A Big Data Analysis on Urban Mobility: Case of Bangkok

Panichpapiboona, Sooksan; Khunsri, Kavepol

doi:10.1109/access.2022.3170068

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Previous studies have examined the steps necessary to promote economic development through RCT activities, focusing on the impact of transportation activities on the economy and emission reduction; however, there are still some problems in understanding the transportation rules. Although big data have been widely used in the field of urban transportation ( 44 , 45 ), statistical data continue to be the most commonly used data in railway container transport research. Compared with statistical data, the railway container loading and unloading station data used in this study have finer granularity and higher accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Evolution of spatial and temporal patterns of railway container transportation: A case study of China cities

Bai

Kuang²,

Yang³

et al. 2022

Front. Public Health

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The healthy development of railway container transport is an important part of railway freight transport and is key for promoting the green transformation of China's transport and supporting a new pattern of transport development. In this study, railway container handling station (RCHS) data, kernel density analysis, standard deviation ellipse, Herfindahl–Hirschman index (HHI), trend surface analysis (TSA), and R index were applied to examine the evolution characteristics of container transport patterns after the market-oriented reform of China's railway freight transport in 2013. The results are as follows: First, the overall scale growth trend is obvious, and the double-center effect of transport scale on the Bohai Rim region and Chengdu–Chongqing Economic Zone is evident, with the transport center of gravity moving northward. Second, the amount of attraction/occurrence is consistent in spatial distribution, and the aggregation effect of both is similar, essentially exhibiting a tendency of being high in the northwest and low in the southeast. Third, the pattern of “export-oriented in the north and import-oriented in the south” has taken shape; nearly half of cities in China have stable traffic functions, stable traffic supply, and demand relationships, and the change of functions shows that the industrial structure is constantly upgrading. This study elucidates the pattern of railway container transport in cities in China and provides empirical guidance for adjusting the functions of urban freight transport, thereby promoting the healthy development of urban freight transport and optimizing urban transport planning.

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Section: Literature Reviewmentioning

confidence: 99%

Evolution of spatial and temporal patterns of railway container transportation: A case study of China cities

Bai

Kuang²,

Yang³

et al. 2022

Front. Public Health

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show abstract

“…Compared with traditional data, big data not only has the advantages of wider sampling range and faster collection speed [47], but it has also been found that there is a strong perceived fit between big data and the urban internal structure [48,49]. At present, there are three kinds of spatial location big data that are widely used in studies related to urban space, including POI data [50], cellular signaling data and population migration data [51,52].…”

Section: Introductionmentioning

confidence: 99%

Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area

Chen

Deng

2022

IEEE Access

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The spatial difference between urban and rural areas is the direct result of urban-rural relations. Accurate identification of urban-rural area is helpful to judge the urban-rural mechanism and promote the integration development of urban-rural area. Previous studies only used single nighttime light (NTL) data to identify urban and rural areas, which is likely to have an impact on the identification results due to the large brightness difference of lights. Therefore, based on NTL data and combine with data level fusion algorithm, this study separately fuses point of interest (POI) data that representing the quantity distribution of urban infrastructure and Baidu migration big (BM)data that representing the change relationship of regional population mobility to identify urban and rural areas by using deep learning method. The results show that the highest accuracy of urban-rural spatial identification with single NTL data is 84.32% and kappa is 0.6952, while the highest accuracy identified by data fusion is 95.02% and kappa is 0.8259. It can be seen that the differences caused by light brightness are effectively corrected after data fusion, which greatly improves the accuracy of urban and rural spatial identification. By comparing the results of NTL data modified by different big data, this study analyzes and identifies the accuracy of urban and rural area by using deep learning method, which not only enriches the study of data fusion in urban area, but also provides a basis for analyzing regional urban-rural relations and urban-rural development. Therefore, this study is believed to have important practical value for the coordinated development of urban and rural areas.

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Research on Multi-Vehicle Power Distribution Method in Charging and Discharging Station Discharge Mode

Zhao,

Yu,

Zhang

et al. 2023

2023 3rd International Conference on Energy, Power and Electrical Engineering (EPEE)

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A Big Data Analysis on Urban Mobility: Case of Bangkok

Cited by 3 publications

References 26 publications

Evolution of spatial and temporal patterns of railway container transportation: A case study of China cities

Evolution of spatial and temporal patterns of railway container transportation: A case study of China cities

Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area

Research on Multi-Vehicle Power Distribution Method in Charging and Discharging Station Discharge Mode

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