Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data

Xia, Dawen; Jiang, Shunying; Yang, Nan; Hu, Yang; Li, Yantao; Li, Huaqing; Wang, Lin

doi:10.1016/j.physa.2021.126056

Cited by 25 publications

(6 citation statements)

References 28 publications

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“…The obtained fragments are adulterated into the 384-well plate, and each plate contains 3000 significant molecules related to their chemical structure. An ensuing ‘Tagmentation’ procedure has been performed with Illumina’s Nextera Technology [ 42 , 60 ]. Here, the plate items are additionally divided, and non-overlapping sequences are added all the while.…”

Section: Drug Discovery Architecturementioning

confidence: 99%

Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network

Pati

Gupta

Banerjee³

et al. 2023

Multimed Tools Appl

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After several waves of COVID-19 led to a massive loss of human life worldwide due to the changes in its variants and the vast explosion. Several researchers proposed neural network-based drug discovery techniques to fight against the pandemic; utilizing neural networks has limitations (Exponential time complexity, Non-Convergence, Mode Collapse, and Diminished Gradient). To overcome those difficulties, this paper proposed a hybrid architecture that will help to repurpose the most appropriate medicines for the treatment of COVID-19. A brief investigation of the sequences has been made to discover the gene density and noncoding proportion through the next gene sequencing. The paper tracks the exceptional locales in the virus DNA sequence as a Drug Target Region (DTR). Then the variable DNA neighborhood search is applied to this DTR to obtain the DNA interaction network to show how the genes are correlated. A drug database has been obtained based on the ontological property of the genomes with advanced D3Similarity so that all the chemical components of the drug database have been identified. Other methods obtained hydroxychloroquine as an effective drug which was rejected by WHO. However, The experimental results show that Remdesivir and Dexamethasone are the most effective drugs, with 97.41 and 97.93%, respectively.

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Section: Drug Discovery Architecturementioning

confidence: 99%

Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network

Pati

Gupta

Banerjee³

et al. 2023

Multimed Tools Appl

View full text Add to dashboard Cite

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“…The traffic flow model method uses speed data to estimate volume based on traffic fundamental diagrams [ 20 ]. Dynamic speed data of large-scale road networks can be easily obtained based on massive data, such as floating car data (FCD) [ 11 ] and travel trajectories collected from mobile applications [ 21 ], which have high temporal and spatial continuity. Dynamic traffic control, such as signal timing optimization, requires small time granularity (five minutes) and high estimation accuracy of traffic volume [ 22 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Environment Estimation Method of Whole Sample Traffic Flows and Emissions Based on Multifactor MFD

Zang

Jiao

Song

et al. 2022

IJERPH

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Vehicle emissions seriously affect the air environment and public health. The dynamic estimation method of vehicle emissions changing over time on the road network has always been the bottleneck of air quality simulation. The dynamic traffic volume is one of the important parameters to estimate vehicle emission, which is difficult to obtain effectively. A novel estimation method of whole sample traffic volumes and emissions on the entire road network based on multifactor Macroscopic Fundamental Diagram (MFD) is proposed in this paper. First, the intelligent clustering and recognition methods of traffic flow patterns are constructed based on neural network and deep-learning algorithms. Then, multifactor MFD models are developed considering different road types, traffic flow patterns and weekday peak hours. Finally, the high spatiotemporal resolution estimation method of whole sample traffic volumes and emissions are constructed based on MFD models. The results show that traffic flow patterns are clustered efficiently by the Self-Organizing Maps (SOM) algorithm combined with the direct time-varying speed index, which describe 91.7% traffic flow states of urban roads. The Deep Belief Network (DBN) algorithm precisely recognizes 92.1% of the traffic patterns based on the speeds of peak hours. Multifactor MFD models estimate the whole sample traffic volumes with a high accuracy of 91.6%. The case study shows that the vehicle emissions are evaluated dynamically based on the novel estimation method proposed in this paper, which is conducive to the coordinated treatment of air pollution.

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“…Taxi OD flow data from April 1st to April 3rd, 2015, were used in this study as reference data to evaluate our measurement of urban polycentric interaction since some scholars have mentioned that taxi data sets can reveal the interaction within cities [60]. The dataset records the beginning and ending points of each taxi trip, which is easy to count the number of trips between two urban centers.…”

Section: B Datamentioning

confidence: 99%

“…Additionally, Xia, et al [60] has demonstrated that people tend to attend entertainment activities by taxi after working on Fridays, which means urban centers with a recreational function will experience a surge of taxi trip on Fridays. Thus, this may lead to an underestimation of interaction (the red points in Fig.…”

Section: A Accuracy Evaluationmentioning

confidence: 99%

Quantitative Analysis of Urban Polycentric Interaction Using Nighttime Light Data: A Case Study of Shanghai, China

Chen

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The urban polycentric structure is connected to the economy and enormously impacts socio-economic development and policies. Unlike traffic data and big geographic data, remote sensing data have shown an accessible way to measure urban spatial interaction. However, most existing studies only focused on the interaction among cities rather than within cities. Meanwhile, the urban spatial interaction, which should be directional, was always expressed as an undirected graph. Therefore, this study developed a network-based radiation model using nighttime light remote sensing data and mapped a directed interaction network (inward and outward direction) among urban centers. Taking the region within the outer ring of Shanghai as an example, the taxi trajectory data were adopted to validate the result with the R 2 of 0.61. We discovered that: (1) The urban polycentric interaction network is dumbbell-shaped with an east-west development corridor crossing the main center and connecting two main urban center clusters. (2) The in-strength and out-strength interaction of each urban center have a similar distribution. The urban centers with higher in-strength and out-strength are mainly concentrated toward the main center, especially in the east-west direction. (3) At the urban center level, the total inward interaction is slightly higher than the total outward interaction of most urban centers. Spatially, an unbalanced distribution was found. In summary, our proposed method effectively indicates the urban polycentric interaction and is applicable to other regions since it requires no arbitrary parameters and the input data (e.g., nighttime light data) is readily available.

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Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data

Cited by 25 publications

References 28 publications

Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network

Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network

A Novel Environment Estimation Method of Whole Sample Traffic Flows and Emissions Based on Multifactor MFD

Quantitative Analysis of Urban Polycentric Interaction Using Nighttime Light Data: A Case Study of Shanghai, China

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