Accuracy Of Time Estimation Research Articles

Estimating Toll Road Travel Times Using Segment-Based Data Imputation

Efficient and sustainable transportation is crucial for addressing the environmental and social challenges associated with urban mobility. Accurate estimation of travel time plays a pivotal role in traffic management and trip planning. This study focused on leveraging machine learning models to enhance travel time estimation accuracy on toll roads under diverse traffic conditions. Two models were developed for travel time estimation under a variety of traffic conditions on the Don Muang Tollway, Bangkok, Thailand: a long short-term memory (LSTM) recurrent neural network model and a support vector regression (SVR) model. Missing data were treated using the proposed segment-based data imputation method. Unlike other studies, the effects of missing input data on the travel time model performance were also analyzed. Traffic parameters, such as speed and flow, along with other relevant parameters (time of day, day of the week, holiday indicators, and a missing data indicator), were fed into each model to estimate travel time on each of the four specific routes. The LSTM and SVR results had similar performance levels based on evaluating the all-day pooled data. However, the mean absolute percentage errors were lower for LSTM during peak periods, while SVR performed slightly better during off-peak periods. Additionally, LSTM coped substantially better than SVR with unusual traffic fluctuations. The sensitivity analysis of the missing input data in this study also revealed that the LSTM model was more robust to the high degree of missing data than the SVR model.

Solar Sail Transfer Trajectory Design for Comet 29P/Schwassmann–Wachmann 1 Rendezvous

The goal of this paper is to analyze the optimal transfer towards the periodic comet 29P/Schwassmann–Wachmann 1 of a solar sail-based spacecraft. This periodic and active comet is an interesting and still unexplored small body that has been regarded as an object of the Centaurs group. In this work, a classical (heliocentric) orbit-to-orbit transfer is studied from an optimal viewpoint, by finding the spacecraft trajectories that minimize the flight time for a given value of the solar sail characteristic acceleration, that is, the typical performance parameter of a photonic sail. In particular, the optimal Earth–comet transfer is studied both in a typical three-dimensional mission scenario and with a simplified two-dimensional approach, whose aim is to rapidly obtain an accurate estimation of the minimum flight time with a reduced computation cost. The numerical simulations illustrate the mission performance, in terms of the characteristics of the rapid transfer trajectory, as a function of the typical propulsive parameter and the solar sail thrust model.

A Novel Machine Learning Model for Predicting Orthodontic Treatment Duration.

In the field of orthodontics, providing patients with accurate treatment time estimates is of utmost importance. As orthodontic practices continue to evolve and embrace new advancements, incorporating machine learning (ML) methods becomes increasingly valuable in improving orthodontic diagnosis and treatment planning. This study aimed to develop a novel ML model capable of predicting the orthodontic treatment duration based on essential pre-treatment variables. Patients who completed comprehensive orthodontic treatment at the Indiana University School of Dentistry were included in this retrospective study. Fifty-seven pre-treatment variables were collected and used to train and test nine different ML models. The performance of each model was assessed using descriptive statistics, intraclass correlation coefficients, and one-way analysis of variance tests. Random Forest, Lasso, and Elastic Net were found to be the most accurate, with a mean absolute error of 7.27 months in predicting treatment duration. Extraction decision, COVID, intermaxillary relationship, lower incisor position, and additional appliances were identified as important predictors of treatment duration. Overall, this study demonstrates the potential of ML in predicting orthodontic treatment duration using pre-treatment variables.

Integrated Performance Model for Building Construction Projects in Higher Institutions, Southwest Nigeria

Performance of construction projects is a crucial driver of economic growth and infrastructural development in Nigeria, but there is still a need for a tool that can accurately analyze construction project performance from the design stage through construction and completion. This study developed a performance model for building construction projects in higher institutions, Southwest Nigeria. Literature was reviewed to identify the previous performance measurement parameters and were classified into sixteen by pilot survey. Qualitative research approach was adopted and a structured questionnaire was administered to 394 project stakeholders and 297 (75.38%) were retrieved from respondents. Mean Score, Kuskal wallis test, analysis of variance and least significant difference post hoc test was used to determine the convergence and divergent views of the stakeholders. Factor analysis as data reduction method via principal component extraction was used to classify project performance into five categories. The study’s findings indicate that client satisfaction with the project (MS = 3.833), Construction time predictability (MS = 3.845) and construction cost predictability (MS = 3.832) were ranked as the top three performance index. From the result of the reliability analysis, project performance was loaded into five (5) integrated factors; construction management, stakeholder management, quality, cost and time performance indicators. It is hereby recommended that client satisfaction, accurate time estimate and good cost forecast should be considered when executing building construction projects to enhance performance. It is also recommended that attention should be given to the five integrated performance indices during construction of building projects to improve performance..

Analysis of cost estimation using the web metrics and cost driver in the high performance of web developers

In recent years, many researchers and software industries have given significant attention to the estimation of software effort. Software effort estimation is a challenge that often appears in the project of making software. To address this issue, various software expansion cost estimations have been familiarized. A basic traditional method for cost estimation is the Constructive Cost Model II (COCOMO II Model), which generates huge gradations of substantiality. The proposed COCOMO II model employs cost drivers, measures and the streak of programmes to predict the energy and the growth period of a web rise. However, the model is still lacking in terms of accuracy in effort and development time estimation. In this study, we do investigate the influence of components and attributes to achieve new better accuracy improvement on the COCOMO II model. The NASA93 dataset is used to test the suggested methodology. The system has outperformed earlier investigations and arrangements capable of imperfect data input, and further advancements in the reliability of the estimated technique.

Estimating the Feeding Time of Individual Broilers via Convolutional Neural Network and Image Processing.

Feeding behavior is one of the critical welfare indicators of broilers. Hence, understanding feeding behavior can provide important information regarding the usage of poultry resources and insights into farm management. Monitoring poultry behaviors is typically performed based on visual human observation. Despite the successful applications of this method, its implementation in large poultry farms takes time and effort. Thus, there is a need for automated approaches to overcome these challenges. Consequently, this study aimed to evaluate the feeding time of individual broilers by a convolutional neural network-based model. To achieve the goal of this research, 1500 images collected from a poultry farm were labeled for training the You Only Look Once (YOLO) model to detect the broilers' heads. A Euclidean distance-based tracking algorithm was developed to track the detected heads, as well. The developed algorithm estimated the broiler's feeding time by recognizing whether its head is inside the feeder. Three 1-min labeled videos were applied to evaluate the proposed algorithm's performance. The algorithm achieved an overall feeding time estimation accuracy of each broiler per visit to the feeding pan of 87.3%. In addition, the obtained results prove that the proposed algorithm can be used as a real-time tool in poultry farms.

Role of the front wing/wheel setting-up on the optimal cornering performances of a Formula 1 car

We propose a hybrid approach based on meta-modelling techniques and machine-learning algorithms to determine the best car configuration for each circuit. By a specific interpolation model, we obtain an accurate estimation of the car’s speed as a function of the front wing configuration and the bend curvature. Some high-fidelity fluid dynamic simulations train the model and extend it to the entire design space. These data are then used as input for a simplified car dynamics model, providing an accurate estimate of the ideal lap time. Comparison with actual telemetry data confirms that the resulting tool is reliable, fast and easy to use.

TRAFFIC SPEED MODELLING TO IMPROVE TRAVEL TIME ESTIMATION IN OPENROUTESERVICE

Abstract. Time-dependent traffic speed information at a street level is important for routing services to estimate accurate travel times and to recommend routes which avoid traffic congestion. Still, most open-source routing machines that use OpenStreetMap (OSM) as the primary data source rely on static driving speeds derived from OSM tags, since comprehensive traffic speed data is not openly available. In this study, a method was developed to model traffic speed by hour of day at a street level using open data from OpenStreetMap, Twitter and population data. The modelled traffic speed data was subsequently integrated into the open-source routing engine openrouteservice to improve travel time estimation in route planning. Machine learning models were trained for ten cities worldwide using traffic speed data from Uber Movement as reference data. Different indicators based on geolocation and timestamp of Twitter data as well as a geographically adapted betweeness centrality indicator were evaluated for their potential to improve prediction accuracy. In all cities, the Twitter indicators improved the model, although this effect was only visible for certain road types. The centrality indicator improved the model as well but to a lesser extent. The Google Routing API was used as reference to evaluate the accuracy in travel time estimation. Deviations in travel times were regionally different and were partly alleviated by including the raw traffic data by Uber or the modelled traffic speed data in openrouteservice.

ABAFT: an adaptive weight-based fusion technique for travel time estimation using multi-source data with different confidence and spatial coverage

The evolution of traffic monitoring systems provides rich traffic data from multiple sensors. Fuzing the data has the potential to enhance the quality of travel time estimation. It also provides better spatial-temporal coverage in traffic observations. However, each sensor’s unique data collection process results in fusion challenges with respect to the coverage and data quality differences between various sources. These factors determine the degree of confidence that should be considered when fuzing different types of data. To this end, this paper proposes an adaptive weight-based fusion technique (ABAFT) that considers data spatial coverage and quality or confidence as the factors constructing the weight. The proposed ABAFT was tested using different scenarios on synthetic GPS and Bluetooth MAC Scanners data from an urban arterial corridor. The results show that the ABAFT can increase the travel time estimation accuracy by over 10%, and reliability by over 8% compared to the single sensor estimators. It also outperforms the simple average and standard-error-based fusion by around 4%. ABAFT is easy to be implemented on multiple sources of information available to transport agencies for a single point of truth.

Causes and Impacts of Delays in Ethiopian Public Construction Projects (Case on Debre Markos University Construction Projects)

The construction project is regarded successful when it is given over to the owner within the time, cost, and standards necessary while minimizing delays in public construction projects. Debre Markos University (DMU) is one of the public universities undergoing massive development projects that are experiencing significant delays. This article evaluates the most prevalent delay issues encountered by Ethiopian government-funded construction projects, particularly those using DMU, analyzes their impacts, and suggests a delay management system. The study applied qualitative as well as quantitative techniques to evaluate primary and secondary data. Per the entirety statistics, the top five most important and highly ranked factors are erroneous time estimates, force majeure, lowest bid, procurement policy, and volatile markets, as well as frequent design changes. Nearly every DMU project schedule overruns up to 268.67% of its contract duration. As a result, the institution suffered from poor public relations and a high cost of supervision and contract administration (6.87% of the overall project cost). Conducting a thorough and accurate project feasibility study, awarding bids to experienced contractors, avoiding drawing discrepancies, preparing an accurate initial time and cost estimate, and strict schedule monitoring were discovered to be effective techniques for reducing delays in public construction projects.

VOCALICIOUS-Voice-Assisted Food Ordering Web Application

Abstract: Vocalicious-voice assisted food ordering website is revolutionizing the way users interact with the platform by incorporating an Artificial Intelligence (AI) voice assistant. This advanced voice assistant not only suggests a wide range of food options available for ordering but also provides users with accurate cooking time estimates. Furthermore, it goes beyond the traditional food ordering experience bysuggesting nearby places to visit during cooking time, ensuring users can make the most of their time in the kitchen.

Inland Vessel Travel Time Prediction via a Context-Aware Deep Learning Model

Accurate vessel travel time estimation is crucial for optimizing port operations and ensuring port safety. Existing vessel travel time prediction models primarily rely on path-finding algorithms and corresponding distance/speed relationships to calculate travel time. However, these models overlook the complex nature of vessel travel time, which is influenced by multiple traffic-related factors such as collision avoidance, shortest path selection, and vessel personnel performance. The lack of consideration for these specific aspects limits the accuracy and applicability of current models. We propose a novel context-aware deep learning approach for inland vessel travel time prediction. Firstly, we introduce a complex network that captures vessel–vessel interaction contexts, providing valuable traffic environment information as an input for the deep learning model. Additionally, we employ a convolutional neural network to extract spatial trajectory information, which is then integrated with interaction contexts and indirect context information. In the vessel travel time prediction procedure, we utilize a long short-term memory network to capture the temporal dependence within consecutive channel sections’ fused multiple context feature sets. Extensive experiments incorporating historical data from the Wuhan section of the Yangtze River in China demonstrate the superiority of our proposed model over classical models in predicting vessel travel time. Importantly, our model accounts for the specific traffic contexts that had previously been overlooked, leading to improved accuracy and applicability in inland vessel travel time prediction.

Accurate estimation of cost and time utilizing risk analysis and simulation (case study: road construction projects in Iran)

Cost and time are essential decision-making factors in the early stages of road construction projects. It has been observed that road construction projects often lack adequate risk analysis before they are scheduled and costed. Also, there is a lack of data in a comprehensive operational plan. The estimated time/cost of the scheduled program (without considering the risks) differs greatly from the time/cost of delivered projects. Risk analysis is essential for preventing enormous losses resulting from delays and cost increases. With the help of an integrated modelling framework, this study examines the effects of risks on the cost/time of road construction based on three delivered projects in Iran. For this purpose, the Delphi method is used to identify the main risks. Next, a questionnaire is developed using the Failure Modes and Effects Analysis method to prioritize critical risks as well as their probability and severity of occurrence. Afterwards, Monte Carlo Simulations and regression models are employed to perform quantitative analysis. On average, the estimated time and cost values of the proposed method differed by only 4% and 6% from the values of the delivered projects, respectively. Furthermore, sensitivity analysis revealed a strong correlation between critical risk occurrence probability and simulated/scheduled time/cost differences. Road project managers can use these findings to estimate the time and cost of projects more accurately. By controlling critical risk probabilities, they can also reduce and control cost overruns and delays.

A Case Study of Automated Simulation for Excavator Cycle Time Estimation

Efficient planning of earthworks is critical for the success of construction projects, accounting for approximately 30% of the project budget. However, traditional approaches, relying on managers' experience, or using standardized estimating systems and equipment manufacturer data, are regarded as inaccurate due to the fact that such methods may are limited in incorporating various site conditions that influence excavator operation, such as digging depth, swing angle, and terrain shape. This study proposes a reinforcement learning-based simulation approach for predicting the cycle time of an excavator given field conditions. For the training of an excavator agent, terrain point clouds of a site are collected and used to build a 3D virtual simulation environment representing a real site where the agent learns how to operate the excavation through trial and error processes. The proposed approach achieves a prediction accuracy of 88.89%, which is higher than that of a traditional method (77.06%). It is thus expected that the proposed approach may allow for improving the accuracy of cycle time estimation for earthmoving planning in practice.

Combining the CSM2020 discrete event simulation model with Decision Aid in Tunneling (DAT) to develop a robust approach for the estimation of completion time for TBM tunnels

The Decision Aids in Tunneling (DAT) are a tool that help in the decision-making process regarding the uncertainty in the project plan, alignment alternatives, and other project aspects. One of the important input parameters in the DAT is the advance rate, and its accurate estimation can significantly improve the accuracy of the DAT. The advance rate values comprise an accurate estimation of the rate of penetration and machine utilization. A few models have been proposed to estimate utilization with different levels of accuracy. The CSM2020, which is a discrete event simulation-based model has proven to be a promising utilization estimation as it incorporates the uncertainties in various tunneling activities in the determination of the delays during the tunneling and in the estimation of machine utilization. In the present study, project data were analyzed, and utilization values were estimated using the CSM2020 model. The results from the CSM2020 model were set as input in the DAT and the average completion time was compared with the actual site data. The results from other available prediction models were also assessed, and a comparison was made with the actual data. The incorporation of CSM2020 results into the DAT showed that the overall difference in completion time was 1.3% compared to the actual project data. Hence, a combination of CSM2020 and DAT can be considered an effective approach that incorporates the uncertainties in the various tunnel operations thereby offering a more accurate and reasonable estimation of completion time for mechanized tunnels.

Time estimation during motor activity.

Several studies on time estimation showed that the estimation of temporal intervals is related to the amount of attention devoted to time. This is explained by the scalar timing theory, which assumes that attention alters the number of pulses transferred by our internal clock to an accumulator that keeps track of the elapsed time. In a previous study, it was found that time underestimation during cognitive-demanding tasks was more pronounced while walking than while sitting, whereas no clear motor-induced effects emerged without a concurrent cognitive task. What remains unclear then is the motor interference itself on time estimation. Here we aim to clarify how the estimation of time can be influenced by demanding motor mechanisms and how different motor activities interact with concurrent cognitive tasks during time estimation. To this purpose, we manipulated simultaneously the difficulty of the cognitive task (solving arithmetic operations) and the motor task. We used an automated body movement that should require no motor or mental effort, a more difficult movement that requires some motor control, and a highly demanding movement requiring motor coordination and attention. We compared the effects of these three types of walking on time estimation accuracy and uncertainty, arithmetic performance, and reaction times. Our findings confirm that time estimation is affected by the difficulty of the cognitive task whereas we did not find any evidence that time estimation changes with the complexity of our motor task, nor an interaction between walking and the concurrent cognitive tasks. We can conclude that walking, although highly demanding, does not have the same effects as other mental tasks on time estimation.

In-plane shear experimental method and mechanical behavior of ceramic matrix mini-composites

In-plane shear mechanical experiments of ceramic matrix mini-composites (CMMC) were realized in this paper by overcoming the difficulties of material preparation, specimen design, gripping and loading, deformation measurement, etc. The in-plane shear stress-strain responses of different matrix volume fractions were obtained based on the method. The stress-strain response of CMMC was strongly non-linear, and its elastic modulus and strength were positively correlated to the matrix volume fraction. The main factors affecting the digital image correlation (DIC) based micro-region shear deformation measurement are analyzed quantitatively, and the corresponding solutions are discussed. The DIC calculation time model is established, and the accurate estimation of DIC processing time is realized. The shear strain field evolution clearly captured the matrix crack initiation and propagation.

Dikkat Mi Nabız Mı: Apne Sürelerinin Tahmin Edilmesinde Yukarıdan Aşağıya ve Aşağıdan Yukarıya Süreçlerin Bütünleştirilmesine Yönelik Bir Ön Çalışma

Accurate perception of time is vital in some athletic activities such as free-diving performance. The exact mechanisms underlying the correct estimation of long intervals using different time estimation paradigms are still an issue to be solved in psychology. To this end, in the current study, by using a prospective paradigm, examined top-down and bottom-up predictors of 25, 50, and 75 seconds of apnea durations. Eleven free-diving athletes performed the target apnea estimations in two experimental conditions: apnea in the air and immersed apnea. In line with our integrative perspective, we obtained heart rate values, attentional control capacities, and affectivity states of the participants, and analyzed the relationship of these with the directional errors of three target apnea estimations. A series of within-participants analyses found the following: first, the heart-rate alone is not the only factor influencing time estimation during long intervals; second, attentional capacity and positive affect contributed to the accuracy of time estimation in a non-constant fashion; third, these three variables affected the accuracy of time estimation differently according to the modality and the duration of the target interval; and fourth, the participants were most accurate in estimating the time during 75s of apnea in the immersed condition.

Artificial intelligence to estimate the tear film breakup time and diagnose dry eye disease

The use of artificial intelligence (AI) in the diagnosis of dry eye disease (DED) remains limited due to the lack of standardized image formats and analysis models. To overcome these issues, we used the Smart Eye Camera (SEC), a video-recordable slit-lamp device, and collected videos of the anterior segment of the eye. This study aimed to evaluate the accuracy of the AI algorithm in estimating the tear film breakup time and apply this model for the diagnosis of DED according to the Asia Dry Eye Society (ADES) DED diagnostic criteria. Using the retrospectively corrected DED videos of 158 eyes from 79 patients, 22,172 frames were annotated by the DED specialist to label whether or not the frame had breakup. The AI algorithm was developed using the training dataset and machine learning. The DED criteria of the ADES was used to determine the diagnostic performance. The accuracy of tear film breakup time estimation was 0.789 (95% confidence interval (CI) 0.769–0.809), and the area under the receiver operating characteristic curve of this AI model was 0.877 (95% CI 0.861–0.893). The sensitivity and specificity of this AI model for the diagnosis of DED was 0.778 (95% CI 0.572–0.912) and 0.857 (95% CI 0.564–0.866), respectively. We successfully developed a novel AI-based diagnostic model for DED. Our diagnostic model has the potential to enable ophthalmology examination outside hospitals and clinics.

A Link Real-time Travel Time Estimation on Speed-time Field Traversing Incorporating LSTM Neural Network

A travel time estimation method based on Speed-time field traversal including LSTM neural network was proposed to increase the real-time and accuracy of travel time estimation. The node departure speed of the traditional piecewise truncated quadratic speed trajectory model was optimized by the road node arrival speed, considering the impact of road conditions on travel time. The node arrival speed was modeled as time series and predicted in the short-term future by combining the LSTM neural network model to construct a spatiotemporally continuous speed trajectory, to estimate the link’s travel time. The method was tested on an actual road and gave considerably improved estimates and results compared to the original method, using LSTM neural network to predict node departure speed and the technique using node arrival speed. According to experimental results, the proposed method performs well in both smooth and crowded traffic circumstances, serving as a benchmark for precise real-time travel time estimation on urban roads.