Origin-Aware Location Prediction Based on Historical Vehicle Trajectories

Next location prediction is of great importance for many location-based applications and provides essential intelligence to various businesses. In previous studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Nevertheless, due to the time and space complexity, these methods (e.g., Markov models) only utilize the just passed locations to predict next locations, neglecting earlier passed locations in the trajectory. In this work, we seek to enhance the prediction performance by incorporating the travel time from all the passed locations in the query trajectory to each candidate next location. To this end, we propose a novel prediction method, namely the Travel Time Difference Model, which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Moreover, we integrate the Travel Time Difference Model with a Sequential and Temporal Predictor to yield a joint model. The joint prediction model integrates local sequential transitions, temporal regularity, and global travel time information in the trajectory for the next location prediction problem. We have conducted extensive experiments on two real-world datasets: the vehicle passage record data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over baseline methods.

Application of Multiparameter Kalman Filter in Ultrasonic Water Meter

With the gradual development of the superior performance of the ultrasonic water meter, the use of the water meter gradually occupies most of the market due to its unique advantages. Through the analysis of the influencing factors of the ultrasonic water meter, the Kalman filter is used to analyze the influencing factors, and the differences are obtained. In this paper, combined with the application scope of the Kalman filter, it is introduced. Combined with the method of data fusion, the influencing factors of the ultrasonic water meter are analyzed. They are the flow rate, temperature, speed of sound, time difference, etc. The appropriate sensor is selected through the sensor selection method, and the corresponding data is obtained by the method of the corresponding sensor. We combine the data fusion method and use Kalman’s method to filter the data. By comparing the data before and after the processing, it is found that the data before and after the filtering of different influencing factors are small. Among them, the flow speed factor has the greatest impact on the accuracy of the ultrasonic water meter; temperature and sound velocity have little effect on the performance of the ultrasonic water meter. When designing an ultrasonic water meter, it is mainly necessary to consider the impact of flow rate and time difference on the performance of the ultrasonic water meter.

Time difference between the 1854 CE Ansei–Tokai and Ansei–Nankai earthquakes estimated from distant tsunami waveforms on the west coast of North America

We estimated the time difference between the 1854 CE Ansei–Tokai and Ansei–Nankai earthquakes from tidal records of two tide gauge stations (San Francisco and San Diego) on the west coast of North America. The first signals of the Ansei–Tokai tsunami were apparent, whereas those of the Ansei–Nankai tsunami were obscured by the later waves of the Ansei–Tokai tsunami. Waveforms of the Ansei–Nankai tsunami simulated with nonlinear dispersive wave theory by assuming an origin time of 07:00 GMT on 24 December arrived earlier than in the observations. The normalized root mean square and the misfit between the simulated and observed waveforms of the Ansei–Nankai tsunami showed a time difference between them of approximately 0.4 h. This finding suggests that the actual origin time of the Ansei–Nankai tsunami was approximately 07:24 GMT on 24 December. A previous study estimated the origin time of the Ansei–Tokai tsunami to be about 00:30 GMT on 23 December. Thus, we concluded that the time difference between the 1854 CE Ansei–Tokai and Ansei–Nankai tsunamis was 30.9 h. Despite the significant difference in the time resolution between the seasonal timekeeping system used in Japan in 1854 and waveform digitization, our result is roughly in agreement with historical descriptions of the tsunamis, suggesting that such information can be effectively used to determine the origin times of historical earthquakes.

Atmospheric Correction of Airborne Hyperspectral CASI Data Using Polymer, 6S and FLAASH

Airborne hyperspectral data play an important role in remote sensing of coastal waters. However, before their application, atmospheric correction is required to remove or reduce the atmospheric effects caused by molecular and aerosol scattering and absorption. In this study, we first processed airborne hyperspectral CASI-1500 data acquired on 4 May 2019 over the Uljin coast of Korea with Polymer and then compared the performance with the other two widely used atmospheric correction approaches, i.e., 6S and FLAASH, to determine the most appropriate correction technique for CASI-1500 data in coastal waters. Our results show the superiority of Polymer over 6S and FLAASH in deriving the Rrs spectral shape and magnitude. The performance of Polymer was further evaluated by comparing CASI-1500 Rrs data with those obtained from the MODIS-Aqua sensor on 3 May 2019 and processed using Polymer. The spectral shapes of the derived Rrs from CASI-1500 and MODIS-Aqua matched well, but the magnitude of CASI-1500 Rrs was approximately 0.8 times lower than MODIS Rrs. The possible reasons for this difference were time difference (1 day) between CASI-1500 and MODIS data, higher land adjacency effect for MODIS-Aqua than for CASI-1500, and possible errors in MODIS Rrs from Polymer.

A SIMULATION-BASED METHODOLOGY FOR EVALUATING THE FACTORS ON SHIP EMERGENCY EVACUATION

There are many hazards on a ship that makes an emergency evacuation process inevitable. Providing safe and effective evacuation of passengers from ships in an emergency situation becomes critical. Handling a real ship evacuation practice is often unaffordable as modelling such an environment is very expensive and may cause severe distress to participants. As an alternative, simulation models have been used to overwhelm the issue above in recent years. Therefore, this paper proposes a novel simulation-based methodology for evaluating the effect of factors including physical as well as psychological passenger characteristics and routeing systematic on emergency evacuation process for public marine transportation. A detailed questionnaire has been conducted in this work to reflect passenger characteristics on simulation model in a more realistic manner. Also, a new routeing systematic is developed to provide an efficient evacuation procedure. As another contribution, a novel grid-based approach where the meshed discretized nodes can contain more than one passenger is proposed in simulation model for the first time. Then, a statistical analysis is included within the methodology to assess the importance level of each factor on evacuation time. The proposed methodology is applied to a real life Ro-Ro ferry. A validation protocol based on IMO regulations is conducted and confirmed the effectiveness of the suggested simulation model. The simulation of different scenario types have indicated the influencing factors in a ship emergency evacuation. According to results, passenger characteristics has been identified as the most dominant factor on evacuation performance. The highest evacuation time difference has been observed for different levels of weight attribute. Moreover, it is concluded that the consideration of load utilization balancing among evacuation systems for routeing decreases evacuation time significantly. Finally, significant evacuation time difference between grid approaches have been demonstrated.

The Study of Generalized Synchronization between Two Identical Neurons Based on the Laplace Transform Method

In this paper, a new method is proposed based on the auxiliary system approach to investigate generalized synchronization between two identical neurons with unidirectional coupling. Different from other studies, the synchronization error system between the response and auxiliary systems is converted into a set of Volterra integral equations according to the Laplace transform method and convolution theorem. By using the successive approximation method in the theory of integral equations, an analytical criterion for the detection of generalized synchronization between two identical neurons is obtained. It is found that there is a time difference between two signals of neurons when the generalized synchronization between them is achieved. Furthermore, the value of the time difference has no relation to the generalized synchronization condition but depends on the coupling function between two neurons. The study in this paper shows that one can construct a coupling function between two identical neurons using the current signal of the drive system to predict its future signal or make its past signal reappear.

An Automatic Conflict Detection Framework for Urban Intersections Based on an Improved Time Difference to Collision Indicator

Urban road intersections are one of the key components of road networks. Due to complex and diverse traffic conditions, traffic conflicts occur frequently. Accurate traffic conflict detection allows improvement of the traffic conditions and decreases the probability of traffic accidents. Many time-based conflict indicators have been widely studied, but the sizes of the vehicles are ignored. This is a very important factor for conflict detection at urban intersections. Therefore, in this paper we propose a novel time difference conflict indicator by incorporating vehicle sizes instead of viewing vehicles as particles. Specially, we designed an automatic conflict recognition framework between vehicles at the urban intersections. The vehicle sizes are automatically extracted with the sparse recurrent convolutional neural network, and the vehicle trajectories are obtained with a fast-tracking algorithm based on the intersection-to-union ratio. Given tracking vehicles, we improved the time difference to the conflict metric by incorporating vehicle size information. We have conducted extensive experiments and demonstrated that the proposed framework can effectively recognize vehicle conflict accurately.