Assessing Urban Travel Patterns: An Analysis of Traffic Analysis Zone-Based Mobility Patterns

Chen, Yanyan; Zhang, Zheng; Liang, Tianwen

doi:10.3390/su11195452

Cited by 20 publications

(10 citation statements)

References 33 publications

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“…Thus, circular economic models can be applied in cities to operationalise and manage sustainable human development simultaneously from a systemic perspective involving problems of social inequality and ecological and economic crises (Chen et al, 2019). Interpretations are related to the purpose of travel from the periphery to the core city, namely: (i) for educational purposes contributed an average of 14.5% of traffic generated due to the concentration of educational activities in the core city; (ii) for work purposes contributed an average of 21.9% of traffic generated; (iii) for business purposes contributed an average of 17.6% of generated traffic; (iv) for building social relations contributed an average of 14.3% of generated traffic; (v) for building business relationships contributed an average of 15.7% of generated traffic; and (vi) trips for tourism/recreation interests contributed an average of 11.2% of traffic generation.…”

Section: Resultsmentioning

confidence: 99%

Population mobility and sustainable development in the Mamminasata Metropolitan South Sulawesi, Indonesia

Surya¹,

Rasyidi²,

Abubakar³

et al. 2021

hgeo

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Economic growth and urban agglomeration have triggered an increase in the size and mobility of the Metropolitan Mamminasata urban population. This study aims to analyse spatial interactions working as a determinant of connectivity of the transportation system and the growth of suburban areas toward smart and sustainable cities in the Mamminasata Metropolitan urban system and the effects that spatial expansion, spatial integration, urban agglomeration, spatial use, and the transportation system have on population mobility. The research method used is a sequential explanatory design that combines quantitative and qualitative research methods. The results show that the coefficients of determination are as follows: spatial expansion to population mobility – 4.90%, spatial integration to population mobility – 2.99%, spatial interaction to population mobility – 4.87%, urban agglomeration to population mobility – 2.09%, space use to population mobility – 2.64%, and transportation system to population mobility – 5.15%. The results of this study will assist in the formulation of development policies, management of the urban transportation system, and allocation of space utilisation going forward.

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Section: Resultsmentioning

confidence: 99%

Population mobility and sustainable development in the Mamminasata Metropolitan South Sulawesi, Indonesia

Surya¹,

Rasyidi²,

Abubakar³

et al. 2021

hgeo

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“…Firstly, our method is capable of simultaneously clustering the mobility data along both spatial dimensions and temporal dimensions, making it easy to perceive the global similarity in spatio-temporal data. Traditional spatio-temporal clustering methods [6,7] usually focus on only one dimension (spatial or temporal) or separately analyzes the dimensions. More importantly, the proposed framework provides a concise and efficient way to present the interaction characteristic among spatial and temporal patterns, rather than interactions represented by the combined probability across different patterns [34].…”

Section: Discussionmentioning

confidence: 99%

“…Traditional spatio-temporal clustering methods focus on three aspects: (1) establish a temporal domain and measure the corresponding spatial distance between two objects [6], [7];…”

Section: Introductionmentioning

confidence: 99%

A Spatio-Temporal Co-Clustering Framework for Discovering Mobility Patterns: A Study of Manhattan Taxi Data

et al. 2021

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Research on clustering spatio-temporal data to extract mobility patterns requires further development, as most existing studies do not simultaneously integrate data along both spatial dimensions and temporal dimensions but instead focus on only one dimension or separate the dimensions in analyses and applications, which could lead to discoveries that are not representative of the overall data or are dificult to interpret. To simultaneously reveal the spatial and temporal patterns of urban mobility datasets, we propose an analytical framework that is based on co-clustering and enables mobility behaviors to be distinguished in spatial and temporal dimensions. We use one month of taxi GPS data from the Manhattan area to explore spatio-temporal co-occurrence patterns. The spatial and temporal dimensions of taxi trip data were coclustered by using the Bregman Block Average co-clustering algorithm with I-divergence (BBAC_I). We performed this process on weekdays and holidays and compared the mobility differences between these two periods. The experimental results demonstrated the effectiveness of this analytical framework, with which we can reveal the spatial patterns and their temporal dynamics as well as temporal patterns and their spatial dynamics in mobility data.INDEX TERMS Mobility patterns, co-clustering, spatio-temporal co-occurrence, taxi trip.

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“…It is well established that cellular signaling data can be used effectively to study human behavior [45][46][47]. The cellular signaling data used in this study were collected by a large mobile operator in Wuhan, whose subscribers account for 67% of all cell phone users, and the cell phone usage data it collects is highly universal and representative, reflecting the travel behavior of most people [45].…”

Section: Cellular Signaling Data Preprocessingmentioning

confidence: 99%

Exploring the Correlation between Block Vitality and Block Environment Based on Multisource Big Data: Taking Wuhan City as an Example

Yang

Jiao

2021

Land

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Block is the basic unit for studying the urban activities of residents, and block vitality is the concrete expression of urban dynamics at the block level. The quality of the block’s residential environment is a crucial medium to satisfy the residents’ pursuit of high-quality life; good block quality is essential for fostering the block vitality and further enhancing the overall vitality of the city. This study used the distribution density of cellular signaling data to quantify block vitality and constructed a block environment index system covering four dimensions—block accessibility, block function, block development degree, and human perception of block environment—innovatively introducing the elements of block environment from the human perspective. Considering the variability of block vitality between workdays and weekends, and between downtown and suburban blocks, this study used a geographically weighted regression model to show the mechanism of the spatial and temporal influence of indicators on block vitality, as well as to suggest how to enhance block vitality at different times of the day based on the influence mechanism. This study was conducted in Wuhan, China. The findings suggest that block vitality exhibited significant spatial and temporal heterogeneity. A high-vitality block can be created by enhancing the block’s accessibility, increasing the degree of block construction, and enriching the functional density and mix of functions in the block. A pleasantly green environment with a moderate degree of openness exerted a significant impact on promoting human activity and enhancing block vitality. The creation of high-vitality blocks should also consider the differences in the impact of various elements on block vitality between weekend and workday. For example, amid the surge in travel demand for education venues on weekends, enhancing the accessibility of blocks can significantly increase the vitality of blocks on weekends. We can truly realize the people-oriented approach to build a livable and high-vitality city by adapting to local conditions and time.

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Assessing Urban Travel Patterns: An Analysis of Traffic Analysis Zone-Based Mobility Patterns

Cited by 20 publications

References 33 publications

Population mobility and sustainable development in the Mamminasata Metropolitan South Sulawesi, Indonesia

Population mobility and sustainable development in the Mamminasata Metropolitan South Sulawesi, Indonesia

A Spatio-Temporal Co-Clustering Framework for Discovering Mobility Patterns: A Study of Manhattan Taxi Data

Exploring the Correlation between Block Vitality and Block Environment Based on Multisource Big Data: Taking Wuhan City as an Example

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