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In maritime operations, instances occur where vessels flee after colliding with buoys. Automatic Identification System (AIS) data collected from these buoys play a vital role in identifying the responsible party and determining liability. However, data from buoys can be lost in rough sea conditions and equipment failure, which makes it difficult to identify accidents where ships collide with buoys. To address this issue, this study proposes a model that combines Long Short-Term Memory Network (LSTM) with Multiple Attention mechanisms to reconstruct buoy trajectories with high precision. In this paper, the buoy trajectory reconstruction test is carried out using Qingdao Harbour Buoy 304 a case study, and the validation results show that the mean squared error (MSE) of this model is 12.15. In addition, the LSTM-Attention model shows a significant improvement in all the metrics compared with other models: compared with the Random sampling model, the mean absolute error (MAE) is improved by 75.15%, and the root mean squared error (RMSE) by 71.02%; MSE by 77.31%, MAE by 36.70%, and RMSE by 52.36% compared to the Cubic spline model; and MSE by 49.92%, MAE by 27.35%, and RMSE by 29.52% compared to the Hermite model. These results show that the LSTM-Attention model significantly improves the accuracy and reliability of trajectory reconstruction. • A novel LSTM-Attention model for reconstructing buoy trajectories is presented. • The model greatly improves the accuracy, and its MSE, MAE and RMSE are better than those of traditional methods. • A case study of buoy 304 in Qingdao harbour demonstrates the effectiveness of the model in a maritime environment. [ABSTRACT FROM AUTHOR]

• A Physics-Informed Neural Network (PINN)-based framework is proposed to address the trajectory stitching problem. • This framework incorporates the advantages of the physics model-based and learning-based methods. • This framework has demonstrated fairly good extrapolation ability, making it suitable for diverse traffic dynamics and ensuring robust performance across varying scenarios. • The dataset processed by the proposed framework, named the High-granularity Highway Simulation (HIGH-SIM) dataset, has been published online for public use. High-accuracy long-coverage vehicle trajectory data can benefit the investigations of various traffic phenomena. However, existing datasets frequently contain broken trajectories due to sensing limitations, which impedes a thorough understanding of traffic. To address this issue, this paper proposes a Physics-Informed Neural Network (PINN)-based method for stitching broken trajectories. The proposed PINN-based method enhances traditional neural networks by integrating physics priors, including vehicle kinematics and boundary conditions, aiming to provide information beyond training domain and regularization, thus increasing method accuracy and extrapolation ability for cross-dynamics scenarios (e.g., extrapolating from low-speed training data to reconstruct high-speed trajectories). Two publicly available vehicle trajectory datasets, NGSIM and HighSIM, were adopted to validate the proposed PINN-based method, and four biased training scenarios were designed to assess the PINN-based method's extrapolation ability. Results indicate that the PINN-based method demonstrated superior performance regarding trajectory stitching accuracy and consistency compared to benchmark models. The dataset processed using our proposed PINN-based method has been made publicly available online to support the traffic research community. Additionally, this PINN-based approach can be applied to a broader range of scenarios that include physics-based priors. [ABSTRACT FROM AUTHOR]

This paper presents a deep auto-encoder model and a phased framework approach to predict the next 12 h of vessel trajectories using 1 to 3 h of Automatic Identification System data as input. The strategy involves fusing spatiotemporal features from AIS messages with probabilistic features engineered from historical AIS data to reduce forecasting uncertainty. The probabilistic features have an F1-Score of approximately 85% and 75% for the vessel route and destination prediction, respectively. Under such circumstances, we achieved an R2 Score of over 98% with different layer structures and varying feature combinations; the high R2 Score is a natural outcome of the well-defined shipping lanes in the study region. However, our proposal stands out among competing approaches as it demonstrates the capability of complex decision-making during turnings and route selection. Furthermore, we have shown that our model achieves more accurate forecasting with average and median errors of 11km and 6km, respectively, a 25% improvement from the current state-of-the-art approaches. The resulting model from this proposal is deployed as part of a broader Decision Support System to safeguard whales by preventing the risk of vessel-whale collisions under the smartWhales initiative and acting on the Gulf of St. Lawrence in Atlantic Canada. [Display omitted] • Probabilistic feature augmentation for deriving trajectory route and destination. • Conditional probability model for spatial feature distillation from AIS data streams. • Feature fusion and augmentation for problem shifting into trajectory reconstruction. • AutoEncoder designed for faster trajectory reconstruction with fewer parameters. • Module of a Decision Support System that avoids vessel-whale collisions in Canada. [ABSTRACT FROM AUTHOR]

Inference of detailed vehicle trajectories is crucial for applications such as traffic flow modeling, energy consumption estimation, and traffic flow optimization. Static sensors can provide only aggregated information, posing challenges in reconstructing individual vehicle trajectories. Shockwave theory is used to reproduce oscillations that occur between sensors. However, as the emerging of connected vehicles grows, probe data offers significant opportunities for more precise trajectory reconstruction. Existing methods rely on Eulerian observations (e.g., data from static sensors) and Lagrangian observations (e.g., data from connected vehicles) incorporating shockwave theory and car-following modeling. Despite these advancements, a prevalent issue lies in the static assignment of shockwave speed, which may not be able to reflect the traffic oscillations in a short time period caused by varying response times and vehicle dynamics. Moreover, driver dynamics while reconstructing the trajectories are ignored. In response, this paper proposes a novel framework that integrates Eulerian and Lagrangian observations for trajectory reconstruction on freeways. The approach introduces a calibration algorithm for time-varying shockwave speed. The shockwave speed calibrated by the CV is then utilized for trajectory reconstruction of other non-connected vehicles based on shockwave theory. Additionally, vehicle and driver dynamics are introduced to optimize the trajectory and estimate energy consumption by applying a vehicle movement model. The proposed method is evaluated using real-world datasets, demonstrating superior performance in terms of trajectory accuracy, reproducing traffic oscillations, and estimating energy consumption. • Integrate Lagrangian and Eulerian observations to reconstruct trajectories. • Calibrate time-varying short-term shockwave speeds using the two types of data. • Reconstruct trajectories for non-connected vehicles based on shockwave theory. • Optimize trajectories by adding driver dynamics for better energy estimation. • Evaluation on real-world datasets shows excellent performances from several aspects. [ABSTRACT FROM AUTHOR]

The study of moving particles (e.g. molecules, virus, vesicles, organelles, or whole cells) is crucial to decipher a plethora of cellular mechanisms within physiological and pathological conditions. Powerful live-imaging approaches enable life scientists to capture particle movements at different scale from cells to single molecules, that are collected in a series of frames. However, although these events can be captured, an accurate quantitative analysis of live-imaging experiments still remains a challenge. Two main approaches are currently used to study particle kinematics: kymographs, which are graphical representation of spatial motion over time, and single particle tracking (SPT) followed by linear linking. Both kymograph and SPT apply a space-time approximation in quantifying particle kinematics, considering the velocity constant either over several frames or between consecutive frames, respectively. Thus, both approaches intrinsically limit the analysis of complex motions with rapid changes in velocity. Therefore, we design, implement and validate a novel reconstruction algorithm aiming at supporting tracking particle trafficking analysis with mathematical foundations. Our method is based on polynomial reconstruction of 4D (3D+time) particle trajectories, enabling to assess particle instantaneous velocity and acceleration, at any time, over the entire trajectory. Here, the new algorithm is compared to state-of-the-art SPT followed by linear linking, demonstrating an increased accuracy in quantifying particle kinematics. Our approach is directly derived from the governing equations of motion, thus it arises from physical principles and, as such, it is a versatile and reliable numerical method for accurate particle kinematics analysis which can be applied to any live-imaging experiment where the space-time coordinates can be retrieved. • 4D space-time trajectory reconstruction for particle kinematics analysis. • Nonlinear limiting technique (WENO). • Arbitrary high order of accuracy. • Assessment of instantaneous velocity and acceleration. • Application to biology and hydraulics. [ABSTRACT FROM AUTHOR]

The Automatic Identification System (AIS) enhances maritime safety and environmental monitoring by providing crucial ship trajectory data. However, this data is often compromised by missing or abnormal values due to signal blockage, transmission errors, and equipment failures, jeopardizing safety and the accuracy of environmental analyses. Addressing these challenges, we propose a novel Customized Tensor-Based Maritime Trajectory Reconstruction (CTMTR) framework that leverages the self-similarity of ship trajectories to reformulate trajectory reconstruction as a matrix rank minimization issue. The CTMTR framework consists of three steps: data preprocessing, trajectory matrix construction, and trajectory reconstruction. We conducted simulation experiments using a dataset comprising vessel trajectories from the east coast of Dover in the United States in 2022. To validate its effectiveness, the CTMTR is compared with four advanced methods (LRMC, LRTC-TNN, TRPCA, and TNN-DCT) under diverse missing and anomalous scenarios. The results substantiate that the performance of CTMTR outperforms other approaches, especially in anomalous cases. Our method outperforms existing methods by an order of magnitude under random missing combined with anomaly scenarios. The CTMTR framework thus has the potential to advance maritime trajectory reconstruction methodologies, providing a solid foundation for future innovations in maritime data analysis and navigation safety technologies. • Introduced a novel CTMTR framework for ship trajectory reconstruction. • Addressed missing and abnormal AIS data issues effectively. • Utilized ship trajectory self-similarity for data reconstruction. • Outperformed existing methods in handling missing and anomalous scenarios. [ABSTRACT FROM AUTHOR]

One of the major challenges in the Hayabusa2 sample-return mission was the second touchdown on the asteroid Ryugu, which was designed to collect subsurface materials near the artificial crater formed by a small carry-on impactor. Due to engineering and scientific requirements, a narrow area with a radius as small as 3.5 m was selected as the target landing site. To achieve pinpoint touchdown at the selected site, an artificial landmark called a target marker (TM) was used for optical navigation. The key to a successful touchdown was precise deployment of the TM in the microgravity environment of the asteroid. This study therefore investigates a viable trajectory for TM deployment, incorporating the uncertainty in the impact and rebound motions of the TM. Following the TM deployment operation, a detailed survey of the landing site around the TM settlement point is performed to assess the terrain conditions. To guarantee the observation quality and spacecraft safety, multi-impulse low-altitude trajectories are introduced in this paper, along with covariance analyses based on the high-fidelity polyhedral gravity model of Ryugu. Subsequently, a pinpoint touchdown trajectory that satisfies various engineering requirements, such as landing accuracy and velocity, is designed, taking advantage of optical TM tracking. The feasibility of the touchdown sequence is further validated by a Monte Carlo dispersion analysis. Consequently, Hayabusa2 successfully touched down within the target site on 11 July 2019. The current research also conducts a post-operation trajectory reconstruction based on the flight data to demonstrate the actual guidance performance in the TM deployment, landing site observations, and pinpoint touchdown. [ABSTRACT FROM AUTHOR]

A key strategy for increasing railway capacity is utilizing infrastructure more efficiently. While much research has been completed on methods for assessing railway capacity, very little has focused on the details of capacity utilisation, such as assessing the various ways trains use capacity, the impacts of specific blocking time components, and how train dynamics (accelerating, cruising, braking, and dwelling) affect capacity. This paper presents a methodology for comparing planned occupancy to actual occupancy under real operations and applies it in a case study. The methodology is based on identifying a critical path which represents an extension of bottleneck concept presented in UIC leaflet 406. The methodology was applied in a case study to determine the specific blocking times and train dynamics which cause a blocking time gap for a sequence of trains, both a-priori and a-posteriori, after considering the operational variations. The analysis of real operations with variations in train trajectories shows that capacity occupation is mostly influenced by train sequence heterogeneity in the original schedule. The varying effects of operations have a smaller but relevant impact. The methods developed in this paper can be used to help assess railway capacity under real operations. • Capacity occupation of planned and realized railway operation are compared. • Train trajectories are reconstructed by simulation based on operation data. • A critical path approach for assessing capacity occupation of railways is proposed. • UIC norm 406 timetable compression method is extended. • Realized operation usually occupies relevantly more capacity than planned. [ABSTRACT FROM AUTHOR]

Automatic Identification System (AIS) is a telecommunication system created to allow ships to communicate with each other by exchanging messages containing information such as vessel's ID, position, speed, heading, etc. AIS is useful in many cases, such as early ship collision detection. However, its terrestrial segment's drawback is a relatively low range (about 74 km, roughly 40 nautical miles). Satellite Automatic Identification System (SAT-AIS) was introduced to overcome this limitation, but it suffers from its own problem known as packet collision. The satellite receives messages from multiple terrestrial cells and communication is synchronized within such cells, but not between them, thus messages got lost or damaged when they appear at the satellite at the same time. In this paper, we present a machine learning-based approach to reconstruct those missing messages and deeply investigate whether or not a density-based spatial clustering of applications with noise (DBSCAN) can be considered in the first stage of the reconstruction. The experiment focuses on findig the optimal parameters for the clustering, running it both on original and damaged data to finally ascertain that DSBCAN can distinguish individual trajectories in a dataset that can be further reconstructed. [ABSTRACT FROM AUTHOR]

Highlights • A method is proposed to extract the instantaneous oscillation factors. • The proposed method has a higher accuracy, compared with signal analysis methods. • A method is proposed to study the interaction of multi-eigenmodes. • Some complex phenomena can be explained by the proposed methods. • The validity and universality of the proposed method have been strictly clarified. Abstract Low-frequency oscillation is usually regarded as a small disturbance problem. Traditional methods describe oscillation through eigenvalues obtained by linearized equations at the equilibrium point. Obviously, nonlinear time-varying factors are ignored. This article introduces a trajectory section eigenvalue method that can reflect the influences of complex models and fault scenarios. Based on this theory, a trajectory reconstruction method is proposed to accomplish the decoupling of multi-eigenmodes in the time domain. On the premise that the numerical integration is accurate, the trajectory section eigenvalue and trajectory reconstruction methods can be used to study the dynamics of the oscillation and interaction of multi-eigenmodes. Simulation test results show the validity of the proposed methods. The mechanism of low-frequency oscillation is illustrated by trajectory section eigenvalues and trajectory reconstruction methods from a new perspective. These methods can be used as effective tools for theoretical research and engineering applications in power systems. [ABSTRACT FROM AUTHOR]

• We present a model reconstructing 2D vehicular trajectory based on optimal control. • The model has a behavioural foundation using decision variables of human driver. • The model is transferable to trajectory reconstruction in any manoeuvre on 2D roads. • All vehicle motion variables are all strictly within the permitted range. • It reconstructs plausible trajectories while keeping consistency with measured data. Vehicle trajectory reconstruction is an indispensable step before using the observed trajectory data for analysis. A recurrent challenge of the existing smoothing/filtering-based methods is the design of the smoothing parameters to avoid over-smoothing while ensuring realistic vehicle dynamics, but they often fall short in two-dimensional (2D) vehicle movements with coupled longitudinal and lateral vehicle motion. To tackle this challenge, we propose a novel approach to reconstruct vehicle trajectories based on constrained optimal control. The proposed approach outputs 2D trajectories to minimize the errors of the reconstructed trajectory with respect to the measured trajectory while respecting the vehicle dynamics and motion constraints. Bounded curvature and acceleration are used as the control variables that resemble human driver behaviour, and plausible ranges of the 2D motion variables are set as the state constraints of the optimal control problem. The proposed model is validated using both the pNEUMA trajectory dataset and a new high-precision trajectory dataset. Results show that the average Euclidean distance between the reconstructed and measured trajectory points is 0.040 m and the vehicle motion variables are all strictly within the permitted range. [ABSTRACT FROM AUTHOR]

Highlights • A trajectory reconstruction method is proposed based on the particle filter. • The path flow can be extracted by analyzing the complete vehicle trajectories. • The OD patterns are estimated based on vehicle trajectories using ALPR data. • The impact of the ALPR sampling rate on OD pattern estimation is investigated. Abstract Origin-destination (OD) pattern estimation is a vital step for traffic simulation applications and active urban traffic management. Many methods have been proposed to estimate OD patterns based on different data sources, such as GPS data and automatic license plate recognition (ALPR) data. These data can be used to identify vehicle IDs and estimate their trajectories by matching vehicles identified by different sensors across the network. OD pattern estimation using ALPR data remains a challenge in real-life applications due to the difficulty in reconstructing vehicle trajectories. This paper proposes an offline method for historical OD pattern estimation based on ALPR data. A particle filter is used to estimate the probability of a vehicle’s trajectory from all possible candidate trajectories. The initial particles are generated by searching potential paths in a pre-determined area based on the time geography theory. Then, the path flow estimation process is conducted through dividing the reconstructed complete trajectories of all detected vehicles into multiple trips. Finally, the OD patterns are estimated by adding up the path flows with the same ODs. The proposed method was implemented on a real-world traffic network in Kunshan, China and verified through a calibrated microscopic traffic simulation model. The results show that the MAPEs of the OD estimation are lower than 19%. Further investigation shows that there exists a minimum required ALPR sampling rate (60% in the test network) for accurately estimating the OD patterns. The findings of this study demonstrate the effectiveness of the proposed method in OD pattern estimation. [ABSTRACT FROM AUTHOR]

Carpooling has been considered a solution for alleviating traffic congestion and reducing air pollution in cities. However, the quantification of the benefits of large-scale carpooling in urban areas remains a challenge due to insufficient travel trajectory data. In this study, a trajectory reconstruction method is proposed to capture vehicle trajectories based on citywide license plate recognition (LPR) data. Then, the prospects of large-scale carpooling in an urban area under two scenarios, namely, all vehicle travel demands under real-time carpooling condition and commuter vehicle travel demands under long-term carpooling condition, are evaluated by solving an integer programming model based on an updated longest common subsequence (LCS) algorithm. A maximum weight non-bipartite matching algorithm is introduced to find the optimal solution for the proposed model. Finally, road network trip volume reduction and travel speed improvement are estimated to measure the traffic benefits attributed to carpooling. This study is applied to a dataset that contains millions of LPR data recorded in Langfang, China for 1 week. Results demonstrate that under the real-time carpooling condition, the total trip volumes for different carpooling comfort levels decrease by 32–49%, and the peak-hour travel speeds on most road segments increase by 5–40%. The long-term carpooling relationship among commuter vehicles can reduce commuter trips by an average of 30% and 24% in the morning and evening peak hours, respectively, during workdays. This study shows the application potential and promotes the development of this vehicle travel mode. [ABSTRACT FROM AUTHOR]

AIS data plays an increasingly important role in collision avoidance, risk evaluation, and navigation behavior study. However, the raw AIS data contains noise that can result in wrong conclusions. We propose a multi-regime vessel trajectory reconstruction model through three-steps processing, including (i) outliers removal, (ii) ship navigational state estimation and (iii) vessel trajectory fitting. This model allows for vessel trajectory reconstruction in different navigation states, namely hoteling, maneuvering, and normal-speed sailing. The normal-speed navigation trajectory is estimated with a spline model, which can fit any types of the trajectory even with circles. Then, the proposed model is tested and compared with other three popular trajectory reconstruction models based on a large AIS dataset containing the movement of more than 500 ships in Singapore Port. The results show that the proposed model performs significantly better than the linear regression model, polynomial regression model, and weighted regression model. The proposed model can decrease the abnormal rate of speed, acceleration, jerk and ROT (Rate of Turn) from 43.42%, 10.65%, 59.25%, 50.33%–0.00%, 0.00%, 17.28% and 15.81%, respectively. More importantly, the navigational behavior, such as turning operation, could be clearly shown in the trajectory reconstructed by the proposed model. [ABSTRACT FROM AUTHOR]

In previous urban planning research, the fine-grained population was considered a crucial factor. However, this population, which is generated from census data, represents only the number of people who live in a region. This static figure cannot indicate the underlying number of people and their temporal variation. Therefore, any decision-making based on the static population may be separated from reality. To overcome this difficulty, in this paper, the urban crowd flux is initially proposed and defined as the number of individuals flowing into or out of a region per unit time interval. Then, the urban crowd flux is estimated using an approach of human trajectory gridding reconstruction based on mobile phone location data. This approach is divided into three steps. First, the trajectory of each individual is extracted from sparse sampling of mobile phone location data after data cleansing. Then, combining with a road network, the trajectory of each individual is reconstructed by network interpolation based on the shortest path algorithm in regular grids. Third, we use the velocities of each user's trajectory record to estimate the urban crowd flux on spatio-temporal grids at each time slice. Finally, urban crowd flux in Beijing, China were estimated using our method and the spatio-temporal characteristics of flux is analyzed. [ABSTRACT FROM AUTHOR]

Changes in sea level are global and cyclical; however, the understanding of the processes that control them is still very limited owing to the low rate of progress in technology to reconstruct the trajectory of global sea level change. Based on the reconstruction of the Cenozoic sea level change curve in the northern South China Sea, discrimination criteria for global sea level (GSL) and relative sea level (RSL) changes are proposed for the first time, and five technical difficulties in single-period trajectory reconstruction are addressed. The findings show that the recorded sea level changes are dominated by the GSL change component in only a small number of basins worldwide. The Cenozoic sea level showed an increasing trend of sea level, and the fluctuation amplitude of the second-order cycle was considerably smaller than that of the third-order cycle. These findings challenge the consensus that the Cenozoic sea level continues to fall and that the magnitude of the second-order cycle is greater than that of the third-order cycle. In terms of technological advancement, the accuracy of the third-order single-period trajectory was improved to approach the cosine locus by correcting the deviation between the sea level indicated by the residual accommodation space and the GSL, and eliminating the trajectory distortion caused by the differences in the deposition rate. The new technique has the potential to reconstruct original single-period trajectories, which can provide technical support for comparing the single-period sea level trajectory with the corresponding celestial orbit and studying the mechanisms driving primary cycles. • The global sea-level (GSL) curve since 33 Ma has been reconstructed. • A criterion for distinguishing GSL and RSL trajectories is proposed. • Five technical difficulties in single-period curve construction are addressed. • New findings challenges the consensus on GSL change. [ABSTRACT FROM AUTHOR]

Globe positioning system (GPS) devices are an increasing importance source of evidence, as more of our devices have built-in GPS capabilities. In this paper, we propose a novel framework to efficiently recover National Marine Electronics Association (NMEA) logs and reconstruct GPS trajectories. Unlike existing approaches that require file system metadata, our proposed algorithm is designed based on the file carving technique without relying on system metadata. By understanding the characteristics and intrinsic structure of trajectory data in NMEA logs, we demonstrate how to pinpoint all data blocks belonging to the NMEA logs from the acquired forensic image of GPS device. Then, a discriminator is presented to determine whether two data blocks can be merged. And based on the discriminator, we design a reassembly algorithm to re-order and merge the obtained data blocks into new logs. In this context, deleted trajectories can be reconstructed by analyzing the recovered logs. Empirical experiments demonstrate that our proposed algorithm performs well when the system metadata is available/unavailable, log files are heavily fragmented, one or more parts of the log files are overwritten, and for different file systems of variable cluster sizes. [ABSTRACT FROM AUTHOR]

In this paper, an automated driving system (ADS) data acquisition and analytics platform for vehicle trajectory extraction, reconstruction, and evaluation based on connected automated vehicle (CAV) cooperative perception are presented. This platform presents a holistic pipeline from the raw advanced sensory data collection to data processing, which is capable of processing the sensor data from multi-CAVs and extracting the objects' Identity (ID) number, position, speed, and orientation information in the map and Frenet coordinates. First, the ADS data acquisition and analytics platform are presented. Specifically, the experimental CAVs platform and sensor configuration are shown, and the processing software, including a deep-learning-based object detection algorithm using LiDAR information, a late fusion scheme to leverage cooperative perception to fuse the detected objects from multi-CAVs, and a multi-object tracking method is introduced. To further enhance the object detection and tracking results, high-definition maps consisting of point cloud and vector maps are generated and forwarded to a world model to filter out the objects off the road and extract the objects' coordinates in Frenet coordinates and the lane information. In addition, to refine trajectories from the object tracking algorithms, a post-processing method is proposed. Given the objects' information from the object detection and tracking and the world model, a Kalman filter and Chi-square test method are applied to reduce the noise and remove the outlier in the trajectories. Aiming at tackling the ID switch issue of the object tracking algorithm, a fuzzy-logic-based approach is proposed to detect the discontinuous trajectories belonging to the same object. Then, a vehicle-kinematics-based trajectory prediction method is used, and a forward–backward-smoothing technique is applied to reconstruct the trajectory between the discontinuous trajectories. Finally, results, including object detection and tracking and a late fusion scheme, are presented, and the improvements by the post-processing algorithm in terms of noise level and outlier removal are discussed, which confirm the functionality and effectiveness of the proposed holistic data collection and processing platform. In another aspect, the extracted objects' information and generated HD maps can be used for several purposes in the transportation research community and ADS development community: analyzing the interaction between human-driven vehicles and ADS-equipped vehicles, car-following behavior analysis of ADS-equipped vehicles, traffic flow status analysis and modeling, and scenario generation for ADS testing. • An automated driving system data processing platform using cooperative perception is designed. • This platform leverages sensor data from connected automated vehicles. • Results of cooperative object detection and tracking in a late fusion scheme is presented. • Postprocessing algorithm is proposed to reduce noise level and outlier removal. [ABSTRACT FROM AUTHOR]

The Intermediate eXperimental Vehicle (IXV) is an ESA re-entry technological platform built to verify in-flight the performance of critical re-entry technologies. The successful flight of the IXV provides the key elements to consolidate the knowledge necessary for the development of future European re-entry systems. The exploitation of in-flight data represents a cornerstone for this flying test bench. In this frame the IXV Vehicle Model Identification subsystem, VMI, represents those steps, techniques and algorithms that shall be applied to the collected data in order to improve the prediction capabilities for future design of re-entry vehicles and specifically to: ● Improve IXV flight dynamics model. ● Validate aerodynamic prediction methods based on Wind Tunnel Tests, WTT, and Computational Fluid Dynamics, CFD. ● Validate the vehicle model identification process and tools. [ABSTRACT FROM AUTHOR]

• A trajectory reconstruction method using everyday smartphone GPS data. • Data-fusion of trajectories and traditional loop detectors. • Driving lane and lane-changing location become identifiable. • Generating lane-accurate data, over long time periods and network length. • Providing a platform for analyzing vehicles' lateral maneuvers. While lane-changing movements are performed on the entire motorway network for various reasons, such as overtaking, their intensity is substantially greater near complex segments, such as weaving areas. Aside from mandatory lane changes, some drivers also conduct lateral maneuvers for cooperation or anticipation near network nodes. Unlike longitudinal driver behavior (car-following models), lateral driver behavior (lane-changing movements) has received fewer research efforts. The scarcity of suitable data resources to analyze these behaviors and movements might be a crucial cause for this research gap. This paper presents a four-step approach for reconstructing and correcting lateral bias in trajectories collected by a commercial traffic information application running on everyday smartphones. The resulting lateral position is accurate enough to allow for identification of the driving lane, and thus, the lane changes. The algorithm's core is built on a data fusion method using trajectory and loop detector data. The evaluation and validation of the proposed algorithm using drones and closed-circuit television (CCTV) data demonstrate that the core of the algorithm can correctly match more than 94% of trajectory and loop detector data. Between each pair of successive detector stations, the lateral position error has been significantly corrected and reduced to less than half the width of a standard lane of motorway networks. As a result, more than 90% of processed trajectory sample points are in the correct lane. The algorithm requires just two calibration parameters, so it is relatively simple to apply to other test networks. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for off-line trajectory reconstruction in air traffic control domain. The proposed algorithm, called model-based reconstruction, performs an accurate IMM smoothing process whose parameters are modified along time according to the flight modes segmented from trajectory measurements. Its competitive performance is demonstrated through comparison with previous reconstruction methods used in ATC and with classical IMM smoothing, using simulated data. [ABSTRACT FROM AUTHOR]

This paper investigates an iterative learning approach integrated with sliding mode control method to accomplish passive rehabilitation therapy tasks for wearable 6 degrees of freedom (DOF) upper-limb exoskeleton. Firstly, the motion data of human body is collected from a healthy subject through VICON motion capture system and reasonable trajectories in joint space are generated by fitting functions. Secondly, an iterative learning controller is developed to estimate the iteration-invariant dynamic parameters which are complicated and difficult to be obtained precisely in practice. Note that the identical initial condition (i.i.c) in traditional iterative learning control (ILC) is released by applying the polynomial reconstruction method. Considering the uncertainties and disturbances which affect the system in the form of friction, backlash and unexpected tissue torques from human body, an adaptive law is proposed to estimate the upper bound of the lumped non-periodic disturbances. Based on that, sliding mode controller is conducted to achieve the robustness over the time domain, while the chattering phenomenon is attenuated by applying t a n h function. Afterwards, the stability and convergence of the overall system is rigorously proved with a composite energy function (CEF) composed of tracking and estimating errors. Finally, co-simulation and experiment results are presented to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

The elastic and inelastic scattering of 16 O ions on 27 Al target nuclei were measured in a broad angular range (5°< θ lab <40°) at 280 MeV incident energy. The beam was accelerated by the K800 Superconducting Cyclotron at the INFN-LNS laboratory. The ejectiles were detected by the MAGNEX large acceptance magnetic spectrometer. The matching of the beam properties with the optical characteristics of the spectrometer allowed to separate the elastic from the inelastic channels in the energy spectra and measure accurate cross-section distributed over more than eight orders of magnitude down to a few tens of nb/sr. [ABSTRACT FROM AUTHOR]

Abstract: The 16O+27Al elastic and inelastic angular distributions have been measured in a broad angular range (13°<θ lab <52°) at about 100MeV incident energy. The use of the MAGNEX large acceptance magnetic spectrometer and of the ray-reconstruction analysis technique has been crucial in order to provide, in the same experiment, high-resolution energy spectra and cross-section measurements distributed over more than seven orders of magnitude down to hundreds of nb/sr. [Copyright &y& Elsevier]

Abstract: A technique to measure the momentum vector of the ions produced in a nuclear reaction and analyzed by a magnetic spectrometer is presented. It consists of a powerful procedure for the reconstruction of the ions trajectory based on the use of a focal plane detector, accurate three-dimensional models of the magnetic fields and efficient algorithms to solve the highly non-linear transport equations. The technique is quite general and reliable even with large acceptance optical devices. Experimental results obtained with the MAGNEX spectrometer show a remarkable resolution of about 0.2° in the horizontal direction, 0.7° in the vertical one and 1/1800 in the momentum modulus. A high degree of accuracy is also found in the reconstruction of both the direction and the modulus of the momentum vector, with the latter strongly depending on the order of reconstruction. [Copyright &y& Elsevier]

Abstract: The MAYA detector is a Time-Charge Projection Chamber based on the concept of active target. These type of devices use a part of the detection system, the filling gas in this case, in the role of the reaction target. The MAYA detector performs three-dimensional tracking, in order to determine physical observables of the reactions occurring inside the detector. The reconstruction algorithms of the tracking use the information from a two-dimensional projection on the segmented cathode, and, in general, they need to be adapted for the different experimental settings of the detector. This work presents some of the most relevant solutions developed for the MAYA detector. [Copyright &y& Elsevier]

Abstract: The European Space Agency’s Huygens probe separated from the NASA Cassini spacecraft on 25 December 2004, after having been attached for a 7-year interplanetary journey and three orbits around Saturn. The probe reached the predefined NASA/ESA interface point on 14 January 2005 at 09:05:52.523 (UTC). It performed a successful entry and descent sequence and softly landed on Titan’s surface on the same day at 11:38:10.77 (UTC) with a speed of about 4.54m/s. Since the publication of the official project entry and descent trajectory reconstruction effort by the Descent Trajectory Working Group in 2007 (referred to as DTWG#4) various other efforts have been performed and published. This paper presents an overview of the most relevant reconstructions and compares their methodologies and results. Furthermore, the results of a new reconstruction effort (DTWG#5) are presented, which is based on the same methodology as DTWG#4 but takes into account new estimates of Titan’s pole coordinates which were derived from radar images of different Cassini Titan flybys. It can be shown that the primary effect can be observed in the meridional direction which is represented by a stark southward shift of the trajectory by about 0.3deg. A much smaller effect is seen in the zonal direction (i.e., less than 0.01deg in the west to east direction). The revised probe landing coordinates are 192.335degW and 10.573degS. A comparison of these coordinates with results of recent landing site investigations using visual and radar images of the Cassini VIMS instrument shows excellent agreement of the two independently derived landing coordinates, i.e., longitude and latitude residuals of respectively 0.035deg and 0.007deg. [Copyright &y& Elsevier]

Abstract: A general technique to study the transmission efficiency of charged particles through a large acceptance magnetic device is presented. This basically involves the measurement of the impact positions and angles of the particles in any plane normal to the optical axis and the use of a powerful algorithm for the reconstruction of the trajectories. This latter is designed to perform the calculations of highly non-linear transport functions and is essential when high-order aberrations are not negligible. The technique is applied to study the transport efficiency of the heavy ions produced in different nuclear reactions and analyzed by the MAGNEX large acceptance spectrometer. Both the angular integral and differential efficiency have been deduced with overall accuracy of about ±1.5% and ±5%, respectively. [Copyright &y& Elsevier]

Abstract: Adherence of target materials or those present on the trajectory of the fired projectile is most common in the offences involving the use of firearms. The recovery and identification of such adhering materials from a projectile may identify the origin or intermediary objects while fixing the trajectory. In numerous cytological preparations, black deposits, contaminated debris from the barrel, perforated materials would be present on the target irrespective of the range i.e. either distant or close. All these must fully compliment and only such results are to be considered in the trajectory reconstruction. The crucial role played by such trace evidences in the reconstruction of scene of crime in general and in the trajectory of the projectile in particular has attracted high levels of appreciations both in the forensic science community and among the user agencies. Despite, trace evidence still remain unexploited and the integrities of potentially valuable material as trace evidence present on the spent projectiles are often infringed by crime scene managers or often unknowingly destroyed while processing. The process of recovery and identification of trace evidence will normally acquire fairly high degree of complication as the discharged projectile would acquire them from their interaction with the intermediate targets. Authors herein report two cases in that trace materials available on the fired projectile that are characterized through microscopic and spectrometric techniques. The results have not only established the nature of the trace evidence material, but also specify the origin of the materials from the intermediaries. [ABSTRACT FROM AUTHOR]

Abstract: A technique to interpolate complex three-dimensional field distributions such as those produced by large magnets is presented. It is based on a modified charge density method where the elementary sources of the magnetic field are image charges with Gaussian shape placed on a three-dimensional surface. The strengths of the charges are found as the solution of a best-fit problem, whose special features are discussed in detail. The method is tested against the measured field of the MAGNEX large acceptance quadrupole, showing a high level of accuracy together with an effective compensation of the effect of the experimental errors present in the data. In addition the model field is in general analytical and Maxwellian. As a consequence, the reliability of the presented technique to the challenging problem of trajectory reconstruction in modern large acceptance spectrometers is demonstrated. [Copyright &y& Elsevier]

Abstract: The VAMOS spectrometer at GANIL is a large acceptance magnetic spectrometer employing a trajectory reconstruction technique to identify the reaction products. The spectrometer when coupled with the EXOGAM clover array provides a powerful tool for the spectroscopy of very weak reaction channels. Parameters are reconstructed using a numerical procedure with a polynomial relationship between the measured final coordinates and the quantities of interest. The coefficients of this polynomial function are determined from a numerical fit to the data generated from ray-tracing calculations using the ion optics code ZGOUBI. To reconstruct each parameter, a single polynomial is used with a set of coefficients that are valid across the full accepted phase space of the spectrometer. To further improve upon the reconstruction method, we developed an alternate procedure for reconstruction based on selecting an optimum set of trajectories and interpolating the data within these trajectories. For each detected event, a set of coordinates lying close to the measured trajectory are selected from the database and the target parameters of interest are determined by polynomial interpolation. The results for the reconstructed parameters using the two methods are presented. [Copyright &y& Elsevier]

Abstract: VAMOS (VAriable MOde Spectrometer) is a large solid angle ray-tracing spectrometer employing numerical methods for reconstructing the particle trajectory. Complete identification of reaction products has been achieved by trajectory reconstruction. Equipped with a versatile detection system, VAMOS is capable of identifying reaction products from diverse reactions using beams at GANIL. The technique for trajectory reconstruction and its application for identifying reaction products are presented. The angular acceptance of the spectrometer has been studied using Monte Carlo simulation by an ion optics code. The spectrometer was coupled to the high efficiency EXOGAM -array to obtain -recoil coincidences for studying nuclei far from stability. The main features of the spectrometer as well as some results applied to experiments in deep inelastic collisions are described. [Copyright &y& Elsevier]

Abstract: The results of the field measurement of the large quadrupole magnet of the MAGNEX spectrometer are presented and analyzed in the view of the possible application of modern techniques of ray reconstruction. The experimental data are checked against the symmetry conditions expected for the magnet. The observed deviations are related both to imperfections on the magnet manufacturing and to the not ideal positioning of the measurement device. In particular a quantitative estimation of the experimental error in the alignment of the probe with respect to the magnet is achieved. The measured field is also compared with the results from three-dimensional finite elements calculation. The obtained discrepancies between the measured and calculated field are too large for a direct application of the latter to ray-reconstruction methods. Nevertheless, these calculations are reliably used to study the impact of the observed inaccuracies in the probe alignment on the overall precision of field reconstruction and to set quantitative constraints on the field interpolation algorithms. [Copyright &y& Elsevier]

Abstract: Space probes entering planetary atmospheres are used for in situ study of their physical structures. During the entry phase aerodynamic forces exerted on the probe depend on atmospheric density. As a consequence accelerations measured by on-board sensors can be used to derive probe trajectory as well as atmospheric density, pressure and temperature profiles. In this work acceleration data acquired by the Huygens Atmospheric Structure Instrument (HASI) have been used to reconstruct the probe trajectory and the Titan''s atmospheric structure from down to of altitude. An accurate six degree of freedom model of Huygens during the entry phase has been developed and a new reconstruction technique based on Kalman filtering is presented. This technique estimates simultaneously the probe trajectory, the attitude profile consistent with measured data and the atmospheric density, pressure and temperature. [Copyright &y& Elsevier]

Abstract: The results of magnetic field measurements of the large bending magnet of the MAGNEX spectrometer are presented. The experimental values are used to build an Enge function by the least-squares method. The resulting field is compared to the measured one, showing too large deviation for application to ray reconstruction techniques. Similarly, the experimental values are compared with results from a three-dimensional finite elements calculation. Again the deviations between measured and calculated field are too large for a direct application of the latter to ray reconstruction, while its reliability is sufficient for analysis purposes. In particular, it has been applied to study the effect of the inaccuracies in the probe location and orientation on the precision of field reconstruction, and to establish the requirements for the field interpolation. These inaccuracies are found to be rather important, especially for the transversal components of the field, with the consequence that their effect on the reconstructed field should be minimized by special interpolation algorithms. [Copyright &y& Elsevier]

Abstract: The problem of the description of magnetic field for large bending magnets is addressed in relation to the requirements of modern techniques of trajectory reconstruction. The crucial question of the interpolation and extrapolation of fields known at a discrete number of points is analysed. For this purpose a realistic field model of the large dipole of the MAGNEX spectrometer, obtained with finite elements three dimensional simulations, is used. The influence of the uncertainties in the measured field to the quality of the trajectory reconstruction is treated in detail. General constraints for field measurements in terms of required resolutions, step sizes and precisions are thus extracted. [Copyright &y& Elsevier]

Abstract: This paper is concerned with the tracking of multiple moving objects in an image sequence and the reconstruction of the entire trajectories of these objects all over the sequence. More specifically, we address the joint issue of trajectory estimation and measurement-to-trajectory associations, which is the key problem in that context due to the occurrence of object occlusions or crossings. An original and efficient scheme is proposed, that adapts the probabilistic multiple hypothesis tracking (PMHT) technique to the case of tracking of regions in video, for which geometry and motion models can be introduced. Moreover, reliable partial associations can be obtained as an initialization. Data association and trajectory estimation are conducted within a probabilistic framework. The latter relies on Kalman filtering, while the former is solved with an EM algorithm for which a suitable initial configuration can be defined. The proposed tracking method is validated by experiments carried out on real image sequences depicting complex situations. [Copyright &y& Elsevier]

Ship trajectory information has made a significant contribution to the data-based research in analyzing maritime transportation and has facilitated the improvement of maritime safety. However, the AIS data, which consists of ship trajectory, inevitably contains noises or missing data that can interfere with the conclusion. In this paper, an improved kinematic interpolation is presented for AIS trajectory reconstruction, which integrates data preprocessing and interpolation that considers the ships' kinematic information. The improved kinematic reconstruction method includes four steps: (1) data preprocessing, (2) analysis of time interval distribution, (3) abnormal data detection and removal, (4) kinematic interpolation that takes the kinematic feature of ships (i.e., velocity and acceleration) into account, adding forward and backward track points to help correct the acceleration function of reconstruction points. The proposed method is tested on the AIS dataset of Zhoushan Port and was compared with traditional ship trajectory reconstruction methods. The comparison indicates that the proposed method can effectively reconstruct the ship trajectory with higher performance on a single ship trajectory and a large AIS data set of certain water areas. • Comprehensive and detailed data processing is integrated into trajectory reconstruction. • Kinematic interpolation is improved considering harmonic points to refine the reconstruction. • The proposed method applies to large-scale data and has high performance. [ABSTRACT FROM AUTHOR]

• The Kalman Filter algorithm is used to estimate the trajectory of vehicles on the basis of vehicle kinematics information via trajectory reconstruction. • The traffic conflict areas are determined by the vehicle trajectory extrapolation. • The Surrogate Safety Assessment Model (SSAM) is used to compare with the proposed model. • K-S modified test and A–D test are applied to verify goodness of fit of conflict distribution. Connected vehicle (CV)technologies offer promising solutions to several problems in transportation systems. The trajectory data generated from CV technology can be used to identify real-time conflicts in intersections. To perform such identification, accurate vehicle localisation should be obtained to clearly recognise the conflicts between left-turning vehicles and straight-through vehicles in the opposite direction at the signal control intersection. This study presents a CV framework that uses the two-way time of arrival to locate the vehicles on the basis of the Intelligent Vehicle Infrastructure Cooperative Environment. Kalman Filter (KF) is used to improve the accuracy of the vehicle location, and the corresponding algorithm is used to estimate the vehicle trajectory to obtain the vehicle kinematics information via the on-board system. The traffic conflict areas of the left-turning vehicles and straight-through vehicles in the opposite direction are determined through vehicle trajectory extrapolation, and the left-turn collision at the signal intersection is identified using the post-encroachment time algorithm and vehicle movement information. In addition, Anderson–Darling and modified Kolmogorov–Smirnov tests are performed to verify the goodness of fit of the data. Results show that the vehicle speed and localisation errors of the proposed method decreased by 66.67 % and 83.33 % compared with the results before filtering, respectively. Moreover, the results of the conflict recognition method based on CV trajectory reconstruction is consistent for both goodness of fit tests under real-time communication conditions. This study can provide driving decision for drivers of left-turning vehicles under the Intelligent Vehicle Infrastructure Cooperative Environment and provide technical support for the research and development of left-turn anti-collision systems. [ABSTRACT FROM AUTHOR]

• An approach to improve the accuracy of reconstructed CDR-based trajectory is proposed. • Radio wave propagation modelling can improve CDR-based localization accuracy. • GSM-based probability localization reduces the uncertainty of Cell-ID positioning. • The proposed approach is effective in individual movement exploration. • The proposed approach can be employed to accurately map the cellular trips to the desired urban zones of an OD matrix. The recent years have witnessed a greater demand for understanding how people move in urban environments. Due to the widespread usage of mobile phones, there have been several trajectory-based studies focusing on extracting the characteristics of human mobility from georeferenced mobile phone data. Mobile positioning data is generally generated as scattered points in CDRs (Call Detail Records). Even though CDR data can be regarded as an inexpensive scalable source of information on human mobility, mobility studies in urban settings based on such data sources still prove to be a research challenge due to the coarseness of CDR spatial granularity. Motivated by the need for transforming large-scale CDRs to movement trajectories, the present study offers a new solution which is made of two principal building blocks: (1) Developing a Bayesian-based induction method through adopting a GIS-based wave propagation model to solve the GSM-based localization problem when methods such as triangulation are not applicable due to the lack of measurements from more than one base station; (2) Reconstruction of movement trajectories from cellular location information using overlapping relations existing between observed cells as well as detection of ping-pong phenomena as auxiliary information. A case study employing CDR and GPS records obtained from an experimental survey on one of the central urban zones of Tehran was conducted, which showed the effectiveness of the proposed methodology in comparison to current approaches with respect to three perspectives, including movement path exploration, individual-oriented movement features extraction, and crowd-movement modelling. [ABSTRACT FROM AUTHOR]

Understanding human mobility is significant in many fields, such as geography, transportation, and sociology. Due to the wide spatiotemporal coverage and low operational cost, mobile phone data have been recognized as a major resource for human mobility research. However, due to conflicts between the data sparsity problem of mobile phone data and the requirement of fine-scale solutions, trajectory reconstruction is of considerable importance. Although there have been initial studies on this problem, existing methods rarely consider the effect of similarities among individuals and the patterns of missing data. To address this issue, we propose a multi-criteria data partitioning trajectory reconstruction (MDP-TR) method for large-scale mobile phone data. In the proposed method, a multi-criteria data partitioning (MDP) technique is used to measure the similarity among individuals in near real-time and investigate the spatiotemporal patterns of missing data. With this technique, the trajectory reconstruction from mobile phone data is then conducted with machine learning models. We verified the method using a real mobile phone dataset in a large city. Results indicate that the MDP-TR method outperforms competing methods in both accuracy and robustness. We argue that the MDP-TR method can be effectively utilized for grasping highly dynamic human movement status and improving the spatiotemporal resolution of human mobility research. • We present a method for reconstructing individuals' trajectories from mobile phone data. • Our method introduces an anchor-point-based clustering algorithm to address the data hungry problem. • Our method learns movement behaviors with different temporal patterns of missing data. • Our method provides more accurate and robust results than competing methods. • Our method can help to improve the spatiotemporal resolution of human mobility research. [ABSTRACT FROM AUTHOR]

Summary Protruding from the apical surface of inner ear sensory cells, hair bundles carry out mechanotransduction. Bundle growth involves sequential and overlapping cellular processes, which are concealed within gene expression profiles of individual cells. To dissect such processes, we developed CellTrails, a tool for uncovering, analyzing, and visualizing single-cell gene-expression dynamics. Utilizing quantitative gene-expression data for key bundle proteins from single cells of the developing chick utricle, we reconstructed de novo a bifurcating trajectory that spanned from progenitor cells to mature striolar and extrastriolar hair cells. Extraction and alignment of developmental trails and association of pseudotime with bundle length measurements linked expression dynamics of individual genes with bundle growth stages. Differential trail analysis revealed high-resolution dynamics of transcripts that control striolar and extrastriolar bundle development, including those that encode proteins that regulate [Ca 2+ ] i or mediate crosslinking and lengthening of actin filaments. [ABSTRACT FROM AUTHOR]