Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

D4.2 Intelligent D-Band wireless systems and networks initial designs

This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project

Modeling, Predicting and Capturing Human Mobility

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility

Cognitive networking for next generation of cellular communication systems

This thesis presents a comprehensive study of cognitive networking for cellular networks with contributions that enable them to be more dynamic, agile, and efficient. To achieve this, machine learning (ML) algorithms, a subset of artificial intelligence, are employed to bring such cognition to cellular networks. More specifically, three major branches of ML, namely supervised, unsupervised, and reinforcement learning (RL), are utilised for various purposes: unsupervised learning is used for data clustering, while supervised learning is employed for predictions on future behaviours of networks/users. RL, on the other hand, is utilised for optimisation purposes due to its inherent characteristics of adaptability and requiring minimal knowledge of the environment. Energy optimisation, capacity enhancement, and spectrum access are identified as primary design challenges for cellular networks given that they are envisioned to play crucial roles for 5G and beyond due to the increased demand in the number of connected devices as well as data rates. Each design challenge and its corresponding proposed solution are discussed thoroughly in separate chapters. Regarding energy optimisation, a user-side energy consumption is investigated by considering Internet of things (IoT) networks. An RL based intelligent model, which jointly optimises the wireless connection type and data processing entity, is proposed. In particular, a Q-learning algorithm is developed, through which the energy consumption of an IoT device is minimised while keeping the requirement of the applications--in terms of response time and security--satisfied. The proposed methodology manages to result in 0% normalised joint cost--where all the considered metrics are combined--while the benchmarks performed 54.84% on average. Next, the energy consumption of radio access networks (RANs) is targeted, and a traffic-aware cell switching algorithm is designed to reduce the energy consumption of a RAN without compromising on the user quality-of-service (QoS). The proposed technique employs a SARSA algorithm with value function approximation, since the conventional RL methods struggle with solving problems with huge state spaces. The results reveal that up to 52% gain on the total energy consumption is achieved with the proposed technique, and the gain is observed to reduce when the scenario becomes more realistic. On the other hand, capacity enhancement is studied from two different perspectives, namely mobility management and unmanned aerial vehicle (UAV) assistance. Towards that end, a predictive handover (HO) mechanism is designed for mobility management in cellular networks by identifying two major issues of Markov chains based HO predictions. First, revisits--which are defined as a situation whereby a user visits the same cell more than once within the same day--are diagnosed as causing similar transition probabilities, which in turn increases the likelihood of making incorrect predictions. This problem is addressed with a structural change; i.e., rather than storing 2-D transition matrix, it is proposed to store 3-D one that also includes HO orders. The obtained results show that 3-D transition matrix is capable of reducing the HO signalling cost by up to 25.37%, which is observed to drop with increasing randomness level in the data set. Second, making a HO prediction with insufficient criteria is identified as another issue with the conventional Markov chains based predictors. Thus, a prediction confidence level is derived, such that there should be a lower bound to perform HO predictions, which are not always advantageous owing to the HO signalling cost incurred from incorrect predictions. The outcomes of the simulations confirm that the derived confidence level mechanism helps in improving the prediction accuracy by up to 8.23%. Furthermore, still considering capacity enhancement, a UAV assisted cellular networking is considered, and an unsupervised learning-based UAV positioning algorithm is presented. A comprehensive analysis is conducted on the impacts of the overlapping footprints of multiple UAVs, which are controlled by their altitudes. The developed k-means clustering based UAV positioning approach is shown to reduce the number of users in outage by up to 80.47% when compared to the benchmark symmetric deployment. Lastly, a QoS-aware dynamic spectrum access approach is developed in order to tackle challenges related to spectrum access, wherein all the aforementioned types of ML methods are employed. More specifically, by leveraging future traffic load predictions of radio access technologies (RATs) and Q-learning algorithm, a novel proactive spectrum sensing technique is introduced. As such, two different sensing strategies are developed; the first one focuses solely on sensing latency reduction, while the second one jointly optimises sensing latency and user requirements. In particular, the proposed Q-learning algorithm takes the future load predictions of the RATs and the requirements of secondary users--in terms of mobility and bandwidth--as inputs and directs the users to the spectrum of the optimum RAT to perform sensing. The strategy to be employed can be selected based on the needs of the applications, such that if the latency is the only concern, the first strategy should be selected due to the fact that the second strategy is computationally more demanding. However, by employing the second strategy, sensing latency is reduced while satisfying other user requirements. The simulation results demonstrate that, compared to random sensing, the first strategy decays the sensing latency by 85.25%, while the second strategy enhances the full-satisfaction rate, where both mobility and bandwidth requirements of the user are simultaneously satisfied, by 95.7%. Therefore, as it can be observed, three key design challenges of the next generation of cellular networks are identified and addressed via the concept of cognitive networking, providing a utilitarian tool for mobile network operators to plug into their systems. The proposed solutions can be generalised to various network scenarios owing to the sophisticated ML implementations, which renders the solutions both practical and sustainable

Discovering critical traffic anomalies from GPS trajectories for urban traffic dynamics understanding

Traffic anomaly (e.g., traffic jams) detection is essential for the development of intelligent transportation systems in smart cities. In particular, detecting critical traffic anomalies (e.g., rare traffic anomalies, sudden accidents) are far more meaningful than detecting general traffic anomalies and more helpful to understand urban traffic dynamics. For example, emerging traffic jams are more significant than regular traffic jams caused by common road bottlenecks like traffic lights or toll road entrances;  and discovering the original location of traffic chaos in an area is more important than finding roads that are just congested. However, using existing traffic indicators that represent traffic conditions, such as traffic flows and speeds, for critical traffic anomaly detection may be not accurate enough. That is, they usually miss some traffic anomalies while wrongly identifying a normal traffic status as an anomaly. Moreover, most existing detection methods only detect general traffic anomalies but not critical traffic anomalies. In this thesis, we provide two new indicators: frequency of jams (captured by stop-point clusters) and Visible Outlier Indexes (VOIs) (based on the Kolmogorov-Smirnov test of speed) to capture critical traffic anomalies more accurately. The advantage of our proposed indicators is that they help separate critical traffic anomalies from general traffic anomalies. The former can discover rare anomalies with low frequency, and the latter can find unexpected anomalies (i.e., when the difference between the predicted VOI and the real VOI is great). Based on these two indicators, we provide three novel methods for comprehensive traffic anomaly analysis, including traffic anomaly identification, prediction, and root cause discovery. First, we provide a novel analysis of spatial-temporal jam frequencies (ASTJF) method for identifying rare traffic anomalies. In the ASTJF method, spatially close stop-points in a time bin are grouped into stop-point clusters (SPCs) using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm; an SPC is an instance of a spatiotemporal jam. Then, we develop a new adapted Hausdorff distance to measure the similarity of two SPCs and put SPCs which are relevant to the same spatiotemporal jam into a group. Finally, we calculate the number of SPCs in a group as the frequency of the corresponding traffic jams; traffic anomalies are classified as regular jams with high frequency and emerging jams with low frequency. The ASTJF method can correctly identify critical traffic anomalies (i.e., emerging jams). Second, we propose a novel prediction approach -  Visible Outlier Indices and Meshed Spatiotemporal Neighborhoods (VOIMSN) method. In this method, the trajectory data from the given region's geographic spatial neighbors and its time-series neighbors are both converted to the abnormal scores measured by VOIs and quantified by the matrix grid as the input of the prediction model to improve the accuracy. This method provides a comprehensive analysis using all relevant data for building a reliable prediction model. In particular, the proposed meshed spatiotemporal neighborhoods with arbitrary shape, which comprises all potential anomalies instead of just past anomalies, is theoretically more accurate than a fixed-size neighborhood for anomaly prediction. Third, we provide an innovative and integrated root cause analysis method using VOI as the probabilistic indicator of traffic anomalies. This method proposes automatically learns spatiotemporal causal relationships from historical data to build an uneven diffusion model for detecting the root cause of anomalies (i.e., the origin of traffic chaos). It is demonstrated to be better than the heat diffusion model. Experiments conducted on a real-world massive trajectory dataset demonstrate the accuracy and effectiveness of the proposed methods for discovering critical traffic anomalies for a better understanding of urban traffic dynamics

A Context Aware Classification System for Monitoring Driver’s Distraction Levels

Understanding the safety measures regarding developing self-driving futuristic cars is a concern for decision-makers, civil society, consumer groups, and manufacturers. The researchers are trying to thoroughly test and simulate various driving contexts to make these cars fully secure for road users. Including the vehicle’ surroundings offer an ideal way to monitor context-aware situations and incorporate the various hazards. In this regard, different studies have analysed drivers’ behaviour under different case scenarios and scrutinised the external environment to obtain a holistic view of vehicles and the environment. Studies showed that the primary cause of road accidents is driver distraction, and there is a thin line that separates the transition from careless to dangerous. While there has been a significant improvement in advanced driver assistance systems, the current measures neither detect the severity of the distraction levels nor the context-aware, which can aid in preventing accidents. Also, no compact study provides a complete model for transitioning control from the driver to the vehicle when a high degree of distraction is detected. The current study proposes a context-aware severity model to detect safety issues related to driver’s distractions, considering the physiological attributes, the activities, and context-aware situations such as environment and vehicle. Thereby, a novel three-phase Fast Recurrent Convolutional Neural Network (Fast-RCNN) architecture addresses the physiological attributes. Secondly, a novel two-tier FRCNN-LSTM framework is devised to classify the severity of driver distraction. Thirdly, a Dynamic Bayesian Network (DBN) for the prediction of driver distraction. The study further proposes the Multiclass Driver Distraction Risk Assessment (MDDRA) model, which can be adopted in a context-aware driving distraction scenario. Finally, a 3-way hybrid CNN-DBN-LSTM multiclass degree of driver distraction according to severity level is developed. In addition, a Hidden Markov Driver Distraction Severity Model (HMDDSM) for the transitioning of control from the driver to the vehicle when a high degree of distraction is detected. This work tests and evaluates the proposed models using the multi-view TeleFOT naturalistic driving study data and the American University of Cairo dataset (AUCD). The evaluation of the developed models was performed using cross-correlation, hybrid cross-correlations, K-Folds validation. The results show that the technique effectively learns and adopts safety measures related to the severity of driver distraction. In addition, the results also show that while a driver is in a dangerous distraction state, the control can be shifted from driver to vehicle in a systematic manner

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency

Road Management Systems to Support Bicycling: A Case Study of Montreal’s Bike Network

Bicycling is a sustainable mode of transportation given its health benefits, reduced air and noise pollution, savings in fuel consumption, and role in shifting demand away from the automobile. A significant increase of bicycle users is an aim of many cities around the world. Responding to this, various cities announced their strategies to extend and/or upgrade their bikeway networks. However, there is a disconnection between the strategies to support bicycles and road management systems, which are typically used for optimal scheduling of maintenance and interventions for roads’ infrastructure. Traditional road management systems consider neither the need to sustain bicycle pathways at good levels of service, nor consider bicycling demand to prioritize their selection. This thesis extends road management systems to support bicycling networks. This enables the ability to optimally allocate available resources for sustaining the surface of bicycle pathways in good condition, and implement physically-separated bicycle lanes to enhance safety conditions and encourage bicycle ridership. A simple formulation of bicycle demand is proposed; it employs the capabilities of smartphones for collecting and estimating bicycling demand based on GPS trajectories of cyclists. Goal programming optimization is applied to address scheduling of maintenance and upgrade investments of pathways. Two scenarios are investigated with different annual budgets. The results show that the first scenario allows a rapid upgrade of existing bicycle lanes to protected paths while accomplishing good conditions of pavements. However, the second scenario is not able to prevent the deterioration of pavement segments