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- Special issue on the 5th International Workshop on Big Mobility Data
Analytics (BMDA’23)-
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PubDate: 2024-08-08
DOI: 10.1007/s10707-024-00526-8
- Resisting TUL attack: balancing data privacy and utility on trajectory via
collaborative adversarial learning-
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Abstract: Abstract Nowadays, large-scale individual trajectories can be collected by various location-based social network services, which enables us to better understand human mobility patterns. However, the trajectory data usually contain sensitive information of users, raising considerable concerns about the privacy issue. Existing methods for protecting user trajectory data face two major challenges. First, existing methods generally emphasize on data privacy but largely ignore the data utility. Second, most existing work focus on protecting the privacy of users’ check-in locations, which is not sufficient to protect against the trajectory-user linking (TUL) attack that infers a user’s identity based on her/his trajectories. In this paper, we for the first time propose a collaborative adversarial learning model named BPUCAL to effectively resist the TUL attack and preserve the data utility simultaneously. The general idea is to fool the TUL model by adding a small perturbation on the original trajectory data to balance the data utility and privacy. BPUCAL perturbs a few numbers of carefully identified check-ins of a trajectory which are pivotal for a TUL model to infer the identity of a user. Specifically, BPUCAL contains three parts: a perturbation generator, a discriminator, and a TUL model. The generator aims to produce learnable noise and adds it to the original trajectories for obtaining perturbed trajectories. The perturbed trajectories with a minimal changes compared to the original trajectories can deceive both the discriminator and the TUL model. Extensive experiments are conducted over two real-world datasets. The results show the superior performance of our proposal in balancing data privacy and utility on trajectory data by comparison with baselines.
PubDate: 2024-07-01
DOI: 10.1007/s10707-023-00507-3
- Efficient spatial queries over complex polygons with hybrid
representations-
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Abstract: Abstract One major goal of spatial query processing is to mitigate I/O costs and minimize the search space. However, geometric computation can be heavy-duty for spatial queries, in particular for complex geometries such as polygons with many edges based on a vector-based representation. Many past techniques have been provided for spatial partitioning and indexing, which are mainly built on minimal bounding boxes or other approximation methods and are not optimized for reducing geometric computation. In this paper, we propose a novel vector-raster hybrid approach through rasterization, where rich pixel-centric information is preserved to help not only filter out more candidates but also reduce geometry computation load. Based on the hybrid model, we implement four typical spatial queries, which can be generalized for other types of spatial queries. We also propose cost models to estimate the latency for those query types. Our experiments demonstrate that the hybrid model can boost the performance of spatial queries on complex polygons by up to one order of magnitude.
PubDate: 2024-07-01
DOI: 10.1007/s10707-023-00508-2
- DGFormer: a physics-guided station level weather forecasting model with
dynamic spatial-temporal graph neural network-
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Abstract: Abstract In recent years, there has been an increased interest in understanding and predicting the weather using weather station data with Spatial-Temporal Graph Neural Networks (STGNN). However, it has large prediction errors as a result of the inherent non-linearities and the influence of dynamic spatio-temporal auto-correlation. Using a continuously-varying graph topology chronologically, while embedding domain knowledge to enforce validity, can effectively resolve the issue, but the implementation of such concept constitutes an interdisciplinary challenge for researchers. A Dynamic Graph Former (DGFormer) model is proposed to address this challenge. It combines a topology learner through a deep generative layer with domain knowledge enhancement inserted into the STGNN structure, where the derived physics-guided method allows for an efficient integration with the earth system. For capture of the optimal topology, we merge a node-embedding-based similarity metric learning and the superposition principle as physical assistants into the dynamic graph module. We evaluate our model with a real-world weather dataset on short-term (12 hours) and medium-range (360 hours) prediction tasks. DGFormer achieves outstanding performance with obvious improvements by up to 34.84% at short-term prediction and by up to 23.25% at medium-range prediction compared with the state-of-the-art methods. We also conducted detailed analyses for cities in three regions and visualized the dynamic graph, revealing the characteristics, advantages, and graph visualization of our model.
PubDate: 2024-07-01
DOI: 10.1007/s10707-024-00511-1
- Meta-learning based passenger flow prediction for newly-operated stations
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Abstract: Abstract By tapping into the human mobility of the urban rail transit (URT) network to understand the travel demands and characteristics of passengers in the urban space, URT managers are able to obtain more support for decision-making to improve the effectiveness of operation and management, the travel experience of passengers, as well as public safety. However, not all URT networks have sufficient human mobility data (e.g., newly-operated URT networks). It is necessary to provide data support for mining human mobility in data-poor URT networks. Therefore, we propose a method called Meta Long Short-Term Memory Network (Meta-LSTM) for passenger flow prediction at URT stations to provide data support for networks that lack data. The Meta-LSTM is to construct a framework that increases the generalization ability of a long short-term memory network (LSTM) to various passenger flow characteristics by learning passenger flow characteristics from multiple data-rich stations and then applying the learned parameter to data-scarce stations by parameter initialization. The Meta-LSTM is applied to the URT network of Nanning, Hangzhou, and Beijing, China. The experiments on three real-world URT networks demonstrate the effectiveness of our proposed Meta-LSTM over several competitive baseline models. Results also show that our proposed Meta-LSTM has a good generalization ability to various passenger flow characteristics, which can provide a reference for passenger flow prediction in the stations with limited data.
PubDate: 2024-07-01
DOI: 10.1007/s10707-023-00510-8
- Discovery of multi-domain spatiotemporal associations
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Abstract: Abstract This paper focuses on the discovery of unusual spatiotemporal associations across multiple phenomena from distinct application domains in a spatial neighborhood where each phenomenon is represented by anomalies from the domain. Such an approach can facilitate the discovery of interesting links between distinct domains, such as links between traffic accidents and environmental factors or road conditions, environmental impacts and human factors, disease spread, and hydrological trajectory, to name a few. This paper proposes techniques to discover spatiotemporal associations across distinct phenomena using a series of anomalous windows from each domain that represent a phenomenon. We propose a novel metric called influence score to quantify the associated influence between the phenomena. In addition, we also propose spatiotemporal confidence, support, and lift measures to quantify these associations. Two novel algorithms for finding multi-domain spatiotemporal associations across phenomena are proposed. We present experimental results across real-world phenomena that are linked and discuss the efficacy of our approach.
PubDate: 2024-07-01
DOI: 10.1007/s10707-023-00506-4
- Efficient algorithms for community aware ridesharing
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Abstract: Abstract Ridesharing services have been becoming a prominent solution to reduce road traffic congestion and environmental pollution in urban areas. Existing ridesharing services fall apart in ensuring the social comfort of the riders. We formulate a Community aware Ridesharing Group Set (CaRGS) query that satisfies the spatial and social constraints of the riders and finds a set of ridesharing groups with the maximum number of served riders. The CaRGS query utilizes user social data in community levels to ensure user privacy. We show that the problem of finding CaRGS query answer is NP-Hard and propose two heuristic approaches: a hierarchical approach and an iterative approach to evaluate CaRGS queries. We evaluate the effectiveness, efficiency, and accuracy of our solution through extensive experiments using real datasets and present a comparative analysis among the proposed algorithms.
PubDate: 2024-07-01
DOI: 10.1007/s10707-023-00509-1
- ICN: Interactive convolutional network for forecasting travel demand of
shared micromobility-
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Abstract: Abstract Accurate shared micromobility demand predictions are essential for transportation planning and management. Although deep learning methods provide robust mechanisms to tackle demand forecasting challenges, current models based on graph neural networks suffer from limited scalability and high computational cost. There is both a need and significant potential to enhance the accuracy and efficiency of existing shared micromobility demand forecasting models. To fill these research gaps, this paper proposes a deep learning model named Interactive Convolutional Network (ICN) to forecast spatiotemporal travel demand for shared micromobility. The proposed model develops a novel channel dilation method by utilizing multi-dimensional spatial information (i.e., demographics, functionality, and transportation supply) based on travel behavior knowledge for building the deep learning model. We use the convolution operation to process the dilated tensor to simultaneously capture temporal and spatial dependencies. Based on a binary-tree-structured architecture and interactive convolution, the ICN model extracts features at different temporal resolutions and then generates predictions using a fully-connected layer. We conducted two practical case studies from Chicago, IL, and Austin, TX to test the proposed model. The results show that the ICN model significantly outperforms all benchmark models. The model predictions have the potential to assist micromobility operators in developing efficient vehicle rebalancing strategies, while also providing cities with guidance on enhancing the management of their shared micromobility system.
PubDate: 2024-06-21
DOI: 10.1007/s10707-024-00525-9
- A transformer-based method for vessel traffic flow forecasting
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Abstract: Abstract In recent years, the maritime domain has experienced tremendous growth due to the exploitation of big traffic data. Particular emphasis has been placed on deep learning methodologies for decision-making. Accurate Vessel Traffic Flow Forecasting (VTFF) is essential for optimizing navigation efficiency and proactively managing maritime operations. In this work, we present a distributed Unified Approach for VTFF (dUA-VTFF), which employs Transformer models and leverages the Apache Spark big data distributed processing framework to learn from historical maritime data and predict future traffic flows over a time horizon of up to 30 min. Particularly, dUA-VTFF leverages vessel timestamped locations along with future vessel locations produced by a Vessel Route Forecasting model. These data are arranged into a spatiotemporal grid to formulate the traffic flows. Subsequently, through the Apache Spark, each grid cell is allocated to a computing node, where appropriately designed Transformer-based models forecast traffic flows in a distributed framework. Experimental evaluations conducted on real Automatic Identification System (AIS) datasets demonstrate the improved efficiency of the dUA-VTFF compared to state-of-the-art traffic flow forecasting methods.
PubDate: 2024-05-30
DOI: 10.1007/s10707-024-00521-z
- MobilityDL: a review of deep learning from trajectory data
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Abstract: Abstract Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior. As data availability and computing power have increased, so has the popularity of deep learning from trajectory data. This review paper provides the first comprehensive overview of deep learning approaches for trajectory data. We have identified eight specific mobility use cases which we analyze with regards to the deep learning models and the training data used. Besides a comprehensive quantitative review of the literature since 2018, the main contribution of our work is the data-centric analysis of recent work in this field, placing it along the mobility data continuum which ranges from detailed dense trajectories of individual movers (quasi-continuous tracking data), to sparse trajectories (such as check-in data), and aggregated trajectories (crowd information).
PubDate: 2024-05-28
DOI: 10.1007/s10707-024-00518-8
- Identifying and recommending taxi hotspots in spatio-temporal space
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Abstract: Abstract The GPS-driven mobile application-based ride-hailing systems, e.g., Uber and Ola, have become integral to daily life and natural transport choices for urban commuters. However, there is an imbalance between demand or pick-up requests and supply or drop-off requests in any area. The city planners and the researchers are working hard to balance this gap in demand and supply situation for taxi requests. The existing approaches have mainly focused on clustering the spatial regions to identify the hotspots, which refer to the locations with a high demand for pick-up requests. This study determined that if the hotspots focus on clustering high demand for pick-up requests, most of the hotspots pivot near the city center or in the two-three spatial regions, ignoring the other parts of the city. This paper (An earlier version of this paper was presented at the Australasian Database Conference and was published in its Proceedings: https://link.springer.com/chapter/10.1007/978-3-030-69377-0_10) presents a hotspot detection method that uses a dominating set problem-based solution in spatial-temporal space, which covers high-density taxi pick-up demand regions and covers those parts of the city with a moderate density of taxi pick-up demands during different hours of the day. The paper proposes algorithms based on k-hop dominating set; their performance is evaluated using real-world datasets and proves the edge over the existing state-of-the-art methods. It will also reduce the waiting time for customers and drivers looking for their subsequent pick-up requests. Therefore, this would maximize their profit and help improve their services.
PubDate: 2024-05-25
DOI: 10.1007/s10707-024-00524-w
- A multistart and recombination algorithm for finding many unique solutions
to spatial aggregation problems-
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Abstract: Abstract Spatial aggregation is essential for applications where data at low level spatial units such as census blocks are grouped into larger regions. This type of problem can be formulated as spatial optimization problems where the goal is to minimize the difference between the grouped regions. These problems are difficult to solve because of their computational intensity. In addition, these problems often have multiple, instead of singular, optimal solutions that have the same or similar objective function values but exhibit different spatial configurations. Existing solution methods often aim to find single solutions to these problems. In this paper, we discuss a new heuristic method that can be used to find a set of unique optimal or near-optimal solutions to spatial aggregation problems. The algorithm consists of two phases. A multistart phase first generates a pool of random solutions to a problem. The size of the pool is specified by the user and contains the number of solutions desired to be found. Each random solution is then improved using an efficient algorithm called give-and-take. The second phase uses a recombination algorithm to create new solutions based on solutions randomly selected from the pool. The worst solution in the pool will be replaced by the new solution if the latter is better and does not exist in the pool. We test this multistart and recombination algorithm (MSRA) using a variety of problems with different sizes and the results suggest the effectiveness of the algorithm in finding multiple unique optimal or near-optimal solutions.
PubDate: 2024-05-21
DOI: 10.1007/s10707-024-00520-0
- An experimental study of existing tools for outlier detection and cleaning
in trajectories-
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Abstract: Abstract Outlier detection and cleaning are essential steps in data preprocessing to ensure the integrity and validity of data analyses. This paper focuses on outlier points within individual trajectories, i.e., points that deviate significantly inside a single trajectory. We experiment with ten open-source libraries to comprehensively evaluate available tools, comparing their efficiency and accuracy in identifying and cleaning outliers. This experiment considers the libraries as they are offered to end users, with real-world applicability. We compare existing outlier detection libraries, introduce a method for establishing ground-truth, and aim to guide users in choosing the most appropriate tool for their specific outlier detection needs. Furthermore, we survey the state-of-the-art algorithms for outlier detection and classify them into five types: Statistic-based methods, Sliding window algorithms, Clustering-based methods, Graph-based methods, and Heuristic-based methods. Our research provides insights into these libraries’ performance and contributes to developing data preprocessing and outlier detection methodologies.
PubDate: 2024-05-18
DOI: 10.1007/s10707-024-00522-y
- A spatial dependency based reinforcement learning model for selecting
features in spatial classification-
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Abstract: Abstract Traditional feature-based classification methods require objects to have the explicit, independent, and identifiable set of features, while most geo-referenced objects do not have the explicit features required by classifiers. Therefore, developing classificatory features under geospatial context is a prerequisite for effective spatial classification. Considering the spatial dependency, objects are correlated with each other, and for the object of interest its features (e.g., the distribution of neighboring objects) exist in a wide range of neighboring areas. However, the uncertainty of neighborhood size makes the dimensionality of potential feature set particularly high for spatial classification. Therefore, we propose a new model to automatically select a subset of spatially explicit features through continuous decision making by multiple agents in reinforcement learning (RL). A novel reward mechanism is developed to feed the knowledge of the downstream classification task back to the loop of feature selection. Through extensive experiments with facility points-of-interest datasets, we demonstrate that the subset of classificatory features selected by our RL model can help significantly improve the accuracy of spatial classification. Moreover, our feature selection has potential explainability for the spatial classification rules as it can determine the neighboring areas which have an impact on the classification result.
PubDate: 2024-05-17
DOI: 10.1007/s10707-024-00523-x
- Editor’s note: Special issue on Deep Modeling and Understanding of
Big Human Mobility Data-
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PubDate: 2024-05-15
DOI: 10.1007/s10707-024-00519-7
- How opportunistic mobile monitoring can enhance air quality
assessment'-
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Abstract: Abstract The deteriorating air quality in urban areas, particularly in developing countries, has led to increased attention being paid to the issue. Daily reports of air pollution are essential to effectively manage public health risks. Pollution estimation has become crucial to expanding spatial and temporal coverage and estimating pollution levels at different locations. The emergence of low-cost sensors has enabled high-resolution data collection, either in fixed or mobile settings, and various approaches have been proposed to estimate air pollution using this technology. The objective of this study is to enhance the data from fixed stations by incorporating opportunistic mobile monitoring (OMM) data. The main research question we are dealing with is: How can we augment fixed station data through OMM' In order to address the challenge of limited OMM data availability, we leverage existing data collected during periods when the pollution maps align with those observed by the fixed stations. By combining the fixed and mobile data, we apply interpolation techniques to produce more accurate pollution maps. The efficacy of our approach is validated through experiments conducted on a real-life dataset.
PubDate: 2024-04-29
DOI: 10.1007/s10707-024-00516-w
- Foresight plus: serverless spatio-temporal traffic forecasting
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Abstract: Abstract Building a real-time spatio-temporal forecasting system is a challenging problem with many practical applications such as traffic and road network management. Most forecasting research focuses on achieving (often marginal) improvements in evaluation metrics such as MAE/MAPE on static benchmark datasets, with less attention paid to building practical pipelines which achieve timely and accurate forecasts when the network is under heavy load. Transport authorities also need to leverage dynamic data sources such as roadworks and vehicle-level flow data, while also supporting ad-hoc inference workloads at low cost. Our cloud-based forecasting solution Foresight, developed in collaboration with Transport for the West Midlands (TfWM), is able to ingest, aggregate and process streamed traffic data, enhanced with dynamic vehicle-level flow and urban event information, to produce regularly scheduled forecasts with high accuracy. In this work, we extend Foresight with several novel enhancements, into a new system which we term Foresight Plus. New features include an efficient method for extending the forecasting scale, enabling predictions further into the future. We also augment the inference architecture with a new, fully serverless design which offers a more cost-effective solution and which seamlessly handles sporadic inference workloads over multiple forecasting scales. We observe that Graph Neural Network (GNN) forecasting models are robust to extensions of the forecasting scale, achieving consistent performance up to 48 hours ahead. This is in contrast to the 1 hour forecasting periods popularly considered in this context. Further, our serverless inference solution is shown to be more cost-effective than provisioned alternatives in corresponding use-cases. We identify the optimal memory configuration of serverless resources to achieve an attractive cost-to-performance ratio.
PubDate: 2024-04-26
DOI: 10.1007/s10707-024-00517-9
- Transfer-learning-based representation learning for trajectory similarity
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Abstract: Abstract Trajectory similarity search is one of the most fundamental tasks in spatial-temporal data analysis. Classical methods are based on predefined trajectory similarity measures, consuming high time and space costs. To accelerate similarity computation, some deep metric learning methods have recently been proposed to approximate predefined measures based on the learned representation of trajectories. However, instead of predefined measures, real applications may require personalized measures, which cannot be effectively learned by existing models due to insufficient labels. Thus, this paper proposes a transfer-learning-based model FTL-Traj, which addresses this problem by effectively transferring knowledge from several existing measures as source measures. Particularly, a ProbSparse self-attention-based GRU unit is designed to extract the spatial and structural information of each trajectory. Confronted with diverse source measures, the priority modeling assists the model for the rational ensemble. Then, sparse labels are enriched with rank knowledge and collaboration knowledge via transfer learning. Extensive experiments on two real-world datasets demonstrate the superiority of our model.
PubDate: 2024-04-13
DOI: 10.1007/s10707-024-00515-x
- A survey on the computation of representative trajectories
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Abstract: Abstract The process of computing a representative trajectory for a set of raw (or even semantically enriched) trajectories is an attractive solution to minimize several challenges related to trajectory management, like trajectory data integration or trajectory pattern analysis. We identify two main strategies for accomplishing such a process (trajectory data summarization and trajectory data fusion), but we argue that this subject is still an open issue, and we did not find a survey with such a focus. In order to fill this literature gap, this paper presents a survey that analyzes several issues around the two aforementioned strategies, like the type of representative data computed by each approach, the dimensions that are considered by the approach (spatial, temporal, and semantics), the accomplished methods of the proposed processes, and how the process is evaluated. Additionally, we compare these two research areas (trajectory summarization and trajectory fusion) in literature to analyze their relationship. Finally, some open issues related to this subject are also pointed out.
PubDate: 2024-04-02
DOI: 10.1007/s10707-024-00514-y
- Learning the micro-environment from rich trajectories in the context of
mobile crowd sensing-
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Abstract: Abstract With the rapid advancements of sensor technologies and mobile computing, Mobile Crowd Sensing (MCS) has emerged as a new paradigm to collect massive-scale rich trajectory data. Nomadic sensors empower people and objects with the capability of reporting and sharing observations on their state, their behavior and/or their surrounding environments. Processing and mining multi-source sensor data in MCS raise several challenges due to their multi-dimensional nature where the measured parameters (i.e., dimensions) may differ in terms of quality, variability, and time scale. We consider the context of air quality MCS and focus on the task of mining the micro-environment from the MCS data. Relating the measures to their micro-environment is crucial to interpret them and analyse the participant’s exposure properly. In this paper, we focus on the problem of investigating the feasibility of recognizing the human’s micro-environment in an environmental MCS scenario. We propose a novel approach for learning and predicting the micro-environment of users from their trajectories enriched with environmental data represented as multidimensional time series plus GPS tracks. We put forward a multi-view learning approach that we adapt to our context, and implement it along with other time series classification approaches. We extend the proposed approach to a hybrid method that employs trajectory segmentation to bring the best of both methods. We optimise the proposed approaches either by analysing the exact geolocation (which is privacy invasive), or simply applying some a priori rules (which is privacy friendly). The experimental results, applied to real MCS data, not only confirm the power of MCS and air quality (AQ) data in characterizing the micro-environment, but also show a moderate impact of the integration of mobility data in this recognition. Furthermore, and during the training phase, multi-view learning shows similar performance as the reference deep learning algorithm, without requiring specific hardware. However, during the application of models on new data, the deep learning algorithm fails to outperform our proposed models.
PubDate: 2024-04-01
DOI: 10.1007/s10707-022-00471-4
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