Revealing the determinants of the intermodal transfer ratio between metro and bus systems considering spatial variations

Buses and metros are two main public transit modes, and these modes are crucial components of sustainable transportation systems. Promoting reciprocal integration between bus and metro systems requires a deep understanding of the effects of multiple factors on transfers among integrated public transportation transfer modes, i.e., metro-to-bus and bus-to-metro. This study aims to reveal the determinants of the transfer ratio between bus and metro systems and quantify the associated impacts. The transfer ratio between buses and metros is identified based on large-scale transaction data from automated fare collection systems. Meanwhile, various influencing factors, including weather, socioeconomic, the intensity of business activities, and built environment factors, are obtained from multivariate sources. A multivariate regression model is used to investigate the associations between the transfer ratio and multiple factors. The results show that the transfer ratio of the two modes significantly increases under high temperature, strong wind, rainfall, and low visibility. The morning peak hours attract a transfer ratio of up to 57.95%, and the average hourly transfer volume is 0.94 to 1.38 times higher at this time than in other periods. The intensity of business activities has the most significant impact on the transfer ratio, which is approximately 1.5 to 15 times that of the other independent variables. Moreover, an adaptative geographically weighted regression is utilized to investigate the spatial divergences of the influences of critical factors on the transfer ratio. The results indicate that the impact of a factor presents spatial heterogeneity and even shows opposite effects (in terms of positive and negative) on the transfer ratio in different urban contexts. For example, among the related socioeconomic variables, the impact of the housing price on the downtown transfer ratio is larger than that in the suburbs. Crowd density positively influences the transfer ratio at most stations in the northern region, whereas it shows negative results in the southern region. These findings provide valuable insights for public transportation management and promote the effective integration of bus and metro systems to provide enhanced transfer services

Global trends in research on cervicogenic headache: a bibliometric analysis

BackgroundThere has been a marked increase in cervicogenic headaches in recent years, significantly affecting sufferers’ daily lives and work. While several treatments exist for this type of headache, their long-term effects could be improved, and additional data from large clinical samples are needed. This study aims to systematically examine the current state of research in cervicogenic headaches through a bibliometric analysis, identify areas of current interest, and provide insight into potential future research directions.MethodsThis article examines research trends in the field of cervicogenic headache through a bibliometric analysis of scholarly articles in the field of cervicogenic headache over the past four decades. The bibliometric analysis method employed included searching the Web of Science database using topics related to cervicogenic headaches. Inclusion criteria were limited to articles and review papers on cervicogenic headaches published between 1982 and 2022. The retrieved dataset was then analyzed using R software and VOSviewer to identify the major research areas, countries and institutions, the most influential authors, journals and keywords, co-citations in the literature, and co-authorship networks.ResultsThis study analyzed 866 articles published between 1982 and 2022, involving 2,688 authors and generating 1,499 unique author keywords. Neuroscience and neurology were the primary focus, with participation from 47 countries, primarily led by the United States, which has the most published articles (n = 207), connections (n = 29), and citations (n = 5,238). In the cervicogenic headache study, which involved 602 institutions, the University of Queensland received the most significant number of citations (n = 876), and Cephalalgia was the journal with the most published articles and received the most local citations (n = 82) and highest growth (n = 36). Two hundred sixty-nine journals have published articles on cervicogenic headaches. Among researchers studying cervicogenic headache, Sjaastad O had the most published articles (n = 51) and citations (n = 22). The most commonly occurring keyword was “cervicogenic headache.” Except for the fourth most impactful paper, as determined by the Local Citation Score, which analyzed clinical treatments, all the top documents emphasized investigating the diagnostic mechanisms of cervicogenic headache. The most commonly occurring keyword was “cervicogenic headache.”ConclusionThis study used bibliometric analysis to provide a comprehensive overview of the current research on cervicogenic headaches. The findings highlight several areas of research interest, including the need for further investigation into the diagnosis and treatment of cervicogenic headaches, the impact of lifestyle factors on cervicogenic headaches, and the development of new interventions to improve patient outcomes. By identifying these gaps in the literature, this study provides a foundation for guiding future research to improve the diagnosis and treatment of cervicogenic headaches

Highly Stable and Conductive Microcapsules for Enhancement of Joule Heating Performance

Nanocarbons show great promise for establishing the next generation of Joule heating systems, but suffer from the limited maximum temperature due to precociously convective heat dissipation from electrothermal system to surrounding environment. Here we introduce a strategy to eliminate such convective heat transfer by inserting highly stable and conductive microcapsules into the electrothermal structures. The microcapsule is composed of encapsulated long-chain alkanes and graphene oxide/carbon nanotube hybrids as core and shell material, respectively. Multiform carbon nanotubes in the microspheres stabilize the capsule shell to resist volume-change-induced rupture during repeated heating/cooling process, and meanwhile enhance the thermal conductance of encapsulated alkanes which facilitates an expeditious heat exchange. The resulting microcapsules can be homogeneously incorporated in the nanocarbon-based electrothermal structures. At a dopant of 5%, the working temperature can be enhanced by 30% even at a low voltage and moderate temperature, which indicates a great value in daily household applications. Therefore, the stable and conductive microcapsule may serve as a versatile and valuable dopant for varieties of heat generation systems

Research on Vehicle Swing Model based on Road Structure: Driving Safety and Comfort

The vehicle swing is a major factor on driving safety and comfort, while the acceleration noise is exactly a description of vehicle swing. Thus, acceleration noise can be a good evaluation indicator of driving safety and comfort. First, the vehicle forces are analyzed in three dimensions, and six vehicle swing models (VSMs) are established based on different road structure by combining acceleration noise theory. Then, the time discrete method is used to further discretize these models for easy calculation and application in practice projects. Finally, a large number of simulation experiments are performed with appropriate roads. The simulation results show that curvature radius, ramp angle, and superelevation slope angle are the major influence factors to driving safety and comfort. This paper not only provides mathematic expression in driving safety and comfort evaluation, but also has certain reference to the geometric design during the design of new highway

Study on Autonomous Path Planning by Mobile Robot for Road Nondestructive Testing Based on GPS

International audienceThis paper analyses the statements of video imaging, CT, infrared thermography and radiography applied in the road nondestructive testing, and design a path planning mobile robot with GPS positioning which can remarkably increase the efficient of road nondestructive testing. Besides, appropriate algorithm for nondestructive testing on the road autonomous mobile robot path planning is given. This method is simplicity, versatility, and efficiency. The mobile robot are selected for example and the simulation results represent the effectiveness of this method

Urban Expressway Travel Time Prediction Method Based on Fuzzy Adaptive Kalman Filter

Abstract: According to the poor adaptive ability of traditional filter algorithm in the estimation for traffic state and travel time with Kalman filter, an improved fuzzy adaptive Kalman filtering method was proposed. The new interest of observation noise was defined, and the fuzzy logic was used to adjust the importance weights of system noise and observation noise through on-line monitoring the interest changes, which changed the trust and utilization degree of the model for the observation information, and this made the filter eventually tend to be stable. To guarantee the real-time performance of system, a direct input- output fuzzy membership function matching method was put forward to take the place of fuzzy reasoning. The method was tested on the urban expressway in Guangzhou by using real-time detection data, and the result show that the traffic state estimation model had better tracking ability than conventional Kalman filter, and results of travel time prediction show that there was a slight difference between the prediction value and that of actual observation in free traffic flow state, and the relative error was under 15 % in traffic congested state. The precision and applicability of this method were acceptable, and it can be used to provide a basis for travel time of urban expressway in traffic control and guidance system

Multi-Agent Based Microscopic Simulation Modeling for Urban Traffic Flow

Traffic simulation plays an important role in the evaluation of traffic decisions. The movement of vehicles essentially is the operating process of drivers, in order to reproduce the urban traffic flow from the micro-aspect on computer, this paper establishes an urban traffic flow microscopic simulation system (UTFSim) based on multi-agent. The system is seen as an intelligent virtual environment system (IVES), and the four-layer structure of it is built. The road agent, vehicle agent and signal agent are modeled. The concept of driving trajectory which is divided into LDT (Lane Driving Trajectory) and VDDT (Vehicle Dynamic Driving Trajectory) is introduced. The “Link-Node” road network model is improved. The driving behaviors including free driving, following driving, lane changing, slowing down, vehicle stop, etc. are analyzed. The results of the signal control experiments utilizing the UTFSim developed in the platform of Visual Studio. NET indicates that it plays a good performance and can be used in the evaluation of traffic management and control

Multivariate Transfer Passenger Flow Forecasting with Data Imputation by Joint Deep Learning and Matrix Factorization

Accurate forecasting of the future transfer passenger flow from historical data is essential for helping travelers to adjust their trips, optimal resource allocation and alleviating traffic congestion. However, current studies have mainly emphasized predicting traffic parameters for a single type of transport, while lacking research into transfer passenger flow influenced by multiple factors across different transport modes. Additionally, efficient traffic prediction relies on high-quality traffic data, yet data loss issues are inevitable but often ignored. To fill these gaps, we present for the first time a reliable joint long short-term memory with matrix factorization deep learning model (i.e., Joint-IF) for accurate imputation and forecasting of transfer passenger flow between metro and bus. This hybrid Joint-IF model uses a repair-before-prediction strategy to deliver the final high-quality outputs. In particular, we simulate a variety of missing combinations under the natural conditions and apply a low-rank matrix factorization to infer those lost values. In addition, we investigate the effects of crucial parameters and spatiotemporal features on transfer flow prediction. To validate the effectiveness of Joint-IF, a large series of experiments are carried out for models’ comparison and validation on the real-world transfer passenger flow dataset of the Shenzhen public transport system, and the results show that the proposed Joint-IF performs better for both imputation and forecasting of transfer passenger flow relative to the baseline models in terms of accuracy and stability

Sulfur- and Nitrogen-Doped, Ferrocene-Derived Mesoporous Carbons with Efficient Electrochemical Reduction of Oxygen

Development of inexpensive and sustainable cathode catalysts that can efficiently catalyze the oxygen reduction reaction (ORR) is of significance in practical application of fuel cells. Herein we report the synthesis of sulfur and nitrogen dual-doped, ordered mesoporous carbon (SN-OMCs), which shows outstanding ORR electrocatalytic properties. The material was synthesized from a surface-templating process of ferrocene within the channel walls of SBA-15 mesoporous silica by carbonization, followed by in situ heteroatomic doping with sulfur- and nitrogen-containing vapors. After etching away the metal and silica template, the resulting material features distinctive bimodal mesoporous carbon frameworks with high nitrogen Brunauer–Emmett–Teller specific surface area (of up to ∼1100 m²/g) and uniform distribution of sulfur and nitrogen dopants. When employed as a noble-metal-free electrocatalyst for the ORR, such SN-OMC shows a remarkable electrocatalytic activity; improved durability and better resistance toward methanol crossover in oxygen reduction can be observed. More importantly, it performs a low onset voltage and an efficient nearly complete four-electron ORR process very similar to the observations in commercial 20 wt % Pt/C catalyst. In addition, we also found that the textural mesostructure of the catalyst has superseded the chemically bonded dopants to be the key factor in controlling the ORR performance

A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting

Short-term traffic speed prediction is a promising research topic in intelligent transportation systems (ITSs), which also plays an important role in the real-time decision-making of traffic control and guidance systems. However, the urban traffic speed has strong temporal, spatial correlation and the characteristic of complex nonlinearity and randomness, which makes it challenging to accurately and efficiently forecast short-term traffic speeds. We investigate the relevant literature and found that although most methods can achieve good prediction performance with the complete sample data, when there is a certain missing rate in the database, it is difficult to maintain accuracy with these methods. Recent studies have shown that deep learning methods, especially long short-term memory (LSTM) models, have good results in short-term traffic flow prediction. Furthermore, the attention mechanism can properly assign weights to distinguish the importance of traffic time sequences, thereby further improving the computational efficiency of the prediction model. Therefore, we propose a framework for short-term traffic speed prediction, including data preprocessing module and short-term traffic prediction module. In the data preprocessing module, the missing traffic data are repaired to provide a complete dataset for subsequent prediction. In the prediction module, a combined deep learning method that is an attention-based LSTM (ATT-LSTM) model for predicting short-term traffic speed on urban roads is proposed. The proposed framework was applied to the urban road network in Nanshan District, Shenzhen, Guangdong Province, China, with a 30-day traffic speed dataset (floating car data) used as the experimental sample. Results show that the proposed method outperforms other deep learning algorithms (such as recurrent neural network (RNN) and convolutional neural network (CNN)) in terms of both calculating efficiency and prediction accuracy. The attention mechanism can significantly reduce the error of the LSTM model (up to 12.4%) and improves the prediction performance