding traffic congestion in urban areas is crucial for ensuring mobility, especially in metropolitan cities of developing countries. This study presents new spatial and temporal data to analyze congestion in Casablanca. Spatial data, collected using QGIS, covers 22 ZIP code areas and includes built environment factors such as land use, road types, and public transport stations. Temporal data consists of 440 randomly generated trajectories per commune, with real-time travel data collected hourly over one week using the Waze Route Calculator. A Python script was used to compute the Travel Time Index (TTI) for each zone. To classify zones based on congestion patterns, we applied fuzzy c-means clustering, allowing for nuanced grouping and interpretation of overlapping characteristics. This dataset supports traffic modeling, simulation, and congestion analysis in developing urban contexts. Full article

Accessible tourism is a critical aspect of sustainable development, yet many Southeast Asian destinations lack sufficient infrastructure and services for elderly and disabled travelers. This study develops a Mixed-Integer Linear Programming (MILP) framework to optimize travel itineraries, balancing cost, accessibility, and cultural–environmental priorities. A national accessibility database for Thailand was created, encompassing airports, hospitals, public transport nodes, cultural landmarks, and natural attractions. Compared to baseline conventional itineraries—defined as standard travel routes planned without specific accessibility considerations or optimization techniques—the MILP-optimized routes reduce average travel time by 15–20% and improve accessibility scores by 25%. Sensitivity analyses reveal trade-offs between economic efficiency, inclusivity, and infrastructure capacity, while a schematic accessibility network highlights structural fragmentation among airports, hospitals, and secondary attractions. Scenario analyses show that stricter accessibility thresholds improve inclusivity (index: 0.65 to 0.80) but restrict destination options, whereas high-demand scenarios increase costs and reduce inclusivity. A survey of 30 smart wheelchair users indicates high satisfaction with individualized programs and GPS connectivity. These findings underscore the need for investment in multimodal integration, accessibility upgrades, and a national database to enhance inclusive tourism planning. The framework is transferable to other ASEAN countries, contributing to SDG 3, 8, and 11. Overall, this study should be viewed as a prototype or exploratory contribution, with limitations in real-time applicability, generalizability, and implementation of environmental and ethical aspects. Full article

Compact urban land use planning and smart growth are essential strategies for tackling the issues of sustainable urban transportation development. In the context of swift global urbanization, examining the intrinsic relationship between urban spatial structure and transport systems might furnish a measurable foundation for urban planning decisions. This study utilizes various data sources, including Chinese city compactness and the Didi traffic index, to integrate exploratory spatial analysis and regression analysis methods. It examines the influence of city compactness on urban transportation by comparing average commuting time and speed relative to city compactness. The following findings are derived: The compactness of Chinese cities demonstrates notable regional differentiation, with western cities expanding uniformly and efficiently, whereas eastern cities display multi-centered, differentiated development in their spatial structures. Furthermore, Chinese cities exhibit a pronounced high-value agglomeration in commuting patterns, where major cities are characterized by high speeds and extended durations. The study reveals that city compactness creates a “concentration paradox” in commuting efficiency, which may reduce commuting distances but significantly decreases speed and extends travel time. The solution to this conflict is to prioritize the enhancement of public transport systems, as the increase in passenger volume is strongly positively connected with improved commuting speed and reduced commuting time. These findings offer a crucial scientific foundation for developing diverse regional spatial plans and transport development strategies. Full article

One of the key challenges for city authorities is to ensure an adequate quality of life for residents while promoting sustainable urban development. Achieving this balance is closely related to transport management which strongly affects urban quality of life, energy consumption, and resource savings. The aim of this article is to propose a new approach of assessing urban transport management quality, with a view to implement urban energy and resource-saving policies. The assessment procedure is based on the Six Sigma methodology and is illustrated using the example of the city of Szczecin for three selected routes. Travel data were obtained based on actual vehicle traffic using crowdsourcing methods. The capacity processes were assessed based on the potential capacity index and the actual capacity index, which characterise deviations in urban traffic from the best way to save energy and resources. Customer specification limits were set based on surveys assessing residents’ expectations regarding car travel times on the analysed routes. The results show that the methodology proposed in the article can be successfully used to assess urban transport management and to identify areas in need of improvement for sustainable transport panning. Full article

Exposure to hypoxia at high altitudes is significantly associated with impairments in learning and memory functions, as well as abnormalities in neuronal function and synaptic plasticity. Recent research has indicated that mitochondrial reactive oxygen species (mtROS) play a role in regulating microglial activation and mediating neurotoxic damage in the hippocampal CA1 region. Nicotinamide riboside (NR), upon absorption, is rapidly converted into nicotinamide adenine dinucleotide (NAD+), which is involved in the production of mitochondrial adenosine triphosphate (ATP). The potential of NR to protect dendritic spine plasticity in hippocampal CA1 neurons following hypoxia exposure, potentially through the inhibition of microglial activation, warrants further investigation. To this end, a mouse model simulating hypoxia at an altitude of 6000 m over a two-week period, along with a BV2 cells and conditional co-culture of BV2 cells and HT22 cells 1%O₂ hypoxia model, was developed. Behavioral assessments indicated that, relative to the normoxia group, mice subjected to hypoxia exhibited a significant reduction in the time spent in the target quadrant, the distance traveled within the target quadrant, the number of platform crossings, and the novel object recognition index. Furthermore, Golgi staining revealed a marked decrease in the density of dendritic spines in the hippocampal CA1 region in the hypoxia-exposed mice compared to the normoxia group. Subsequently, A daily dosage of 400 mg/kg of NR was administered for two weeks and 0.5 mM NR was used in a conditional co-culture model. Results demonstrated that, in comparison to the hypoxia group, the group receiving combined hypoxia and NR treatment showed significant improvements in the time spent in the target quadrant, the distance traveled within the target quadrant, the number of platform crossings, the novel object recognition index, and the density of dendritic spines in the hippocampal CA1 region. Additionally, transmission electron microscopy indicated a significant increase in the synaptic density of hippocampal neurons in the combined hypoxia exposure and NR treatment group compared to the hypoxia exposure group. Simultaneously, when compared to the hypoxia group, the combination of hypoxia and NR treatment resulted in an increased concentration of mitochondrial ATP. This treatment also partially restored mitochondrial membrane integrity, reduced mtROS levels, decreased the percent of Iba1⁺CD68⁺Iba1⁺ microglia, and lowered the interleukin-1β (IL-1β), interleukin-6 (IL-6), tumor necrosis factor-α (TNFα), and inducible nitric oxide synthase (iNOS) mRNA levels. These findings indicate that NR treatment may mitigate neurotoxic damage in the hippocampal CA1 region induced by hypoxia exposure, primarily through the attenuation of microglial activation and the reduction in mtROS production. Full article

The multi-objective optimization of an electric prototype racing vehicle is addressed in this study. The goal was to identify the optimal combination of battery type, pilot weight, and power mode to maximize operational time and distance while minimizing energy consumption. A structured $2\times 3\times 3$ factorial design was implemented, and the resulting data were analyzed through Response Surface Methodology (RSM) in combination with the Desirability Function Approach (DFA). The experimental design included two battery configurations, three weight levels, and three power settings, while data acquisition was performed through a custom Arduino-based system validated against commercial instruments. The results revealed that the configuration with the smallest battery, the lowest weight (66 kg), and the lowest power mode (N5) achieved the most efficient performance, yielding an operating time of 1.12 h, a travel distance of 24.63 km, and an energy performance index of 2.90 km/Ah. The integration of RSM with DFA provided a robust framework for identifying optimal multiparameter conditions under competition constraints. Unlike previous studies that examined these variables in isolation, this work advances the state of the art by demonstrating the feasibility of multiparameter optimization in real-world racing contexts, offering methodological and practical insights for sustainable electric mobility. Full article

The intensification of extreme hydrologic events, such as flash floods and landslides, has amplified the challenges of ensuring timely and effective emergency response. A key factor in the efficiency of such interventions is the accessibility of affected areas, which often becomes compromised during hazard events. In this context, the present study focuses on the Vărbilău River catchment in Romania, a region highly exposed to frequent flash floods and terrain instability. The research evaluates the spatial accessibility of emergency intervention units. Four major intervention centers were assessed under both normal and constrained scenarios. Accessibility was quantified through travel-time thresholds, incorporating variables such as road quality, network density, topography, and hazard-induced disruptions. Findings indicate that southern localities enjoy relatively short intervention times (less than 10 or between 10 and 20 min) due to favorable terrain and proximity to well-equipped centers. In such cases, the speed on main roads is 50–60 km/h, while the accessibility index is 5. Conversely, northern areas and villages like Lutu Roşu face elevated isolation risks, as single-road access and weak connectivity heighten their vulnerability during floods or landslides. In such cases, speeds reduce to 10 km/h and accessibility is very low, with the accessibility index of 1. Scenario modeling further demonstrated that the loss of key hubs (e.g., Ploieşti or Văleni) severely undermines coverage efficiency, particularly in high-risk zones, where the access times increases over 40 min. These results emphasize the need for dynamic intervention planning, infrastructure reinforcement, and the systematic integration of hazard-prone areas into emergency response strategies. Moreover, the methodological framework developed here can be adapted to other regions exposed to hydrologic hazards. Full article

With the intensification of global climate change, extreme weather events increasingly threaten water resources and agricultural systems. This study focuses on the Naoli River Basin, employing the Standardized Precipitation Actual Evapotranspiration Index (SPAEI), the Standardized Runoff Index (SRI), and the Standardized Surface Moisture Index (SSMI) to assess the spatiotemporal variability of meteorological, hydrological, and agricultural droughts. Drought events are identified based on travel time theory, and joint distributions of drought characteristics are modeled using optimized two- and three-dimensional copula functions. Lagged correlation and Bayesian conditional probability analyses are used to explore drought propagation processes. Key findings include (1) the SWAT model showed strong runoff simulation performance (R² > 0.75, NSE > 0.97), while the PLUS model achieved high land use simulation accuracy (overall accuracy > 0.93, Kappa > 0.85); (2) future projections suggest continued forest expansion and farmland decline, with water areas increasing under SSP245 and urban areas expanding under SSP585; (3) five CMIP6 models with high skill (r = 0.80, RMSE = 26.15) were selected via a Taylor diagram for scenario simulation; (4) copula-based joint drought probabilities vary temporally, with meteorological drought risks increasing under long-term moderate-emission scenarios, while hydrological and agricultural droughts show contrasting trends; (5) and under extreme meteorological drought, the conditional probability of extreme agricultural drought doubles from 0.12 (SSP245) to 0.24 (SSP585), indicating heightened vulnerability under high-emission pathways. These results offer critical insights for regional drought risk assessment and adaptive management under future climate scenarios. Full article

Rapid urban growth in Middle Eastern cities has intensified congestion-related challenges, yet traffic data-based decision making remains limited. This study leverages crowd-sourced travel time data from the Google Maps API to evaluate temporal and spatial patterns of congestion across multiple strategic routes in Jeddah, Saudi Arabia, a coastal metropolis with a complex road network characterized by narrow, high-traffic corridors and limited public transit. A real-time Congestion Index quantifies traffic flow, incorporating free-flow speed benchmarking, dynamic profiling, and temporal classification to pinpoint congestion hotspots. The analysis identifies consistent peak congestion windows and route-specific delays that are critical for travel behavior modeling. In addition to congestion monitoring, the framework contributes to urban sustainability by supporting reductions in traffic-related emissions, enhancing mobility equity, and improving economic efficiency through data-driven transport management. To our knowledge, this is the first study to systematically use the validated, real-time Google Maps API to quantify route-specific congestion in a Middle Eastern urban context. The approach provides a scalable and replicable framework for evaluating urban mobility in other data-sparse cities, especially in contexts where traditional traffic sensors or GPS tracking are unavailable. The findings support evidence-based transport policy and demonstrate the utility of publicly accessible traffic data for smart city integration, real-time traffic monitoring, and assisting transport authorities in enhancing urban mobility. Full article

In this study, a new nonlinear dynamic model was established for functionally graded material (FGM) beams with layered/porous fractal microstructures, aiming to reveal the cross-scale propagation mechanism of flexural waves under large deflection conditions. The characteristics of layered/porous microstructures were equivalently mapped to the fractal dimension index. In the framework of the fractal derivative, a fractal nonlinear wave governing equation integrating geometric nonlinear effects and microstructure characteristics was derived, and the coupling effect of finite deformation and fractal characteristics was clarified. Four groups of deflection gradient traveling wave analytical solutions were obtained by solving the equation through the extended minimal (G′/G) expansion method. Compared with the traditional (G′/G) expansion method, the new method, which is concise and expands the solution space, generates additional csch² soliton solutions and csc² singular-wave solutions. Numerical simulations showed that the spatiotemporal fractal dimension can dynamically modulate the amplitude attenuation, waveform steepness, and phase rotation characteristics of kink solitary waves in beams. At the same time, it was found that the decrease in the spatial fractal dimension will make the deflection curve of the beam more gentle, revealing that the fractal characteristics of the microstructure have an active control effect on the geometric nonlinearity. This model provides theoretical support for the prediction and regulation of the wave behavior of fractal microstructure FGM components, and has application potential in acoustic metamaterial design and engineering vibration control. Full article

This study assesses future agricultural drought risk in the Ganjiang River Basin under climate change and land use change. A coupled analysis framework was established using the SWAT hydrological model, the CMIP6 climate models (SSP1-2.6, SSP2-4.5, SSP5-8.5), and the PLUS land use simulation model. Key methods included the Standardized Soil Moisture Index (SSMI), travel time theory for drought event identification and duration analysis, Mann–Kendall trend test, and the Pettitt change-point test to examine soil moisture dynamics from 2027 to 2100. The results indicate that the CMIP6 ensemble performs excellently in temperature simulations, with a correlation coefficient of R² = 0.89 and a root mean square error of RMSE = 1.2 °C, compared to the observational data. The MMM-Best model also performs well in precipitation simulations, with R² = 0.82 and RMSE = 15.3 mm, compared to observational data. Land use changes between 2000 and 2020 showed a decrease in forestland (−3.2%), grassland (−2.8%), and construction land (−1.5%), with an increase in water (4.8%) and unused land (2.7%). Under all emission scenarios, the SSMI values fluctuate with standard deviations of 0.85 (SSP1-2.6), 1.12 (SSP2-4.5), and 1.34 (SSP5-8.5), with the strongest drought intensity observed under SSP5-8.5 (minimum SSMI = −2.8). Drought events exhibited spatial and temporal heterogeneity across scenarios, with drought-affected areas ranging from 25% (SSP1-2.6) to 45% (SSP5-8.5) of the basin. Notably, abrupt changes in soil moisture under SSP5-8.5 occurred earlier (2045–2050) due to intensified land use change, indicating strong human influence on hydrological cycles. This study integrated the CMIP6 climate projections with high-resolution human activity data to advance drought risk assessment methods. It established a framework for assessing agricultural drought risk at the regional scale that comprehensively considers climate and human influences, providing targeted guidance for the formulation of adaptive water resource and land management strategies. Full article

Personal Rapid Transit (PRT) and Group Rapid Transit (GRT) are classes of fully automated public transport systems, where passengers can travel in small vehicles on an interconnected, grade-separated network of guideways, non-stop, from origin to destination. PRT and GRT are considered sustainable as they are low-emission and able to attract car drivers. The parameterized cost modeling framework developed in this paper has the advantage that profitability of different PRT/GRT systems can be rapidly verified in a transparent way and in function of a variety of relevant system parameters. This framework may contribute to a more transparent, rapid, and low-cost evaluation of PRT/GRT schemes for planning and decision-making purposes. The main innovation is the introduction of the “peak hour network saturation” S: the number of vehicles in circulation during peak hour divided by the maximum number of vehicles running at line speed with minimum time headways. It is an index that aggregates the main uncertainties in the planning process, namely the demand level relative to the supply level. Furthermore, a maximum S can be estimated for a PRT/GRT project, even without a detailed demand estimation. The profit per trip is analytically derived based on S and a series of more certain parameters, such as fares, capital and maintenance costs, daily demand curve, empty vehicle share, and physical properties of the system. To demonstrate the ability of the framework to analyze profitability in function of various parameters, we apply the methods to a single vehicle PRT, a platooned PRT, and a mixed PRT/GRT. The results show that PRT services with trip length proportional fares could be profitable already for $S>0.25$. The PRT capacity, profitability, and robustness to tripled infrastructure costs can be increased by vehicle platooning or GRT service during peak hours. Full article