Search Results (72)

With the electrification of generator sets, electric locomotives, new energy vehicles, and other industries, AC motors subject bearings to an electric field environment, leading to galvanic corrosion due to the use of variable frequency power supply drives. The phenomenon of bearing discharge breakdown in subway traction motors is a critical issue in understanding the relationship between shaft current strength and the extent of bearing damage. This paper analyzes the mechanism of impulse discharge that leads to galvanic corrosion damage in bearings at a microscopic level and conducts electric thermal coupling simulations of the traction motor bearing discharge breakdown process. It examines the temperature rise associated with lubricant film discharge breakdown during the dynamic operation of the bearing and investigates how breakdown channel parameters and operational conditions affect the temperature rise in the micro-region of bearing lubrication. Ultimately, the results of the electric thermal coupling simulation are validated through experimental tests. This study revealed that in an electric field environment, the load-bearing area of the outer ring experiences significantly more severe corrosion damage than the inner ring, whereas non-bearing areas remain unaffected by electrolytic corrosion. When the inner ring reaches a speed of 4500_rpm, the maximum widths of electrolytic corrosion pits for the outer and inner rings are measured at 89 um and 51 um, respectively. Additionally, the highest recorded temperatures for the breakdown channels in the outer and inner rings are 932 °C and 802 °C, respectively. Furthermore, as the inner ring speed increases, both the width of the electrolytic corrosion pits and the temperature of the breakdown channels rise. Specifically, at inner ring speeds of 2500_rpm, 3500_rpm, and 4500_rpm, the widths of the electrolytic pits in the outer ring raceway load zone were measured at 34 um, 56 um, and 89 um, respectively. The highest temperatures of the lubrication film breakdown channels were recorded as 612 °C, 788 °C, and 932 °C, respectively. This study provides a theoretical basis and data support for the protective and maintenance practices of traction motor bearings. Full article

The COVID-19 pandemic has highlighted critical vulnerabilities in urban logistics systems, particularly in last-mile delivery. To enhance logistics resilience and efficiency, the Korean government has initiated an innovative project that repurposes idle spaces in subway vehicle bases within the Seoul Metropolitan Area into logistics centers. This study proposes a comprehensive multi-criteria evaluation framework combining the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assess the suitability of ten candidate sites. The evaluation criteria span four dimensions, facility, geographical, environmental, and social factors, derived from the literature and expert consultations. AHP results indicate that geographical factors, especially proximity to urban centers and major logistics facilities, hold the highest weight. Based on the integrated analysis using TOPSIS, the most suitable locations identified are Sinnae, Godeok, and Cheonwang. The findings suggest the strategic importance of aligning infrastructure development with spatial accessibility and stakeholder cooperation. Policy implications include the need for targeted investment, public–private collaboration, and sustainable logistics planning. Future research is encouraged to incorporate dynamic data and consider social equity and environmental impact for long-term urban logistics planning. Full article

Based on the current research status of fault prediction in rail transit reliability, this paper proposes a cumulative failure rate prediction method for key components of subway vehicles based on the RF-CNN-LSTM model. The article describes the prediction method based on cumulative failure rate data and takes the subway EDCU as an example of cumulative failure rate prediction. Three models, ARIMA, MLP, and LSTM, are introduced and compared with the RF–CNN–LSTM model by R² and adjusted R² index. The results show that the RF–CNN–LSTM model can predict the failure rate of the underground door controller well, with accuracy rates of 99.78% and 97.88%. Based on the prediction results, the cumulative failure rate of the EDCU peaks in about 10 years at 4.5% and 10.6%, respectively; the maintenance strategies can be adjusted through the actual situation of the EDCU to reduce maintenance costs and optimize maintenance plans. Full article

To address the challenges posed by the interdependence between subway vehicle scheduling and maintenance planning, which complicates joint optimization modeling and solution processes, this study proposes a spatiotemporal network-based joint optimization method for subway vehicle preventive maintenance and scheduling. First, based on spatiotemporal network theory, the transition process between train operation scheduling and preventive maintenance states is analyzed, and a spatiotemporal state network graph is constructed to represent the temporal and spatial transitions of subway vehicles throughout the planning period. The vehicle’s operational workflow is represented as a path within this network. Next, leveraging the generated spatiotemporal network path set, a joint optimization model for preventive maintenance and scheduling is formulated, integrating optimization objectives and constraints to achieve coordinated optimization. Finally, an improved genetic algorithm is employed to solve the model and determine the optimal scheduling and maintenance strategy. The experimental results demonstrate that the proposed method effectively addresses the challenges in modeling and solving the joint optimization problem, enabling efficient coordination between maintenance and scheduling while enhancing the overall operational efficiency in subway vehicle management. Full article

Urban rail transit systems, while alleviating traffic congestion, generate environmental vibrations that impact adjacent structures and residents, particularly during train acceleration and deceleration near stations. Existing research predominantly focuses on constant-speed operations, leaving a gap in understanding vibration propagation during variable-speed phases. This study investigates vibration characteristics and propagation behaviors using field measurements from a subway station in Foshan, China. Wireless vibration sensors were deployed across nine measuring points at varying distances (15–35 m) from the subway station’s external wall, capturing time-domain and frequency-domain data during train operations. The analysis incorporated China’s JGJ/T 170-2009 standards, evaluating vibration acceleration levels (VAL) and 1/3 octave band spectra. Key findings revealed background vibrations (0–10 Hz) exhibited negligible interference, whereas vehicle-induced vibrations (40–60 Hz) demonstrated directional disparities: urban-bound trains produced higher accelerations (0.004–0.008 m/s² vertically) than suburban-bound ones (0.001–0.005 m/s²) due to track damping measures and propagation distance. Vibration attenuation with distance was found to be non-linear, influenced by soil hardening and train speed. Vertical vibrations near the station (15 m) approached the 70 dB regulatory limit, emphasizing proximity risks. Doppler effects were observed during train acceleration/deceleration, though data limitations precluded precise quantification of speed impacts. This work supplements knowledge on non-uniform train-induced vibrations, offering insights for urban planning and mitigation strategies. Full article

This study selected green spaces from three residential areas in Hefei as the research subjects, combining behavioral observation methods and a natural experiment to collect behavioral data from 2010 and 2024. The data were then compared using Poisson regression models. Additionally, home visits were conducted to gather residents’ perceptions of the factors contributing to the decline in vitality. Based on the survey data, multilevel regression analysis was performed to explore the decline in RQGS usage vitality and its influencing factors in the context of rapid urbanization. This study found a significant decline in green space visits, particularly during the afternoon (16:00–18:00) and in areas adjacent to roadways. The main influencing factors include emerging leisure choices (such as taking the subway to large parks or preferring indoor activities) and residents’ satisfaction with RQGS characteristics (such as functional zoning, noise pollution, and neighborhood familiarity). Notably, there was no significant correlation between “disposable leisure time” and visit frequency. These findings suggest that, despite the inherent advantages of proximity, the vitality of RQGS faces increasing challenges due to emerging diverse leisure demands and growing environmental disturbances. In contrast to the traditional emphasis on accessibility, this study recommends that future RQGS planning prioritize functional zoning (e.g., dog-walking areas, sports zones), address the needs of vulnerable groups, and focus on mitigating vehicle noise and air pollution rather than merely expanding parking facilities. Interventions should be scheduled for the afternoon and emphasize strengthening community interaction and cohesion to enhance user experience. This research provides valuable scientific evidence and practical guidance for urban planners and policymakers to optimize residential green spaces in the context of rapid urbanization, offering new perspectives for the empirical evaluation of RQGS upgrades. Full article

With the rapid development of urban rail transit, the intensity and impact range of train-induced vibrations are increasing. Investigating the transmission characteristics and attenuation patterns of these vibrations in track structures aids in understanding train-induced environmental vibrations. This study conducted rail impact experiments on a long sleeper integrated slab of a straight section of a subway tunnel. The hammer struck the rail at various positions, and acceleration sensors recorded the responses of the rail, slab, and tunnel. In order to determine the impact force, the vertical wheel–rail force and the vibration response of track structures were measured. Then, the Lance-LC1304B force hammer was selected for the experiment, and the hammer impact force reached 30 kN, the magnitude of which reached the measured wheel–rail force size for the line. Based on the results of the impact tests, the vibration attenuation characteristics of the track structure were analyzed. Accordingly, reference values for the truncation time and truncation distance in the vehicle–track coupled dynamics model’s moving window were provided. By comparing the results of the hammering experiment with the train-induced vibration results, the main excitation frequencies during train operation were determined. These findings provide valuable insights for the development of rail transit systems. Full article

Demand Responsive Transit (DRT) is gaining attention as a flexible and efficient solution for connecting urban transit hubs, but challenges such as travel time variability and punctuality remain significant barriers. This study develops a robust optimization framework with variable travel speed to address these issues, minimizing user and operator costs while reducing transfer waiting times. The framework incorporates variable travel speeds and employs a genetic algorithm to optimize routes and operations compared to many studies using constant commercial speed. Experiments conducted in Hwaseong, South Korea, analyzed scenarios with varying service rates, vehicle capacities, and detour ratios. Results show that implementing punctuality-constrained DRT reduces total travel times by 14% compared to subways and 36% compared to buses, highlighting its potential to significantly improve user convenience and operational efficiency. The findings suggest that carefully designed DRT systems with highly reliable punctuality can enhance urban mobility by integrating seamlessly with existing transit networks, providing a cost-effective and reliable alternative to traditional public transport. Full article

Urban air mobility (UAM) and drones can significantly improve traffic movement in saturated cities because the skies above them are not frequently used; furthermore, they do not require large-scale infrastructure, like roads and subways do. Thus, UAM vehicles and drones present themselves as new means of transportation in cities. They can be rapidly deployed if their operational safety is secured. However, to date, no precise numerical study has been conducted on the safety of UAM vehicles and drones. In this study, the accident rates of UAM vehicles and drones are predicted based on the accident rates of conventional aircraft. Additionally, control measures for UAM vehicles and drones are presented at a basic level. The results can be summarized as follows: First, in terms of accident rates, for a projected total UAM vehicle flight distance of 650 km and 177,147 h of flight in Seoul in 2035, 0.000221 crashes, 0.45 takeoff/landing accidents, and 0.0011446 deaths are expected. Second, if drones handle 0.5% of the logistics in Seoul in 2035, 38.35 crashes and 7.51 takeoff/landing accidents are projected per year. However, these numbers are plausible only if the infrastructure required for UAM vehicle and drone flights, such as taxiways and flight paths, is built similarly to that for large aircraft. Additionally, UAM vehicles and drones, as with large aircraft, can cause serious damage to facilities and human lives on the ground in the event of a crash. Therefore, thorough response mechanisms for crashes are required even if the crash probability is extremely low. Finally, integration with smart city systems is suggested to monitor UAM vehicle and drone flights and the safety of urban residents. The transportation services of smart cities include emergency dispatch and disaster notification services, which help in immediately notifying the degree of risk to potentially affected urban residents and facilities in the event of a UAM vehicle/drone crash or an emergency. The transportation services of smart cities are also typically equipped with accident handling processes. Therefore, integrating UAM and drone systems into smart city systems is highly recommended. Full article

The deployment of electric vehicle charging stations (EVCSs) is crucial for the large-scale adoption of electric vehicles and the sustainable energy development of global cities. However, existing research on the spatial distribution of EVCSs has provided limited analysis of spatial equity from the perspective of supply–demand relationships. Furthermore, studies examining the influence of the built environment on EVCS accessibility are scarce, and often rely on single methods and perspectives. To explore the spatial characteristics of EVCS accessibility and its influencing factors, using multi-source urban spatial data, this study initially employs the Gaussian two-step floating catchment area (G2SFCA) method to measure and analyze the spatial distribution characteristics of EVCS accessibility in Guangzhou, China, with consideration of supply–demand relationships. Subsequently, it integrates the MGWR and random forest (RF) models to comprehensively investigate the impact mechanism of the built environment on EVCS accessibility from the perspectives of spatial heterogeneity and non-linear relationship. The results show that the EVCS accessibility exhibits a “ higher in the west and lower in the east, with extreme core concentration” distribution pattern, and has significant spatial autocorrelation. The built-environment variables exhibit different scale effects and spatial non-stationarity, with widespread non-linear effects. Among them, the auto service, distance to regional center, and distance to subway station play important roles in influencing EVCS accessibility. These findings offer important guidance for the efficient and equitable layout of EVCSs in high-density cities. Full article

Dense urban environments pose significant challenges when it comes to detecting and measuring crowd size due to their nature of being free-flow environments containing many dynamic factors. In this paper, we use a wireless sensor network (WSN) to perform device-free crowd size estimation in a subway station. Our sensing solution uses the change in attenuation of the communication links between sensor nodes to estimate the number of people standing on the platform. In order to achieve this, we use the same attenuation information coming from the WSN to detect the presence of a rail vehicle in the station and compensate for the channel fading caused by the introduced rail vehicle. We make use of two separately trained regression models depending on the presence or absence of a rail vehicle to estimate the people count. The detection of rail vehicles occurred with a near-perfect accuracy. When evaluating the resulting estimation model on our test set, we achieved a mean average error of 3.567 people, which is a significant improvement over 6.192 people when using a single regression model. This demonstrates that device-free sensing technologies can be successfully implemented in dynamic environments by implementing detection techniques and using different regression models depending on the environment’s state. Full article

A smart city (SC) includes different systems that are highly interconnected. Transportation and energy systems are two of the most important ones that must be operated and planned in a coordinated framework. In this paper, with the complete implementation of the SC, the performance of each of the network elements has been fully analyzed; hence, a nonlinear model has been presented to solve the operation and planning of the SC model. In the literature, water treatment issues, as well as energy hubs, subway systems (SWSs), and transportation systems have been investigated independently and separately. A new method of subway and electric vehicle (EV) interaction has resulted from stored energy obtained from subway braking and EV parking. Hence, considering an SC that simultaneously includes renewable energy, transportation systems such as the subway and EVs, as well as the energy required for water purification and energy hubs, is a new and unsolved challenge. In order to solve the problem, in this paper, by presenting a new system of the SC, the necessary planning to minimize the cost of the system is presented. This model includes an SWS along with plug-in EVs (PEVs) and different distributed energy resources (DERs) such as Photovoltaics (PVs), Heat Pumps (HPs), and stationary batteries. An improved grey wolf optimizer has been utilized to solve the nonlinear optimization problem. Moreover, four scenarios have been evaluated to assess the impact of the interconnection between SWSs and PEVs and the presence of DER technologies in the system. Finally, results were obtained and analyzed to determine the benefits of the proposed model and the solution algorithm. Full article

In this study, we consider the introduction of new mobility services and technologies into the megacity of Beijing, China, as per developed strategy and action plans, in order to investigate their potential contribution to sustainable mobility. This includes population relocation (decentralization), the construction of new rail lines, the introduction of shared bike services as a feeder to subway stations, the electrification of passenger vehicles and the adoption of automated and shared vehicles. The well-established, system dynamics-based MARS model is adapted to Beijing and further improved via the inclusion of these new services, technologies and policies. We find that decentralization can have a profound effect on overall sustainability if not considered in conjunction with other policies and that new rail lines and shared bikes may only have benefits in specific zones. Shared and automated vehicles could increase VKT by 60% and reduce active and public transport trips by a quarter. As such, nuanced integrated policy approaches will be required that are similar to those currently in place, such as imposed car shedding and taxi fleet control. Full article

To further enhance the crashworthiness of subway vehicle anti-climb energy-absorbing devices, this paper proposes a novel collapsible structure, which is embedded with honeycomb aluminum blocks and consists of an inner and outer double-layer square tube. An impact finite element model of the subway vehicle’s anti-climb energy-absorbing device was established using LS-DYNA (v.17.2). The effects of the connecting diaphragm thickness, the inner and outer tube thicknesses of the thin-walled tubes, and the yield strength of the honeycomb aluminum on the energy absorption and impact force of the anti-climb energy-absorbing device were investigated. The results indicate that changes in the inner and outer tube thicknesses of the thin-walled tubes significantly affect the energy absorption and impact force of the anti-climb energy-absorbing device. However, changes in the connecting diaphragm thickness only increase the maximum energy absorption by up to 7.5% but have a significant impact on the maximum peak force. It was also found that the difference in energy absorption due to changes in the yield strength of the honeycomb aluminum is only 3.6%, suggesting that the yield strength of the honeycomb aluminum and the connecting diaphragm thickness have a limited influence on the crashworthiness design of the anti-climb energy-absorbing device. Furthermore, a multi-objective surrogate model characterizing the specific energy absorption and maximum peak force was established for the aforementioned four influential parameters using the response surface method. The model was then optimized using a genetic algorithm, resulting in optimal parameters that increased the energy absorption and specific energy absorption by 27.3% and 13.8%, respectively, compared to the original values, leading to a significant improvement in crashworthiness. The findings provide valuable references for and insights into the crashworthiness design and optimization of subway vehicles’ anti-climb energy-absorbing devices. Full article

New energy vehicles (NEVs) offer a sustainable private transportation alternative. Charging points are the source of power for NEVs; thus, their construction can significantly lower the costs associated with their use, thereby encouraging their adoption. This could potentially impact the subway demand, which is reflected by the relationship between housing prices and subway proximity in this paper, leading to a decrease in the premium for properties near subway stations. Utilizing a comprehensive data set of 599,916 housing transactions in Beijing and a difference-in-differences approach based on the hedonic price model, we found that China’s NEV charging point subsidy policy significantly decreases the subway premium of housing prices and mitigates housing price disparities. Furthermore, we explored the spatial heterogeneity of this impact, finding that the policy has less influence on residents living near the city center. Our findings indicate that the policy has resulted in a considerable decrease in the subway premium, ranging from ¥19,217 to ¥55,936 ($2745 to $7991) per transaction, which is equivalent to the annual income for an average individual at the time of the policy. The results address the far-reaching implications and significant role of NEV development in urban transportation. Full article