Wuhan encountered a serious attack in the first round of the coronavirus disease 2019 (COVID-19) pandemic, which has resulted in a public health social impact, including public mental health. Based on the Weibo help data, we inferred the spatial distribution pattern of the epidemic situation and its impacts. Seven urban factors, i.e., urban growth, general hospital, commercial facilities, subway station, land-use mixture, aging ratio, and road density, were selected for validation with the ordinary linear model, in which the former six factors presented a globally significant association with epidemic severity. Then, the geographically weighted regression model (GWR) was adopted to identify their unevenly distributed effects in the urban space. Among the six factors, the distribution and density of major hospitals exerted significant effects on epidemic situation. Commercial facilities appear to be the most prevalently distributed significant factor on epidemic situation over the city. Urban growth, in particular the newly developed residential quarters with high-rise buildings around the waterfront area of Hanyang and Wuchang, face greater risk of the distribution. The influence of subway stations concentrates at the adjacency place where the three towns meet and some near-terminal locations. The aging ratio of the community dominantly affects the hinterland of Hankou to a broader extent than other areas in the city. Upon discovering the result, a series of managerial implications that coordinate various urban factors were proposed. This research may contribute toward developing specific planning and design responses for different areas in the city based on a better understanding of the occurrence, transmission, and diffusion of the COVID-19 epidemic in the metropolitan area.

It is important to assess environmental impact of intelligent transportation systems, and hence developing a vehicle emission model with high accuracy has been a long-standing topic in transportation research. However, current vehicle emission models are either overly simple using average speed, resulting in low estimation accuracy, or they are too complicated requiring excessive inputs, relying on too much prior knowledge. In this study, we develop and evaluate a deep learning-based vehicle emission model (DL-VEM) to estimate the instantaneous CO2 emissions of taxicabs. First, we examine the correlation between observed emissions and vehicle driving condition data collected in a PEMS experiment. Then, an end-to-end deep learning structure is developed to model patterns of vehicle emissions. Specifically, LSTM networks are used to learn temporal dependencies of historical driving patterns, and fully connected networks are employed to extract deep features of current driving behaviors and external environment. Our model aggregates the outputs of these networks using different learnable weights. Experiments were conducted in Wuhan, China, where our model was trained and validated using observed datasets. Compared with the state-of-the-art models, our model achieved higher accuracy in estimating CO2 emissions. Thereafter, it was applied to a taxicab trajectory dataset in one day, and spatiotemporal patterns of CO2 emissions were presented using different fuel types. Importantly, we find that an increment of 24.94% emissions can be expected if petrol instead of compressed natural gas was used by each taxicab in Wuhan.

Human activity hotspots are the clusters of activity locations in space and time, and a better understanding of their functionality would be useful for urban land use planning and transportation. In this article, using trajectory data, we aim to infer the functionality of human activity hotspots from their scaling pattern in a reliable way. Specifically, a large number of stopping locations are extracted from trajectory data, which are then aggregated into activity hotspots. Activity hotspots are found to display scaling patterns in terms of the sublinear scaling relationships between the number of stopping locations and the number of points of interest (POIs), which indicates economies of scale of human interactions with urban land use. Importantly, this scaling pattern remains stable over time. This finding inspires us to devise an allometric ruler to identify the activity hotspots, whose functionality could be reliably estimated using the stopping locations. Thereafter, a novel Bayesian inference model is proposed to infer their urban functionality, which examines the spatial and temporal information of stopping locations covering 75 days. Experimental results suggest that the functionality of identified activity hotspots are reliably inferred by stopping locations, such as the railway station.

Wuhan encountered a serious attack in the first round of the COVID-19 pandemic which has resulted in serious worldwide consequences politically and economically. Based on the Weibo help data, we inferred the spatial distribution pattern of the epidemic situation and its impacts. Seven urban factors, i.e. urban growth, general hospital, commercial facilities, subway station, landuse mixture, aging ratio, and road density, were selected for validation with the ordinary linear model, and the former six presented globally significant association with the epidemic severity; thereafter, the geographically weighted regression model was further adopted for local test to identify their unevenly distributed effects in urban space. Among the six, the place where general hospitals exert effects on epidemic situation highly is associated with their distribution and density; commercial facilities appear the most prevalently distributed factor over the city; newly developed residential quarters with high-rise buildings face greater risks, mainly distributed around the waterfront area of Hanyang and Wuchang; the influence of subway stations concentrates at the adjacency place where the three towns meet and near-terminal locations; aging ratio dominantly affects the hinterland of Hankou in a broader extent than other areas in the city. Upon the result, a series of managerial implications that coordinate various urban factors have been proposed. This research is conductive to developing specific planning and design responses for different areas in the city based on a better understanding of the occurrence, transmission, and diffusion of the COVID-19 epidemic in the metropolitan area.

Prediction of short-term traffic flow has been examined recently, but little attention has been paid to the prediction of citywide turning traffic flow at intersections. Based on an in-depth analysis of turning traffic flow patterns, we propose a novel attention-based spatiotemporal deep learning model to predict citywide short-term turning traffic flow at road intersections with high accuracy. First, we examine the spatiotemporal patterns of turning traffic flow. Then, an end-to-end deep learning structure with four components is designed to model turning traffic flow. In our model, graph convolutional network is revised to learn spatial dependencies and sparseness, and gate recurrent unit network with an attention mechanism is developed to learn temporal dependencies and fluctuations. Experiments were conducted in Wuhan, China, where taxicab trajectory data were used to train and validate our model. The results suggest that our model outperforms current state-of-the-art models with higher accuracy on estimating turning traffic flow.