Visual exploration of urban functions via spatio-temporal taxi OD data

Visual analytics of route recommendation for tourist evacuation based on graph neural network

The overcrowding of scenic spots not only threatens tourists' safety but also affects the travel experience. Traditional methods for addressing tourist overload have involved limited access and guided evacuation. While limited access has been effective, it often results in a diminished tourist experience. Moreover, the existing guided evacuation rarely considers the impact on tourists' experience, resulting in a low willingness to cooperate and making it difficult to estimate evacuation effort efficiency. To solve these problems, this paper proposed a tourist evacuation route recommendation algorithm based on a graph neural network considering the similarity of tourism styles (PER-GCN) and designed a visualization system to simulate and analyse evacuation efficiency. First, the interaction matrix of tourists and scenic spots was constructed using graph mining to extract the high-order interaction information. In the output layer, the similarity between scenic spots and tourism styles was calculated to further improve the accuracy of scenic spot recommendations. Second, due to route complexity and the real-time carrying capacity of scenic spots, the researchers optimized the evacuation routes. Finally, taking the West Lake spot as the case study, the effectiveness of PER-GCN was verified. Additionally, a visualization system was designed to monitor tourist flow in real time and analyse tourist portraits according to the clustering results of scenic spot styles. In addition, the evacuation efficiency of scenic spots was analysed by adjusting the parameters of tourists' willingness to cooperate, evacuation batch, and the weight of route complexity and scenic spot carrying capacity.

Unfolding Spatial-Temporal Patterns of Taxi Trip based on an Improved Network Kernel Density Estimation

Taxi mobility data plays an important role in understanding urban mobility in the context of urban traffic. Specifically, the taxi is an important part of urban transportation, and taxi trips reflect human behaviors and mobility patterns, allowing us to identify the spatial variety of such patterns. Although taxi trips are generated in the form of network flows, previous works have rarely considered network flow patterns in the analysis of taxi mobility data; Instead, most works focused on point patterns or trip patterns, which may provide an incomplete snapshot. In this work, we propose a novel approach to explore the spatial-temporal patterns of taxi travel by considering point, trip and network flow patterns in a simultaneous fashion. Within this approach, an improved network kernel density estimation (imNKDE) method is first developed to estimate the density of taxi trip pick-up and drop-off points (ODs). Next, the correlation between taxi service activities (i.e., ODs) and land-use is examined. Then, the trip patterns of taxi trips and its corresponding routes are analyzed to reveal the correlation between trips and road structure. Finally, network flow analysis for taxi trip among areas of varying land-use types at different times are performed to discover spatial and temporal taxi trip ODs from a new perspective. A case study in the city of Shenzhen, China, is thoroughly presented and discussed for illustrative purposes.

Identification of urban functions enhancement and weakening based on urban land use conversion: A case study of Changchun, China

A deep understanding of urban functions is the basis for the optimization of urban spatial structure and for the sustainable development of urban space. Previous research mainly focused on the identification and classification of urban functions using a variety of data and methods; however, little attention has been paid to the quantitative measurement of changes in urban functions, and to their temporal and spatial evolution. With the help of urban land use maps, we used the overlay analysis and the transition matrix to quantitatively measure the enhancement and weakening of urban functions and their spatial differences, by analyzing the relationship between urban land development and redevelopment. Accordingly, the change state of urban function enhancement and weakening could be divided into six categories. Moreover, the results of research on residential, commercial, public service, and industrial functions show that changes in different urban functions have different temporal and spatial characteristics. Our findings on the enhancement and weakening of urban functions in the central city of Changchun provide theoretical support for the sustainable development of urban space in the future.

Integrating Geovisual Analytics with Machine Learning for Human Mobility Pattern Discovery

Understanding human movement patterns is of fundamental importance in transportation planning and management. We propose to examine complex public transit travel patterns over a large-scale transit network, which is challenging since it involves thousands of transit passengers and massive data from heterogeneous sources. Additionally, efficient representation and visualization of discovered travel patterns is difficult given a large number of transit trips. To address these challenges, this study leverages advanced machine learning methods to identify time-varying mobility patterns based on smart card data and other urban data. The proposed approach delivers a comprehensive solution to pre-process, analyze, and visualize complex public transit travel patterns. This approach first fuses smart card data with other urban data to reconstruct original transit trips. We use two machine learning methods, including a clustering algorithm to extract transit corridors to represent primary mobility connections between different regions and a graph-embedding algorithm to discover hierarchical mobility community structures. We also devise compact and effective multi-scale visualization forms to represent the discovered travel behavior dynamics. An interactive web-based mapping prototype is developed to integrate advanced machine learning methods with specific visualizations to characterize transit travel behavior patterns and to enable visual exploration of transit mobility patterns at different scales and resolutions over space and time. The proposed approach is evaluated using multi-source big transit data (e.g., smart card data, transit network data, and bus trajectory data) collected in Shenzhen City, China. Evaluation of our prototype demonstrates that the proposed visual analytics approach offers a scalable and effective solution for discovering meaningful travel patterns across large metropolitan areas.

Green Travel Mode: Trajectory Data Cleansing Method for Shared Electric Bicycles

Location-based service (LBS) technologies provide a new perspective for the analysis of the spatiotemporal dynamics of urban systems. Previous studies have been performed using data from mobile communications, public transport vehicles (taxis and buses), wireless hotspots and shared bicycles. However, corresponding analyses based on shared electric bicycle (e-bike) have not yet been reported in the literature. Data cleaning and extraction of the origin-destination (O-D) are prerequisites for the study of the spatiotemporal patterns of urban systems. In this study, based on a dataset of a week of shared e-bike GPS data in the city of Tengzhou (Shandong Province), sparse characteristics of discontinuities and nonuniformities of the GPS trajectory and a lack of riding status are observed. Based on the characteristics and the actual road, we proposed a method for the extraction of O-D pairs for every trajectory segment from continuous and stateless trajectory GPS data. This method cleans the incomplete and invalid trajectory records, which is suitable for sparse trajectory data. A week of shared e-bike GPS data in Tengzhou is scrubbed and, by the sampling method, the extraction accuracy of 91% is verified. We provide preliminary cleaning rules for sparse trajectory shared e-bike data for the first time, which are highly reliable and suitable for data mining from other forms of sparse GPS trajectory data.