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With the advent of the information age and rapid population growth, the urban transportation environment is deteriorating. Travel-route planning is a key issue in modern sustainable transportation systems. When conducting route planning, identifying the spatiotemporal disparities between planned routes and the routes chosen by actual drivers, as well as their underlying reasons, is an important method for optimizing route planning. In this study, we explore the spatial–temporal differences between planned routes and actual routes by studying the popular roads which are avoided by drivers (denoted as: PRAD) from car-hailing trajectories. By applying an improved Hidden Markov Model (HMM) map-matching algorithm to the original trajectories, we obtain the Origin-Destination (OD) matrix of vehicle travel and its corresponding actual routes, as well as the planned routes generated by the A* routing algorithm. We utilize the Jaccard index to quantify the similarity between actual and planned routes for the same OD pairs. The causes of PRADs are detected and further analyzed from the perspective of traffic conditions. By analyzing ride-hailing trajectories provided by DiDi, we examine the route behavior of drivers in Wuhan city on weekdays and weekends and discuss the relationship between traffic conditions and PRADs. The results indicate that the average accuracy of GNSS trajectory point-to-road map-matching reaches 88.83%, which is approximately 12% higher than the accuracy achieved by the HMM map-matching method proposed by Hu et al. Furthermore, the analysis of PRAD causes reveals that PRADs occurring on weekdays account for approximately 65% and are significantly associated with traffic congestion and accidents during that time. The findings of this study provide insights for future research on sustainable transportation systems and contribute to the development of improved route-planning strategies. [ABSTRACT FROM AUTHOR]

Points‐of‐interest (POIs) geographic information system data are increasingly important for supporting map generation and navigation services, although updating their semantic and location information still largely depends on manual labor. In this study, we propose a novel method to automatically detect the changes in POIs from Chinese text and check‐in position data provided by the Chinese social media platform, Weibo. The proposed method includes three steps: (1) POI name recognition; (2) location confirmation; (3) and change detection. First, we propose recognizing a POI's name from Weibo text using the improved conditional random field algorithm. Then, we detect the location of each named POI by integrating the text address with the check‐in position. The changes in the detected POIs are recognized by extracting the status words from Weibo text and a three‐level status word database. To verify the effectiveness of the proposed method, we examine Wuhan as a case and detect the changes in the commercial POI using real‐world Weibo data collected from January to September 2020. Based on the validation of three common map platforms, the data provided and the manual field investigation of 55 random samples, the identification accuracies for newly added POIs, the unchanged POIs, and expired POIs are approximately 100, 95.8, and 91.7%, respectively. [ABSTRACT FROM AUTHOR]

The first reported case of coronavirus disease 2019 (COVID-19) occurred in Wuhan, Hubei, China. Thereafter, it spread through China and worldwide in only a few months, reaching a pandemic level. It can cause severe respiratory illnesses such as pneumonia and lung failure. Since the onset of the disease, the rapid response and intervention of traditional Chinese medicine (TCM) have played a significant role in the effective control of the epidemic. Yinqiaosan (YQS) was used to treat COVID-19 pneumonia, with good curative effects. However, a systematic overview of its active compounds and the therapeutic mechanisms underlying its action has yet to be performed. The purpose of the current study is to explore the compounds and mechanism of YQS in treating COVID-19 pneumonia using system pharmacology. A system pharmacology method involving drug-likeness assessment, oral bioavailability forecasting, virtual docking, and network analysis was applied to estimate the active compounds, hub targets, and key pathways of YQS in the treatment of COVID-19 pneumonia. With this method, 117 active compounds were successfully identified in YQS, and 77 potential targets were obtained from the targets of 95 compounds and COVID-19 pneumonia. The results show that YQS may act in treating COVID-19 pneumonia and its complications (atherosclerosis and nephropathy) through Kaposi sarcoma-related herpesvirus infection and the AGE-RAGE signaling pathway in diabetic complications and pathways in cancer. We distinguished the hub molecular targets within pathways such as TNF, GAPDH, MAPK3, MAPK1, EGFR, CASP3, MAPK8, mTOR, IL-2, and MAPK14. Five of the more highly active compounds (acacetin, kaempferol, luteolin, naringenin, and quercetin) have anti-inflammatory and antioxidative properties. In summary, by introducing a systematic network pharmacology method, our research perfectly forecasts the active compounds, potential targets, and key pathways of YQS applied to COVID-19 and helps to comprehensively clarify its mechanism of action. [ABSTRACT FROM AUTHOR]

Road intersection data are critical in many spatial applications and analyses. Approaches to identifying road intersections from various sensor data have been widely discussed in many existing studies, which focused on macroscopic information detection (e.g., location and size confirmation) and microscopic structure extraction (e.g., traffic rules at lane level). As the premise and basis for microscopic structure construction, the accuracy of macroscopic information has an important influence on extracting the detailed spatial structure of road intersections. In this article, we proposed applying a mask region convolutional neural network (Mask‐RCNN) framework to automatically detect the macroscopic information of road intersections from crowdsourced big trace data. There are two key points: (1) Mask‐RCNN‐based road intersection detection; and (2) result optimization and localization. Two real‐world GNSS (global navigation satellite system) trace datasets collected in Wuhan and Rome, respectively, were used to verify the applied Mask‐RCNN system. The results showed that the identification of various common types of road intersections achieved an overall precision of 97, 99, and 96%; recall of 93, 87, and 90%; and F1 score of 95, 92, and 93 in Wuhan, Shanghai, and Rome, respectively. These indices revealed a better model performance than the existing popular RCNN‐based models. [ABSTRACT FROM AUTHOR]

Background: The recent COVID-19 outbreak in Wuhan, China, has quickly spread throughout the world. In this study, we systematically reviewed the clinical features and outcomes of pregnant women with COVID-19.Methods: PubMed, Web of Science, EMBASE and MEDLINE were searched from January 1, 2020, to April 16, 2020. Case reports and case series of pregnant women infected with SARS-CoV-2 were included. Two reviewers screened 366 studies and 14 studies were included. Four reviewers independently extracted the features from the studies. We used a random-effects model to analyse the incidence (P) and 95% confidence interval (95% CI). Heterogeneity was assessed using the I2 statistic.Results: The meta-analysis included 236 pregnant women with COVID-19. The results were as follows: positive CT findings (71%; 95% CI, 0.49-0.93), caesarean section (65%; 95% CI, 0.42-0.87), fever (51%; 95% CI, 0.35-0.67), lymphopenia (49%; 95% CI, 0.29-0.70), coexisting disorders (33%; 95% CI, 0.21-0.44), cough (31%; 95% CI, 0.23-0.39), fetal distress (29%; 95% CI, 0.08-0.49), preterm labor (23%; 95% CI, 0.14-0.32), and severe case or death (12%; 95% CI, 0.03-0.20). The subgroup analysis showed that compared with non-pregnant patients, pregnant women with COVID-19 had significantly lower incidences of fever (pregnant women, 51%; non-pregnant patients, 91%; P < 0.00001) and cough (pregnant women, 31%; non-pregnant patients, 67%; P < 0.0001).Conclusions: The incidences of fever, cough and positive CT findings in pregnant women with COVID-19 are less than those in the normal population with COVID-19, but the rate of preterm labor is higher among pregnant with COVID-19 than among normal pregnant women. There is currently no evidence that COVID-19 can spread through vertical transmission. [ABSTRACT FROM AUTHOR]

Pedestrian network is an important part of urban transportation systems. The connectivity among networks substantially influences the residential access to surrounding amenity resources. Although studies to date have explored the evaluation and analysis of connectivity in pedestrian networks, the impact of mobility associated with network, e.g., public transport transfer, was not captured. In this study, we built a novel model that integrates network structural complexity and public transport transfer into connectivity index (CI i). The structural complexity of pedestrian networks as the first factor for the proposed model is calculated using the LNR (Link- Node Ratio) method. The degree of public transport transfer freedom as the second factor is quantified based on the number of public transport stations and their categories. We applied this model to the city of Wuhan, and the correlation between pedestrian network connectivity and urban amenities is further explored using Spearmans' rank correlation coefficient. This study provides two findings: first, difference in connectivity was identified in some residential areas after considering public transport transfer freedom, compared with traditional LNR. Denser pedestrian network and transit stops were revealed in the locations with higher CI i , with differences between old and new districts. Second, newly proposed CI i improved the significance in the association between connectivity and urban amenities. In residential areas with advanced connectivity, urban planners are expected to expand urban amenities to improve the living environmental. • A pedestrian network connectivity assessment model was proposed. • Difference in connectivity was identified in some residential areas after considering public transport transfer freedom. • The relationship between the connectivity and the distribution of urban amenities was studied. • Newly proposed CI i improved the significance in the association between connectivity and urban amenities. [ABSTRACT FROM AUTHOR]

The question of whether each building of housing estate has equal access to nearby social service resources (e.g., public transportation service, catering, entertainment, etc.) is a major concern of citizens. This paper takes Wuhan as a case to explore the equality in social service resource sharing of the housing estate at a microscopic level by analyzing the accessibility of each building under different travel patterns. To estimate the accessibility of each building, we developed a novel model with multi-travel modes and residential suitability evaluation of residents. The specific values of the parameters involved in the proposed model were extracted from the multi-source spatial data such as social media data, census data, point of interest, and road network data. These data were acquired from multiple platforms, e.g., Gaode map, OSM (OpenStreetMap), and GeoQ. We chose three types of districts in the city of Wuhan, including the old central district, new central district, and suburban district. We applied the proposed model to assess the accessibility of communities in these districts. Based on the results, we further analyzed whether and to what extent the distribution of each building in urban communities is equitable for social service resource sharing in China. [ABSTRACT FROM AUTHOR]

The debates about patients' decisions regarding which hospital to visit have been shown in many health research literature for the past few years. Researchers have developed various methods to understand hospital bypass behaviour of patients; however, previous studies ignore the impact of spatial heterogeneity and awareness of patients about surrounding hospitals such as travel distance and hospital distributions in the process of bypass behaviour definitions and evaluations. To address these limitations, this study puts forward a Hospital Bypass Index (HBI) to understand urban hospital bypass behaviour by using big trace data collected by urban taxis. To evaluate the bypass behaviour, we defines three evaluation indicators for HBI including a potential bypass rate, an overall distance decay parameter, and a diurnal variation in distance decay parameter by mining large-scale patient-hospital trips from a spatiotemporal perspective. Experiments are conducted with 30 general hospitals and 13 specialty hospitals in Wuhan city, China, by using one month of taxi traces. The results of bypass behaviour evaluation and comparisons indicate that the proposed method is effective and feasible, which is promising for health departments to optimize the medical services and rationally allocate the medical facilities. • This study introduces a new method to understand urban hospital bypass behaviour based on taxi trace data; • The new method addresses the absence of spatial heterogeneity and patient awareness in previous studies; • The results indicate that the diurnal variation in distance decay parameter can improve the accuracy of bypass analysis; • Taking Wuhan, China as an example, this study analyses the urban hospital bypass phenomenon with real taxi traces. [ABSTRACT FROM AUTHOR]

In taxi management, taxi-driver shift behaviors play a key role in arranging the operation of taxis, which affect the balance between the demand and supply of taxis and the parking spaces. At the same time, these behaviors influence the daily travel of citizens. An analysis of the distribution of taxi-driver shifts, therefore, contributes to transportation management. Compared to the previous research using the real shift records, this study focuses on the spatiotemporal analysis of taxi-driver shifts using big trace data. A two-step strategy is proposed to automatically identify taxi-driver shifts from big trace data without the information of drivers' identities. The first step is to pick out the frequent spatiotemporal sequential patterns from all parking events based on the spatiotemporal sequence analysis. The second step is to construct a Gaussian mixture model based on prior knowledge for further identifying taxi-driver shifts from all frequent spatiotemporal sequential patterns. The spatiotemporal distribution of taxi-driver shifts is analyzed based on two indicators, namely regional taxi coverage intensity and taxi density. Taking the city of Wuhan as an example, the experimental results show that the identification precision and recall rate of taxi-driver shift events based on the proposed method can achieve about 95% and 90%, respectively, by using big taxi trace data. The occurrence time of taxi-driver shifts in Wuhan mainly has two high peak periods: 1:00 a.m. to 4:00 a.m. and 4:00 p.m. to 5:00 p.m. Although taxi-driver shift behaviors are prohibited during the evening peak hour based on the regulation issued by Wuhan traffic administration, experimental results show that there are still some drivers in violation of this regulation. By analyzing the spatial distribution of taxi-driver shifts, we find that most taxi-driver shifts distribute in central urban areas such as Wuchang and Jianghan district. [ABSTRACT FROM AUTHOR]