A benchmark and real-time estimator for the passenger arrival rate to elevator system

Median busways have caused many pedestrian crossing problems, especially when the bus station is placed beside an intersection. The Lishan Road station, which is set beside an intersection, is ad opted as the case study for the analysis of pedestrian crossing characteristics. Th e pedestrian crossing characteristics found near the Lishan Road station are different from the characteristics seen at intersections without a bus way in a number of ways. There is a larger pedestrian volume, the pedestrian arriv al rate on the road sides obey the Negative Binomial Distribution, the passenger arriv al rate in the middle of the road, influenced by the Bus Rapid Transit (BRT) operation, is impulsive and its cycle and peak value are not stable, the walking sp eed of pedestrians can be modeled by a normal distribution, and the walking s peed on the crosswalk proximal to the BRT station is 7% higher than that of the distal. Since the setting up of BRT stations, the pedestrians’ violation time s has increased by 10-15%. The main reasons for this increase include the increase of pedestrian volume and the pedestrian desire to catch a bus causes the pedestr ian to disregard any danger. The pedestrian crossing characteristics analysis in thi s paper can serve as a reference for signal-timing, policy making and the setting up of management facilities.

In the passenger transfer process, passenger congestion conflict is a very important factor in transfer delays in large passenger transport hubs. By analyzing passenger walking speed, passenger arrival rate, the capacity of walking facilities and passengers’ expected travel time, a model for calculating conflict delay in the passenger transfer process is established in this paper. The relationship between the average delay due to passenger conflict, the arrival rate and the absolute value of the difference in passenger arrival rates from different directions is calculated, and a simulation platform of passenger travel behavior, which is based on NetLogo, is established to simulate the passenger transfer conflict process. The outcomes verify that the model is effective, feasible and accurate. Therefore, the research results make great contributions towards the evaluation of the layout plans of passenger transfer facilities in large hubs, and provide a theoretical foundation for perfecting hub transfer facilities.

The design of an elevator system heavily relies on the calculation of the round-trip time under up-peak (incoming) traffic conditions. The round-trip time can either be calculated analytically or by the use of Monte Carlo simulation. However, the calculation of the round-trip time is only part of the design methodology. This paper does not discuss the round-trip time calculation methodology as this has been addressed in detail elsewhere. This paper presents a step-by-step automated design methodology which gives the optimum number of elevators in very specific, constrained arrival situations. A range of situations can be considered and a judgement can be made as to what is the best cost–performance tradeoff. It uses the round trip value calculated by the use of other tools to automatically arrive at an optimal elevator design for a building. It employs rules and graphical methods. The methodology starts from the user requirements in the form of three parameters: the target interval; the expected passenger arrival rate (AR%) which is the passenger arrival in the busiest 5 min expressed as a percentage of the building population; and the total building population. Using these requirements, the expected number of passengers boarding an elevator car is calculated. Then, the round-trip time is calculated (using other tools) and the optimum number of elevators is calculated. Further iterations are carried out to refine the actual number of passengers boarding the elevator and the actual achieved target. The optimal car capacity is then calculated based on the final expected passengers boarding the car. The HARint plane is presented as a graphical tool that allows the designer to visualise the solution. Three different rated speeds are suggested and used in order to explore the possibility of reducing the number of elevator cars. Moreover, the average passenger travel time is used to indicate the need for zoning of buildings. Practical application: This paper has an important application in allowing the designer to arrive at the optimum design for the elevator system using a clearly defined methodology. This ensures that the number of elevators, their speed and their capacity are optimised, thus ensuring that the cost of the elevator system and the space it occupies within the building are minimised. The method also employs a graphical method (the HARint) in order to allow the designer to visualise the optimality and the feasibility of the different design options.

Spike detection approaches for noisy neuronal data: Assessment and comparison

The amount of time spent waiting at a public transport station is a key element in a passenger’s assessment of service quality and in mode choice decisions. Many transport models estimate the average wait time is half the headway for small headways and use a maximum waiting time for headways over a given value. The assumption is that at small headways passengers do not bother to consult schedules since vehicles arrive frequently; therefore these passengers arrive regularly at the station. In contrast, at longer headways passengers do consult schedules to reduce their waiting time; these passengers arrive clustered around the departure time. This research evaluated the influence of headway and other factors on passenger arrival rates at public transport stations based on data collected at 28 stations in Zurich’s public transport network. It found that even at 5-minute headways, some passengers consulted schedules and did not arrive randomly at the station. This finding is interesting since 5-minutes is much lower than many models assume, therefore these models may be overstating passenger wait time. The research also found time-of-day and reliability had an important influence on passenger arrival rates. The research proposes a model for passenger arrival rates at stations that combines a uniform distribution with a shifted Johnson SB distribution.

Missing values in time series data is a well-known and important problem which many researchers have studied extensively in various fields. In this paper, a new nonparametric approach for missing value imputation in time series is proposed. The main novelty of this research is applying the [Formula: see text] norm-based version of Singular Spectrum Analysis (SSA), namely [Formula: see text]-SSA which is robust against outliers. The performance of the new imputation method has been compared with many other established methods. The comparison is done by applying them to various real and simulated time series. The obtained results confirm that the SSA-based methods, especially [Formula: see text]-SSA can provide better imputation in comparison to other methods.

A task-level adaptive MapReduce framework for real-time streaming data in healthcare applications

Internet Protocol (IP) has brought rapid development in many applications such as video and audio streaming, Voice-over-IP (VoIP) and e-commerce. However, these applications suffer from congestion problem, which severely worsens the network performance of real time data transmissions. In this paper, we propose a fuzzy based congestion detection technique for queuing in IP networks. This technique classifies the flow as real time and non real time and the priority scheduler prioritizes the classified flows as high and low, respectively. The congestion level of high priority flows is detected by means of fuzzy logic system. The queue delay and arrival rate are considered as input for Fuzzy logic and the level of congestion is estimated. According to the congestion level of flows, they are scheduled in two different queuing mechanisms. We simulate our technique using NS-2 and simulation results prove the efficiency of our approach. It eradicates unsolicited packet drops and improves packet delivery ratio.

Many technologies need to be established to realize autonomous ships. In particular, accurate state estimation in real time is one of the most important technologies. In the ship and ocean engineering fields, there have been many studies on state estimation using nonlinear Kalman filters. Several methods have been proposed for nonlinear Kalman filters. However, there is insufficient verification on the selection of which filter should be applied among them. Therefore, this study aims to validate the filter selection to provide a guideline for filter selection. The effects of modeling error, observation noise, and type of maneuvers on the estimation accuracy of the unscented Kalman filter (UKF) and ensemble Kalman filter (EnKF) used in this study were investigated. In addition, it was verified whether filtering could be performed in real time. The results show that modeling error significantly impacts the estimation accuracy of the UKF and EnKF. However, the observation noise and types of maneuvers did not have an impact like the modeling error. Thus, we obtained the guideline that UKF and EnKF should be used differently depending on the required computation time. We also obtained that keeping the modeling error sufficiently small is essential to improving the estimation accuracy.

Pose estimation in real time is a challenging research area with numerous applications. Obtaining detected key points in each pose presents us with useful applications, one such application being in the augmented reality domain. This paper presents an approach to 3D model overlay on an image with pose key point information. The model used in this work is based on the paper titled Multi-Person Pose Estimation. An FCN is employed for extracting key point information from the data. The method used in this work involves intersecting the image plane and the desired 3D model in real time and orienting the model correctly in accordance with the key point information. The performance will greatly depend on the GPU specifications.KeywordsAugmented realityPose estimation3D modelReal-time applications

The three‐dimensional macroscopic fundamental diagram (3D‐MFD) provides a novel approach to characterize the complex interactions between cars and buses in multimodal urban networks, offering particular value for designing efficient bimodal perimeter control strategies. In this study, a perimeter control strategy for cars is implemented by regulating the transfer flow rate at subregion boundaries, while bus numbers are dynamically adjusted through optimized dispatch frequencies. A multimodal traffic system state equation integrating both car and bus dynamics is constructed. Building on operational state factors for both modes, a passenger mode choice model based on the Logit model is established. With the dual objectives of maximizing the overall passenger arrival rate and minimizing total network energy consumption, an integrated multimodal traffic control framework (I‐MPC) is developed using model predictive control (MPC). The comparative analysis against the no boundary control (NBC) method, the MPC‐based boundary control method for private cars (C‐MPC), and the bus scheduling optimization method (B‐MPC) demonstrates that the proposed I‐MPC method achieves outstanding performance across multiple key metrics, including passenger arrival efficiency, network energy consumption, and average bus occupancy rate, thereby enabling the optimized allocation and efficient utilization of traffic resources. Moreover, the method maintains reasonable bus occupancy levels while significantly enhancing passenger comfort and reducing overall system energy consumption.

Possibilities of using mathematical models of the theory of queues for the purpose of control of processes of discrete productions modeled by it in real time are investigated. As a model example, a system for managing the rearrangements of service items in queues was considered. For the analysis the methods of calculation of BMAP-controlled systems and the methods of the theory of recovery on periods of employment for queues with vacancies of requirements were used.As a result of research it is found out that mathematical modeling in the theory of queues can be used for simulation of discrete production for the purpose of design and the production analysis, but cannot be used for management of the modeled processes in real time.

The article proposes an integrated approach to modeling the initial stage of tracking air targets in radar systems based on a combination of a probabilistic-temporal model of the GERT type and a recursive state estimation procedure using the Kalman filter. At the macro level, the process of interaction "radar-tracked object" is described by an exponential GERT network using moment generating functions, which allows analytically forming an equivalent W-function, determining the distribution and probability density functions of the interaction time, as well as taking into account multivariate transition trajectories, returns to previous states, and detection confirmation delays. It is shown that the GERT model effectively reproduces global temporal-probabilistic regularities of the process, but does not provide recursive correction of coordinates and velocities estimates in real time and minimization of the mean square error. To overcome these limitations, it is proposed to integrate the Kalman filter as a micro-level tool for trajectory estimation. A discrete linear stochastic model in the state space is formulated, a two-step recursion "prediction-correction", principles of selection of motion models (CV, CA, CT), structures of noise covariance matrices and tuning procedures based on RMSE and NEES criteria are presented. It is shown that the time characteristics obtained from GERT can be used to initialize the initial state and covariances, as well as for adaptive tuning of the sampling step and process noise parameters. The modeling results demonstrate the complementarity of the approaches: GERT forms a structural-probabilistic framework of the tracking process, while the Kalman filter provides smoothing of measurements, reduction of the spread of estimates and a significant reduction of RMSE compared to the macro-level estimate. The proposed integration framework "GERT → Kalman" creates a methodological basis for building a comprehensive model of goal tracking in a cluttered environment, combining the analysis of the temporal-probabilistic structure of the process with highly accurate recursive real-time state estimation.

The performance of a Kalman filter that estimates core power in a pressurized water reactor was evaluated using data from the Loss of Fluid Test (LOFT) Reactor and linear error analysis techniques. The Kalman filter provides a more accurate core power estimate in real time than does the conventional method of power estimation. Accuracies of better than 0.75% of rated power are obtained with the Kalman filter. If this accuracy improvement can be translated into higher levels of power operation, then more revenue can be generated by a commercial power plant. Scaling the LOFT 150-MW(thermal) l accuracy improvement up to a 1000-MW(electric) commercial power plant size, assuming that average power production is increased by the amount of the accuracy improvement, results in more than $800 000 of extra revenue per year ($0.06/kW·h).

BART is a state-of-the-art active end-to-end bandwidth measurement method that estimates not only the available bandwidth but also the link capacity of the bottleneck link. It uses a Kalman filter to give estimates in real time during a measurement session. In this paper, we have studied the impact of 802.11 networks on the bandwidth estimates produced by BART. The Kalman filter used by BART is tunable, and one of the contributions of this paper is to show how the Kalman filter should be adjusted to improve real-time tracking and estimation accuracy when the bottleneck is an 802.11 link. Further, the paper contributes by discussing how to interpret the estimates produced by BART and similar bandwidth estimation tools relying on self-induced congestion when used in wireless scenarios. An analysis show that the BART estimates produced are correct - but corresponds to a fair share of the wireless link rather than to the unused capacity. However, the estimates do indicate how much bandwidth an application or device in the wireless network can expect when sending and/or receiving network traffic.