Search

Describing travel patterns and identifying significant locations is a crucial area of research in transportation geography and social dynamics. Our study aims to contribute to this field by analyzing taxi trip data from Chengdu and New York City. Specifically, we investigate the probability density distribution of trip distance in each city, which enables us to construct long- and short-distance trip networks. To identify critical nodes within these networks, we employ the PageRank algorithm and categorize them using centrality and participation indices. Furthermore, we explore the factors that contribute to their influence and observe a clear hierarchical multi-centre structure in Chengdu's trip networks, while no such phenomenon is evident in New York City's. Our study provides insight into the impact of trip distance on important nodes within trip networks in both cities and serves as a reference for distinguishing between long and short taxi trips. Our findings also reveal substantial differences in network structures between the two cities, highlighting the nuanced relationship between network structure and socio-economic factors. Ultimately, our research sheds light on the underlying mechanisms shaping transportation networks in urban areas and offers valuable insights into urban planning and policy making. [ABSTRACT FROM AUTHOR]

This is a retrospective analysis. Quantitative prediction of the children's and adolescents' spherical equivalent based on their variable-length historical vision records. From October 2019 to March 2022, we examined uncorrected visual acuity, sphere, astigmatism, axis, corneal curvature and axial length of 75,172 eyes from 37,586 children and adolescents aged 6–20 years in Chengdu, China. 80% samples consist of the training set, the 10% form the validation set and the remaining 10% form the testing set. Time-Aware Long Short-Term Memory was used to quantitatively predict the children's and adolescents' spherical equivalent within two and a half years. The mean absolute prediction error on the testing set was 0.103 ± 0.140 (D) for spherical equivalent, ranging from 0.040 ± 0.050 (D) to 0.187 ± 0.168 (D) if we consider different lengths of historical records and different prediction durations. Time-Aware Long Short-Term Memory was applied to captured the temporal features in irregularly sampled time series, which is more in line with the characteristics of real data and thus has higher applicability, and helps to identify the progression of myopia earlier. The overall error 0.103 (D) is much smaller than the criterion for clinically acceptable prediction, say 0.75 (D). [ABSTRACT FROM AUTHOR]

The outbreak of Coronavirus disease 2019 (COVID-19) led to the widespread stagnation of urban activities, resulting in a significant reduction in industrial pollution and traffic pollution. This affected how urban form influences air quality. This study reconsiders the influence of urban form on air quality in five urban agglomerations in China during the pandemic period. The random forest algorithm was used to quantitate the urban form–air quality relationship. The urban form was described by urban size, shape, fragmentation, compactness, and sprawl. Air quality was evaluated by the Air Quality Index (AQI) and the concentration of six pollutants (CO, O 3 , NO 2 , PM 2.5 , PM 10 , SO 2). The results showed that urban fragmentation is the most important factor affecting air quality and the concentration of the six pollutants. Additionally, the relationship between urban form and air quality varies in different urban agglomerations. By analyzing the extremely important indicators affecting air pollution, the urban form–air quality relationship in Beijing-Tianjin-Hebei is rather complex. In the Chengdu-Chongqing and the Pearl River Delta, urban sprawl and urban compactness are extremely important indicators for some air pollutants, respectively. Furthermore, urban shape ranks first for some air pollutants both in the Triangle of Central China and the Yangtze River Delta. Based on the robustness test, the performance of the random forest model is better than that of the multiple linear regression (MLR) model and the extreme gradient boosting (XGBoost) model. • The effect of urban form on air quality was reconsidered during the COVID-19 period. • Urban fragmentation was the extreme important factor affecting air quality. • The urban form-air quality relationship varied in China's five urban agglomerations. [ABSTRACT FROM AUTHOR]

Urban livability and sustainability require a deeper understanding of the impact of urban spatial structure on human mobility. Here two mobility networks generated from two taxi data from two cities (Chengdu and New York City) are used to explore human intra-urban mobility. Through the network analysis, we found that many measures of two mobility networks present a two-segment feature. Combined with the map and population distribution, we demonstrate that the two segments correspond to the central and non-central regions of the city, respectively, which means hierarchical mobility related to urban-rural structure exists in both cities, although they have different levels of urbanization. We comprehensively analyze the impact of urban-rural spatial structure on the nature of mobility networks. The central and non-central regions differ significantly in the heterogeneity of human mobility and present different ranges and directions of trips, like the large degree nodes, showing a flared entry and exit pattern. We improved a gravity model by introducing flows to and from the node instead of population to predict the traffic between different nodes, and the prediction results also vary significantly between the two regions. These findings are valuable for understanding the relationship between urban-rural spatial structure and human mobility. • By establishing the dependence relationship between network structure and urban spatial structure, we explore the effect of urban-rural spatial structure on mobility network structure. • Based on the segmentation form of network metrics, we found a hierarchy of mobility between central and non-central regions of the city. • We found an asymmetry in the spatial extent of incoming and outgoing flows at central region and no-central regions, with high-traffic nodes showing a flared entry and exit pattern. • We improved a gravity model by introducing flows to and from the node instead of population to predict traffic more accurately between regions within the city. [ABSTRACT FROM AUTHOR]

As an essential mode of travel for city residents, taxis play a significant role in meeting travel demands in an urban city. Understanding the modal characteristics of taxis is vital to addressing many difficulties regarding urban sustainability. The movement trajectory of taxis reflects not only the operating features of taxis themselves but also urban structure and human mobility. In this work, the taxi trajectory data of Chengdu and New York City is processed, and the corresponding urban trip networks are constructed based on geographic information systems. We empirically and systematically analyze these urban trip networks according to the network hierarchy based on complex network theory. First, we studied the low-order organization of the urban trip networks (i.e., degree distribution, cluster-degree coefficient, rich-club coefficient, and so on.). We uncover the nontrivial relationship between network density and trip distance and find that the urban trip network in Chengdu is more heterogeneous than that in New York City. Second, we investigate the meso-order organization of the urban trip networks by using community detection. The community detection results show that the community boundaries are more or less mismatched with the administrative boundaries. Finally, we detect the higher-order organizations of the urban trip networks and find some critical nodes and regions. These empirical results from the perspective of complex networks provide insight to better understand the urban structure and human mobility, and potentially amend urban planning. • Urban trip networks are constructed based on the taxi trajectory data. • We explore the topological characteristics of urban trip network according to its hierarchy. • These trip networks show the commonalities and non-commonalities of topological characteristics. • These trip networks reflect the differences of urban structure and human mobility. [ABSTRACT FROM AUTHOR]