Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic

In this paper, we present our approach for solving the DEBS Grand Challenge 2018. The challenge asks to provide a prediction for (i) a destination and the (ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the maritime context. Novel aspects of our approach include the use of ensemble learning based on Random Forest, Gradient Boosting Decision Trees (GBDT), XGBoost Trees and Extremely Randomized Trees (ERT) in order to provide a prediction for a destination while for the arrival time, we propose the use of Feed-forward Neural Networks. In our evaluation, we were able to achieve an accuracy of 97% for the port destination classification problem and 90% (in mins) for the ETA prediction

Bayesian Intent Prediction in Object Tracking Using Bridging Distributions.

In several application areas, such as human computer interaction, surveillance and defence, determining the intent of a tracked object enables systems to aid the user/operator and facilitate effective, possibly automated, decision making. In this paper, we propose a probabilistic inference approach that permits the prediction, well in advance, of the intended destination of a tracked object and its future trajectory. Within the framework introduced here, the observed partial track of the object is modeled as being part of a Markov bridge terminating at its destination, since the target path, albeit random, must end at the intended endpoint. This captures the underlying long term dependencies in the trajectory, as dictated by the object intent. By determining the likelihood of the partial track being drawn from a particular constructed bridge, the probability of each of a number of possible destinations is evaluated. These bridges can also be employed to produce refined estimates of the latent system state (e.g., object position, velocity, etc.), predict its future values (up until reaching the designated endpoint) and estimate the time of arrival. This is shown to lead to a low complexity Kalman-filter-based implementation of the inference routine, where any linear Gaussian motion model, including the destination reverting ones, can be applied. Free hand pointing gestures data collected in an instrumented vehicle and synthetic trajectories of a vessel heading toward multiple possible harbors are utilized to demonstrate the effectiveness of the proposed approach

Bayesian Approaches to Tracking, Sensor Fusion and Intent Prediction

This thesis presents work on the development of model-based Bayesian approaches to object tracking and intent prediction. Successful navigation/positioning applications rely fundamentally on the choice of appropriate dynamic model and the design of effective tracking algorithms capable of maximising the use of the structure of the dynamic system and the information from sensors. While the tracking problem with frequent and accurate position data has been well studied, we push back the frontiers of current technology where an object can undergo fast manoeuvres and position fixes are limited. On the other hand, intent prediction techniques which extract higher level information such as the intended destination of a moving object can be designed, given the ability to perform successful tracking. Such techniques can play important roles in various application areas, including traffic monitoring, intelligent human computer interaction systems and autonomous route planning. In the first part of this thesis Bayesian tracking methods are designed based on a standard fix-rate setting in which the dynamic system is formulated into a Markovian state space form. We show that the combination of an intrinsic coordinate dynamic model and sensors in the object's body frame leads to novel state space models according to which efficient proposal kernels can be designed and implemented by the sequential Monte Carlo (SMC) methods. Also, sequential Markov chain Monte Carlo schemes are considered for the first time to tackle the sequential batch inference problems due to the presence of infrequent position data. Performance evaluation on both synthetic and real-world data shows that the proposed algorithms are superior to simpler particle filters, implying that they can be favourable alternatives to tracking problems with inertial sensors. The modelling assumption that leads to Markovian state space models can be restrictive for real-world systems as it stipulates that the state sequence has to be synchronised with the observations. In the second major part of this thesis we relax this assumption and work with a more natural class of models, termed variable rate models. We generalise the existing variable rate intrinsic model to incorporate acceleration, speed, distance and position data and introduce new variable rate particle filtering methods tailored to the derived model to accommodate multi-sensor multi-rate tracking scenarios. The proposed algorithms can achieve substantial improvements in terms of tracking accuracy and robustness over a bootstrap variable rate particle filter. Moreover, full Bayesian inference schemes for the learning of both the hidden state and system parameters are presented, with numerical results illustrating their effectiveness. The last part of the thesis is about designing efficient intent prediction algorithms within a Bayesian framework. A pseudo-observation based approach to the incorporation of destination knowledge is introduced, making the mathematics of the dynamical model and the observation process consistent with the Markov state process. Based on the new interpretation, two algorithms are proposed to sequentially estimate the probability of all possible endpoints. Whilst the synthetic maritime surveillance data demonstrate that the proposed methods can achieve comparable prediction performance with reduced computational cost in comparison to the existing bridging distribution based methods, the results on an extensive freehand pointing database, which contains 95 three-dimensional pointing trajectories, show that the new algorithms can outperform other state-of-the-art techniques. Some sensitivity tests are also performed, confirming the good robustness of the introduced methods against model mismatches

Valuing the Prevention of an Infestation: The Threat of the New Zealand Mud Snail in Northern Nevada

The Truckee / Carson / Walker River Watershed in Northern Nevada is under an imminent threat of infestation by the New Zealand Mud Snail, an aquatic nuisance species with the potential to harm recreational fisheries. We combine a utility-theoretic system-demand model of recreational angling with a Bayesian econometric framework to provide estimates of trip and welfare losses under different types of regulatory control policies. We find that such losses can be substantial, warranting immediate investments in preemptive strategies via public outreach and awareness campaigns.New Zealand Mud Snail; Incomplete Demand System; Hierarchical Modeling; Bayesian Simulation

Bayesian Intent Prediction in Object Tracking Using Bridging Distributions.

In several application areas, such as human computer interaction, surveillance and defence, determining the intent of a tracked object enables systems to aid the user/operator and facilitate effective, possibly automated, decision making. In this paper, we propose a probabilistic inference approach that permits the prediction, well in advance, of the intended destination of a tracked object and its future trajectory. Within the framework introduced here, the observed partial track of the object is modeled as being part of a Markov bridge terminating at its destination, since the target path, albeit random, must end at the intended endpoint. This captures the underlying long term dependencies in the trajectory, as dictated by the object intent. By determining the likelihood of the partial track being drawn from a particular constructed bridge, the probability of each of a number of possible destinations is evaluated. These bridges can also be employed to produce refined estimates of the latent system state (e.g., object position, velocity, etc.), predict its future values (up until reaching the designated endpoint) and estimate the time of arrival. This is shown to lead to a low complexity Kalman-filter-based implementation of the inference routine, where any linear Gaussian motion model, including the destination reverting ones, can be applied. Free hand pointing gestures data collected in an instrumented vehicle and synthetic trajectories of a vessel heading toward multiple possible harbors are utilized to demonstrate the effectiveness of the proposed approach.The authors would like to thank Jaguar Land Rover and the UK Engineering and Physical Science Research Council (BTaRoT grant EP/K020153/1) for funding this research

Ports’ congestion factors: Applying risk analysis as a problem identification tool to figure out the interrelated complex factors that contribute to the problem by assigning weights and probabilities to each factor

Ports’ congestion is a recurring problem that is caused by several factors. There are several past attempts to resolve ports’ congestion by applying governing and constructional reforms. Due to divergence and instability of congestion causal factors, the available studies and solutions are specific to individual ports. The main objective of this master thesis is to apply risk analysis as a problem identifier to figure out the interrelated complex factors that contribute to the congestion problem by assigning weights and probabilities to each factor. The research is based on qualitative data from secondary sources to gather all available information about the causal factors for ports’ congestion. A structured questionnaire was carried out and sent to various ports’ managers to figure out the most effective causal factors globally, as a means of validation for the secondary data and to ensure that the data reflect the current congestions causing factors from the port’s users themselves. Congestion’s factors can be human, technical, or organizational with different magnitudes based on the port’s features and capabilities. They are vulnerable to sudden and quick changes due to their interrelated and complex structure. Bayesian network (BN) is a risk analysis tool that fits the complex and changing scenarios of the congestion problem. It can incorporate the newly received information into the pre-established network of causal factors for port congestion. BN managed to reflect the cause-and-effect relationship between the causal factors and by means of appropriate software, the effect of any new event on congestion occurrence is visualized. Furthermore, the application of BN needs to be integrated into the port information management system as a permanent warning system that predicts the congestion and virtually shows the results of applying suggested solutions before applying it. Keywords: port congestion, congestion factors, Bayesian network, port productivit

Prediction of late/early arrivals in container terminals - A qualitative approach

Vessel arrival uncertainty in ports has become a very common problem worldwide. Although ship operators have to notify the Estimated Time of Arrival (ETA) at predetermined time intervals, they frequently have to update the latest ETA due to unforeseen circumstances. This causes a series of inconveniences that often impact on the efficiency of terminal operations, especially in the daily planning scenario. Thus, for our study we adopted a machine learning approach in order to provide a qualitative estimate of the vessel delay/advance and to help mitigate the consequences of late/early arrivals in port. Using data on delays/advances at the individual vessel level, a comparative study between two transshipment container terminals is presented and the performance of three algorithmic models is evaluated. Results of the research indicate that when the distribution of the outcome is bimodal the performance of the discrete models is highly relevant for acquiring data characteristics. Therefore, the models are not flexible in representing data when the outcome distribution exhibits unimodal behavior. Moreover, graphical visualisation of the importance-plots made it possible to underline the most significant variables which might explain vessel arrival uncertainty at the two European ports

Simultaneous intent prediction and state estimation using an intent-driven intrinsic coordinate model

The motion of an object (e.g. ship, jet, pedestrian, bird, drone, etc.) is usually governed by premeditated actions as per an underlying intent, for instance reaching a destination. In this paper, we introduce a novel intent-driven dynamical model based on a continuous-time intrinsic coordinate model. By combining this model with particle filtering, a seamless approach for jointly predicting the destination and estimating the state of a highly manoeuvrable object is developed. We examine the proposed inference technique using real data with different measurement models to demonstrate its efficacy. In particular, we show that the introduced approach can be a flexible and competitive alternative, in terms of prediction and estimation performance, to other existing methods for various measurement models including nonlinear ones