Unfolding Spatial-Temporal Patterns of Taxi Trip based on an Improved Network Kernel Density Estimation

Shen, Boxi; Xu, Xinhe; Li, Jun; Huang, Qunying

doi:10.3390/ijgi9110683

Cited by 5 publications

(3 citation statements)

References 67 publications

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“…As the industry matures, more methods are applied to charging infrastructure demand forecasting, such as: travel chain theory [4][5][6][7] Grey forecast model [8] , Cumulative Prospect Theory [9,10] and simulation methods based on Monte Carlo, etc. [11][12][13][14] . Historical studies have shown that each method has its own unique advantages and applicability, however, current studies mainly focus on micro scenarios, such as a small or mediumsized area within a city, and lack of macro-view forecasting and research, which makes it difficult to provide direct support for the industry's high-quality development as well as for the business planning decisions of enterprises.…”

Section: Forewordmentioning

confidence: 99%

Monte Carlo-based Charging Demand Forecasting Model and Market Space Study

Zhang,

Peng,

et al. 2023

IJE

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With the rapid development of China's new energy vehicle industry, charging infrastructure has gradually become a key factor affecting the further development of the new energy vehicle industry. Scientific and reasonable forecasts of the future development scale of the domestic charging infrastructure market have become an important element supporting the business planning of various related parties such as vehicle enterprises, charging pile enterprises, and the energy industry. Based on the distribution data of electric vehicle traveling and charging characteristics obtained from the industry research, this study proposes a set of charging infrastructure prediction model based on the principle of energy supply-demand balance, predicts the development scale of charging infrastructure in the country and key cities, and further analyzes the market competition and future market margin of the key cities, and evaluates the difficulty of future market entry in the key cities, which provides a basis for further development of the charging infrastructure industry. The difficulty of entering the future market in each key city is assessed, providing business decision support for industry participants. In the future, the charging infrastructure development scale prediction results proposed by this study can be used as the input parameters of the charging pile layout planning model to enhance the scientific nature of the charging infrastructure layout and help the industry to develop in a high-quality manner.

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Section: Forewordmentioning

confidence: 99%

Monte Carlo-based Charging Demand Forecasting Model and Market Space Study

Zhang,

Peng,

et al. 2023

IJE

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“…Zheng et al used the taxi trajectory data to investigate the spatial layout and the allocation of management resource of the urban public green space from the spatial interaction perspective [62]. Shen et al analyzed the spatiotemporal pattern of taxi travel based on an improved network kernel density estimation method [63]. Gao et al investigated the impact of the modifiable areal unit problem (MAUP) for understanding the relationships between commuting demand and built environment [64].…”

Section: Conclusion and Prospectmentioning

confidence: 99%

Revealing Taxi Interaction Network of Urban Functional Area Units in Shenzhen, China

Lai

Shang

et al. 2022

IJGI

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Characterizing the taxi travel network is of fundamental importance to our understanding of urban mobility, and could provide intellectual support for urban planning, traffic congestion, and even the spread of diseases. However, the research on the interaction network between urban functional area (UFA) units are limited and worthy of notice. Therefore, this study has applied the taxi big data to construct a travel flow network for the exploration of spatial interaction relationships between different UFA units in Shenzhen, China. Our results suggested that taxi travel behavior was more active in UFA units dominated by functions, including residential, commercial, scenic, and greenspace during weekends, while more active in UFA units dominated by industrial function during weekdays. In terms of daily average volume, the characteristics of spatial interaction between the various UFA types during weekdays and weekends were similar. During the morning peak period, the sink areas were mainly distributed in Futian District and Nanshan District, while during the evening peak period, the sink areas were mainly distributed in the southern part of Yantian District, the southwestern part of Longgang District, and the eastern part of Luohu District. The average daily taxi mobility network during weekdays showed a spatial pattern of “dense in the west and north, sparse in the south and east”, exhibiting significant spatial unevenness. Compared with weekdays, the daily taxi mobility network during weekends was more dispersed and the differences in node sizes decreased, indicating that taxi travel destinations were more diverse. The pattern of communities was more consistent with the administrative division during weekdays, indicating that taxi trips are predominantly within the districts. Compared with weekdays, the community pattern of network during weekends was clearly different and more in line with the characteristics of a small world network. The findings can provide a better understanding of urban mobility characteristics in Shenzhen, and provide a reference for urban transportation planning and management.

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“…The two NKDEs correcting the density at intersections have been widely adopted in practice (Mohaymany, Shahri, and Mirbagheri, 2013; Dai and Jaworski, 2016; Harirforoush and Bellalite, 2019; Shen et al, 2020), in part, due to its implementation in the SANET software (Okabe, Okunuki, and Shiode, 2006). The approach proposed by Xie and Yang (2008) is still used (Li et al, 2011; Lesage‐Mann and Apparicio, 2016; Rui et al, 2016) because it can easily be implemented in traditional GIS software and because of its intuitive character.…”

Section: Introductionmentioning

confidence: 99%

Temporal Network Kernel Density Estimation

Gelb

Apparicio

2023

Geographical Analysis

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Kernel density estimation (KDE) is a widely used method in geography to study concentration of point pattern data. Geographical networks are 1.5 dimensional spaces with specific characteristics, analyzing events occurring on networks (accidents on roads, leakages of pipes, species along rivers, etc.). In the last decade, they required the extension of spatial KDE. Several versions of Network KDE (NKDE) have been proposed, each with their particular advantages and disadvantages, and are now used on a regular basis. However, scant attention has been given to the temporal extension of NKDE (TNKDE). In practice, when the studied events happen at specific time points and are constrained on a network, the methodologies used by geographers tend to overlook either the network or the temporal dimension. Here we propose a TNKDE based on the recent development of NKDE and the product of kernels. We also adapt classical methods of KDE (Diggle's correction, Abramson's adaptive bandwidth and bandwidth selection by leave‐one‐out maximum likelihood). We also illustrate the method with Montreal road crashes involving a pedestrian between 2016 and 2019.

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Unfolding Spatial-Temporal Patterns of Taxi Trip based on an Improved Network Kernel Density Estimation

Cited by 5 publications

References 67 publications

Monte Carlo-based Charging Demand Forecasting Model and Market Space Study

Monte Carlo-based Charging Demand Forecasting Model and Market Space Study

Revealing Taxi Interaction Network of Urban Functional Area Units in Shenzhen, China

Temporal Network Kernel Density Estimation

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