Clustering Analysis of the Spatio-Temporal On-Street Parking Occupancy Data: A Case Study in Hong Kong

Wu, Fan; Ma, Wei

doi:10.3390/su14137957

Cited by 14 publications

(5 citation statements)

References 42 publications

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“…The study shows that the proposed method can significantly improve the accuracy of parking predictions compared to traditional methods. Wu et al in [29] propose a clustering analysis method to identify the spatiotemporal patterns of on-street parking occupancy. The study shows that the method can help identify areas with high parking demand and optimize parking management strategies, such as adjusting parking fees and improving enforcement.…”

Section: Spatiotemporal Correlations Between Parking Lotsmentioning

confidence: 99%

“…The works discussed above show that there are limited studies that build a pattern to characterise a parking lot using both spatial and temporal features [15,26,28,29,32,33]. These works do not take into account several characteristics, such as exogenous factors, including the weather and the type of day (weekday or weekend), which have a significant impact on parking occupancy.…”

Section: Spatiotemporal Correlations Between Parking Lotsmentioning

confidence: 99%

“…Clustering is an essential data mining technique for grouping data points into homogeneous groups (or clusters). Clustering techniques are well-known to provide a simple solution for spatial and temporal grouping [3,5,15,29,35,36]. In our work, we try to group similar spatial and temporal parking lot profiles.…”

Section: Spatiotemporal Clustering Of the Set Of Parking Lotsmentioning

confidence: 99%

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Spatiotemporal Clustering of Parking Lots at the City Level for Efficiently Sharing Occupancy Forecasting Models

Mufida

Cadi

Delot

et al. 2023

Sensors

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This study aims to address the challenge of developing accurate and efficient parking occupancy forecasting models at the city level for autonomous vehicles. Although deep learning techniques have been successfully employed to develop such models for individual parking lots, it is a resource-intensive process that requires significant amounts of time and data for each parking lot. To overcome this challenge, we propose a novel two-step clustering technique that groups parking lots based on their spatiotemporal patterns. By identifying the relevant spatial and temporal characteristics of each parking lot (parking profile) and grouping them accordingly, our approach allows for the development of accurate occupancy forecasting models for a set of parking lots, thereby reducing computational costs and improving model transferability. Our models were built and evaluated using real-time parking data. The obtained correlation rates of 86% for the spatial dimension, 96% for the temporal one, and 92% for both demonstrate the effectiveness of the proposed strategy in reducing model deployment costs while improving model applicability and transfer learning across parking lots.

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Section: Spatiotemporal Correlations Between Parking Lotsmentioning

confidence: 99%

Section: Spatiotemporal Correlations Between Parking Lotsmentioning

confidence: 99%

Section: Spatiotemporal Clustering Of the Set Of Parking Lotsmentioning

confidence: 99%

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Spatiotemporal Clustering of Parking Lots at the City Level for Efficiently Sharing Occupancy Forecasting Models

Mufida

Cadi

Delot

et al. 2023

Sensors

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“…Moreover, besides the weather factors, the transfer ridership is also influenced by other factors, such as socioeconomic factors and built environment factors [23]. Some existing studies consider the impact of the travel time, travel cost, environmental factors [24][25][26][27], and transfer penalty on the transfer during the morning peak period [12,17]. However, these studies were mainly based on survey data, with a limited sample coverage area and small sample size [28].…”

Section: Introductionmentioning

confidence: 99%

How Determinants Affect Transfer Ridership between Metro and Bus Systems: A Multivariate Generalized Poisson Regression Analysis Method

Pian

et al. 2022

Sustainability

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Understanding the determinants of transfer ridership is important for providing insights into improving the attractiveness of transit systems and building reliable and resilient metro stations. This study focuses on the transfer ridership between bus and metro systems under different dates and severe weather conditions to quantify the impacts of various attributes on the transfer ridership of different transfer modes (metro-to-bus and bus-to-metro). A multivariate generalized Poisson regression (GPR) model is applied to investigate the effects of critical factors on the transfer ridership of different transfer modes on weekdays, holidays, and typhoon days, respectively. The results indicate that the transfer-related variables, real-time weather, socioeconomic characteristics, and built environment significantly affect the transfer ridership. Concretely, the influence of socioeconomic and demographic factors on transfer ridership is the most significant on different types of dates, which is approximately 1.19 to 9.28 times that of the other variables. Weather variables have little effect on transfer ridership on weekdays, but they have a more significant impact on the transfer ridership on holidays and typhoon days. Specifically, during typhoons, transfer ridership is more affected by the weather factors: the coefficients are about 2.36 to 4.74 times higher than that in the other periods. Moreover, under strong wind speed, heavy rain, and high-temperature conditions, transfer ridership of the metro-to-bus mode significantly increases. In contrast, transfer ridership of the bus-to-metro mode rapidly decreases. Additionally, the peak hours have a strong positive influence on the transfer ridership, and the average hourly transfer ridership during peak hours is 1.16 to 4.02 times higher than that during the other periods. These findings indicate that the effect of each factor on transfer ridership varies with dates and transfer modes. This can also provide support for improving metro stations and increasing the attractiveness of public transport.

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“…In North Karachi, 2022 four parks have a -1 score and one park has a-2 score in parking issues. These numbers have increased significantly due to the annual growth rate of 2.13% in both towns Wu and Ma (2022). found an average on-street parking occupancy in Hong Kong was up to 80% during the day, and three parking patterns are reliably identified by direct data imagining and clustering consequences.…”

mentioning

confidence: 95%

Geospatial techniques for comparative case study of spatiotemporal changes in New Karachi and North Karachi parks

Arif

Kausar

Afzal

et al. 2023

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The well-known fact is that parks play a significant role in sustaining the urban environment. Megacities like Karachi are developing rapidly with a simultaneous increase in the city area, putting immense pressure on open green spaces. The widespread built-up development is replacing the previously existing vegetative cover. The lack of green spaces is the main concern, and this problem will only worsen due to overpopulation associated with the rapid growth of cities. The lack of evidence-based planning contributes to the unbalanced spatial distribution of parks in quantity and quality. The present research aimed to compare and find out the quality and status of parks such as park areas under encroachment and temporal changes in the vegetative cover of parks in the predominantly low to middle-income residential areas of New Karachi and North Karachi Towns of Karachi metropolitan. Geospatial techniques have been used for mapping, assessments, and analyses. Results indicate that boundary walls are a good solution to stop or reduce park encroachments as correlation indicates the parks with boundary walls have a significantly lower percentage of encroachment in 2022. The existing work indicated that the number of trees has increased in most parks in both towns in 2022. The overall correlation results indicate that factors affecting park quality positively have a positive association with other positively affecting factors and a negative association with factors that affect park quality negatively. There is a dire need to implement better planning strategies to enhance the quality of existing parks and construct new parks in the study area.

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Clustering Analysis of the Spatio-Temporal On-Street Parking Occupancy Data: A Case Study in Hong Kong

Cited by 14 publications

References 42 publications

Spatiotemporal Clustering of Parking Lots at the City Level for Efficiently Sharing Occupancy Forecasting Models

Spatiotemporal Clustering of Parking Lots at the City Level for Efficiently Sharing Occupancy Forecasting Models

How Determinants Affect Transfer Ridership between Metro and Bus Systems: A Multivariate Generalized Poisson Regression Analysis Method

Geospatial techniques for comparative case study of spatiotemporal changes in New Karachi and North Karachi parks

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