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Low-sampling-rate floating car data (FCD) are more challenging than those with high-sampling-rate FCD for map matching (MM) algorithms. Some MM algorithms for low-sampling-rate FCD lack sufficient efficiency nor accuracy, especially related to complex urban road networks. This paper proposes a new method named the trajectory restoration algorithm, which is based on geometry MM algorithms to ensure efficiency and accuracy. The proposed algorithm adopts the modified A* shortest path algorithm to reduce the number of function calls and fully considers road network topology and historical matched points to improve its accuracy. We test the efficiency and accuracy of the trajectory restoration algorithm with FCD data for the complex urban road networks in Beijing. The results have strong continuity which greatly improves the utilization of FCD. We show that the proposed algorithm outperforms related MM methods in efficiency and accuracy and its robustness to restore trajectories of both high and low sampling rates in complex urban road networks. [ABSTRACT FROM AUTHOR]

Geographical flow (hereafter flow) can be modeled as an orderly connected point pair composed of an origin (O) and a destination (D). Aggregation is the most common form of spatial heterogeneity of flows, which we define as their deviation from complete spatial randomness (CSR), and the aggregation scale is an important indicator for its perception. Nevertheless, quantifying the aggregation scale of flows is still an unsolved problem. In this paper, we propose the L-function for flows as a solution, derive theoretical null models of the K-function and L-function in a flow space. We conduct simulation experiments to validate the L-function and its capability to detect aggregation scales. Finally, we apply the solution in a case study with taxi data in Beijing and identify nine aggregation scales of taxi OD flows, ranging from 170 m to 22.1 km. These scales correspond to three classes: less than 300 m, from 600 m to 700 m and more than 1500 m. The classes are related to the sizes of the urban facilities where the dominant flow clusters occur, indicating that the L-function in flow space can detect the aggregation scale of flows at the building scale, the block scale and the district scale. [ABSTRACT FROM AUTHOR]

Studies on the riding modes of shared bicycles have aimed to heighten the understanding of cycling characteristics. This paper analyzes the spatiotemporal riding modes of shared bicycles based on tensor decomposition in Beijing, China. Two third-order tensors are constructed for the origin and destination points of shared bicycles in the day, hour, and space dimensions. Three factor matrices explicitly reveal two modes, three modes, and six modes in the day dimension, hour dimension, and space dimension, respectively. The relationships among the different modes in the three dimensions are demonstrated in an interaction table. Further, the density for different types of points of interest (POIs) are calculated to further analyze the potential riding purpose for different riding modes. Notably, the main POI types for the areas of O2 and D2 modes are consistent with the areas of D3 and O3 modes, which reflects the tidal characteristics of the commuting activities of shared bicycles. The main functional areas are inferred according to the riding modes and POIs, which enables verification of the correctness of the obtained riding modes to some extent. By method comparison, tensor decomposition shows the advantage of being able to reveal the spatiotemporal modes among multiple dimensions. [ABSTRACT FROM AUTHOR]

Regionalization attempts to group units into a few subsets to partition the entire area. The results represent the underlying spatial structure and facilitate decision-making. Massive amounts of trajectories produced in the urban space provide a new opportunity for regionalization from human mobility. This paper proposes and applies a novel regionalization method to cluster similar areal units and visualize the spatial structure by considering all trajectories in an area into a word embedding model. In this model, nodes in a trajectory are regarded as words in a sentence, and nodes can be clustered in the feature space. The result depicts the underlying socio-economic structure at multiple spatial scales. To our knowledge, this is the first regionalization method from trajectories with natural language processing technology. A case study of mobile phone trajectory data in Beijing is used to validate our method, and then we evaluate its performance by predicting the next location of an individual's trajectory. The case study indicates that the method is fast, flexible and scalable to large trajectory datasets, and moreover, represents the structure of trajectory more effectively. [ABSTRACT FROM AUTHOR]