Accurate traffic forecasting plays a critical role in modern transportation systems by enabling effective congestion management and route optimization. Although recent deep learning-based models have shown substantial progress in modeling complex spatiotemporal dependencies, most existing methods overlook the challenges posed by anomalous traffic conditions. To address this gap, we propose a two-stage anomaly-aware forecasting correction framework. The first stage employs an unsupervised auto-regressive anomaly detector that combines representation learning and spatiotemporal attention to capture normal traffic patterns and filter anomalous inputs through error thresholding. In the second stage, an anomaly optimization predictor leverages a memory module and a sparsity regularization learning strategy to enhance the representation of normal patterns and suppress noise. It models spatiotemporal dependencies using an information propagation layer composed of sequential small-kernel temporal convolutions and memory-guided graph convolutions. Extensive experiments on real-world traffic flow datasets demonstrate that our framework outperforms state-of-the-art models under anomalous conditions.

Next Point of Interest (POI) recommendation primarily predicts future activities based on users’ past check-in data and current status, providing significant value to users and service providers. We observed that the popular check-in times for different POI categories vary. For example, coffee shops are crowded in the afternoon because people like to have coffee to refresh after meals, while bars are busy late at night. However, existing methods rarely explore the relationship between POI categories and time, which may result in the model being unable to fully learn users’ tendencies to visit certain POI categories at different times. Additionally, existing methods for modeling time information often convert it into time embeddings or calculate the time interval and incorporate it into the model, making it difficult to capture the continuity of time. Finally, during POI prediction, various weighting information is often ignored, such as the popularity of each POI, the transition relationships between POIs, and the distances between POIs, leading to suboptimal performance. To address these issues, this article proposes a novel next POI recommendation framework called Graph Disentangler with POI Weighted Module (GDPW) . This framework aims to jointly consider POI category information and multiple POI weighting factors. Specifically, the proposed GDPW learns category and time representations through the Global Category Graph and the Global Category-Time Graph. Then, we disentangle category and time information through contrastive learning. After prediction, the final POI recommendation for users is obtained by weighting the prediction results based on the transition weights and distance relationships between POIs. We conducted experiments on two real-world datasets, and the results demonstrate that the proposed GDPW outperforms other existing models, improving performance by 3% to 11%.

Dengue outbreaks pose significant public health and socio-economic risks in endemic regions. We propose SEIR-DST, a forecasting model that predicts district-level Dengue cases by integrating SEIR-based epidemiological simulation with spatio-temporal deep learning. The model combines compartmental disease dynamics, attention mechanisms, and dilated temporal convolutions to capture both temporal trends and spatial diffusion. Evaluations on real-world data from Tainan City, Taiwan, show that SEIR-DST outperforms nine baseline models in predictive accuracy, enabling earlier risk identification and more targeted interventions. Our findings highlight the value of combining epidemiological priors with graph-based deep learning for fine-grained epidemic forecasting.

With growing environmental concerns and the push for sustainable urban development, promoting green travel has become a critical initiative. Urban transit systems face the challenge of integrating green initiatives with efficient transport routes, while sophisticated graph modeling enhances travel efficiency. However, blending historical and contemporary elements introduces complex variations in traffic networks, complicating feature extraction and clustering for information retrieval due to multi-scale spatial heterogeneity. Traditional methods often overlook key nuances by oversimplifying data relationships. We proposed M3 and validated the integration of GIS-based Attention-Cluster-GCN with Dueling Double Deep Q Network across various cities, enhancing urban travel with detailed information on green attraction recommendations, considering the usage of Multi-Purpose and Multi-Stakeholder for Multi-Scale Spatial Heterogeneity scenarios. Utilizing Attention-Based Reinforcement Graph Clustering refines modeling and emphasizes vital connections, enhancing personalized recommendation precision and clustering performance. Our method surpasses both conventional and advanced GNN methods, even in graph convolution-based deep reinforcement learning, achieving superior cluster separation and accuracy. Our sampling and ablation studies confirm the pivotal role of the attention mechanism and multi-scale features, showing a significant performance decline without attention. Our findings underscore the potential of graph clustering in making public transport more engaging and aligned with green attractions policies by recommendations, even amidst significant spatial heterogeneity.

Spatio-temporal forecasting is critical for urban applications such as epidemic control and crime prevention, yet many existing methods assume dense and consistent sensor data, which is often unavailable due to infrastructural or cost constraints. This work explores the challenge of forecasting in sensor-less regions, where direct temporal observations are missing. Building on two prior studies: Multi-View Graph Fusion Approach with Approximation Module (MVGAM) for disease risk prediction and Graph Disentangler with POI Weighted Module (GDPW), a contrastive learning framework for enhancing POI embedding, we outline a new research direction. Our framework integrates large language models (LLMs), gated recurrent units (GRUs) and multi-layer perceptron (MLP) to encode multi-modal signals, with contrastive learning aligning heterogeneous representations. A dual-stage graph propagation mechanism consolidates knowledge in sensor-rich areas and transfer it to sensor-less regions via localized subgraphs. We anticipate using crime forecasting in Chicago as a case study, this work lays the foundation for robust and interpretable forecasting in data-scarce urban settings.

Dengue fever is an emergency disease spread by mosquitoes. The most direct way to prevent the disease is to predict risky areas and bolster mosquito preventive strategies. Risk is usually evaluated by monitoring the number of eggs in the ovitraps set up by the government. However, areas without sensors still need to be checked and managed for dengue risk. In this study, we focus on forecasting each region’s fine-grained dengue fever risk, especially in regions without sensor coverage. The paucity of historical data makes this endeavor challenging. Furthermore, determining how to effectively blend different features is another important research challenge and practical issue. We propose a Multi-View Graph Fusion Approach with Approximation Module (MVGAM) to address these two issues. For the regions that have no sensor coverage, MVGAM first uses a feature extractor to learn their representation based on their dynamic and static features. Then, we use a graph constructor to formulate the relationship between sensors from different perspectives and a multi-view graph fusion module to learn the embedding of sensors. Finally, we use an approximation module to deal with the lack of historical data. We conducted experiments using a real-world dataset from the urban area of Tainan, Taiwan. The results show that the proposed MVGAM outperforms the state-of-the-art methods and baselines. The ablation study also shows that every component in MVGAM has a significant impact on boosting the prediction effectiveness.

We present LINKin-PARK, an innovative system that seamlessly merges geographic visualization with an advanced Dual Attention Double Channel Convolutional Neural Network with Multilayer Perceptron (Dual Attention-DCCNN+MLP) to facilitate the efficient analysis of land valuation. LINKin-PARK provides robust visualization capabilities for intuitive comprehension. Our model outperforms traditional methods, e.g., linear regression, multilayer perceptron (MLP), Extreme Gradient Boosting (XGBoost), and the combination of CNN (Convolutional Neural Network) with MLP. An ablation study further evaluates the influence of specific components within the model, revealing that spatial and channel-wise attention mechanisms and the integration of DCCNN and skip connections are crucial for capturing spatial details and improving prediction accuracy. Users have the flexibility to explore and predict developable land valuation based on their specific requirements and provide their feedback to minimize errors in model prediction. For instance, this system can forecast future development potential and market demand for everywhere in an urban space, enabling users to make informed decisions before purchasing a property. Similarly, retailers can anticipate future revenues to aid in strategic decisions, such as selecting optimal locations for establishing new retail outlets. In summary, LINKin-PARK effectively combines geographic visualization and Dual Attention-DCCNN+MLP to assist users in analyzing and predicting land valuation and other scenarios.

We introduce a GIS-based AR to promote public transport and environmental awareness. It transforms bus rides into gamified journeys of virtual plant cultivation at bus stops. Featuring AR scanning for plant growth, navigation aids, and a digital herbarium, it enriches user interaction with their surroundings. The system integrates nature-inspired virtual installations, supported by a database and species map for green education, utilizing Cluster-GCN for plant, flower, and crop information and mapping into bus stops to recommend travel sites for passengers. An immersive AR interface enables users to access plant information at nearby stations through a custom graph clustering pipeline. User tests showed no significant change in bus ridership interest, slightly fluctuating from 52% to 43%, attributed to bus punctuality and frequency issues. However, the app significantly increased user engagement with the environment and species knowledge from 54% to 82%, underscoring a positive relationship between public transport and environmental awareness.

We introduce ACCEPT, a geospatial decision support system that merges robust, intuitive visualization with grid-based data processing and neural networks to enhance spatial data analysis and interpretation in context-sensitive scenarios. It offers versatile machine learning modules with multiple prediction models, tailored to specific requirements with user-defined configurable parameters and flexible predictive target selection. The system serves as an accessible introduction to geographic information systems (GIS) for the general public. The system maps Points of Interest (POIs) to grids, simplifying processes like weighting, intersection, and interpolation, enhancing data accessibility and manipulation. Our case studies show effective handling of spatial data, reflecting similar distribution patterns of POIs, spatial separation, local feature sensitivity, and proximity to infrastructure and kernel size affect evaluations. The extensible and user-friendly web interface includes geospatial data inquiries, overlay, import/export, statistic, and multiple map views, facilitating informed decisions in resource distribution and urban planning. It supports urban planners, analysts, and policymakers in achieving equitable resource distribution and enhancing residential justice, while also providing non-experts an introduction to advanced geospatial analyses, promoting wider engagement and understanding in spatial decision-making.

Parking behaviors near popular destinations often exhibit a preference for proximity, resulting in poor habits, limited parking availability, and a range of consequential issues such as traffic chaos, economic challenges due to congestion, and imbalanced parking utilization. Taiwan has also faced escalating challenges in this regard. To effectively address these issues, the Government of Taiwan has initiated the Smart City program, encompassing various initiatives to enhance urban functionality. One notable solution implemented under this program is the Smart Parking Meter System (SPMS), designed to enhance the overall parking experience. The SPMS incorporates intelligent billing and secure parking data transmission, ensuring a safer and improved parking environment. In this paper, we propose ParkFlow, a comprehensive software-based solution that seamlessly integrates with smart parking hardware, presenting a holistic approach to tackling these challenges. ParkFlow intelligently disperses parking shortages in highly frequented areas and addresses the problem from multiple perspectives, including user, engineering, and government scenarios. By exploring and addressing these scenarios, we aim to provide valuable insights and inspiration to regions worldwide grappling with similar parking-related difficulties. Based on historical data analysis, the implementation of ParkFlow in resolving the parking imbalance problem is anticipated to lead to a significant increase of up to 10% to 20% in available parking hours in popular areas of Tainan, Taiwan. ParkFlow is in the process of being integrated into the Tainan City Government's Parking application, indicating its potential to address real-world parking challenges.

Dengue fever is an emergency disease spread by mosquitoes. The most direct way to prevent the disease is to predict risky areas and increase mosquito preventive strategies. Risk is evaluated by monitoring the sensors set up by the government. However, areas without sensors still need to be managed for dengue risk. In this study, we focus on forecasting each region's fine-grained dengue fever risk, especially in regions without sensor coverage. The lack of historical data makes this endeavor challenging. Furthermore, determining how to effectively blend different features is another challenge. We propose a Multi-View Graph Fusion Recurrent Neural Network (MVGFRNN), which consists of a multi-view graph constructor, graph fusion module, and an approximation module to address these two issues. We conducted experiments using a real-world dataset from the urban area of Tainan, Taiwan. The results show that MVGFRNN outperforms state-of-the-art methods.

Self-supervised contrastive learning has achieved promising results in computer vision, and recently it also received attention in the medical domain. In practice, medical data is hard to collect and even harder to annotate, but leveraging multi-modality medical images to make up for small datasets has proved to be helpful. In this work, we focus on mining multi-modality Magnetic Resonance (MR) images to learn multi-modality contrastive representations. We first present multi-modality data augmentation (MDA) to adapt contrastive learning to multi-modality learning. Then, the proposed cross-modality group convolution (CGC) is used for multi-modality features in the downstream fine-tune task. Specifically, in the pre-training stage, considering different behaviors from each MRI modality with the same anatomic structure, yet without designing a handcrafted pretext task, we select two augmented MR images from a patient as a positive pair, and then directly maximize the similarity between positive pairs using Simple Siamese networks. To further exploit multi-modality representation, we combine 3D and 2D group convolution with a channel shuffle operation to efficiently incorporate different modalities of image features. We evaluate our proposed methods on liver MR images collected from a well-known hospital in Taiwan. Experiments show our framework has significantly improved from previous methods.

In this paper, we propose "Petition Executing Process Optimizer (PEPO)," an AI-based petition processing system that features three components, (a) Department Classification, (b) Importance Assessment, and (c) Response Generation for improving the Public Work Bureau (PWB) 1999 Hotline petitions handling process in Taiwan. Our Department Classification algorithm has been evaluated with NDCG, achieving an impressive score of 86.48%, while the Important Assessment function has an accuracy rate of 85%. Besides, Response Generation enhances communication efficiency between the government and citizens. The PEPO system has been deployed as an online web service for the Public Works Bureau of the Tainan City Government. With PEPO, the PWB benefits greatly from the effectiveness and efficiency of handling citizens' petitions.

Hybrid computational intelligent systems that synergize learning-based inference models and route planning strategies have thrived in recent years. In this article, we focus on the non-monotonicity originated from heterogeneous urban data, as well as heuristics based on neural networks, and thereafter formulate the traveling transporter problem (TTP). TTP is a multi-criteria optimization problem and may be applied to the circular route deployment in public transportation. In particular, TTP aims to find an optimized route that maximizes passenger flow according to a neural-network-based inference model and minimizes the length of the route given several constraints, including must-visit stations and the requirement for additional ones. As a variation of the traveling salesman problem (TSP), we propose a framework that first recommends new stations’ location while considering the herding effect between stations, and thereafter combines state-of-the-art TSP solvers and a metaheuristic named Trembling Hand , which is inspired by self-efficacy for solving TTP. Precisely, the proposed Trembling Hand enhances the spatial exploration considering the structural patterns, previous actions, and aging factors. Evaluation conducted on two real-world mass transit systems, Tainan and Chicago, shows that the proposed framework can outperform other state-of-the-art methods by securing the Pareto-optimal toward the objectives of TTP among comparative methods under various constrained settings.

In this work, a novel decision assistant system for urban transportation, called Route Scheme Assistant (RSA), is proposed to address two crucial issues that few former researches have focused on: route-based passenger flow (PF) inference and multivariant high-PF route recommendation. First, RSA can estimate the PF of arbitrary user-designated routes effectively by utilizing Deep Neural Network (DNN) for regression based on geographical information and spatial-temporal urban informatics. Second, our proposed Bidirectional Prioritized Spanning Tree (BDPST) intelligently combines the parallel computing concept and Gaussian mixture model (GMM) for route recommendation under users’ constraints running in a timely manner. We did experiments on bus-ticket data of Tainan and Chicago and the experimental results show that the PF inference model outperforms baseline and comparative methods from 41% to 57%. Moreover, the proposed BDPST algorithm's performance is not far away from the optimal PF and outperforms other comparative methods from 39% to 71% in large-scale route recommendations.

In this work, we propose a novel deep learning framework to predict the future financial potential of urban spaces. We use the number of financial institutions as our prediction target in an urban area. Our model offers three kinds of interpretability, providing a better way for decision makers to understand the decision processes of the model: a) critical rules that determine the prediction; b) influential surrounding grids; and c) critical regional features. Our module takes advantage of a tree-based model, which can effectively extract cross features. Our proposed model also leverages convolutional neural networks to obtain more complex and inclusive features around the target area. Experimental results on real-world datasets demonstrate the superiority of our proposed model against the existing state-of-art methods.

Transportation between satellite cities or inside the city center has always been a crucial factor in contributing to a better quality of life. This paper focuses on a multi-criteria distributed competitive route planning for parking slot cruising in regions where neither real-time nor historical availability of parking slots is accessible. An inference-than-planning framework is proposed for solving the parking slot searching using a zero-information distributed model with an availability inference for parking slots in areas with no sensor coverage. Meanwhile, a proposed Conntrans algorithm is suggested as a two-stage structure with three relaxing policies: adjacent cruising, on-orbital annealing, and orbital transitioning. The evaluation is conducted based on the simulation in a publicly accessible real-world parking data from SFPark in San Francisco; the area is divided into 3 separated regions with different urban characteristics. Overall results show that the proposed availability inference model can retrieve decent performance. Furthermore, Conntrans is able to outperform baselines and state-of-the-arts in overall score by at most 77% with a success rate at around 97% and maintains the quality of solutions under various circumstances.

Traffic-related forecasting plays a critical role in determining transportation policy, unlike traditional approaches, which can only make decisions based on statistical results or historical experience. Through machine learning, we are able to capture the potential interactions between urban dynamics and find their mutual interactions in a spatial context. However, despite a plethora of traffic-related studies, few works have explored predicting the impact of congestion. Therefore, this paper focuses on predicting how a car accident leads to traffic congestion, especially the length of time it takes for the congestion to occur. Accordingly, we propose a novel model named Dual-Attention Multi-Scale Graph Convolutional Networks (DAMGNet) to address this issue. In this proposed model, heterogeneous data such as accident information, urban dynamics, and various highway network characteristics are considered and combined. Next, the context encoder encodes the accident data, and the spatial encoder captures the hidden features between multi-scale Graph Convolutional Networks (GCNs). With our designed dual attention mechanism, the DAMGNet model is able to effectively learn the correlation between features. The evaluations conducted on a real-world dataset prove that our DAMGNet has a significant improvement in RMSE and MAE over other comparative methods.

In this work, we propose a novel deep learning framework, called Attention-Based 2-layer Bi-ConvLSTM (denoted as Att-2BiConvLSTM) model, to predict the number of illegal-parking events in urban spaces. We model the research as a "next frame" prediction problem, which aims to improve urban transportation conditions and enhance the security and right-of-way for pedestrians. Various features in the prediction model are considered: some of them (e.g., hourly weather, traffic volumes) are dynamic every hour, while others (e.g., road network, point-of-interests) are static. To boost the effectiveness of static features, we propose a dynamic training process to transform the static features into dynamics. After that, all features can vary with time so that they are capable of handling a real-time prediction scenario. Moreover, we propose an attention mechanism for enhancing our bi-directional ConvLSTM model. With experimental verifications, we find that our proposed Att-2BiConvLSTM model can outperform other state-of-art and baseline methods. Besides, our model is useful for combining all features to make an accurate prediction.

Multi-criteria path planning is an important combinatorial optimization problem with broad real-world applications. Finding the Pareto-optimal set of paths ideal for all requiring features is time-consuming and unclear to obtain the subset of optimal paths efficiently for multiple origin states in the planning space. Meanwhile, due to the rise of deep learning, hybrid systems of computational intelligence thrive in recent years. When facing non-monotonic data or heuristics derived from pre-trained neural networks, most of the existing methods for the one-to-all path problem fail to find an ideal solution. We employ Gaussian mixture model to propose a target-prioritized searching algorithm called Multi-Source Bidirectional Gaussian-Prioritized Spanning Tree (BiasSpan) in solving this non-monotonic multi-criteria route planning problem given constraints including range, must-visit vertices, and the number of recommended vertices. Experimental results on mass transportation system in Tainan and Chicago cities show that BiasSpan outperforms comparative methods from 7% to 24%and runs in a reasonable time compared to state-of-art route-planning algorithms.

Urban areas need to deploy a lot of services and stations. This work considers the issue of establishing new branches for a certain service. Given a number of stations we plan to construct, our goal is to recommend locations as deploy placements and transportation cost could be efficiently reduced by jointly considering road network, existing stations and spatial event data. Our model can be divided into four parts: 1) Adopting DBSCAN clustering method to find hot spots of spatial events. 2) Doing community detection for road network to split the road network to smaller components. 3) Exploiting a refined closeness centrality to identify a good candidate location in each community. 4) Developing a greedy-based distance minimized method to establish stations sequentially. The results show our solution is effective and efficient for a large crime event dataset of Chicago.

As mobile devices are becoming ubiquitous nowadays, the geographical activities and interactions of human beings can be easily recorded and accessed. Each mobile individual can belong to an online social network. Unfortunately, the underlying online social relationships are hidden and only available to service providers. Acquiring the social network of mobile users would enrich lots of mobile applications, such as friend recommendation and energy-saving mobile database management. In this work, we propose to infer online social ties using purely offline geographical activities of users, such as check-in records and spatial meeting events. To tackle the problem, we devise a novel inference framework, O2O-I nf , which consists of two components, Feature Modeling and Link Inference . Feature modeling is to characterize both direct and indirect geographical interactions between nodes from co-location and graph features. Link inference aims to infer the social ties based on a small set of observed social links, and the idea is that pairs of nodes sharing similar geographical behaviors have the same tendency of linkage (i.e., either being friends or non-friends). Experiments conducted on a G owalla location-based social network and a M eetup event-based social network exhibit a satisfying performance in comparison to state-of-the-art prediction methods under the settings of offline-to-online network inference and geo-link prediction.

In this work, we propose a novel system, called Route Planning Maker (RPM) to help the government or transportation companies to design new route services in the city. The RPM system has a flexible user interface that allows users design the nearby areas of a new route and further deploying new stations. Moreover, based on user-designed arbitrary transportation routes and the expected locations of stations, the RPM system provides an intelligent function to infer passenger flows in certain time intervals so that the user can estimate the effectiveness of designed routes. To capture the spatial-temporal factors correlated with passenger flows, we propose to combine dynamic features such as human mobility, passenger volume of existing routes, and static features, including road network structure, point-of-interests (POI), station placement of existing routes and local population structure. Finally, to combine these features, we modified Deep Neural Network (DNN) for regression to derive the passenger flow for each given designated route. The experiments on the Tainan's bus-ticket data outperform baseline methods for 75%.

In recent years, online social networks are among the most popular websites with high PV (Page View) all over the world, as they have renewed the way for information discovery and distribution. Millions of users have registered on these websites and hence generate formidable amount of user-generated contents every day. The social networks become "giants", likely eligible to carry on any research tasks. However, we have pointed out that these giants still suffer from their "Achilles Heel", i.e., extreme sparsity. Compared with the extremely large data over the whole collection, individual posting documents such as microblogs seem to be too sparse to make a difference under various research scenarios, while actually these postings are different. In this paper we propose to tackle the Achilles Heel of social networks by smoothing the language model via influence propagation. To further our previously proposed work to tackle the sparsity issue, we extend the socialized language model smoothing with bi-directional influence learned from propagation. Intuitively, it is insufficient not to distinguish the influence propagated between information source and target without directions. Hence, we formulate a bi-directional socialized factor graph model, which utilizes both the textual correlations between document pairs and the socialized augmentation networks behind the documents, such as user relationships and social interactions. These factors are modeled as attributes and dependencies among documents and their corresponding users, and then are distinguished on the direction level. We propose an effective learning algorithm to learn the proposed factor graph model with directions. Finally we propagate term counts to smooth documents based on the estimated influence. We run experiments on two instinctive datasets of Twitter and Weibo. The results validate the effectiveness of the proposed model. By incorporating direction information into the socialized language model smoothing, our approach obtains improvement over several alternative methods on both intrinsic and extrinsic evaluations measured in terms of perplexity, nDCG and MAP measurements.

This paper aims to investigate how the geographical footprints of users correlate to their social ties. While conventional wisdom told us that the more frequently two users co-locate in geography, the higher probability they are friends, we find that in real geo-social data, Gowalla and Meetup, almost all of the user pairs with friendships had never met geographically. In this sense, can we discover social ties among users purely using their geographical footprints even if they never met? To study this question, we develop a two-stage feature engineering framework. The first stage is to characterize the direct linkages between users through their spatial co-locations while the second is to capture the indirect linkages between them via a co-location graph. Experiments conducted on Gowalla check-in data and Meetup meeting events exhibit not only the superiority of our feature model, but also validate the predictability (with 70% accuracy) of detecting social ties solely from user footprints.

This paper tries to answer two questions. First, how to infer real-time air quality of any arbitrary location given environmental data and historical air quality data from very sparse monitoring locations. Second, if one needs to establish few new monitoring stations to improve the inference quality, how to determine the best locations for such purpose? The problems are challenging since for most of the locations (>99%) in a city we do not have any air quality data to train a model from. We design a semi-supervised inference model utilizing existing monitoring data together with heterogeneous city dynamics, including meteorology, human mobility, structure of road networks, and point of interests (POIs). We also propose an entropy-minimization model to suggest the best locations to establish new monitoring stations. We evaluate the proposed approach using Beijing air quality data, resulting in clear advantages over a series of state-of-the-art and commonly used methods.

Searching for a particular person by specifying her name is one of the essential functions in online social networking services such as Facebook. So many times, however, one would like to find a person but what she knows is few social labels about the target, such as interests, skills, hometown, school, employment, etc. Assume each user is associated a set of social labels, we propose a novel search in online social network, Person-of-Interest (POI) Search, which aims to find a list of desired targets based on a set of user-specified query labels that depict the targets. We develop a greedy heuristic graph search algorithm, which finds the target who not only covers the query labels, but also either possesses better social interactions with peers or has higher social proximity towards the user. Experiments conducted on Facebook and Twitter datasets exhibit the satisfying accuracy and encourage more advanced efforts on POI search.

Location-based services allow users to perform check-in actions, which not only record their geo-spatial activities, but also provide a plentiful source for data scientists to analyze and plan more accurate and useful geographical recommender system. In this paper, we present a novel Time-aware Route Planning (TRP) problem using location check-in data. The central idea is that the pleasure of staying at the locations along a route is significantly affected by their visiting time. Each location has its own proper visiting time due to the category, objective, and population. To consider the visiting time of locations into route planning, we develop a three-stage time-aware route planning framework. First, since there is usually either noise time on existing locations or no visiting information on new locations constructed, we devise an inference method, LocTimeInf, to predict and recover the location visiting time on routes. Second, we aim to find the representative and popular time-aware location-transition behaviors from user check-in data, and a Time-aware Transit Pattern Mining (TTPM) algorithm is proposed correspondingly. Third, based on the mined time-aware transit patterns, we develop a Proper Route Search (PR-Search) algorithm to construct the final time-aware routes for recommendation. Experiments on Gowalla check-in data exhibit the promising effectiveness and efficiency of the proposed methods, comparing to a series of competitors.

Location-based services allow users to perform geospatial recording actions, which facilitates the mining of the moving activities of human beings. This article proposes to recommend time-sensitive trip routes consisting of a sequence of locations with associated timestamps based on knowledge extracted from large-scale timestamped location sequence data (e.g., check-ins and GPS traces). We argue that a good route should consider (a) the popularity of places, (b) the visiting order of places, (c) the proper visiting time of each place, and (d) the proper transit time from one place to another. By devising a statistical model, we integrate these four factors into a route goodness function that aims to measure the quality of a route. Equipped with the route goodness, we recommend time-sensitive routes for two scenarios. The first is about constructing the route based on the user-specified source location with the starting time. The second is about composing the route between the specified source location and the destination location given a starting time. To handle these queries, we propose a search method, Guidance Search , which consists of a novel heuristic satisfaction function that guides the search toward the destination location and a backward checking mechanism to boost the effectiveness of the constructed route. Experiments on the Gowalla check-in datasets demonstrate the effectiveness of our model on detecting real routes and performing cloze test of routes, comparing with other baseline methods. We also develop a system TripRouter as a real-time demo platform.

Information about urban air quality, e.g., the concentration of PM2.5, is of great importance to protect human health and control air pollution. While there are limited air-quality-monitor-stations in a city, air quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, traffic volume, and land uses. In this paper, we infer the real-time and fine-grained air quality information throughout a city, based on the (historical and real-time) air quality data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs). We propose a semi-supervised learning approach based on a co-training framework that consists of two separated classifiers. One is a spatial classifier based on an artificial neural network (ANN), which takes spatially-related features (e.g., the density of POIs and length of highways) as input to model the spatial correlation between air qualities of different locations. The other is a temporal classifier based on a linear-chain conditional random field (CRF), involving temporally-related features (e.g., traffic and meteorology) to model the temporal dependency of air quality in a location. We evaluated our approach with extensive experiments based on five real data sources obtained in Beijing and Shanghai. The results show the advantages of our method over four categories of baselines, including linear/Gaussian interpolations, classical dispersion models, well-known classification models like decision tree and CRF, and ANN.