Departure Time Research Articles

Investigation on the Impact of the 2022 Luding M6.8 Earthquake on Regional Low-Frequency Time Code Signals in Northern China

Low-Frequency Time Code time service technology, as an important means of ground-based radio time dissemination, can be divided into ground wave zone and sky wave zone according to different receiving and transmitting distances. Ground waves travel primarily along the Earth’s surface, while sky waves propagate over long distances by reflecting off the ionosphere. This paper utilizes the raw observation data received by the Low-Frequency Time Code dissemination monitoring stations before and after the 6.8 magnitude earthquake in Luding, Sichuan, China on 5 September 2022. A Low-Frequency Time Code time service monitoring system was built in Xi’an to continuously monitor the 68.5 kHz time signal broadcast by the BPC station. The data was then processed and analyzed through visualization. Simultaneously, we analyzed the signal fluctuation for multiple days before and after the earthquake to see the changes in the Low-Frequency Time Code signal during the earthquake. By combining seismic activity, solar activity, and geomagnetic data, this study aims to explore the causes and patterns of signal parameter variations. The results show that the field strength of the Low-Frequency Time Code signal fluctuated significantly within a short period during the earthquake. The value began to decrease about 60 min before the earthquake, dropping by approximately 8.9 dBμV/m, and gradually recovered 2 h after the earthquake. The phase also mutated by 1.36 μs at the time of the earthquake, and the time deviation fluctuated greatly compared to the 2 days before and after. Earthquake occurrences influence ionospheric variations, leading to changes in the sky wave propagation of Low-Frequency Time Code signals. Analysis of the influence of earthquakes on the propagation of Low-Frequency Time Code signals can provide references for research on Low-Frequency Time Code signal propagation models and earthquake prediction.

Efficacy and safety of Le Fort colpocleisis in the treatment of stage III-IV pelvic organ prolapse

BackgroundLe Fort colpocleisis is an obliterative surgery for the treatment of pelvic organ prolapse (POP). In this study, we aimed to investigate the efficacy and safety of Le Fort colpocleisis in the treatment of patients with stage III-IV POP.MethodsThe study was designed as a retrospective analysis of a single-center case series. Patients with stage III-IV POP treated with Le Fort colpocleisis were included. Perioperative indicators, subjective and objective outcomes and complications were assessed. The POP-Quantification (POP-Q) system was used for objective outcome evaluation. Pelvic Floor Distress Questionnaire (PFDI-20) and Pelvic Floor Impact Questionnaire (PFIQ-7) were used for subjective outcome evaluation.ResultsA total of 54 patients with complete pre- and postoperative data completed follow-up and were included in our study. The mean ± standard deviation of operative time, intraoperative blood loss, recovery activity time, postoperative hospitalization time and total hospitalization time were 146.85 ± 37.01 min, 92.04 ± 68.31 ml, 2.81 ± 0.85 days, 6.5 ± 2.11 days and 13.52 ± 4.78 days, respectively. Within the median follow-up period of 38.5 months, the objective cure rate of Le Fort colpocleisis reached 98.15% (53/54), and the subjective cure rate reached 92.59% (50/54). No serious complications were reported during the perioperative period and follow-up period.ConclusionLe Fort colpocleisis is an effective and safe procedure for the treatment of severe POP. Because of its lower operative risk, better subjective and objective outcomes, lower rates of prolapse recurrence and perioperative complications, Le Fort colpocleisis should be considered as the recommended procedure for elderly patients with severe POP (stage III-IV).

In Situ Synthesis of Hypercrosslinked Polymer as Stationary Phase for Capillary Gas Chromatography

Hypercrosslinked polymers (HCPs) constructed by the Friedel–Crafts alkylation reaction of aromatic compounds have emerged as a new class of porous materials with unique merit. Herein, a HCP named HCP-TPB was coated onto a capillary column through in situ synthesis. The prepared column exhibited a nonpolar nature, and the column efficiency for n-dodecane was 3003 plates m−1. Moreover, the relative standard deviations of retention time and peak area for six replicate injections of the C3–C6 were lower than 0.1% and 1.5%, respectively. The results of this study showed that it is very promising to utilize HCPs as stationary phases for the separation of C3–C6.

Research on airport express train schedule optimization based on demand-driven air-rail intermodal transportation

The optimization of the train frequency of the Airport express line (AEL) is crucial for improving the efficiency of air-rail intermodal transport. It directly influences passenger transfer convenience and overall service quality, thereby bolstering the competitiveness of the transport system This study focuses on the optimization of “AEL and Flight Succession” in the context of air-rail intermodal transport. By analyzing the departure and landing time of airport flights, we assess the demand from various passenger flows and identify key factors that impact the connection between the AEL and flights. Based on these factors, we develop a demand-driven optimization model for AEL frequency, aimed at minimizing total travel time and the number of unserved passengers. A simulated annealing algorithm is employed to solve this model. The Lanzhou-Zhongchuan AEL serves as a case study for validation. The results demonstrate that the optimized schedule reduces total passenger travel time costs by 0.93% and 3.82%, respectively, while accounting for passenger time sensitivity and fairness principles, with a difference of 2.89% between these scenarios. In addition, the optimization scheme decreases the number of unserved passengers by 14.7% and reduces the percentage of flights and trains failing to meet occupancy constraints by 17%. This study illustrates that the schedule optimization strategy not only effectively increases the number of served passengers but also significantly reduces total intermodal and commuter travel time. Such findings provide a solid scientific foundation for AEL operations and management to develop a more efficient and rational train schedule in the context of air-rail intermodal transport.

Wavelength multicasting quantum clock synchronization network

Quantum clock synchronization (QCS) can measure out the high-precision clock difference among distant users, which breaks through the standard quantum limit by employing the properties of quantum entanglement. Currently, the wavelength division multiplexed QCS network has been demonstrated with a spontaneous parametric down-conversion entangled photon source. In this paper, we propose a more efficient QCS network scheme with the wavelength multicasting entangled photon source, which can decrease at least 25% of wavelength channel consumption under the identical network scale. Afterwards, a four node QCS network is demonstrated, where the wavelength multicasting entangled photon source is utilized with dual-pumped four-wave mixing silicon chip. The experimental results show that the measured time deviation is 3.4 ps with an average time of 640 s via the multiple fiber links of more than 10 km.

Bus Schedule Time Prediction Based on LSTM-SVR Model

With the acceleration of urbanization, urban bus scheduling systems are facing unprecedented challenges. Traditional bus scheduling provides the original schedule time and the planned time of arrival at the destination, where the schedule time is the departure time of the bus. However, various factors encountered during the drive result in significant differences in the driving time of the bus. To ensure timely arrivals, the bus scheduling system has to rely on manual adjustments to optimize the schedule time to determine the actual departure time. In order to reduce the scheduling cost and align the schedule time closer to the actual departure time, this paper proposes a dynamic scheduling model, LSTM-SVR, which leverages the advantages of LSTM in capturing the time series features and the ability of SVR in dealing with nonlinear problems, especially its generalization ability in small datasets. Firstly, LSTM is used to efficiently capture features of multidimensional time series data and convert them into one-dimensional effective feature outputs. Secondly, SVR is used to train the nonlinear relationship between these one-dimensional features and the target variables. Thirdly, the one-dimensional time series features extracted from the test set are put into the generated nonlinear model for prediction to obtain the predicted schedule time. Finally, we validate the model using real data from an urban bus scheduling system. The experimental results show that the proposed hybrid LSTM-SVR model outperforms LSTM-BOA, SVR-BOA, and BiLSTM-SOA models in the accuracy of predicting bus schedule time, thus confirming the effectiveness and superior prediction performance of the model.

Electric vehicle drivers’ choices of expressway usage and peak avoidance: an empirical analysis considering the random effects among individuals

ABSTRACT Traffic congestion is a persistent challenge in urban areas, particularly with increased private car ownership post-COVID-19. Traditional administrative measures to manage traffic demand have proven unsustainable, necessitating more effective strategies. This study examines trajectory data from 3,064 commuting (CMT) and 2,690 non-commuting (Non-CMT) electric vehicles in Shanghai to analyze how travel purposes, distances, directions, and departure times influence expressway usage and peak avoidance decisions. It identifies significant differences in choices between CMT and Non-CMT users, highlighting the need for personalized travel management methods based on vehicle usage patterns. Route management strategies should focus on commuting trips for CMT vehicle users, while spatial control measures considering the trip distance and direction can reduce Non-CMT vehicle users’ dependence on expressways during peak hours. This research contributes to enhancing our understanding of the heterogeneity of travelers’ behaviors and offers personal and practical insights for managing traffic congestion sustainably in metropolitan cities.

Travel route recommendation with a trajectory learning model

This study addresses a critical issue in location-based services: travel route recommendation. It leverages historical trajectory data to predict the actual route on a road network from a starting point to a destination, given a specific departure time. However, capturing the latent patterns in complex trajectory data for accurate route planning presents a significant challenge. Existing route recommendation methods commonly face two major problems: first, inadequate integration of multi-source data, which fails to fully consider the potential factors affecting route choice; and second, limited capability to capture road network characteristics, which restricts the effective application of node features and negatively impacts recommendation accuracy. To address these issues, this research introduces a Trajectory Learning Model for Route Recommendation (TLMR) based on deep learning techniques. TLMR enhances the understanding of user route choice behavior in complex environments by integrating multi-source data. Moreover, by incorporating road network features, TLMR more effectively captures and utilizes the structural and dynamic information of the road network. Specifically, TLMR first employs a Position-aware Graph Neural Network to learn features of intersections from the road network, incorporating context features like weather and traffic conditions. Then, it integrates this information through neural networks to predict the next intersection. Finally, a beam search algorithm is applied to generate and recommend multiple candidate routes. Extensive experiments on four large real-world datasets demonstrate that TLMR outperforms existing methods in four key performance metrics. These results prove the effectiveness and superiority of TLMR in route recommendation.

Response inhibition as a critical executive function in differentiating attention-deficit/hyperactivity disorder from autism spectrum disorder: a comprehensive attention test study.

Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) are both associated with impairment in executive function, particularly in complex attention. Although previous studies using clinical assessments have attempted to delineate differences between these disorders, the findings have been inconclusive. Our study aims to elucidate the differences of endophenotype between ASD, ADHD, and their co-occurring condition utilizing a uniform computerized test. The study included children diagnosed with ASD, ASD co-occurring with ADHD (ASD+ADHD), or ADHD who completed the comprehensive attention test (CAT) at Severance Hospital between October 2013 to May 2023. We excluded children with intellectual disability and comorbid major psychiatric or neurologic disorders possibly affecting attention measurement. The participants were categorized into three groups for the comparative analysis of CAT measures: (a) ASD (n=112), (b) ASD+ADHD (n=155), and (c) ADHD (n=104). The study also conducted an exploratory analysis utilizing multivariate linear regression analysis to examine the association between the CAT measures and parent-reported scales. Notably, the ASD+ADHD and ADHD groups exhibited higher frequency of commission errors (CE) and perseveration errors (PE) compared to the ASD group. In the exploratory analysis, a significant negative association was observed between reaction time (RT) and both the social communication questionnaire (SCQ) and the child behavior checklist (CBCL) externalization scores in the ASD+ADHD and ADHD groups. The ASD+ADHD group tended to show higher standard deviation of reaction time (RTSD) compared to the ASD group. Our findings suggest that impaired response inhibition is more pronounced in ADHD compared to ASD. We propose altered visual attention, reflecting response inhibition, may serve as potential endophenotypic markers differentiating ADHD from ASD in attentional assessment. Elevated RTSD in the ASD+ADHD group demonstrates additive pathology, suggesting that the neurological mechanisms underpinning impaired sustained attention may differ between the two conditions.

Attendance patterns of provisioned Australian humpback dolphins (Sousa sahulensis) in Tin Can Bay, Australia – Further indication of male bonding and alliance

Tin Can Bay, Queensland, is one of four free-ranging dolphin feed provisioning sites in Australia and the only one featuring Australian humpback dolphins (Sousa sahulensis). The site attracts the same 5–10 dolphins a year, most of which return regularly, and some which have been in attendance since the 1990s. Historical records were obtained for the period March 2010 to March 2021 covering dolphin attendance to the provisioning station including dolphin name, arrival time, and departure time. Tourist numbers were also obtained which included the total number and how many of that total directly took part in provisioning. Attendance of dolphins to the provisioning site has increased over the years both in frequency and total numbers. There was evidence of group cohesion between four particular dolphins (one mother-offspring pair and one male pair). The strongest pairing was seen between two males, indicating bond formation in adult male humpback dolphins, which may be indicative of an alliance. Specific events were also looked at in detail including cyclones, flooding COVID-19 restrictions, which reduced dolphin and/or human attendance.

Research on Distributed Autonomous Timekeeping Algorithm for Low-Earth-Orbit Constellation

The time of a satellite navigation system is primarily generated by the main control station of the ground system. Consequently, when ground stations fail, there is a risk to the continuous provision of time services to the equipment and users. Furthermore, the anticipated launch of additional satellites will further strain the satellite–ground link. Next-generation satellite navigation systems will rely on time deviation measurements from inter-satellite links to independently establish and maintain a space-based time reference, enhancing the system’s reliability and robustness. The increasing number of low-Earth-orbit satellite navigation constellations provides ample resources for establishing a space-based time reference. However, this also introduces challenges, including extensive time scale computations, increased link noise, and low clock resource utilization. To address these issues, this paper proposes a Distributed Kalman Plus Weight (D-KPW) algorithm, which combines the benefits of Kalman filtering and the weighted average algorithm, balancing the performance with computational resources. Furthermore, an adaptive clock control algorithm, D-KPW (Control), is developed to account for both the short-term and long-term frequency stability of the time reference. The experimental results demonstrate that the frequency stability of the time reference established by the D-KPW (Control) algorithm reaches 7.40×10−15 and 2.30×10−15 for sampling intervals of 1000 s and 1,000,000 s, respectively, outperforming traditional algorithms such as ALGOS. The 20-day prediction error of the time reference is 1.55 ns. Compared to traditional algorithms such as AT1, ALGOS, Kalman, and D-KPW, the accuracy improves by 65%, 65%, 66%, and 67%, respectively.

Effects of one night of sleep deprivation on whole brain intrinsic connectivity distribution using a graph theory neuroimaging approach

Neuroimaging studies have revealed disturbances in brain functional connectivity (FC) after one night of sleep deprivation (SD). These researches explored the alterations of FC using classical regions of interest (ROI)-based analysis or functional connectivity density. However, these methods need for a priori information about the selected ROIs and a specific correlation threshold to define a connection between two ROIs or voxels, which may bring inconsistent results. In the present study, we adopted a data-driven, whole brain voxel-based graph-theoretical approach, intrinsic connectivity distribution (ICD) analysis, to examine changes of brain connectivity after SD in 52 normal young subjects without any prior knowledge. The cross-hemisphere ICD (ch-ICD) analysis was also performed to discover the effect of SD on cerebral lateralization. We found that sleep-deprived subjects showed significant reduced ICD in default mode network (DMN) and limbic network, and increased ICD in sensorimotor network. Furthermore, after SD, the ICD in the right precuneus showed significant correlation with psychomotor vigilance test (PVT) performance following the stepwise regression analysis after Bonferroni correction (ICD = 0.43 - 0.62*10% fast reaction time + 0.31*the standard deviation of reaction time, p = 0.0012). Follow-up seed-based FC analyses in the right precuneus revealed decreased FC to regions in DMN, visual network, ventral attentional network and frontal-parietal network. Nevertheless, no striking difference of ch-ICD was found following SD. In conclusion, these findings suggested that DMN, especially precuneus may be hubs of FC disturbances associated with vigilance after SD, and may provide new insights into the intervention for SD.

Cortical brain potentials in response to lower limb proprioceptive stimuli in young adults with probable developmental coordination disorder

BackgroundProprioceptive deficits have been shown to underlie motor problems in individuals with a probable developmental coordination disorder (pDCD). Behavioral studies have employed response times to passive limb movement to evaluate proprioceptive function in individuals with pDCD. However, the underlying neural mechanisms involved in the cortical processing of proprioceptive input and its corresponding motor response are unclear. To address this issue, this study aims to investigate neuropsychological and neurophysiological performances using event-related potentials (ERP) on proprioceptive-motor tasks in young adults with pDCD. MethodsFrom a total of 149 young adults screened using the Bruininks-Oseretsky Test of Motor Proficiency 2nd Edition Complete Form (BOT-2), 12 individuals with pDCD were identified (mean age ± SD: 20.50 ± 1.08 years) along with 12 age- and sex-matched controls (mean age ± SD: 20.75 ± 1.05 years). Participants placed their dominant foot on a passive ankle motion apparatus that plantarflexed the ankle at a constant velocity of 22°/s for a total of 75 trials in each proprioceptive condition. With vision occluded, participants had to press the trigger button held by the dominant hand when they sensed the passive motion of the ankle (voluntary response, VR), or purely receive the movement without a voluntary response (non-voluntary response, NVR). Behavioral performances [i.e., mean movement detection time (MDTmean), the standard deviation of the movement detection time (MDTSD)] and ERP indices (i.e., N1, P3 amplitude, and latency) related to ankle kinesthetic stimuli were obtained to determine the proprioceptive-motor function. ResultsThe results showed that young adults with pDCD exhibited longer MDTmean (p < 0.001) and MDTSD (p = 0.002) compared to their controls. Electrophysiological indices measured at frontal and central electrode sites, showed that young adults with pDCD exhibited significantly smaller N1 (p = 0.019) and P3 amplitudes (p = 0.032) during VR and NVR conditions. Notably, correlation analysis revealed a significant negative relationship between MDTmean and N1 (r = 0.62, p < 0.001) and P3 amplitudes (r = − 0.55, p = 0.005) in the VR condition in young adults with and without pDCD. ConclusionsThis study sheds light on the central brain mechanisms underlying proprioceptive-motor deficits in young adults with pDCD. The combined analysis of behavioral and ERP data suggests that longer MDTmean and larger MDTSD in young adults with pDCD are associated with weaker proprioceptive afferent inflow shown by decreased N1 amplitude to the frontal and parietal cortices. Such degraded proprioceptive signals are followed by reduced P3 amplitude, suggesting that young adults with pDCD allocate fewer neural resources to modulate motor processes with regard to proprioceptive stimuli. These findings contribute to a better understanding of the neurophysiological basis of proprioceptive-motor deficits in pDCD and may inform the development of targeted sensorimotor interventions.

Small Deviations for the Mutual Intersection Local Time of Brownian Motions

Small Deviations for the Mutual Intersection Local Time of Brownian Motions

Optimal Energy Management of EVs at Workplaces and Residential Buildings Using Heuristic Graph-Search Algorithm

As the adoption of electric vehicles (EVs) continues to rise, efficient scheduling methods that minimize operational costs are critical. This paper introduces a novel EV scheduling method utilizing a heuristic graph-search algorithm for cost minimization due to its admissible nature. The approach optimizes EV charging and discharging schedules by considering real-time energy prices and battery degradation costs. The method is tested on systems with solar generation, electric loads, and EVs featuring vehicle-to-grid (V2G) connections. Various charging rates, such as standard, fast, and supercharging, along with uncertainties in EV arrival and departure times, are factored into the analysis. Results from various case studies demonstrate that the proposed method outperforms popular heuristic optimization techniques, such as particle swarm optimization and genetic algorithms, by 3–5% for different real-time energy prices. Additionally, the method’s effectiveness in reducing operational costs for workplace EVs is confirmed through extensive case studies under varying uncertain conditions. Finally, the system is implemented on a digital real-time simulator with DNP3 communication, where real-time results align closely with offline simulations, confirming the algorithm’s efficacy for real-world applications.

Collaborative Optimization Framework for Coupled Power and Transportation Energy Systems Incorporating Integrated Demand Responses and Electric Vehicle Battery State-of-Charge

The growing adoption of electric vehicles (EVs) and advancements in dynamic wireless charging (DWC) technology have strengthened the interdependence between power distribution networks (PDNs) and electrified transportation networks (ETNs), leading to the emergence of coupled power and transportation energy systems (CPTESs). This development introduces new challenges, particularly as DWC technology shifts EV charging demand from residential plug-in charging to charging-while-driving during commuting hours, causing simultaneous congestion in both ETNs and PDNs during peak times. The present work addresses this issue by developing a collaborative optimization framework for CPTESs that incorporates integrated demand responses (IDRs) and EVs battery state-of-charge (SOC). In the ETN, a multiperiod traffic assignment model with time-shiftable traffic demands (MTA-TSTD) is established to optimize travelers’ routes and departure times while capturing traffic flow distribution. Meanwhile, effective path generation models with EVs battery SOC are proposed to optimize charging energy during driving and construct the effective path sets for MTA-TSTD. In the PDN, a multiperiod optimal power flow model with time-shiftable power demands (MOPF-TSPD) is formulated to schedule local generators and flexible power demands while calculating the power flow distribution. To enhance temporal and spatial coordination in CPTESs, a distributed coordinated operation model considering IDRs is proposed, aiming to optimize energy consumption, alleviate congestion, and ensure system safety. Finally, an adaptive effective path generation algorithm and an ETN–PDN interaction algorithm are devised to efficiently solve these models. Numerical results on two test systems validate the effectiveness of the proposed models and algorithms.

Fluorinated covalent triazine frameworks as stationary phase for the separation of fluorinated compounds by capillary electrochromatography

Three covalent triazine frameworks (CTFs) named as H-CTF, TF50-CTF and TF-CTF, containing different numbers of fluorine atoms were constructed to serve as stationary phases for capillary electrochromatography (CEC), and fluorine interactions were investigated by changing the number of fluorine atoms in the stationary phases and separating the targeted analytes. The developed CEC column was used to separate fluoroquinolones (FQs), β-phenylalanine and trifluoromethyl β-phenylalanine analogs. Characterization of the materials and the electrochromatographic columns revealed that the materials were successfully synthesized and that the inner surfaces of the capillary columns were uniformly coated with CTFs. The evaluation of the TF-CTF-coated capillary column proved that it had good resolution and reproducibility. The intraday relative standard deviations (RSDs) of the retention time (n = 9) and peak area were 0.72 %-0.85 % and 1.19 %-2.60 %, respectively. The interday RSDs (n = 9) of the retention time and peak area were 0.85 %-1.06 % and 2.59 %-3.68 %, respectively. The column-to-column RSDs of the retention time (n = 9) and peak area were 0.54 %-0.69 % and 2.95 %-4.55 %, respectively. The interbatch RSDs of the retention time and peak area (n = 9) were 2.11 %-2.89 % and 2.74 %-6.46 %, respectively. A comparsion of the peak order of fluorinated compounds in the same stationary phase and the separation efficiency of three stationary phases relative to the same target substance, it demonstrated that fluorine interaction can improve the separation efficiency of CEC.

Clock synchronization characterization of the Washington DC metropolitan quantum network (DC-QNet)

Quantum networking protocols relying on interference and precise time-of-flight measurements require high-precision clock synchronization. This study describes the design, implementation, and characterization of two optical time transfer methods in a metropolitan-scale quantum networking research testbed. With active electronic stabilization, sub-picosecond time deviation (TDEV) was achieved at integration times between 1 and 105 s over 53 km of deployed fiber. Over the same integration periods, 10-ps level TDEV was observed using the White Rabbit–Precision Time Protocol over 128 km. Measurement methods are described to understand the sources of environmental fluctuations on clock synchronization toward the development of in situ compensation methods. Path delay gradients, chromatic dispersion, polarization drift, and optical power variations all contributed to clock synchronization errors. The results from this study will inform future work in the development of compensation methods essential for enabling experimental research in developing practical quantum networking protocols.

Short-Term Prediction of Origin–Destination Passenger Flow in Urban Rail Transit Systems with Multi-Source Data: A Deep Learning Method Fusing High-Dimensional Features

Short-term origin–destination (OD) passenger flow forecasting is crucial for urban rail transit enterprises aiming to optimise transportation products and increase operating income. As there are large-scale OD pairs in an urban rail transit system, OD passenger flow cannot be obtained in real time (temporal hysteresis). Additionally, the distribution characteristics are also complex. Previous studies mainly focus on passenger flow prediction at metro stations, while few methods solve the OD passenger flow prediction problems of an urban rail transit system. In view of this, we propose a novel deep learning method fusing high-dimensional features (HDF-DL) with multi-source data. The HDF-DL method is combined with three modules. The temporal module incorporates the time-varying, trend, and cyclic characteristics of OD passenger flow, while the latest OD passenger flow time sequence (within 1 h) is excluded from the time-varying characteristics. In the spatial module, the K-means and K-shape algorithms are used to classify OD pairs from multiple perspectives and capture the spatial features, reducing the difficulty of OD passenger flow predictions with large-scale and complex characteristics. Weather factors are considered in the external feature module. The HDF-DL method is tested on a large-scale metro system in China, in which eight baseline models are designed. The results show that the HDF-DL method achieves high prediction accuracy across multiple time granularities, with a mean absolute percentage error of about 10%. OD passenger flow in every departure time interval can be predicted with high and stable accuracy, effectively capturing temporal characteristics. The modular design of HDF-DL, which fuses high-dimensional features and employs appropriate neural networks for different data types, significantly reduces prediction errors and outperforms baseline models.

EKF-based parameter estimation method for radar maneuvering target with unknown time information

Moving target detection (MTD) is a research hotspot in radar signal processing. Generally, the time information of non-cooperative moving targets entering and leaving a radar coverage area is unknown, which would lead to severe performance loss for target parameter estimation, detection, and imaging. Unlike our previous research work, this paper addresses the motion parameters estimation and refocusing problem for a radar maneuvering target with unknown entry and departure time. A computationally efficient method that utilizes extended Kalman filtering (EKF) for phase tracking is proposed to estimate the entry and departure times. The proposed method first performs range cell migration correction (RCMC) on the pulse compression echo signal. Then, the maneuvering target signal is modeled as a polynomial phase signal (PPS) and utilizes the EKF to construct a binary state-space equation for polynomial phase tracking. Finally, by comparing the phase tracking results of the noise cell and the signal cell, one can derive estimates for the entry/departure time and motion parameters. Compared with existing methods, the proposed method avoids multi-dimension searching on the parameter space, so it has a prominent advantage in computational complexity. Moreover, the core of the proposed method lies in tracking the polynomial phase, which is not constrained by the order of target motion, and has wider applicability in practice. Both simulated and public radar data are used to validate the effectiveness of the proposed method.