Time Estimation Method Research Articles

Deep Learning-Based Travel Time Estimation in Hiking with Consideration of Individual Walking Ability

Abstract Hiking is popular, but mountain accidents are serious problems. Accurately predicting hiking travel time is an essential factor in preventing mountain accidents. However, it is challenging to accurately reflect individual hiking ability and the effects of fatigue in travel time estimation. Therefore, this study proposes a deep learning model, “HikingTTE”, for estimating arrival times when hiking. HikingTTE estimates hiking travel time by considering complex factors such as individual hiking ability, changes in walking pace, terrain, and elevation. The proposed model achieved significantly higher accuracy than existing hiking travel time estimation methods based on the relation between slope and speed. Furthermore, HikingTTE demonstrated higher accuracy in predicting hiking arrival times than a deep learning model originally developed to estimate taxi arrival times. The source code of HikingTTE is available on github for future development of the travel time estimation task.

Dynamic Routing and Scheduling Approach for Aircraft Taxi Automation with Adaptive Surface Situation

The emergence of surface trajectory-based operation (STBO) has promoted the development of taxi automation systems to plan conflict-free aircraft trajectories for efficient airport operations. Unlike traditional airport ground movement, aircraft guided by the taxi automation system possess a significant degree of freedom during taxiing, requiring the system to coordinate aircraft movement on the surface with timely responses to uncertainty. This study proposes a dynamic routing and scheduling approach with an adaptive surface situation characterization mechanism. A link-level unimpeded taxi time estimation method is designed to fine-grain characterize airport operation. A comprehensive evaluation system is then developed to identify similar situation patterns. Finally, a dynamic routing and scheduling approach is proposed to optimize airport ground movement with an adaptive and refined surface situation mechanism. The case study of actual operational data collected from a hub airport reveals that the proposed approach achieves efficient movement plans and collaborative control for all aircraft, as well as a more balanced utilization of the runway. The embedded adaptive situation mechanism also makes a better tradeoff between computational efficiency and optimal solutions. The study provides a promising approach to generating conflict-free four-dimensional trajectories for airport operations in future STBO scenarios.

An assessment of the Henssge method for forensic death time estimation in the early post-mortem interval.

Time-since-death (TSD) diagnostics are crucial in forensic medical casework. The compound method by Henssge and Madea, which combines temperature and non-temperature-based techniques, is widely used to estimate TSD. This study aims to validate the predictive ability of this method in a cohort of 76 deceased individuals with known times of death (TOD). A convenience sample of 76 deceased individuals was examined at the Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf. The study included individuals who died at the hospital and those with sudden death in public. Exclusion criteria included age under 18, known infection or sepsis, polytrauma, bleeding, and hyperthermia. The TSD interval was calculated using the Deathtime software. The overall agreement between the actual TOD and the 95% prediction interval for the TSD was 36.8% (95% CI: 26.1 to 48.7). Warm-stored corpses showed a higher agreement (61.9% [95% CI: 38.4 to 81.9]) compared to cold-stored corpses (27.3% [95% CI: 16.1 to 41.0]). Factors such as body mass index (BMI) and body surface area (BSA) were found to influence the odds of agreement. Assuming a plausible range of ambient temperatures between death and admission improved the agreement in cold-stored cases. The study found low to moderate agreement between the actual TOD and the 95% prediction interval using the Henssge method. Incorporating BMI and BSA could improve the predictive accuracy of TSD estimations. Further research with larger sample sizes and external validation is recommended to refine the model.

Reducing Instrumental Errors in Parkes Pulsar Timing Array Data

This paper demonstrates the impact of state-of-the-art instrumental calibration techniques on the precision of arrival times obtained from 9.6 yr of observations of millisecond pulsars using the Murriyang 64 m CSIRO Parkes Radio Telescope. Our study focuses on 21 cm observations of 25 high-priority pulsars that are regularly observed as part of the Parkes Pulsar Timing Array project, including those predicted to be the most susceptible to calibration errors. We employ measurement equation template matching (METM) for instrumental calibration and matrix template matching (MTM) for arrival time estimation, resulting in significantly improved timing residuals with up to a sixfold reduction in white noise compared to arrival times estimated using scalar template matching and conventional calibration based on the ideal feed assumption. The median relative reduction in white noise is 33%, and the maximum absolute reduction is 4.5 μs. For PSR J0437−4715, METM and MTM reduce the best-fit power-law amplitude (2.7σ) and spectral index (1.7σ) of the red noise in the arrival time residuals, which can be tentatively interpreted as mitigation of 1/f noise due to otherwise unmodeled steps in polarimetric response. These findings demonstrate the potential to directly enhance the sensitivity of pulsar timing array experiments through more accurate methods of instrumental calibration and arrival time estimation.

Optimal acquisition time estimation method for CSEM with high-order pseudo-random signal

High-order pseudo-random signal is gradually being applied in controlled-source electromagnetic (CSEM) exploration. In contrast to the conventional single-frequency sweep mode, the high-order pseudo-random signal enables simultaneous transmission of multiple frequencies. However, estimating a fixed acquisition time based on observed noise levels often results in poor adaptability for high-order pseudo-random signal, which only require reception of one set of waveform. In this study, we presented an estimation method for acquisition time for CSEM with high-order pseudo-random signal using an improved logistic function. The improved logistic function was proposed to introduce a time-decay factor into the governing equation for the first time. By considering the transformation rule of noise statistical characteristics with time, the specific parameters of the function have been determined to better describe the dynamic evolution process of the signal quality. The effective frequencies were extracted at various acquisition times based on the noise evaluation number, and the resulting quantity of effective frequencies was used as the fitting target. Guidance for the fieldwork was determined based on the average time of the saturation period, in accordance with the properties of the function. The reliability of the improved logistic function was validated through a transmission current data simulation. The proposed method was demonstrated through the measured data from both strong and weak interference areas.

Bayesian Modeling of Travel Times on the Example of Food Delivery: Part 1—Spatial Data Analysis and Processing

Online food delivery services are rapidly growing in popularity, making customer satisfaction critical for company success in a competitive market. Accurate delivery time predictions are key to ensuring high customer satisfaction. While various methods for travel time estimation exist, effective data analysis and processing are often overlooked. This paper addresses this gap by leveraging spatial data analysis and preprocessing techniques to enhance the data quality used in Bayesian models for predicting food delivery times. We utilized the OSRM API to generate routes that accurately reflect real-world conditions. Next, we visualized these routes using various techniques to identify and examine suspicious results. Our analysis of route distribution identified two groups of outliers, leading us to establish an appropriate boundary for maximum route distance to be used in future Bayesian modeling. A total 3% of the data were classified as outliers, and 15% of the samples contained invalid data. The spatial analysis revealed that these outliers were primarily deliveries to the outskirts or beyond the city limits. Spatial analysis shows that the Indian OFD market has similar trends to the Chinese and English markets and is concentrated in densely populated areas. By refining the data quality through these methods, we aim to improve the accuracy of delivery time predictions, ultimately enhancing customer satisfaction.

Wildland Firefighter Estimated Ground Evacuation Time Modeling to Support Risk-Informed Decision-Making

Wildland firefighters often work in remote settings with multiple hazards that can cause life-threatening injuries. Prompt access to medical care is key to reducing injury consequences. For the last decade, a spatial model of wildland firefighter estimated ground evacuation time (GET) has been used when developing operational response strategies in the contiguous United States (CONUS). This paper describes our updated and improved GET model and the resulting decision support spatial data representing the estimated time to evacuate to a hospital from anywhere within the CONUS using ground transportation only. The new GET model leverages updated input datasets and has improved off-road travel time estimation methods that incorporate the latest science on how terrain slope influences pedestrian travel rates. It also accounts for a novel set of landscape variables not previously considered, including minor roads and trails, streams, woody debris, cliffs, and an improved handling of shrub cover. When compared to a set of recent safety incidents, the reported evacuation times were correlated with the model predictions. The spatial patterns of GET from the new model are similar to the old product; however, we found that, on average, the new version of GET yields slightly faster evacuation times, but with regional variation in this trend.

Study of dead time estimation method based on pulse interval distribution

In nuclear radiation detection, some measured radiation counts are lost due to dead time. Estimation of dead time is necessary to restore the counting information in quantitative analysis. This study aims to propose a method for estimating dead time at high counting rate. First of all, a measurement system of pulse interval distribution was used to estimate the true input counting rate of the detection system and implemented in FPGA. Then, a digital pole-zero cancellation technique was introduced in the measurement system to decrease the decay time constant of pulse. This was aimed at mitigating the impact of pile-up on the pulse interval distribution spectrum, especially at high counting rate. The feasibility of this measurement system at high counting rate was verified by the experimental platform of EDXRF. The dead time was calculated using the measured counting rate and the estimated true input counting rate. The accuracy of the dead time relies on the precision of the estimated true count rate. When the counting rate reaches 600 kCPS, the relative error between the theoretical counting rate and the estimated true input counting rate is less than 5%.

The thixotropic characteristics of rheological parameters of waste stone-tailings cemented paste backfill and the time effect of pipeline resistance

The cemented paste backfill (CPB) involves the optimal combination of one or more solid materials with water to prepare a paste-like slurry with certain stability, fluidity, and plasticity. The paste flows as a piston flow under external force (pumping pressure) or gravity through pipelines into the underground goaf to complete backfilling operations. In order to improve the performance of the CPB, coarse aggregate is added to the CPB to form a coarse aggregate cemented paste backfill (C-CPB). The C-CPB belonged to non-Newtonian fluids and exhibited thixotropic behavior as its rheological parameters determined its flow characteristics. During the initial stage of pipeline transportation, the rheological parameters of the paste material were influenced by its thixotropic behavior and exhibited a certain degree of time dependence. The present study employed the stress relaxation method for thixotropy test to ascertain the rheological parameters of the C-CPB with respect to shear time. The results indicated that the yield stress decreased with increasing shear time with a negative exponential growth trend. A mathematical function describing the variation of yield stress with time for the paste material was established, and an estimation method for thixotropic equilibrium time of the yield stress was proposed. A Gaussian distribution function describing the variation of plastic viscosity with shear time was developed, and the thixotropic stabilization time of plastic viscosity was estimated. On this basis, a pipeline transportation resistance model considering the time-dependent rheological parameters of the C-CPB was established. Subsequently, a formula for calculating the boundary layer thickness during the initial stage of pipeline transportation was derived. The establishment of the model enables a better understanding of the resistance characteristics of the paste during the initial stage of pipeline transportation, as well as the process and patterns of boundary layer formation, which is of great significance.

P–S–N curves fitting method of small samples obeying Weibull distribution

AbstractA three‐parameter Weibull small sample aggregation method is proposed to address the problem of small sample fatigue data in practical engineering. First, based on the empirical assumption that the shape parameter is a constant and data equivalent conversion and aggregation, the small sample fatigue life data are converted into “equivalent large samples.” Second, through the linear relationship between logarithmic scale parameter and stress, an iterative procedure is established to estimate the scale parameters of small sample data. At the same time, the moment and rank estimation methods are used to solve the location parameters quickly. Finally, P–S–N curves with different survival rates are given, and a 7‐series aluminum alloy is selected to verify the correctness and reliability of the method proposed in this study. The results show that the predicted life obtained by the method still has high accuracy under a high survival rate, which can provide a theoretical basis for solving practical engineering problems.

A METHOD FOR EMPIRICAL ESTIMATION OF PLANCK’S LENGTH, MASS, AND TIME THROUGH THE CHARACTERISTICS OF AN ELECTRON. IMPROVING THE ACCURACY OF SOME PHYSICAL CONSTANTS

A method for empirical estimation of Planck's length, mass, and time is proposed, which is based on the characteristics of the electron, Avogadro, and Euler numbers, and the fine structure constant. Basic physical constants can be expressed in terms of Planck's length, mass, and time. The disadvantage of this method is that Planck's elementary particle does not exist in nature. Planck's particle is presented in a hypothetical, virtual form, its characteristics are theoretically calculated through the reduced Planck's constant, Newton's gravitation constant, and the constant speed of light in a vacuum, and the accuracy of these characteristics is low. This is due to the low accuracy of the Newtonian constant of gravity. This shortcoming can be eliminated if the characteristics of the hypothetical Planck particle are associated with a real elementary particle, for example, with an electron, and through its characteristics with the characteristics of leptons and baryons. Since the characteristics of leptons and baryons are determined experimentally and are among the most accurate, for example, for a proton now is 11 decimal places, establishing a connection with them of a hypothetical Planck particle will increase the accuracy of the values: of Planck's length, mass and time, of Planck constant, of elementary electric charge, of the Newtonian gravitational constant, of electron mass, of Planck temperature up to the accuracy level of a proton. As is known, the 26th General Conference on Weights and Measures decided to define one of the basic SI units of measurement through physical constants. In particular, with this solution, the kilogram is now defined in terms of Planck's constant, and the ampere is now determined in terms of the value of the elementary electric charge. Accordingly, with increasing accuracy of the values of Planck's constant and elementary electric charge, the accuracy of the values of a kilogram and Ampere will increase.

İleriye Yönelik ve Geriye Dönük Zamanlama Süreçleri: Teoriler ve Yöntemler

We experience a flow of time in everyday life. However, according to modern physics, time does not flow. In the psychology and neuroscience literature, different models and explanations have been made to try to answer the question of what time is besides an illusion of flow and to figure out how time is perceived. Different methods have also been used to learn how time is handled and to test how accurate these models are. People process time in two ways: implicitly and explicitly. We are talking about explicit timing when estimating the time interval length. Another distinction, according to the measurement method of time perception paradigms, is between prospective and retrospective time estimations. Although there are theorists who claim that these two methods involve similar cognitive processes, it is thought that different cognitive processes serve to experience and remember a time, depending on whether time perception is measured by the prospective or retrospective time estimation methods. This brings to mind different method-dependent constraints and advantages. As with any other kind of perception study, the method chosen for time perception research should fit the question being asked. This review presents a selective review of time perception studies to make it easier for a researcher planning to study time perception to choose the appropriate method for the study question. The time perception processes and related evidence were examined using prospective and retrospective time estimation measurement methods. But it's important to note that the time perception models and methods talked about in this review are only a small part of a very large field.

Shrinkage-based Random Local Clocks with Scalable Inference.

Molecular clock models undergird modern methods of divergence-time estimation. Local clock models propose that the rate of molecular evolution is constant within phylogenetic subtrees. Current local clock inference procedures exhibit one or more weaknesses, namely they achieve limited scalability to trees with large numbers of taxa, impose model misspecification, or require a priori knowledge of the existence and location of clocks. To overcome these challenges, we present an autocorrelated, Bayesian model of heritable clock rate evolution that leverages heavy-tailed priors with mean zero to shrink increments of change between branch-specific clocks. We further develop an efficient Hamiltonian Monte Carlo sampler that exploits closed form gradient computations to scale our model to large trees. Inference under our shrinkage clock exhibits a speed-up compared to the popular random local clock when estimating branch-specific clock rates on a variety of simulated datasets. This speed-up increases with the size of the problem. We further show our shrinkage clock recovers known local clocks within a rodent and mammalian phylogeny. Finally, in a problem that once appeared computationally impractical, we investigate the heritable clock structure of various surface glycoproteins of influenza A virus in the absence of prior knowledge about clock placement. We implement our shrinkage clock and make it publicly available in the BEAST software package.

Neural Network Mapping of Industrial Robots’ Task Times for Real-Time Process Optimization

The ability to predict the maximal performance of an industrial robot executing non-deterministic tasks can improve process productivity through time-based planning and scheduling strategies. These strategies require the configuration and the comparison of a large number of tasks in real time for making a decision; therefore, an efficient task execution time estimation method is required. In this work, we propose the use of neural network models to approximate the task time function of a generic multi-DOF robot; the models are trained using data obtained from sophisticated motion planning algorithms that optimize the shape of the trajectory and the executed motion law, taking into account the kinematic and dynamic model of the robot. For scheduling purposes, we propose to evaluate only the neural network models, thus confining the online use of the motion planning software to the full definition of the actually scheduled task. The proposed neural network model presents a uniform interface and an implementation procedure that is easily adaptable to generic robots and tasks. The paper’s results show that the models are accurate and more efficient than the full planning pipeline, having evaluation times compatible with real-time process optimization.

Timing Synchronization Based on Supervised Learning of Spectrogram for OFDM Systems

This paper proposes a supervised convolutional neural network (CNN) based symbol timing synchronization method using the spectrogram image for preamble-less orthogonal frequency division multiplexing (OFDM) systems. With the development of mobile terminals, OFDM has become an increasingly widespread fundamental technology for wireless communications. While OFDM can achieve high-speed transmission, it is sensitive to synchronization timing for decoding. Thus, the accurate synchronization timing estimation method has become essential for reliable communication. Conventional synchronization timing estimation methods without the preamble lack investigations of estimation accuracy under varying environments, comprehensive performance evaluation, and robustness to Doppler shift. Focusing on the spectrum fluctuations observed when synchronization errors occur, our proposed approach is to train the CNN using spectrogram images to find accurate synchronization points even in noisy and fluctuating environments. The simulation results show that the proposed method achieves better synchronization accuracy than other existing methods. Furthermore, it shows the near-optimal bit error rate (BER) characteristics and superior processing time for BER in an environment close to realistic settings, such as broader synchronization timing and various Doppler shifts.

A hardware-independent time estimation method for inference process of convolutional layers on GPU

Nowadays, various AI applications based on Convolutional Neural Networks (CNNs) are widely deployed on GPU-accelerated devices. However, due to the lack of visibility into GPU internal scheduling, accurately modeling the performance of CNN inference tasks or estimating the latency of CNN tasks that are executing or waiting on the GPU is challenging. This hurts the multi-model scheduling on multi-device and CNN real-time inference. Therefore, in this paper, we propose a time estimation method to estimate the forward execution time of a convolutional layer with an arbitrary shape on a GPU. The proposed method divides an explicit General Matrix Multiplication (GEMM) convolution operation into a series of estimatable GPU operations and constructs performance models at the level of sub-operations rather than hardware instructions or entire models. Also, the proposed method can be easily adapted to different hardware devices or underlying algorithm implementations, since it focuses on the variation of execution time relative to the input data scale, without focusing on specific instructions or hardware actions. According to the experiments on four typical CUDA compatible platforms, the proposed method has an average error rate of less than 5% for convolutional layers in some practical CNN models, and has about 8% error rate in estimating GEMM convolution implementations provided by cuDNN library. Experiments show that the proposed method can predict the forward execution time of convolutional layers of arbitrary size in CNN inference tasks on different GPU models.

Method of Time Estimation for the Bathymetric Surveys Conducted with a Multi-Beam Echosounder System

Exact and complete preparation of a hydrographic survey project allows for the avoidance or reduction of additional costs and unexpected delays and, at the same time, increases the efficiency of the survey. One of the essential requirements at the survey planning stage is a calculation of time necessary for performing bathymetric measurements with a multi-beam echosounder. Based on these calculations, many decisions related to the costs and methodology are made. The article presents the method of time estimation for the hydrographic surveys and takes into account many variables that directly affect the final duration of the project. The paper demonstrates the influence of water depth, multi-beam echosounder swath angle, and other planning parameters related to the scheme of survey lines on the total time of stay at sea. The main findings are based on the author’s over twenty years of experience aboard the Polish Navy hydrographic ship Arctowski and include thorough analysis of specialist literature, publications, manuals, and international standards.

Traffic arrival pattern estimation at urban intersection using license plate recognition data

Traffic arrival pattern is an indispensable part of the traffic state analysis and optimization of signal control schemes at the urban intersection. In some previous research relevant to the traffic parameters at the intersection, such as the queue length estimation, delay estimation, and vehicle trajectory reconstruction, the vehicle arrival patterns are assumed to obey a Normal or Poisson distribution. However, the actual traffic arrival patterns at the intersection are dynamic and more complex. License plate recognition (LPR) data containing rich vehicle trajectory information are promising in data-driven applications of traffic control and management. We propose a framework based on the LPR data, to infer the dynamic traffic arrival pattern at the intersection. The proposed integrative data-driven framework consists of three main parts, a method for inferring traffic signal cycle parameters, deriving the cycle start time and cycle length from time headway sequences; a link travel time estimation method, extracting the valid travel time at the selected time interval, and an arrival pattern estimation model based on the above modules to perform the second-level estimation at the downstream intersection. The unique characteristics of LPR data are exploited and utilized in this study. To validate the proposed method, the LPR data from two adjacent intersections in the city of Changsha in China are used. Numerical results reveal that the proposed framework can achieve satisfactory estimation under different traffic scenarios. The findings in this study can be extended for supporting efficient traffic control applications.

A New Estimation Method for Time–Space Sampled-Data Synchronization of RDNNs With Random Delays

The asymptotical synchronization in mean square of reaction–diffusion neural networks (RDNNs) with random delays is studied in this article. By sampling on both the time domain and spatial domain, a time–space sampled-data controller (TSSDC) is designed, which can efficiently save the network communication resources for RDNNs. A new processing method for the TSSDC is provided. Compared with the existing methods, the processing method here can capture more sampling information and is more concise. An extended Poincaré–Wirtinger inequality is proposed, which is in matrix form and less conservative. Then by constructing a sampling-dependent LKF, using the extended Poincaré–Wirtinger inequality and Hölder inequality, new mean square asymptotical synchronization criteria are set up for RDNNs with random delays, and the desired TSSDC gain is obtained. At length, a numerical example is given to verify the effectiveness and superiority of the obtained results.

A COMPARATIVE STUDY OF METHODS FOR DRIVE TIME ESTIMATION ON GEOSPATIAL BIG DATA: A CASE STUDY IN USA

Abstract. Travel time estimation is crucial for several geospatial research studies, particularly healthcare accessibility studies. This paper presents a comparative study of six methods for drive time estimation on geospatial big data in the USA. The comparison is done with respect to the cost, accuracy, and scalability of these methods. The six methods examined are Google Maps API, Bing Maps API, Esri Routing Web Service, ArcGIS Pro Desktop, OpenStreetMap NetworkX (OSMnx), and Open Source Routing Machine (OSRM). Our case study involves calculating driving times of 10,000 origin-destination (OD) pairs between ZIP code population centroids and pediatric hospitals in the USA. We found that OSRM provides a low-cost, accurate, and efficient solution for calculating travel time on geospatial big data. Our study provides valuable insight into selecting the most appropriate drive time estimation method and is a benchmark for comparing the six different methods. Our open-source scripts are published on GitHub (https://github.com/wybert/Comparative-Study-of-Methods-for-Drive-Time-Estimation) to facilitate further usage and research by the wider academic community.