A Spatio-Temporal Schedule-Based Neural Network for Urban Taxi Waiting Time Prediction

You, Liangzhi; Guan, Zhengyi; Li, Na; Zhang, Jiahe; Cui, Haibo; Claramunt, Christophe; Cao, Rui

doi:10.3390/ijgi10100703

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…e formulation of the planning scheme is seriously affected by nature, society, economy, and other aspects. e technical method of cultural space layout planning has developed from early linear programming to multi-objective linear programming and even nonlinear planning, making the planning form develop from static planning to dynamic planning, and changing the original too rigid planning to the direction of exible planning with certain adaptability [1][2][3]. erefore, it shows the shortcomings of traditional planning methods, and it is very adaptable to the requirements of complex planning process.…”

Section: Introductionmentioning

confidence: 99%

Layout Optimization of Urban Cultural Space Construction Based on Forward Three-Layer Neural Network Model

Zhou

2022

Computational Intelligence and Neuroscience

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The change of urban cultural space layout is a multi-variable, multi-objective, and restricted research process. The optimization of urban cultural space construction and layout is a multi-objective decision-making problem that needs to be solved urgently. Based on the forward three-layer neural network theory, this paper constructs an optimization model for the construction and layout of urban cultural space evaluation of the layout of cultural space. This paper first analyzes the feasibility of combining the forward three-layer neural network model with the optimization and adjustment of cultural space layout structure. Taking the three-layer feedforward network as an example, the structure optimization model based on the forward three-layer neural network is selected, and the established model is used to reflect the internal environment of the objective world. Structure and perform dynamic simulation. In the process of simulation modeling, from the aspects of system description, model structure, logical analysis, reasoning, and interpretation, two effective computer dynamic simulation methods, namely, forward three-layer neural network model and system dynamics SD model, were carried out for theoretical comparison and identification. The experimental results show that the feasibility and calculation error of the application of the optimization model are relatively good, reaching 0.897 and 6.21%, respectively. The number of newly added cultural spaces and the expansion speed show an increasing trend, expanding at an average annual speed of about 35 km2, effectively increasing the quality of regional planning and construction layout.

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Section: Introductionmentioning

confidence: 99%

Layout Optimization of Urban Cultural Space Construction Based on Forward Three-Layer Neural Network Model

Zhou

2022

Computational Intelligence and Neuroscience

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“…In earlier practice of GIScience, the automation of space subdivision with database attribution and geometric modelling conceives the real world, in addition to the perceptual dimensions of humanities as well [for example 16,17,18]. The current practice extends neural cognition of human-like robot in liveable geographic contexts that merges virtual reality with reality in cyberspace [for example 19,20,21,22,23]. In parallel, the artificial intelligence of computing architectural space flips the process from topologic space configuration to realized geometrical forms of shape grammar [for example 24,25,26,27,28,29].…”

Section: Methodsmentioning

confidence: 99%

Voronoi Structure of Space Integration with Applied Roundabout Network

El-Shazly

2024

Preprint

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Network objective of structuring space integration addresses topologic and geometric dimensions to be framed-out in holistic method. In this sense, the spatial network of multidiscipline voronoi convex-hull encapsulates the varied integral properties with their correlative exploration. In practice, conservational network of roundabout Cairo investigates its integral structure against decomposing. Resulted structural essence defines individual topologic versus plural geometric role at global integration per generator. The integrated topology cross-correlates control syntax with degree centrality at the local context, whereas the global structure of asymmetry complements betweenness dynamics of generators’ distributiveness. Meanwhile, geometrical network of gabriel-graph forms cyclic spaces of global integrity and correlated by voronoi nodes at equidistant meeting-point per cycle. Overlaid voronoi properties classify the layout into clustered generators of correlated interval integrity with functional cognition to infinity. Overview of voronoi network adopts the intangible space integration along its comprehensive framework of interrelated algorithms in a virtual reality of urbanism.

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“…11 (2) 2024 | 741 taksi terdekat. Waktu tunggu tersebut menjadi hal yang krusial bagi penumpang taksi dalam memilih lokasi penjemputan yang ramai di lingkungan perkotaan (You, et al, 2021).…”

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Identifikasi Pengelompokan Titik Penjemputan Dan Titik Pengantaran Perjalanan Taksi Menggunakan Algoritma Dbscan

Salsabila,

Iswari

2024

EDUSAINTEK

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Pesatnya pertumbuhan wilayah perkotaan dalam beberapa tahun terakhir mengakibatkan keterkaitan dan saling mempengaruhi antara rutinitas perjalanan harian penduduk kota dan penggunaan transportasi umum. Kepuasan pelayanan kepada penumpang merupakan hal yang sangat terkait dengan usaha armada taksi, dimulai dari proses penjemputan hingga pengantaran ke tujuan. Waktu menunggu taksi menjadi faktor penting bagi penumpang dalam memilih titik penjemputan yang tepat di lingkungan perkotaan. Mengidentifikasi sejarah permintaan penumpang taksi secara akurat dapat membantu pengelola armada taksi dalam mengalokasikan sumber daya terutama di wilayah perkotaan. Dalam penelitian ini, algoritma DBSCAN digunakan untuk mengidentifikasi pola cluster yang muncul dari titik penjemputan dan titik pengantaran penumpang berdasarkan data perjalanan taksi. Data yang digunakan berasal dari Kaggle, dan fokus penelitian ini adalah perjalanan taksi di Kota Brooklyn. Pada titik penjemputan penumpang, teridentifikasi 3 cluster dengan pola sebaran jalur yang memiliki potensi area yang signifikan terletak di sekitar pusat perbelanjaan seperti Atlantic Avenue Barclays Center, Arena Barclays Center, Flatbush Avenue, dan Atlantic Avenue. Sementara itu, pada titik pengantaran penumpang, terdapat 2 cluster dengan pola sebaran jalur yang memiliki potensi area signifikan terletak di Myrtle Avenue, Flatbush Avenue, Lafayette Avenue, Williamsburg, dan Dumbo.

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A Spatio-Temporal Schedule-Based Neural Network for Urban Taxi Waiting Time Prediction

Cited by 7 publications

References 21 publications

Layout Optimization of Urban Cultural Space Construction Based on Forward Three-Layer Neural Network Model

Layout Optimization of Urban Cultural Space Construction Based on Forward Three-Layer Neural Network Model

Voronoi Structure of Space Integration with Applied Roundabout Network

Identifikasi Pengelompokan Titik Penjemputan Dan Titik Pengantaran Perjalanan Taksi Menggunakan Algoritma Dbscan

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