Search Results (87)

Metro stations serve as critical hubs for passenger gathering and scattering. Under disturbing scenarios, a station’s ability to respond to disturbances, named resilience, fundamentally governs the operational stability, sustainability and emergency performance of the metro network. Existing metro network resilience studies typically treated stations merely as topological nodes, making it impossible to account for the internal passenger flow organization and facility capacities of the station. The resilience of the station itself cannot be characterized and quantified. This study focuses on the metro station’s resilience. From the perspective of sustainable operation, considering the passenger flow management of the station, the station’s resilience is defined as the ability of the station to maintain its basic service capabilities and minimize the number of delayed passengers within the station during disturbances. A passenger delay coefficient is introduced to quantify variations in passenger delay volumes within the station. The total number of passengers entering and leaving a station is used to quantify its service capacity. A resilience measurement model for the station is constructed by coupling the passenger delay coefficient and the service capacity. A case study of a transfer station experiencing a sudden passenger surge is conducted for model validation, considering passenger flow control measures and train capacity constraints. The results demonstrate that the model measures the station’s resilience across varying passenger flow management strategies effectively. This study provides a quantitative tool for measuring metro station resilience, enabling emergency responses, operational optimization and policy formulation that support the sustainable and stable operation of metro stations and networks. Full article

Passive transit signal priority (TSP) strategies are widely recognized as effective tools for mitigating bus delays along urban arterials. However, existing TSP models primarily focus on through movements of transit vehicles, leading to potential delays for buses making turning movements. Moreover, these models do not adequately address signal coordination in multi-modal traffic systems involving both buses and private vehicles, resulting in increased delays and frequent stops for private vehicles. To address these limitations, this study proposes a binary mixed-integer linear programming (BMILP)-based signal progression band optimization model designed for multi-modal, path-level signal coordination. The model creates multiple progression bands for both straight and turning buses to minimize potential transit delays and enhance public transport service levels. By incorporating the mutual interactions between buses and private vehicles, progression bands for private vehicles are simultaneously optimized, enabling coordinated signal control that considers all users. The objective function maximizes passenger-equivalent service demand satisfied by the progression bands, explicitly accounting for mixed traffic flows and passenger loads. Numerical experiments on an urban arterial corridor demonstrate that, compared with the benchmark BUSBAND method, the proposed model achieves a 26% reduction in average bus delays, a 37% reduction in passenger car delays, and a 22% decrease in total stops, while also improving overall travel time reliability. Full article

Elevator Group Control Systems (EGCSs) play a key role in managing the passenger flow and consumption of energy in modern buildings. However, existing EGCS algorithms are typically only applied to real-time passenger calls, which does not take the long-term statistics of passenger requirement into account. To address this gap, we propose a standby strategy that proactively repositioning idle elevators even if there is no passenger call. It calculates a combined score that balances the expected waiting time and the energy consumption to determine the optimal standby floors for idle elevators. We implement this strategy on a simple baseline dispatcher using the closest car algorithm and introduce tunable parameters to adjust the standby behavior. Experiments on mid-rise and high-rise building scenarios show that the standby strategy significantly reduces the average waiting time for passengers by more than 24% in both cases. Moreover, because this strategy operates independently of the core dispatcher, it can be combined with existing EGCS algorithms to further improve waiting time without compromising core energy optimizations. These findings demonstrate that proactive standby repositioning is an effective complementary approach for next-generation elevator control systems and offers a practical way to reduce waiting times under realistic office building traffic conditions. Full article

The global aviation sector underwent an unprecedented shock due to the COVID-19 pandemic, severely disrupting the passenger flows, flight operations, and revenues of Polish airports. In response, the government launched protective measures under the Anti-Crisis Shield and the COVID-19 Counteraction Fund. This study examines the financial impact of such public support on 12 Polish airports between 2016 and 2022, applying a two-step cointegration ECM framework with Driscoll–Kraay inference. Profitability (ROA, ROE, OM), liquidity, debt, and operational activity indicators were analysed, with particular attention to methodological distortions arising from including subsidies in operating revenues. The results indicate a material decline in profitability from 2020 to 2022, albeit with pronounced heterogeneity across airports. Larger hubs (Warsaw–Chopin, Kraków, Gdansk, Katowice, Poznan, and Wroclaw) demonstrated relative resilience, while many smaller, regionally owned airports (e.g., Bydgoszcz, Lodz, Lublin, Olsztyn-Mazury, Zielona Gora) remained structurally unprofitable despite substantial subsidies. In several cases, profitability, liquidity, and operating activity recovered by 2021–2022, yet the improvement was not uniform: for fiscally dependent airports, transfers merely masked persistent inefficiencies. Passenger volumes, flight operations, and employment emerged as the primary performance drivers, while capital expenditure, turnover of current assets, and liquidity were particularly relevant for ROE. The novelty of this research lies in disentangling the stabilising effect of subsidies from underlying profitability, revealing how non-market revenues distort standard performance metrics and accelerate short-run adjustment dynamics once netted out. The findings demonstrate asymmetric impacts of state aid across ownership structures, i.e., central state control at Warsaw versus regional self-government involvement elsewhere, and highlight structural inefficiencies that weaken systemic resilience. These insights underline the importance of subsidy-adjusted financial indicators, more selective allocation of support, and reporting standards that separate operating from non-market revenues to enhance resilience and ensure sustainable airport operations. Full article

Among other uses, DC-DC converters are employed in the auxiliary power modules (APMs) of electric vehicles (EVs), connecting the high-voltage traction battery to the low-voltage auxiliary system (AS). Traditionally, the APM is an isolated two-port, two-level (2L) DC-DC converter, and the auxiliary loads are fed at a fixed voltage level, e.g., 12 V in passenger cars. Dual-active-bridge (DAB) converters are commonly used for this application, as they provide galvanic isolation, high power density and efficiency, and bidirectional power flow capability. However, the auxiliary loads do not present a uniform optimum supply voltage, hindering overall efficiency. Thus, a more flexible approach, providing multiple supply voltages, would be more suitable for this application. Multiport DC-DC converters capable of feeding auxiliary loads at different voltage levels are a promising alternative. Multilevel neutral-point-clamped (NPC) DAB converters offer several advantages compared to conventional two-level (2L) ones, such as greater efficiency, reduced voltage stress, and enhanced scalability. The series connection of the NPC DC-link capacitors enables a multiport configuration without additional conversion stages. Moreover, the modular nature of the ML NPC DAB converter enables scalability while using semiconductors with the same voltage rating and without requiring additional passive components, thereby enhancing the converter’s power density and efficiency. This paper proposes a modulation strategy and decoupled closed-loop control strategy for the generalized multiport 2L-NL NPC DAB converter interfacing the EV traction battery with the AS, and its performance is validated through hardware-in-the-loop testing and simulations. The proposed modulation strategy minimizes conduction losses in the converter, and the control strategy effectively regulates the LV battery modules’ states of charge (SoC) by varying the required SoC and the power sunk by the LV loads, with the system stabilizing in less than 0.5 s in both scenarios. Full article

Underground rail transit transfer stations are large-scale, complex structures with high-passenger flows, making them more vulnerable to fires and rescue challenges than other stations. Taking Zhongnan Road Metro Transfer Station in Wuhan as a project example, this study simulates two typical fire scenarios—flammable package ignition and equipment short circuits—using PyroSim to analyze changes in smoke movement, temperature, visibility, and CO concentration within the station. The required safety egress time (T_RSET) was determined according to the critical threshold. Then, the critical evacuation phase time (t_move’) at each key evacuation node was calculated by working backward from T_RSET. The threshold control of the open/close time nodes of the evacuation passages in the Pathfinder calculation was realized based on this time parameter. Based on the improved optimization algorithm method, personnel evacuation simulations are conducted to analyze evacuation characteristics, efficiency, and safety levels. Results show that the combustion characteristics of the fire source significantly affect the efficiency of passenger evacuation. The evacuation fails in Scenario 1 (flammable package) but succeeds in Scenario 2 (short circuit of an elevator circuit). Safety ratings for exits A–F are Level 1 (Good), Staircase 1 is Level 2 (Qualified), Staircases 2 and 3 are Level 3 (At Risk), and Staircase 4 is Level 4 (Poor). Finally, suggestions for improvement were proposed regarding size, quantity, and layout optimization of egress staircases. Full article

This paper reviews the variability of passenger service time (PST) at the platform–train interface (PTI), a critical performance indicator in metro systems shaped by the infrastructure design, affecting passenger behavior and accessibility. Despite its operational importance, PST remains underexplored in relation to crowd management strategies. This review synthesizes findings from empirical and experimental research to clarify the main factors influencing PST and their implications for platform-level interventions. Key contributors to PST variability include door width, gap dimensions, crowd density, and user characteristics such as mobility impairments. Design elements—such as platform edge doors, yellow safety lines, and vertical handrails—affect flow efficiency and spatial dynamics during boarding and alighting. Advanced tracking and simulation tools (e.g., PeTrack and YOLO-based systems) are identified as essential for evaluating pedestrian behavior and supporting Level of Service (LOS) analysis. To complement traditional LOS metrics, the paper introduces Level of Interaction (LOI) and a multidimensional LOS framework that captures spatial conflicts and user interaction zones. Control strategies such as platform signage, seating arrangements, and visual cues are also reviewed, with experimental evidence showing that targeted design interventions can reduce PST by up to 35%. The review highlights a persistent gap between academic knowledge and practical implementation. It calls for greater integration of empirical evidence into policy, infrastructure standards, and operational contracts. Ultimately, it advocates for human-centered, data-informed approaches to PTI planning that enhance efficiency, inclusivity, and resilience in high-demand transit environments. Full article

When in operation, ultra-high-speed elevators encounter transverse vibrations due to uneven guide rails and airflow disturbances, which can greatly undermine passenger comfort. To alleviate these adverse effects and boost passenger comfort, a gas–solid coupling dynamic model for ultra-high-speed elevator cars is constructed, and a vibration suppression approach is proposed. To start with, the flow field model of the elevator car-shaft under different motion states is simulated, and the calculation formula of air excitation is derived. Next, by incorporating the flow field excitation into the four degrees of freedom dynamic model of the separation between the car and the frame, a transverse vibration model of the elevator car based on gas–solid coupling is established. Finally, an LQR controller is used to suppress elevator transverse vibration, and a multi-objective optimization algorithm is applied to optimize the parameters of the weight matrix to obtain the optimal solution of the LQR controller. A set of controllers with moderate control cost and system performance meeting the requirements was selected, and the effectiveness of the controller was verified. Compared with other methods, the proposed LQR-based method has greater advantages in suppressing the transverse vibration of ultra-high-speed elevators. This work provides an effective solution for enhancing the ride comfort of ultra-high-speed elevators and holds potential for application in the vibration control of high-speed transportation systems. Full article

The long-standing need for a modern public transportation system in Lebanon, a developing country of the Middle East with an almost exclusive dependence on costly and polluting passenger cars, has become more pressing in recent years due to the worsening economic crisis and the onset of hyperinflation. This study investigates the potential reductions in energy use, emissions, and costs from the possible introduction of natural gas, hybrid, and battery-electric buses compared to traditional diesel buses in local real driving conditions. Four operating conditions were considered including severe congestion, peak, off-peak, and bus rapid transit (BRT) operation. Battery-electric buses are found to be the best performers in any traffic operation, conditional on having clean energy supply at the power plant and significant subsidy of bus purchase cost. Natural gas buses do not provide significant greenhouse gas emission savings compared to diesel buses but offer substantial reductions in the emission of all major pollutants harmful to human health. Results also show that accounting for additional energy consumption from the use of climate-control auxiliaries in hot and cold weather can significantly impact the performance of all bus technologies by up to 44.7% for electric buses on average. Performance of all considered bus technologies improves considerably in free-flowing traffic conditions, making BRT operation the most beneficial. A vehicle mix of diesel, natural gas, and hybrid bus technologies is found most feasible for the case of Lebanon and similar developing countries lacking necessary infrastructure for a near-term transition to battery-electric technology. Full article

This study introduces an innovative set of guide vanes that increase the efficiency of Vertical Axis Wind Turbines (VAWT) using winds generated by vehicles traveling on highways. The increase in efficiency is based on enhancing the airflow interaction as the vehicle moves past the turbine. Initial Computational Fluid Dynamics (CFD) simulations with two guide vanes setups demonstrated a 56.81% increase in power output under wind generated by passenger vehicles. Further design enhancements, incorporating three guide vanes with optimized geometries, led to a 242% improvement in power generation. Additional simulations evaluated the performance under wind conditions generated by larger vehicles, such as buses. The three guide vanes configuration yielded a 102% increase in energy capture efficiency in these scenarios. The findings suggest that vehicle-induced winds—typically an untapped energy source—can be effectively harvested using tailored turbine system designs. By integrating passive flow control strategies such as guide vanes, VAWTs can operate more efficiently in highway environments. This research highlights a novel pathway for enhancing renewable energy systems and supports broader efforts toward sustainable energy development through the utilization of unconventional wind sources. This performance enhancement is primarily due to the aerodynamic redirection of airflow toward the advancing blade and away from the returning blade, reducing drag and improving torque generation. Full article

Due to the unbalanced temporal and spatial distribution of the passenger flow on metro lines during peak hours, the implementation of passenger flow control strategies effectively ensures operational safety and travel efficiency for passengers. In this study, we analyze the coupling relationship between trains and passengers, introduce train-stopping state variables, and synergistically optimize both train operation schedules and station passenger flow control. Aiming to minimize the total passenger delay time and maximize the number of boarding passengers, we consider four constraints: the train operation process, the passenger entry process, the passenger–train interaction process, and system constraints. This framework enables us to construct a cooperative passenger flow control optimization model for oversaturated metro lines. Subsequently, we propose an improved artificial bee colony algorithm to solve this model. We utilize evolutionary operators and an enhanced tabu search to create new food sources for employed bees and enhance their local search capabilities during the employed phase. Finally, Shanghai Metro Line 9 is used as a case study for the model validation. The computational results indicate that the proposed Collaborative passenger flow control strategy significantly reduces the number of stranded passengers on platforms and decreases the total passenger delay time by 36.26% compared to the existing passenger flow control strategy. The findings demonstrate that the cooperative control strategy proposed in this paper can effectively alleviate the pressure from passenger flow on oversaturated lines, balance the asymmetry between supply and demand, and markedly improve both safety and efficiency in the metro system during peak hours. Full article

This study investigates the mechanism behind the cavitation-induced noise in an automotive heater core and proposes a structural solution to eliminate it. Abnormal noise during cold-start conditions in a compact passenger vehicle was traced to cavitation in the heater core of the heating, ventilation, and air conditioning (HVAC) system. Controlled bench tests, in-vehicle measurements, and computational fluid dynamics (CFD) simulations were conducted to analyze flow behavior and identify the precise location and conditions for cavitation onset. Results showed that high flow rates and low coolant pressure generated vapor bubbles near the junction of the upper tank and outlet pipe, producing distinctive impulsive noise and vibration signals. Flow visualization using a transparent pipe and accelerometer data confirmed cavitation collapse at this location. CFD analysis indicated that the original geometry created a high-velocity, low-pressure region conducive to cavitation. A redesigned outlet with a tapered transition and larger diameter significantly improved flow conditions, raising the cavitation index and eliminating cavitation events. Experimental validation confirmed the effectiveness of the modified design. These findings contribute to improving the acoustic performance and reliability of automotive HVAC systems and offer broader insights into cavitation mitigation in fluid systems. Full article

This study proposes a methodology that integrates real-time data and predictive modeling to identify the passenger flow and occupancy levels within a multimodal transport hub. This tool enables the implementation of control and planning strategies to ensure a high Level of Service (LOS). The tool is based on a Long Short-Term Memory (LSTM) model and heterogeneous data sources, including an Automatic Passenger Counting (APC) system, which are utilized to estimate the real-time passenger flow and area occupancy. The Module A of the Moncloa Interchange in Madrid is the case study, and the results reveal that transport-dedicated zones have higher occupancy levels. Methodologically, time series data were standardized to a uniform frequency to ensure consistency, and the training set consisted of seven months of available data. The model performs better in high-occupancy zones. Despite maintaining a LOS A, some periods experience temporary congestion. These findings indicate that the variations in occupancy levels influence the service quality and highlight the essential role of dynamic interchange management. Tailored operational strategies can optimize the service levels and improve the user experience by anticipating congestion through predictive modeling. This can help enhance public transport’s attractiveness, minimize the perceived transfer penalties, make transfers more efficient, and reinforce transport hubs’ role in sustainable urban mobility. Full article

The emergence of autonomous vehicles offers the potential to eliminate traditional traffic lanes, enabling vehicles to navigate freely in two-dimensional spaces. Unlike conventional traffic constrained by physical lanes, autonomous vehicles rely on real-time data exchange within platoons to adopt cooperative movement strategies, similar to synchronized flocks of birds. Motivated by this paradigm, this paper introduces an innovative traffic flow model based on the principles of particle swarm intelligence. In the proposed model, each vehicle within a platoon is treated as a particle contributing to the collective dynamics of the system. The motion of each vehicle is determined by the following two key factors: its local optimal velocity, influenced by the preceding vehicle, and its global optimal velocity, derived from the average of the optimal velocities of M vehicles within its observational range. To implement this framework, we develop a novel particle swarm optimization algorithm for autonomous vehicles and rigorously analyze its stability using linear system stability theory, as well as evaluate the system’s performance through four distinct indices inspired by traditional control theory. Numerical simulations are conducted to validate the theoretical assumptions of the model. The results demonstrate strong consistency between the proposed swarm intelligent model and the Bando model, providing evidence of its effectiveness. Additionally, the simulations reveal that the stability of the traffic flow system is primarily governed by the learning parameters $c_1$ and $c_2$, as well as the field of view parameter M. These findings underscore the potential of the swarm intelligent model to improve traffic flow system dynamics and contribute to the broader application of autonomous traffic systems management. In addition, it is worth noting that this paper explores the operational control of an AV platoon from a theoretical perspective, without fully considering passenger comfort, as well as “soft” instabilities (vehicles joining/leaving) and “hard” instabilities (technical failures/accidents). Future research will expand on these related aspects. Full article

In pursuit of sustainable development, worldwide adoption of hydrogen fuel cell vehicles (HFCVs) is growing to cut carbon emissions in the transportation sector. The construction of hydrogen refueling stations (HRSs) is the key to popularizing HFCVs. The popularity of HRSs is hindered by cost, site selection, and user expectations. Selecting mature gas stations with large passenger flow to expand HRSs can improve the accuracy of the hydrogen refueling network. Reducing the range anxiety of HFCV users to improve the path coverage of HFCVs is a favorable way to expand the hydrogen vehicle industry chain. Therefore, this study proposes a bi-level programming model, which considers hydrogen source (HS), hydrogen delivery mode (HDM), initial remaining range, range anxiety, and other factors. The upper-level model is designed to optimize economic costs, including the total chain cost of the HRS. The lower level aims to optimize the range anxiety of HFCV users and more accurately reflect their autonomy by controlling the maximum remaining range of the vehicle. Finally, the expressway in the Liaoning Province of China is taken as an example to verify that the optimization model had the advantages of low hydrogen cost and minimal range anxiety. The cost analysis of several HSs and HDMs was discussed from the perspective of the best site selected, and it was found that the Anshan HS using coal to produce hydrogen and the long tube trailer can provide lower hydrogen cost for the HRS. This method is generalizable to other regions or all types of HFCVs. Full article