Regional socioeconomic indicators are critical across various domains, yet their acquisition can be costly. Inferring global socioeconomic indicators from a limited number of regional samples is essential for enhancing management and sustainability in urban areas and human settlements. Current inference methods typically rely on spatial interpolation based on the assumption of spatial continuity, which does not adequately address the complex variations present within regional spaces. In this paper, we present GeoHG, the first space-aware socioeconomic indicator inference method that utilizes a heterogeneous graph-based structure to represent geospace for non-continuous inference. Extensive experiments demonstrate the effectiveness of GeoHG in comparison to existing methods, achieving an R2 score exceeding 0.8 under extreme data scarcity with a masked ratio of 95%. The code and data are available at https://github.com/CityMind-Lab/GeoHG.

In IoT (Internet of Things) scenarios, massive floating-point time series data are generated in a streaming manner and transmitted within limited bandwidth for real-time analysis. To enhance the efficiency, it is acknowledged to compress the data before transmission. Existing floating-point compression methods are either for batched compression that may cause long delays, or for streaming lossless compression that has an unsatisfactory compression ratio when certain errors are allowed. In this paper, we propose the first Streaming ERror-bounded Floating-point compression Serf , which has two implementations: Serf-Qt and Serf-XOR . Serf-Qt first quantizes each floating-point value into an integer, and then encodes the integer with Elias gamma coding. Serf-XOR is the first lossy floating-point compression based on the XORing operation. To enhance the compression ratio of Serf-XOR , we propose a novel data offset technique to increase the leading zeros of the XORed values, and design a novel approximation technique to search for an error-qualified value that produces an XORed value with many trailing zeros. To improve the compression efficiency, we propose a pruning strategy to accelerate the process of approximated values search. We further build a streaming transmission prototype system based on a real development board, and deploy the proposed methods to it. Extensive experiments using 13 datasets show that, compared with 17 competitors, both Serf-Qt and Serf-XOR enjoy remarkable compression ratios with high efficiency in streaming scenarios. The transmission experiments based on the proposed system also showcase that Serf-XOR always takes the least overall time when the bandwidth is limited.

Representation learning of road networks is essential for various downstream traffic-related tasks, as road network contain multi-modal data with rich information, and the learned embeddings can be directly used in machine learning models. However, due to the dynamic changes in road networks with respect to topology and associated data, as well as the local and long-range dependency caused by complex mobility semantics, learning robust and effective representations remains challenging. To this end, we exploit the properties of the road network and the mobility semantics embedded in trajectories, and propose a novel S emantic- E nhanced G raph C ontrastive L earning (SE-GCL) framework, for learning general-purpose embeddings of road networks. Specifically, in this framework, we propose (1) a multi-modal feature embedding module to capture both the attribute and visual information of road segments, (2) a semantic-enhanced graph augmentation strategy to simulate topological changes and data missing in the road network, and (3) a semantic-enhanced contrastive optimization module that leverages geo-locality and mobility semantics to guide representation learning. Extensive experiments are conducted on two real-world road networks with three representative downstream tasks. The result demonstrate that SE-GCL yields more robust and effective representations, outperforming the state-of-the-art baselines. The source code is available at https://github.com/csjiezhao/SE-GCL .

Dynamic ridesharing has garnered significant attention in recent years due to its numerous benefits. Existing ridesharing algorithms often employ a “filter-and-refine” frame-work, where a large set of candidate vehicles is initially selected for each ride order, followed by computationally intensive route planning for each candidate. However, this process can lead to significant response delays and limit system efficiency. To address this challenge, we propose an order-vehicle pre-matching recommendation approach (PreMR) that refines the candidate set before route planning. PreMR leverages spatial-temporal intervals and a self-attention mechanism to encode diverse order and vehicle information into uniform and informative representations, enabling it to accurately identify the most suitable vehicles for each order. Extensive experiments using real-world datasets and four representative ridesharing algorithms demonstrate that PreMR significantly reduces order response time (by 46.78% on average) while maintaining high service quality, with a slight trade-off in the order completion rate.

Urban public nuisance events, like garbage exposure, illegal parking, facilities damage, and etc., impair the quality of life for city residents. Predicting and controlling these nuisances is crucial but complicated due to their ties to subjective and psychological factors. In this study, we reveal a significant correlation between such nuisances and meteorological indicators, influenced by the impact of climate on people's psychological states. We employ meteorology predictions that are integrated in Hawkes processes to enhance the accuracy of predicting the category and timing of these nuisances. To this end, we propose Spatial-Temporal Two-Tower Transformer (ST-T3), which simultaneously considers spatial data and further improves the prediction accuracy. Evaluated by about three-year data from both downtown and suburban Shanghai, our method outperforms both traditional and advanced prediction systems. We share a portion of the de-identified dataset for open research.

Trip purpose is a meaningful aspect of travel behaviour for the understanding of urban mobility. However, it is non-trivial to automatically obtain trip purposes. On one hand, trip purposes are naturally diverse and complicated, but the available predictive data sources are limited in real-world scenarios. On the other hand, since trip purpose labeling is costly and the development levels of cities are unbalanced, it is infeasible to access large-scale labeled data in less developed cities to train advanced prediction models. To narrow the gaps, this article presents A new Graph Embedding Network and active Domain Adaptation based framework (AGENDA) that only requires open data sources and is capable of predicting in both label-rich cities and label-scarce cities. Specifically, in label-rich source cities, we first use the vehicle’s GPS trajectory and open POI check-ins to augment trip contexts. Then we establish a supervised graph embedding network with two attention mechanisms to extract the passenger’s latent activity semantics and a classifier to predict trip purpose. To enable the prediction in label-scarce target cities, we further devise an active domain adaptation framework, in which adversarial domain adaptation is used to transfer the source-learned knowledge, and active learning is used to integrate human intelligence in the model training. A group of experiments are conducted with real-world datasets in Beijing and Shanghai. Evaluation results demonstrate that the proposed framework significantly outperforms existing trip purpose prediction algorithms, and could make accurate trip purpose prediction in label-scarce cities with much fewer labeling efforts.

There are a prohibitively large number of floating-point time series data generated at an unprecedentedly high rate. An efficient, compact and lossless compression for time series data is of great importance for a wide range of scenarios. Most existing lossless floating-point compression methods are based on the XOR operation, but they do not fully exploit the trailing zeros, which usually results in an unsatisfactory compression ratio. This paper proposes an Erasing-based Lossless Floating-point compression algorithm, i.e., Elf. The main idea of Elf is to erase the last few bits (i.e., set them to zero) of floating-point values, so the XORed values are supposed to contain many trailing zeros. The challenges of the erasing-based method are three-fold. First, how to quickly determine the erased bits? Second, how to losslessly recover the original data from the erased ones? Third, how to compactly encode the erased data? Through rigorous mathematical analysis, Elf can directly determine the erased bits and restore the original values without losing any precision. To further improve the compression ratio, we propose a novel encoding strategy for the XORed values with many trailing zeros. Elf works in a streaming fashion. It takes only O ( N ) (where N is the length of a time series) in time and O (1) in space, and achieves a notable compression ratio with a theoretical guarantee. Extensive experiments using 22 datasets show the powerful performance of Elf compared with 9 advanced competitors.

Map matching is a fundamental research topic with the objective of aligning GPS trajectories to paths on the road network. However, existing models fail to achieve satisfactory performance for low-quality (i.e., noisy, low-frequency, and non-uniform) trajectory data. To this end, we propose a general and robust deep learning-based model, L2MM , to tackle these issues at all. First, high-quality representations of low-quality trajectories are learned by two representation enhancement methods, i.e., enhancement with high-frequency trajectories and enhancement with the data distribution . The former employs high-frequency trajectories to enhance the expressive capability of representations, while the latter regularizes the representation distribution over the latent space to improve the generalization ability of representations. Secondly, to embrace more heuristic clues, typical mobility patterns are recognized in the latent space and further incorporated into the map matching task. Finally, based on the available representations and patterns, a mapping from trajectories to corresponding paths is constructed through a joint optimization method. Extensive experiments are conducted based on a range of datasets, which demonstrate the superiority of L2MM and validate the significance of high-quality representations as well as mobility patterns.

Traditional relational databases are nowadays over-whelmed by the increasing data volume and concurrent access. NoSQL databases can manage large-scale data, but most of them do not support complete transactions and standard SQL languages. NewSQL is proposed for both high scalability and transactional properties with SQL languages support. One type of NewSQL builds distributed systems from scratch, which is too radical for some critical applications. The other type of NewSQL, i.e., data sharding among relational databases, is a better option for these scenarios. This paper presents Apache ShardingSphere, the first top-level open-source platform for data sharding in Apache, which enables developers to use sharded databases like one database. Specifically Apache, ShardingSphere integrates six databases and designs and implements a complete SQL engine to route requests correctly and intelligently. Additionally it encapsulates three types of distributed transactions and provides two adaptors for different scenarios. Moreover it proposes a novel AutoTable strategy and a query language i.e DistSQL allowing database maintainers to easily configure the sharded databases. Further-more it provides many other pluggable features to better shard data. Extensive experiments are conducted using two famous benchmarking tools proving that Apache ShardingSphere is more efficient than eight state-of-the-art systems in our settings. All experimental source codes are publicly released. More than 170 companies are currently using Apache ShardingSphere.

The rapid development of positioning technology produces an extremely large volume of spatio-temporal data with various geometry types such as point, line string, polygon, or a mixed combination of them. As one of the most basic but time-consuming operations, k nearest neighbors join (kNN join) has attracted much attention. However, most existing works for kNN join either ignore temporal information or consider point data only.

Data quality and budget are two primary concerns in urban-scale mobile crowdsensing. Traditional research on mobile crowdsensing mainly takes sensing coverage ratio as the data quality metric rather than the overall sensed data error in the target-sensing area. In this article, we propose to leverage spatiotemporal correlations among the sensed data in the target-sensing area to significantly reduce the number of sensing task assignments. In particular, we exploit both intradata correlations within the same type of sensed data and interdata correlations among different types of sensed data in the sensing task. We propose a novel crowdsensing task allocation framework called SPACE-TA (SPArse Cost-Effective Task Allocation) , combining compressive sensing, statistical analysis, active learning, and transfer learning, to dynamically select a small set of subareas for sensing in each timeslot (cycle), while inferring the data of unsensed subareas under a probabilistic data quality guarantee. Evaluations on real-life temperature, humidity, air quality, and traffic monitoring datasets verify the effectiveness of SPACE-TA. In the temperature-monitoring task leveraging intradata correlations, SPACE-TA requires data from only 15.5% of the subareas while keeping the inference error below 0.25°C in 95% of the cycles, reducing the number of sensed subareas by 18.0% to 26.5% compared to baselines. When multiple tasks run simultaneously, for example, for temperature and humidity monitoring, SPACE-TA can further reduce ∼10% of the sensed subareas by exploiting interdata correlations.