62,325 research outputs found

    SST: A Simplified Swin Transformer-based Model for Taxi Destination Prediction based on Existing Trajectory

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    Accurately predicting the destination of taxi trajectories can have various benefits for intelligent location-based services. One potential method to accomplish this prediction is by converting the taxi trajectory into a two-dimensional grid and using computer vision techniques. While the Swin Transformer is an innovative computer vision architecture with demonstrated success in vision downstream tasks, it is not commonly used to solve real-world trajectory problems. In this paper, we propose a simplified Swin Transformer (SST) structure that does not use the shifted window idea in the traditional Swin Transformer, as trajectory data is consecutive in nature. Our comprehensive experiments, based on real trajectory data, demonstrate that SST can achieve higher accuracy compared to state-of-the-art methods.Comment: Accepted by IEEE ITS

    Predicting a User's Next Cell With Supervised Learning Based on Channel States

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    Knowing a user's next cell allows more efficient resource allocation and enables new location-aware services. To anticipate the cell a user will hand-over to, we introduce a new machine learning based prediction system. Therein, we formulate the prediction as a classification problem based on information that is readily available in cellular networks. Using only Channel State Information (CSI) and handover history, we perform classification by embedding Support Vector Machines (SVMs) into an efficient pre-processing structure. Simulation results from a Manhattan Grid scenario and from a realistic radio map of downtown Frankfurt show that our system provides timely prediction at high accuracy.Comment: The 14th IEEE International Workshop on Signal Processing Advances for Wireless Communications (SPAWC), Darmstadt : Germany (2013

    Exploiting Map Topology Knowledge for Context-predictive Multi-interface Car-to-cloud Communication

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    While the automotive industry is currently facing a contest among different communication technologies and paradigms about predominance in the connected vehicles sector, the diversity of the various application requirements makes it unlikely that a single technology will be able to fulfill all given demands. Instead, the joint usage of multiple communication technologies seems to be a promising candidate that allows benefiting from characteristical strengths (e.g., using low latency direct communication for safety-related messaging). Consequently, dynamic network interface selection has become a field of scientific interest. In this paper, we present a cross-layer approach for context-aware transmission of vehicular sensor data that exploits mobility control knowledge for scheduling the transmission time with respect to the anticipated channel conditions for the corresponding communication technology. The proposed multi-interface transmission scheme is evaluated in a comprehensive simulation study, where it is able to achieve significant improvements in data rate and reliability

    It’s a long way to Monte-Carlo: probabilistic display in GPS navigation

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    We present a mobile, GPS-based multimodal navigation system, equipped with inertial control that allows users to explore and navigate through an augmented physical space, incorporating and displaying the uncertainty resulting from inaccurate sensing and unknown user intentions. The system propagates uncertainty appropriately via Monte Carlo sampling and predicts at a user-controllable time horizon. Control of the Monte Carlo exploration is entirely tilt-based. The system output is displayed both visually and in audio. Audio is rendered via granular synthesis to accurately display the probability of the user reaching targets in the space. We also demonstrate the use of uncertain prediction in a trajectory following task, where a section of music is modulated according to the changing predictions of user position with respect to the target trajectory. We show that appropriate display of the full distribution of potential future users positions with respect to sites-of-interest can improve the quality of interaction over a simplistic interpretation of the sensed data

    Modeling Taxi Drivers' Behaviour for the Next Destination Prediction

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    In this paper, we study how to model taxi drivers' behaviour and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well studied problem in human mobility, which finds several applications in real-world scenarios, from optimizing the efficiency of electronic dispatching systems to predicting and reducing the traffic jam. This task is normally modeled as a multiclass classification problem, where the goal is to select, among a set of already known locations, the next taxi destination. We present a Recurrent Neural Network (RNN) approach that models the taxi drivers' behaviour and encodes the semantics of visited locations by using geographical information from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to predict the exact coordinates of the next destination, overcoming the problem of producing, in output, a limited set of locations, seen during the training phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge 2015 dataset - based on the city of Porto -, obtaining better results with respect to the competition winner, whilst using less information, and on Manhattan and San Francisco datasets.Comment: preprint version of a paper submitted to IEEE Transactions on Intelligent Transportation System
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