With the increasing improvement of the living standard of the residents in urban areas and their pursuit of quality of life, urban green spaces have become the main places of leisure and recreation for residents. Under this background, how to fairly evaluate the rationality of the layout of urban green spaces and put forward suggestions for improvement has become an important part of urban transportation and land use planning. Urban green space accessibility is a key indicator for evaluating the layout of urban green spaces. In response to the limitations of assessing attractiveness based solely on urban green space area in the past, this paper takes Suzhou urban area as an example. In addition to calculating accessibility using objective attributes in the traditional framework, the paper delves into social media data to incorporate urban residents' subjective sentiment towards urban green space quality indicators into the consideration scope of attractiveness. Through this innovative integration, the paper improves the Two-Step Floating Catchment Area (2SFCA) method, analyzing in-depth the accessibility of urban residents to urban green spaces and the dynamic changes in accessibility before and after public health emergencies. The improved 2SFCA method, combined with Sentiment Knowledge Enhanced Pre-training (SKEP) model, incorporates residents' emotional evaluations of urban green spaces to measure their subjective attractiveness. Meanwhile, considering the skewness characteristic of area indicators, the paper innovatively proposes the Scale Index (SI) as an objective attractiveness evaluation indicator for urban green spaces, providing more scientific and robust support for urban green space planning. The research findings reveal that during public health emergencies, individuals tend to prefer urban green spaces that offer convenient access, such as community parks. However, as daily life gradually resumes, there is a greater preference for urban green spaces equipped with high-quality facilities, such as specialized parks. Only considering objective area as the attractiveness of urban green space leads to overestimation of the accessibility of large-area and underestimation of small-area urban green space. Moreover, solely based on visitors' subjective quality perception of urban green space may underestimate the accessibility of communities around large urban green spaces. The improved 2SFCA method, considering both visitors' subjective perception and objective attributes of urban green space attractiveness, can more accurately assess urban green space accessibility, broadening the perspective of traditional urban green space accessibility assessment. This method can not only be applied to urban green space planning, but also provides a new idea and computational framework for the accessibility analysis of public service facilities.

The rapid development of information and communication technologies and mobile computing has generated a variety of mobility big data, providing new opportunities for understanding and exploring the spatiotemporal distribution and mobility characteristics of resident travel, and further contributing to the construction of smart cities. However, the emerging mobile data have experienced significant growth in both scale and complexity compared to traditional data, posing challenges for its structural characteristic analysis. To address these issues, this paper proposes an analytical framework to deal with the spatiotemporal distribution characteristics of high-dimensional ride-hailing travel pattern. Compared to traditional square partitions, a regular hexagon is closer to a circle, and the six adjacent hexagons connected to its edges are symmetrically equivalent, which can be more advantageous in aggregating demands with similar travel characteristics into the same partition. Therefore, hexagonal partition is selected as the basic clustering unit, and different spatiotemporal patterns are identified by clustering homogeneous travel distribution groups. Firstly, the spatiotemporal characteristics of travel distribution aggregated in the hexagonal partition are summarized into three main components: the departure demand distribution at the origin partition, the spatial distribution at the destination partition, and the arrival demand distribution at the destination partition. The spatiotemporal similarity between two partitions can be expressed as the product of these three types of distribution similarity. Furthermore, a Clustering Algorithm with Fast Search and Find of Spatiotemporal Density Peaks (CFSFSTDP) is proposed to identify the spatiotemporal patterns of ride-hailing travel distribution in each partition. The spatiotemporal distances between different partitions are obtained through the calculation of spatiotemporal similarity. Finally, affinity propagation clustering algorithm is used to perform clustering analysis on the time series variation pattern of spatiotemporal pattern of travel distribution in each partition. The time series similarity of spatiotemporal patterns between different partitions is represented by the sum of Euclidean distances between time series of each interval, and the model converges through continuous updates of attractiveness and affiliation indices. Through the empirical analysis of Didi Chuxing order data in Chengdu for one month, the validity of the method is verified. Based on the identified seven spatiotemporal distribution patterns, the differences of spatiotemporal patterns in the size, location, and time of demand are analyzed, and the functional types of ride-hailing travel in different partitions are discussed. The identified six time series patterns better grasp the time continuity of spatiotemporal patterns of ride-hailing travel distribution and help to better build the corresponding spatiotemporal evolution digital.

As an important transportation infrastructure, the timely updating of road network data is of great significance in the fields of traffic management, emergency response, and urban planning. Road network matching that determines the correspondence between the features of road network data from different sources serves this purpose. It also provides technical support for tasks such as the quality assessment of crowdsourced road network data, which has attracted a lot of attention in the field of geographic information. However, traditional road network matching methods mainly measure the similarity of road network structure through the geometric and topological attributes of road network data to determine the matching relationship of road network elements. Such methods with manually designed features and thresholds are easily limited by experts' experience, which degrades their performance under complex road network structures. In recent years, road network data modeling based on graph neural networks has become a research hotspot and has achieved excellent performance in several road network modeling tasks. However, most of the existing methods use direct neighborhood aggregation on the graph topology to learn the embedded representation of the road network structure, without considering the spatial relationship of road network features in this key step, and failing to make full use of the representation learning capability of graph neural networks. For this reason, this study proposes an improved neighborhood aggregation that performs a spatially explicit graph-based embedding learning method for road network matching. First, a road graph model of the road network data is constructed, and geometric, semantic, and location features are extracted. Then, based on the GraphSAGE framework, three kinds of neighborhood aggregation operators (i.e., spatial, classified, and hybrid) are proposed, and the computation of spatial relationships and attribute types of road network features is introduced in the neighborhood aggregation operations. Finally, the similarity of graph node embedding is utilized to determine the matching relationship of road network features. To verify the effectiveness of the proposed method, extensive experiments are carried out using real-world road network data. The proposed method achieves the optimal performance in all metrics on the test data of the study region, which improves the matching correctness rate by more than 11% and the recall rate by more than 6.8% compared to the baseline graph neural network method. Furthermore, the road network graph embedding features are analyzed from the aspects of graph embedding structure and embedded road network structure, which helps explore the role of improved neighborhood aggregation on the graph embedding representation capability and provides a new perspective for further improving the graph neural network road network modeling.

In the transition of urban taxi fleets toward electrification in large cities, the charging demand of taxis exhibits characteristics of high charging load and strong spatiotemporal randomness, with a noticeable mismatch between charging supply and demand in time and space. To accurately estimate the potential charging demand post- the full electrification of the taxi fleet, this study introduces a bottom-up conceptual framework and a binary tree algorithm based on trajectory map matching, leveraging fuel taxi trajectory data exclusively. This approach provides a new paradigm for estimating charging demands in regions lagging behind in the electrification transition. The model and algorithm are validated using trajectory data from 890 electric taxis with battery status fields (State of Charge, SOC). Results show that the estimation errors of indicators such as the number of charging segments and charging amount are less than 6.5%. Moreover, the model also exhibits high spatiotemporal distribution estimation accuracy under different parameter settings (battery depletion threshold θ) and spatial scales (with grid sizes of 500 m, 1 000 m, and 10 000 m), ensuring their applicability in real-world scenarios. Specifically, the temporal distribution error of charging amount is less than 8.5% in the best-case scenario, and over half of the charging amount within 500 m grids has a spatial distribution error less than 0.3, with 59% of the 500 m grids having an estimated error of charging segment count less than 0.3. Building upon this, the Singular Value Decomposition (SVD) algorithm is used to decompose and reduce the dimensionality of the spatiotemporal matrix of charging demands, identifying spatiotemporal patterns of potential charging demands within the real road network of Beijing's Sixth Ring Road at road level. Finally, a case study is conducted using trajectory data from 1 913 taxis in Beijing over three consecutive days from March 9th (Monday) to March 11th (Wednesday) in 2019, and the results indicate that the spatial distribution of potential charging demands for taxis in Beijing exhibits prominent clustering features in key areas and critical corridors, corresponding to high-density charging demands associated with residents' high activity levels and long-distance travel. The decomposed charging demands reveal a spatiotemporal structural pattern dominated by regular charging demands, with supplementary heterogeneity in charging demands between morning and afternoon, as well as during working and non-working hours. This analysis method assists in uncovering the spatial distribution structural characteristics of potential charging demand and spatiotemporal coupling relationships, providing decision-making references for long-term planning of charging infrastructure, grid load scheduling, and charging demand management in the electrification transformation of taxi fleets.

The lightweighting of three-dimensional models is a pressing need in the construction of realistic three-dimensional environments in China. Simplifying three-dimensional models to a certain extent is a suitable approach. In the realm of model simplification algorithms, the Quadric Error Metrics (QEM) algorithm is considered a classic method. However, traditional QEM algorithms lack a dedicated mechanism to preserve crucial details during the simplification process, and the post-simplification mesh quality needs further optimization. To address these challenges, this paper proposes a real-world three-dimensional model simplification algorithm based on edge collapsing. The aim is to better preserve key details and enhance the mesh quality of the simplified model in the process of three-dimensional model lightweighting. The simplification algorithm in this method introduces vertex approximation curvature and volume error as constraints, altering the edge collapsing cost to maintain important details while achieving effective simplification. By incorporating boundary preservation conditions while modifying edge collapsing costs, the algorithm effectively safeguards the model's boundaries. Finally, post-simplification mesh optimization is performed using the Laplacian mesh optimization method to optimize triangle shapes and preserve features. Experiments were conducted to verify the proposed algorithm and compare it with the traditional QEM algorithm. The research results indicate that our proposed method retains regions with rich details better than the traditional QEM algorithm, significantly reducing the number of elongated triangles and improving mesh quality. This algorithm is applicable to the lightweighting of real-world three-dimensional models, contributing to enhanced model performance and visualization effects.

The short-term prediction of urban traffic states serves as a fundamental application supporting traffic management and online navigation. Floating car trajectory data, characterized by low cost and high spatio-temporal coverage, has been widely employed for real-time traffic state extraction, thereby facilitating short-term traffic prediction. However, the spatio-temporal coverage of floating car trajectories is extremely uneven, resulting in a significant amount of missing or insufficient coverage of road networks over many time periods. It is difficult to accurately estimate the traffic states of all segments of the entire road network directly using the trajectory data of floating cars. The accuracy and reliability cannot meet the needs of real-time traffic state estimation and short-term prediction. This issue therefore poses a significant technical challenge for online short-term prediction of traffic states based on uneven trajectory data across the entire road network in large cities, hindering the refinement of traffic monitoring and management. To address the issue of uneven spatiotemporal distribution in trajectory data, this study proposes a dynamic layering method for the road network, dividing it into multiple layers based on the spatio-temporal distribution of trajectories, including a main road network with high-quality trajectories and other secondary road networks with sparse trajectory distributions. Building upon the layered road network, we propose a trajectory-driven multi-level spatio-temporal graph neural network method for short-term traffic state prediction. Leveraging the hierarchical structure of road networks, the proposed prediction method incorporates intra- and inter-layer message passing mechanisms that consider the spatio-temporal distribution of trajectories. We apply dilated causal convolution and graph attentional mechanisms to describe the complex spatio-temporal correlations of traffic states among road networks. Based on the correlation representation, we develop a unified graph neural network prediction model that integrates "representation" and "prediction" into an end-to-end learning scheme. The proposed method enables online prediction of speed and congestion state of all road segment in the road network simultaneously, significantly enhancing the predictive performance of traffic state for road segments with sparse trajectories. Through testing on real trajectory data from the large road network of Wuhan City, the proposed method showcases a notable improvement in prediction accuracy compared to recent popular baseline methods, particularly achieving satisfactory performance in segments with severe trajectory data deficiencies. The training efficiency is also significantly improved. Experimental results indicate that the proposed multi-level spatio-temporal graph neural network prediction method can effectively handle the prediction challenges caused by the uneven distribution of trajectories.

Simultaneous Localization and Mapping (SLAM) based on Light Detection and Ranging (LiDAR) is one of the core technologies widely used in the field of indoor and outdoor integrated positioning and spatial data acquisition. It has the advantage of strong anti-interference without considering light conditions. However, in degenerate environments lacking geometric features, the traditional LiDAR odometry based on feature registration algorithm or Iterative Closest Point (ICP) algorithm usually produce inaccurate results. To address the above problem, this paper proposes a LiDAR odometer positioning method for degenerate environments using modified laser intensity and an accurate LiDAR degenerate environment detection method. Firstly, an intensity correction method is proposed based on laser intensity properties and the elimination idea, and an unsupervised position correction method is used to jointly optimize the original point cloud. Based on this, four point cloud features are extracted, and the fuzzy comprehensive evaluation method is used to accurately detect the degradation of each frame point cloud. For well-conditioned point clouds, line and surface features are extracted to construct the interest point set. For degenerate point clouds, a " MI word search " method is proposed to extract well-conditioned features based on laser intensity, and the interest point set is constructed with effective line and surface features. Finally, the robust LiDAR odometry is constructed by processing the inter-frame interest point sets through suitable registration algorithms. The experimental results show that the correct rate of laser intensity correction is 93.34 %. The correct detection rate of LiDAR degenerate point cloud is 98.58 %, and the error detection rate of well-conditioned point cloud is only 2.24 %. Compared with the LiDAR odometry based on feature registration algorithm and ICP algorithm, the positioning Root Mean Square Error (RMSE) of the LiDAR odometry positioning method proposed in this paper is reduced by 90.00% and 83.96%, respectively, and the Maximum Error (MAXE) is reduced by 86.23 % and 79.07 %, respectively. The time required for single inter-frame registration is 0.006 9 s, which effectively improves the positioning accuracy of traditional LiDAR odometry in degenerate environment while maintaining data processing efficiency. Compared with the other methods in the same field, our method shows more significant advantages. In addition, since the method in this paper is completely innovated and improved on the basis of traditional LiDAR odometer, it has a low overall complexity and significant advantages in terms of secondary development and portability.

The road network has a distinct spatial distribution pattern, and its pattern recognition plays a crucial role in many fields such as mapping, map matching, and spatial queries. The current road network pattern recognition methods are mostly based on two models: mesh structure and road segment structure, which have achieved good recognition results, but also have certain limitations. Existing mesh based recognition algorithms are often limited to the grid pattern of the smallest unit, and cannot recognize patterns of globally regular and locally fragmented mesh groups; Pattern recognition based on road segment structure involves complex preprocessing and recognition processes, and can only recognize a single road network pattern. As a branch of information entropy, directional entropy can effectively describe the spatial distribution characteristics and patterns of geographic data. Therefore, this article combines the recognition advantages of mesh structure in the case of regular mesh and the advantages of road section structure in analyzing the arrangement of road sections. Based on this, feature parameters such as directional entropy, rectangularity, and concavity are introduced to construct an effective road network pattern recognition algorithm. This article selects the road networks of Wuhan, Shanghai, Nanjing, and Xi'an as experimental objects for sample construction. The sample sets of Nanjing and Xi'an are used to calculate the parameter classification thresholds corresponding to different road network modes and determine the road network pattern recognition rules. The sample sets of Wuhan and Shanghai are used to verify the feasibility and transferability of the algorithm proposed in this article. This method not only completes the pattern recognition of grid networks and irregular road networks, but also divides them into linear mesh groups and grid mesh groups based on the different arrangement of mesh groups in grid networks by constructing a minimum mesh centroid spanning tree and using directional entropy. The experimental results show that the pattern classification accuracy of the road network pattern recognition algorithm combining road segments and mesh structures reaches over 97%, which can effectively complete the pattern recognition of grid networks and irregular road networks, and can well subdivide grid patterns according to the arrangement of mesh groups in the grid network. The algorithm in this paper has good transferability and the recognition results are consistent with human cognition. Compared with existing road network pattern recognition methods, this paper constructs a simple, fast, and relatively accurate road network pattern recognition algorithm, providing a new approach for subsequent research in fields such as map synthesis, pattern recognition, and urban planning.

The rapid growth in the scale and complexity of spatiotemporal trajectory data in the era of big data presents significant challenges for efficient data management and querying. Graph databases, with their inherent advantages in modeling, storing, and managing complex relationships, have emerged as a powerful tool for handling spatiotemporal trajectory data. However, as the scale of road network spatiotemporal trajectory data continues to increase, the query performance of graph databases tends to decline due to the extensive node traversal required. To address this challenge, this paper proposes a novel method for road network spatiotemporal trajectory modeling and efficient indexing within a graph database framework. The proposed method employs the Compressed Linear Reference (CLR) model to represent road network spatiotemporal trajectories. This model is specifically designed to compress and streamline the representation of trajectory data, making it more manageable within large-scale datasets. The CLR model is implemented within a graph database, where a three-layer spatiotemporal indexing structure has been designed. This structure consists of three key components: a road network spatial index, a temporal index, and a spatiotemporal path segment index. The road network spatial index is used to index the underlying road network segments, while the temporal index and the spatiotemporal path segment index handle the temporal and combined spatiotemporal aspects of trajectory data. This integrated indexing structure is designed to minimize the need for extensive node traversal during query execution, significantly improving query efficiency. In addition to the indexing structure, two spatiotemporal query methods have been developed that leverage this efficient index. These methods are tailored to meet the requirements of different application scenarios, such as identifying intersections between spatiotemporal paths and performing spatiotemporal window queries to retrieve relevant trajectory segments. To validate the effectiveness of the proposed method, extensive experiments were conducted using artificially synthesized road network spatiotemporal trajectory data based on the road network of Wuhan. The Nebula Graph database was selected as the platform for managing and storing the spatiotemporal trajectory data, and the proposed indexing and query methods were implemented within this environment. The performance of our approach was tested across datasets of varying scales to evaluate scalability and efficiency. The experimental results demonstrated that the efficient spatiotemporal index significantly outperforms the native indexing mechanisms of Nebula Graph. Specifically, the method improved the performance of spatiotemporal window-path intersection queries by a factor of at least 16.59 and enhanced spatiotemporal path-path intersection queries by a factor of at least 2.74 compared to the baseline performance. These results highlight the substantial improvements in query performance achieved by this method.

Satellite video can quickly provide city-level information on vehicle dynamics, providing reliable data for urban informatics, smart cities, and urban development planning. However, the image background captured by current video satellites contains a large amount of noise and pseudo-motion targets due to perspective shifts. The moving vehicles are presented as dark, weak, and tiny spots in the on-board video, which makes it very difficult to accurately detect the moving vehicles. In this paper, we propose a motion vehicle detection method using tracking information in the on-board video. The method first extracts the road mask in the first frame of the video using the D-LinkNet to eliminate the interference of noise and pseudo-motion targets in the background. Then the foreground detection algorithm is used for the preliminary detection of moving vehicles in the road mask. The preliminary detection results are used for the inter-frame tracking of moving vehicles. Finally, the tracking information-based regional fine detection is performed for the moving vehicle targets that failed to be tracked due to omission of detection, to obtain highly accurate moving vehicle targets. In order to verify the effectiveness of the proposed method, we accurately reannotate the existing dataset by supplementing the missing targets and adding 2 120 new moving vehicle targets in the dataset. Based on the reannotated dataset and two study areas with Jilin-1 video data, the proposed method is compared with a bunch of existing methods using different indexes. The results show that: (1) the proposed detection method can significantly improve the recall rate of on-board video motion vehicle detection and obtains the highest F-value in all tests; (2) by evaluating the step-by-step performance of the proposed method, we found that the proposed method's mask has the effect of eliminating the false detections in the background region and the tracking information has the effect of facilitating the redetection of omitted targets; and (3) in the test of the tracking phase, the proposed method obtains higher multi-target tracking accuracy and tracker identity maintenance capability. Therefore, the method proposed in this paper has good robustness in detection of moving vehicles from satellite video and provides a new method and idea for the acquisition of vehicle dynamic information at the city level.