Multiple Extreme Learning Machines Based Arrival Time Prediction for Public Bus Transport

Due to fast-growing urbanization,the traffic management system becomes a crucial problem owing to the rapid growth in the number of vehicles The research proposes an Intelligent public transportation system where informa-tion regarding all the buses connecting in a city will be gathered,processed and accurate bus arrival time prediction will be presented to the user.Various linear and time-varying parameters such as distance,waiting time at stops,red signal duration at a traffic signal,traffic density,turning density,rush hours,weather conditions,number of passengers on the bus,type of day,road type,average vehi-cle speed limit,current vehicle speed affecting traffic are used for the analysis.The proposed model exploits the feasibility and applicability of ELM in the travel time forecasting area.Multiple ELMs(MELM)for explicitly training dynamic,road and trajectory information are used in the proposed approach.A large-scale dataset(historical data)obtained from Kerala State Road Transport Corporation is used for training.Simulations are carried out by using MATLAB R2021a.The experiments revealed that the efficiency of MELM is independent of the time of day and day of the week.It can manage huge volumes of data with less human intervention at greater learning speeds.It is found MELM yields prediction with accuracy in the range of 96.7%to 99.08%.The MAE value is between 0.28 to 1.74 minutes with the proposed approach.The study revealed that there could be regularity in bus usage and daily bus rides are predictable with a better degree of accuracy.The research has proved that MELM is superior for arrival time pre-dictions in terms of accuracy and error,compared with other approaches.

Bus Arrival Time Prediction Based on Mixed Model

How to predict the bus arrival time accurately is a crucial problem to be solved in Internet of Vehicle. Existed methods cannot solve the problem effectively for ignoring the traffic delay jitter. In this paper,a three-stage mixed model is proposed for bus arrival time prediction. The first stage is pattern training. In this stage,the traffic delay jitter patterns(TDJP)are mined by K nearest neighbor and K-means in the historical traffic time data. The second stage is the single-step prediction,which is based on real-time adjusted Kalman filter with a modification of historical TDJP. In the third stage,as the influence of historical law is increasing in long distance prediction,we combine the single-step prediction dynamically with Markov historical transfer model to conduct the multi-step prediction. The experimental results show that the proposed single-step prediction model performs better in accuracy and efficiency than short-term traffic flow prediction and dynamic Kalman filter. The multi-step prediction provides a higher level veracity and reliability in travel time forecasting than short-term traffic flow and historical traffic pattern prediction models.

P-wave velocity structure in the crust and the uppermost mantle of Chao Lake region of the Tan-Lu Fault inferred from teleseismic arrival time tomography

Chao Lake is a Geoheritage site on the active Tan-Lu Fault between the Yangtze craton,the North China craton,and the Dabie orogenic belt in the southeast.This segment of the fault is not well constrained at depth partly due to the overprinting of the fault zone by intrusive materials and its relatively low seismic activity and sparse seismic station coverage.This study took advantage of a dense seismic array deployed around Chao Lake to delineate the P-wave velocity variations in the crust and uppermost mantle using teleseismic earthquake arrival time tomography.The station-pair double-difference with waveform crosscorrelation technique was employed.We used a multiscale resolution 3-D initial model derived from the combination of highresolution 3-D v S models within the region of interest to account for the lateral heterogeneity in the upper crust.The results revealed that the velocity of the upper crust is segmented with structures trending in the direction of the strike of the fault.Sedimentary basins are delineated on both sides of the fault with slow velocities,while the fault zone is characterized by high velocity in the crust and uppermost mantle.The high-velocity structure in the fault zone shows characteristics of magma intrusion that may be connected to the Mesozoic magmatism in and around the Middle and Lower Yangtze River Metallogenic Belt(MLYMB),implying that the Tan-Lu fault might have formed a channel for magma intrusion.Magmatic material in Chao Lake is likely connected to the partial melting,assimilation,storage,and homogenization of the uppermost mantle and the lower crustal rocks.The intrusions,however,seem to have suffered severe regional extension along the Tan-Lu fault driven by the eastward Paleo-Pacific plate subduction,thereby losing its deep trail due to extensional erosion.

Route Temporal⁃Spatial Information Based Residual Neural Networks for Bus Arrival Time Prediction

Bus arrival time prediction contributes to the quality improvement of public transport services.Passengers can arrange departure time effectively if they know the accurate bus arrival time in advance.We proposed a machine⁃learning approach,RTSI⁃ResNet,to forecast the bus arrival time at target stations.The residual neural network framework was employed to model the bus route temporal⁃spatial information.It was found that the bus travel time on a segment between two stations not only had correlation with the preceding buses,but also had common change trends with nearby downstream/upstream segments.Two features about bus travel time and headway were extracted from bus route including target section in both forward and reverse directions to constitute the route temporal⁃spatial information,which reflects the road traffic conditions comprehensively.Experiments on the bus trajectory data of route No.10 in Shenzhen public transport system demonstrated that the proposed RTSI⁃ResNet outperformed other well⁃known methods(e.g.,RNN/LSTM,SVM).Specifically,the advantage was more significant when the distance between bus and the target station was farther.

Arrival time measurements of first arrival phases P and PKIKP using the method of fixed scale wavelet transformation ratio

The arrival times of first teleseismic phases are difficult to be measured precisely because of slowly and gradually changed onsets and weak amplitudes. The arrival times measured manually are usually behind the real ones. In this paper, using the ratio method of fixed scale wavelet transformations improved by us, the arrival times for the first arrival phases (such as P and PKIKP) at the teleseismic and far-teleseismic distances were measured. The results are reasonable and reliable based on the analysis and discussion of the reliabilities and errors.

Backward Dijkstra Algorithms for Finding the Departure Time Based on the Specified Arrival Time for Real-Life Time-Dependent Networks

A practical transportation problem for finding the “departure” time at “all source nodes” in order to arrive at “some destination nodes” at specified time for both FIFO (i.e., First In First Out) and Non-FIFO “Dynamic ” Networks is considered in this study. Although shortest path (SP) for dynamic networks have been studied/documented by various researchers, contributions from this present work consists of a sparse matrix storage scheme for efficiently storing large scale sparse network’s connectivity, a concept of Time Delay Factor (TDF) combining with a “general piece- wise linear function” to describe the link cost as a function of time for Non-FIFO links’ costs, and Backward Dijkstra SP Algorithm with simple heuristic rules for rejecting unwanted solutions during the backward search algorithm. Both small-scale (academic) networks as well as large- scale (real-life) networks are investigated in this work to explain and validate the proposed dynamic algorithms. Numerical results obtained from this research work have indicated that the newly proposed dynamic algorithm is reliable, and efficient. Based on the numerical results, the calculated departure time at the source node(s), for a given/specified arrival time at the destination node(s), can be non-unique, for some Non-FIFO networks’ connectivity.

RHMX:Bus Arrival Time Prediction via Mixed Model

With the widespread use of information technologies such as IoT and big data in the transportation business,traditional passenger transportation has begun to transition and upgrade into intelligent transportation,providing passengers with a better riding experience.Giving precise bus arrival times is a critical link in achieving urban intelligent transportation.As a result,a mixed model-based bus arrival time prediction model(RHMX)was suggested in this work,which could dynamically forecast bus arrival time based on the input data.First,two sub-models were created:bus station stopping time prediction and interstation running time prediction.The former predicted the stopping time of a running bus at each downstream station in an iterative manner,while the latter projected its running time on each downstream road segment(stations as the break points).Using the two models,a group of time series data on interstation running time and bus station stopping time may be predicted.Following that,the time series data from the two sub-models was fused using long short-term memory(LSTM)to generate an approximate bus arrival time.Finally,using Kalman filtering,the LSTM prediction results were dynamically updated in order to eliminate the influence of aberrant data on the anticipated value and obtain a more precise bus arrival time.The experimental findings showed that the suggested model's accuracy and stability were both improved by 35%and 17%,respectively,over AutoNavi and Baidu.

Assessing the efficacy of health countermeasures on arrival time of infectious diseases

Public health measures to control the international spread of infectious diseases include strengthening quarantines and sealing borders.Although these measures are effective in delaying the importation of infectious diseases,they also have a significant economic impact by stopping the flow of people and goods.The arrival time of infectious diseases is often used to assess quarantine effectiveness.Although the arrival time is highly dependent on the number of infected cases in the endemic country,direct comparisons have not yet been made.Therefore,this study derives an explicit relationship between the number of infected cases and arrival time.Transmission behavior is stochastic,and deterministic models are not always realistic.In this study,random differential equations,which are differential equations with stochastic processes,were used to describe the dynamics of infection in an endemic country.Furthermore,the flow of travelers from the endemic country was described in terms of survival time,and the arrival time in each country was calculated.A scenario in which PCR kits were distributed between endemic and diseasefree countries was also considered,and the impact of different distribution rates on arrival time was evaluated.The simulation results showed that increasing the distribution of PCR kits in the endemic country was more effective in delaying arrival times than using PCR kits in quarantine in disease-free countries.It was also found that increasing the proportion of identified infected persons in the endemic country,leading to isolation,was more important and effective in delaying arrival times than increasing the number of PCR tests.

Arrival time fluctuation of higher order normal modes caused by solitary internal waves

The sensitivities of the normal modes arrival time to solitary internal waves （IWs） are analyzed by using the SW06 environments. Simulation results show that the arrival time of mode 1 is relatively stable. But, there are some higher-order normal modes which arrive earlier than mode 1, and fluctuate with the appearance of solitary IWs. Explanation of the phenomenon is given based on ray theory. It is shown that, when thermocline falls down to some depths, those higher-order modes with a group of definite grazing angles mainly propagate above the thermocline and arrive earlier.

Exit identities for diusion processes observed at Poisson arrival times

For diffusion processes,we extend various two-sided exit identities to the situation when the process is only observed at arrival times of an independent Poisson process.The results are expressed in terms of solutions to the differential equations associated with the diffusions generators.

Measurement of X-ray photon energy and arrival time using a silicon drift detector

Detecting the X-ray emission of pulsars and obtaining the photons＇ time of arrival are the foundational steps in autonomous navigation via X-ray pulsar measurement.The precision of a pulse＇s time of arrival is mainly determined by the precision of photon arrival time measurement.In this work,a silicon drift detector is used to measure photon energy and arrival time.The measurement system consists of a signal detector,a processing unit,a signal acquisition unit and a data receiving unit.This system acquires the energy resolution and arrival time information of photons.In particular,background noise with different energies disturbs pulse profile forming,the system can also achieve a high signal-to-noise ratio profile.Ground test results show that this system can be applied in autonomous navigation based on X-ray pulsar measurement.

High-Precision Time Delay Estimation Based on Closed-Form Offset Compensation

To improve the estimation accuracy,a novel time delay estimation(TDE)method based on the closed-form offset compensation is proposed.Firstly,we use the generalized cross-correlation with phase transform(GCC-PHAT)method to obtain the initial TDE.Secondly,a signal model using normalized cross spectrum is established,and the noise subspace is extracted by eigenvalue decomposition(EVD)of covariance matrix.Using the orthogonal relation between the steering vector and the noise subspace,the first-order Taylor expansion is carried out on the steering vector reconstructed by the initial TDE.Finally,the offsets are compensated via simple least squares(LS).Compared to other state-of-the-art methods,the proposed method significantly reduces the computational complexity and achieves better estimation performance.Experiments on both simulation and real-world data verify the efficiency of the proposed approach.

Performance Evaluation of Three-Dimensional UWB Real-Time Locating Auto-Positioning System for Fire Rescue

Fire rescue challenges and solutions have evolved from straightfor-ward plane rescue to encompass 3D space due to the rise of high-rise city buildings.Hence,this study facilitates a system with quick and simplified on-site launching and generates real-time location data,enabling fire rescuers to arrive at the intended spot faster and correctly for effective and precise rescue.Auto-positioning with step-by-step instructions is proposed when launching the locating system,while no extra measuring instrument like Total Station(TS)is needed.Real-time location tracking is provided via a 3D space real-time locating system(RTLS)constructed using Ultra-wide Bandwidth technology(UWB),which requires electromagnetic waves to pass through concrete walls.A hybrid weighted least squares with a time difference of arrival(WLS/TDOA)positioning method is proposed to address real path-tracking issues in 3D space and to meet RTLS requirements for quick computing in real-world applications.The 3D WLS/TDOA algorithm is theoretically constructed with the Cramer-Rao lower bound(CRLB).The computing complexity is reduced to the lower bound for embedded hardware to directly compute the time differential of the arriving signals using the time-to-digital converter(TDC).The results of the experiments show that the errors are controlled when the positioning algorithm is applied in various complicated situations to fulfill the requirements of engineering applications.The statistical analysis of the data reveals that the proposed UWB RTLS auto-positioning system can track target tags with an accuracy of 0.20 m.

Three-Dimensional Sound Source Location Algorithm for Subsea Leakage Using Hydrophone

Leakages from subsea oil and gas equipment cause substantial economic losses and damage to marine ecosystem,so it is essential to locate the source of the leak.However,due to the complexity and variability of the marine environment,the signals collected by hydrophone contain a variety of noises,which makes it challenging to extract useful signals for localization.To solve this problem,a hydrophone denoising algorithm is proposed based on variational modal decomposition(VMD)with grey wolf optimization.First,the average envelope entropy is used as the fitness function of the grey wolf optimizer to find the optimal solution for the parameters K andα.Afterward,the VMD algorithm decomposes the original signal parameters to obtain the intrinsic mode functions(IMFs).Subsequently,the number of interrelationships between each IMF and the original signal was calculated,the threshold value was set,and the noise signal was removed to calculate the time difference using the valid signal obtained by reconstruction.Finally,the arrival time difference is used to locate the origin of the leak.The localization accuracy of the method in finding leaks is investigated experimentally by constructing a simulated leak test rig,and the effectiveness and feasibility of the method are verified.

Measuring Acoustic Wave Transit Time in Furnace Based on Active Acoustic Source Signal

Accurate measurement of transit time for acoustic wave between two sensors installed on two sides of a furnace is a key to implementing the temperature field measurement technique based on acoustical method. A new method for measuring transit time of acoustic wave based on active acoustic source signal is proposed in this paper, which includes the followings： the time when the acoustic source signal arrives at the two sensors is measured first; then, the difference of two arriving time arguments is computed, thereby we get the transit time of the acoustic wave between two sensors installed on the two sides of the furnace. Avoiding the restriction on acoustic source signal and background noise, the new method can get the transit time of acoustic wave with higher precision and stronger ability of resisting noise interference.

Aviation multi-station collaborative detecting based on time-frequency correlation of data-link

As an important application research topic of the intelligent aviation multi-station, collaborative detecting must overcome the problem of scouting measurement with status of 'fragmentation', and the NP-hardness problem of matching association between target and measurement in the process of scouting to data-link, which has complicated technical architecture of network construction. In this paper, taking advantage of cooperation mechanism on signal level in the aviation multi-station sympathetic network, a method of obtaining target time difference of arrival (TDOA) measurement using multi-station collaborative detecting based on time-frequency association is proposed. The method can not only achieve matching between target and its measurement, but also obtain TDOA measurement by further evolutionary transaction through refreshing sequential pulse time of arrival (TOA) measurement matrix for matching and correlating. Simulation results show that the accuracy of TDOA measurement has significant superiority over TOA, and detection probability of false TDOA measurement introduced by noise and fake measurement can be reduced effectively.

Triangles Technique for Time and Location Finding of the Lightning Discharge in Spherical Model of the Earth

The spherical model of time and location calculation of the lightning discharge is given. The calculations are made by means of radio signals detection by sensors of the distributed network. The full solution of a problem of lightning discharge cloud-ground type location for three sensors is given. Based on this task the lightning location method for a network of sensors was developed. By means of computational experiments, the analysis of accuracy of the model depending on radio signals detection accuracy at observing stations was done.

Stochastic Model of a Cold-Stand by System with Waiting for Arrival &Treatment of Server

The service facility or server is the key constituent to keep a system operational for desired period of time. As any eventuality with the system necessitates immediate presence of it (server) so the time point of arrival and treatment of server significantly affects the system performance. This paper works out the steady state behavior of a cold standby system equipped with two similar units and a server with elapsed arrival and treatment times following general probability distributions. It practices the theory of semi-Markov processes, regenerative point technique and Laplace transforms to derive the expressions for state transition probabilities, mean sojourn times, mean time to system failure, system availability, server busy period and expected frequencies of repairs and treatments. The profit function is also developed taking different costs and revenue in to account. For tracing wider applicability of the model for different reliability and cost-effective systems, a particular case study is also presented as an illustration.

Experiments on two-phase flow in hydraulic jump on pebbled rough bed:Part 2–Bubble clustering

A survey on bubble clustering in air–water flow processes may provide significant insights into turbulent two-phaseflow.These processes have been studied in plunging jets,dropshafts,and hydraulic jumps on a smooth bed.As a first attempt,this study examined the bubble clustering process in hydraulic jumps on a pebbled rough bed using experimental data for 1.70<Fr_(1)<2.84(with Fr_(1) denoting the inflow Froude number).The basic properties of particle grouping and clustering,including the number of clusters,the dimensionless number of clusters per second,the percentage of clustered bubbles,and the number of bubbles per cluster,were analyzed based on two criteria.For both criteria,the maximum cluster count rate was greater on the rough bed than on the smooth bed,suggesting greater interactions between turbulence and bubbly flow on the rough bed.The results were consistent with the longitudinal distribution of the interfacial velocity using one of the criteria.In addition,the clustering process was analyzed using a different approach:the interparticle arrival time of bubbles.The comparison showed that the bubbly flow structure had a greater density of bubbles per unitflux on the rough bed than on the smooth bed.Bed roughness was the dominant parameter close to the jump toe.Further downstream,Fr_(1) predominated.Thus,the rate of bubble density decreased more rapidly for the hydraulic jump with the lowest Fr_(1).

Delivery Service Management System Using Google Maps for SMEs in Emerging Countries

This paper proposes a Delivery Service Management(DSM)system for Small and Medium Enterprises(SMEs)that own a delivery fleet of pickup trucks to manage Business-to-Business(B2B)delivery services.The proposed DSM system integrates four systems:Delivery Location Positioning(DLP),Delivery Route Planning(DRP),Arrival Time Prediction(ATP),and Communication and Data Sharing(CDS)systems.These systems are used to pinpoint the delivery locations of customers,plan the delivery route of each truck,predict arrival time(with an interval)at each delivery location,and communicate and share information among stakeholders,respectively.The DSM system deploys Google applications,a GPS tracking system,Google Map APIs,ATP algorithms(embedded in Excel Macros),Line,and Telegram as supporting tools.To improve the accuracy of the ATP system,three tech-niques are applied considering driver behaviors.The proposed DSM system has been implemented in a Thai SME.From the process perspective,the DSM system is a systematic procedure for end-to-end delivery services.It allows the interactions between planner-driver decisions and supporting tools.The supporting tools are simple,can be easily used with little training,and require low capital expenditure.The statistical analysis shows that the ATP algorithm with the three techniques provides high accuracy.Thus,the proposed DSM system is beneficial for practitioners to manage delivery services,especially for SMEs in emerging countries.