Data-driven traffic zone division in smart city: Framework and technology

Cai, Ming; Hong, Lantian; Xiong, Chen

doi:10.1016/j.seta.2022.102251

Cited by 5 publications

(5 citation statements)

References 22 publications

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“…For example, Martinez et al [35] designed the hierarchical heuristic algorithm to cluster grid cells using the origin-destination (OD) data to maximize the similarity of interzonal trips of each zone, while Xing et al [36] combined the methods of Voronoi diagram and natural boundaries and clustered small units to zones, not only to maximize the similarity of interzonal trips of each zone but also to keep a similar level of population among zones. Besides, the K-means algorithm can be used to cluster grids to zones so that the zones have a similar level of population or demand for a transport mode, e.g., the taxi demand [33], while Cai et al [10] suggested a genetic-algorithm-based clustering algorithm to cluster units into zones with consideration of the homogeneity of resident travel characteristics.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, there are few feature-based methods for bus service analysis, as far as we know. Hence, the grid-based method [9] and the naturalboundary-based method [10] are used for comparison in this section, whereas the proposed BS-based method is selected to represent the feature-based method. Later, a real-world case is introduced to compare these three types of zone division methods in the spatial analysis of public bus.…”

Section: Analysis and Comparisonmentioning

confidence: 99%

“…(2) Te natural-boundary-based method uses natural boundaries (e.g., road and river) to generate zones with consideration of land use or resident information. Tis zone division method is widely used in the traditional trafc planning, and the new extension includes the cellular signaling data to improve the demand homogeneity within each zone [10]. Te new extension is adopted in this study for the further tests.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Bus‐Service‐Based Zone Division Approach for the Spatial Analysis of Public Bus: A Case Study in South China

Fu,

Xie,

Cai

et al. 2024

Journal of Advanced Transportation

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Public bus is one of the major green transport modes in most cities, and a proper analysis of public bus can enhance its sustainable development. Hence, the analysis of public bus is always a hot research topic. Generally, to consider the heterogeneity in the studied area and reduce the computation difficulties, the study area is divided into multiple zones and then various analytical methods are applied. However, an improper zone division could lower the quality of the analysis results and then probably mislead the improvement strategy design. This study examines the influence of the two commonly used zone division methods (the grid-based method and the natural-boundary-based method) on the spatial analysis of public bus service using the analytical example. The result shows that the impact of zone division exists. Therefore, this study aims to identify the possible approach to reduce the negative impact and suggests a new zone division method, the bus-service-based (BS-based) method, for the spatial analysis of public bus service. The BS-based method is compared with the commonly used zone division methods using the real-world data, and the comparison results demonstrate that the BS-based method can be more efficient to support the spatial analysis of bus service.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Analysis and Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Bus‐Service‐Based Zone Division Approach for the Spatial Analysis of Public Bus: A Case Study in South China

Fu,

Xie,

Cai

et al. 2024

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

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“…• Smart mobility -data-driven applications are used to offer and monitor complex multi-modality systems of transportation to achieve sustainable transport systems that are efficient. Smart parking and smart traffic management techniques that are used to coordinate and integrate different transportation modes are part of smart mobility applications (Kaluarachchi , 2022;Cai, Hong and Xiong, 2022) • Smart governance can be achieved when citizens and other stakeholders are part of operations in cities, contributing to planning, supporting key decisions, and making processes with the help of smart platforms and applications. The aim is to attain synergies through collaboration and improve public services and the transparency of institutions to promote sustainable communities.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Data-Driven Approach in Knowledge-Based Smart City Management

Radziszewska

2023

ECKM

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The concept of smart city management is based on the implementation and use of advanced technologies, such as wireless sensors, intelligent vehicles, mobile networks, and data storage technologies. It involves integrating various information and communication technology solutions to efficiently manage a city's resources. Cities are investing in data-driven smart technologies to enhance performance and efficiency, thereby generating a large amount of data. Finding innovative ways to use this data helps improve city management and urban development. A data-driven city utilizes datafication to optimize its operations, functions, services, strategies, and policies. Datafication involves transforming various aspects of urban life into computerized data and extracting value from this information. This transformation is dependent on controlling the storage, management, processing, and analysis of the data, as well as utilizing the extracted knowledge to develop useful smart city solutions. Access to real-time data and information enables the provision of effective services that improve productivity, leading to environmental, social, and economic benefits. Both current and future smart cities have the potential to generate vast amounts of real-time data due to complex physical infrastructure and data-driven applications supported by social networks. This paper investigates how the emerging data-driven smart city is practiced and justified in terms of its innovative applied solutions. The aim of the paper is to explore the general conditions for implementing advanced data-driven technologies for smart city management, using knowledge from literature analysis and case studies. To understand this new urban phenomenon, a descriptive case study is used as a qualitative research methodology to examine and compare the possibilities of implementing data-driven approaches in knowledge-based smart city management. Seventeen case studies that use data-driven applications in real-world settings were identified from secondary sources and evaluated based on smart city indicators and related data-driven applications. Smart Cities were selected based on their rankings in the Digital Cities Index 2022, the Smart City Index 2022, and the IESE Cities in Motion Index 2022.

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“…The method used in the present study for generating perceptual regions of tourist attractions is similar to this method, but we also consider Traffic Analysis Zone (TAZ) data when generating the perceptual regions of tourist attractions. TAZ is closely related to the urban road traffic and, to some extent, represents the ways in which urban residents use urban space [40,41]. Therefore, perceptual regions that consider urban traffic information may be more in line with human cognition.…”

Section: Vague Region Perceptionmentioning

confidence: 99%

A Deep Transfer Learning Toponym Extraction and Geospatial Clustering Framework for Investigating Scenic Spots as Cognitive Regions

Zhang

et al. 2023

IJGI

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In recent years, the Chinese tourism industry has developed rapidly, leading to significant changes in the relationship between people and space patterns in scenic regions. To attract more tourists, the surrounding environment of a scenic region is usually well developed, attracting a large number of human activities, which creates a cognitive range for the scenic region. From the perspective of tourism, tourists’ perceptions of the region in which tourist attractions are located in a city usually differ from the objective region of the scenic spots. Among them, social media serves as an important medium for tourists to share information about scenic spots and for potential tourists to learn scenic spot information, and it interacts to influence people’s perceptions of the destination image. Extracting the names of tourist attractions from social media data and exploring their spatial distribution patterns is the basis for research on the cognitive region of tourist attractions. This study takes Hangzhou, a well-known tourist city in China, as a case study to explore the human cognitive region of its popular scenic spots. First, we propose a Chinese tourist attraction name extraction model based on RoBERTa-BiLSTM-CRF to extract the names of tourist attractions from social media data. Then, we use a multi-distance spatial clustering method called Ripley’s K to filter the extracted tourist attraction names. Finally, we combine road network data and polygons generated using the chi-shape algorithm to construct the vague cognitive regions of each scenic spot. The results show that the classification indicators of our proposed tourist attraction name extraction model are significantly better than those of previous toponym extraction models and algorithms (precision = 0.7371, recall = 0.6926, F1 = 0.7141), and the extracted vague cognitive regions of tourist attractions also generally conform to people’s habitual cognition.

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Data-driven traffic zone division in smart city: Framework and technology

Cited by 5 publications

References 22 publications

A Bus‐Service‐Based Zone Division Approach for the Spatial Analysis of Public Bus: A Case Study in South China

A Bus‐Service‐Based Zone Division Approach for the Spatial Analysis of Public Bus: A Case Study in South China

Data-Driven Approach in Knowledge-Based Smart City Management

A Deep Transfer Learning Toponym Extraction and Geospatial Clustering Framework for Investigating Scenic Spots as Cognitive Regions

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