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.

Collaborative prediction for bus arrival time based on CPS

To improve the accuracy of real-time public transport information release system, a collaborative prediction model was proposed based on cyber-physical systems architecture. In the model, the total bus travel time was divided into three parts: running time, dwell time and intersection delay time, and the data were divided into three categories of historical data, static data and real-time data. The bus arrival time was obtained by fusion computing the real-time data in perception layer together with historical data and static data in collaborative layer. The validity of the collaborative model was verified by the data of a typical urban bus line in Shanghai, and 1538 sets of data were collected and analyzed from three different perspectives. By comparing the experimental results with the actual results, it is shown that the experimental results are with higher prediction accuracy, and the collaborative prediction model adopted is able to meet the demand for bus arrival prediction.

Automatic pickup of arrival time of channel wave based on multi-channel constraints

Accurately detecting the arrival time of a channel wave in a coal seam is very important for in-seam seismic data processing. The arrival time greatly affects the accuracy of the channel wave inversion and the computed tomography （CT） result. However, because the signal-to-noise ratio of in-seam seismic data is reduced by the long wavelength and strong frequency dispersion, accurately timing the arrival of channel waves is extremely difficult. For this purpose, we propose a method that automatically picks up the arrival time of channel waves based on multi-channel constraints. We first estimate the Jaccard similarity coefficient of two ray paths, then apply it as a weight coefficient for stacking the multi- channel dispersion spectra. The reasonableness and effectiveness of the proposed method is verified in an actual data application. Most importantly, the method increases the degree of automation and the pickup precision of the channel-wave arrival time.

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.

Average Arrival Time:an Alternative Approach for Studying Fluorescent Behavior of Single Quantum Dotst

Due to photoluminescence intermittency of single tional exponential fluorescence lifetime analysis is colloidal quantum dots （QDs）, the tradinot perfect to characterize QDs＇ fluores- cent emission behavior. In this work we used the time-tagged time-resolved （TTTR） mode to record the fluorescent photons from single QDs. We showed that this method is compatible with the traditional lifetime analysis. In addition, by constructing the trajectory over time and the distribution of average arrival time （AAT） of the fluorescent photons, inore details about the emission behavior of QDs were revealed.

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.

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.

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.

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.

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.

A PSO microseismic localization method based on group waves' time difference information

Aiming at the lower microseismic localization accuracy in underground shallow distributed burst point localization based on time difference of arriva（TDOA）,this paper presents a method for microseismic localization based on group waves’ time difference information Firstly, extract the time difference corresponding to direct P wavers dominant frequency by utilizing its propagation characteristics. Secondly, construct TDOA model with non-prediction velocity and identify objective function of particle swarm optimization （PSO）. Afterwards, construct the initial particle swarm by using time difference information Finally, search the localization results in optimal solution space. The results of experimental verification show that the microseismic localization method proposed in this paper effectively enhances the localization accuracy of microseismic explosion source with positioning error less than 50 cm, which can satisfy the localization requirements of shallow burst point and has definite value for engineering application in underground space positioning field.

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.

Bilevel Programming Model for Joint Scheduling of Arrival and Departure Flights Based on Traffic Scenario

In order to meet the needs of collaborative decision making,considering the different demands of air traffic control units,airlines,airports and passengers in various traffic scenarios,the joint scheduling problem of arrival and departure flights is studied systematically.According to the matching degree of capacity and flow,it is determined that the traffic state of arrival/departure operation in a certain period is peak or off-peak.The demands of all parties in each traffic state are analyzed,and the mathematical models of arrival/departure flight scheduling in each traffic state are established.Aiming at the four kinds of joint operation traffic scenarios of arrival and departure,the corresponding bi-level programming models for joint scheduling of arrival and departure flights are established,respectively,and the elitism genetic algorithm is designed to solve the models.The results show that:Compared with the first-come-firstserved method,in the scenarios of arrival peak&departure off-peak and arrival peak&departure peak,the departure flight equilibrium satisfaction is improved,and the runway occupation time of departure flight flow is reduced by 38.8%.In the scenarios of arrival off-peak&departure off-peak and departure peak&arrival off-peak,the arrival flight equilibrium delay time is significantly reduced,the departure flight equilibrium satisfaction is improved by 77.6%,and the runway occupation time of departure flight flow is reduced by 46.6%.Compared with other four kinds of strategies,the optimal scheduling method can better balance fairness and efficiency,so the scheduling results are more reasonable.

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.

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.