Statistical Analysis and Prediction of Parking Behavior

Feng, Ningxuan; Zhang, Feng; Lin, Jiazao; Zhai, Jidong; Du, Xiaoyong

doi:10.1007/978-3-030-30709-7_8

Cited by 11 publications

(5 citation statements)

References 27 publications

(30 reference statements)

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“…Considering RMSE value, our proposed approach showed almost similar performance compared to Shao et al's parking data availability prediction with LSTM [15] with a narrow margin. On the other hand, our approach with Random Forest regression outperforms Feng's approach [8] of parking behavior.…”

Section: Results and Analysismentioning

confidence: 79%

“…We have analyzed the performance of our approach in comparison to previous approaches used in related works such as Shao et al's framework [15] and Feng et al 's experiment [8].…”

Section: Results and Analysismentioning

confidence: 99%

“…Many researchers have proposed different machine learning approaches to solve the parking occupancy prediction problem such as Neural Network approaches, regression models and so on. [8] analyzed the parking behavior under the impact of different weather conditions such as temperature, humidity, rainfall and wind speed. To categorize features and test the correlation, they used Anova Test.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Car Parking Availability Prediction: A Comparative Study of LSTM and Random Forest Regression Approaches

Sadia,

Reza,

Alam

et al. 2021

Int J Auto AI Mach learn

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Drivers spend an enormous amount of time searching for parking spots every year. Waste of time, emission of carbon and air pollution have been issues in hunting for parking spots without proper prediction. In this paper, we have proposed to build a framework based on Recurrent Neural Network (RNN) using Long Short Term Memory (LSTM) and Random Forest Regression model to provide prediction of parking availability and compared results afterwards. A real-world case of parking spots availability consisting of 5,500 parking spots in Kuala Lumpur City Centre (KLCC), Malaysia, has been used for regression implementation in this comparative analysis. The results showed that random forest outperformed LSTM approach based on performance metrics.

show abstract

Section: Results and Analysismentioning

confidence: 79%

“…We have analyzed the performance of our approach in comparison to previous approaches used in related works such as Shao et al's framework [15] and Feng et al 's experiment [8].…”

Section: Results and Analysismentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Car Parking Availability Prediction: A Comparative Study of LSTM and Random Forest Regression Approaches

Sadia,

Reza,

Alam

et al. 2021

Int J Auto AI Mach learn

View full text Add to dashboard Cite

show abstract

“…There is a wide range of machine learning models that have been employed by researchers in the last few years. The following models have been tested in the reviewed studies: clustering [15], [21], [32], different linear regression algorithm like Lasso, Ridge, or basic linear regression [33], vector spatio-temporal autoregression [13], ARIMA [25], Support Vector Machine classifier [34], decision tree [15], [28], random forest [7], Support Vector Regression [14], [25], and tree-based algorithms like Gradient Boosting Regression Tree (GBRT) [15], [35] among others. Despite longer run times and in the hopes that unsupervised learning can enhance models, many studies have utilized deep learning approaches using neural networks like multi-layer perceptron [15], [36], CNN, Hybrid CNN, Graph CNN, RNN, LSTM, [2], [3], [6], [8], [9], [25].…”

Section: Popular Parking Prediction Modelsmentioning

confidence: 99%

Development of a Data-Driven On-Street Parking Information System Using Enhanced Parking Features

Gomari

Domakuntla

Knoth

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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On-street parking information (OSPI) systems help reduce congestion in the city by lessening parking search time. However, current systems use features mainly relying on costly manual observations to maintain a high quality. In this paper, on top of traditional location-based features based on spatial, temporal and capacity attributes, vehicle parked-in and parked-out events are employed to fill the quality assurance gap. The parking events (PEs) are used to develop dynamic features to make the system adaptive to changes that impact on-street parking availability. Additionally, a parking behavior change detection (PBCD) model is developed as an OSPI supplementary component to trigger potential parking map updates. The evaluation shows that the developed OSPI availability prediction model is on par with state-of-the-art models, despite having simpler but more enhanced and adaptive features. The foundational temporal and aggregated spatial parking capacity features help the most, while the PE-based features capture variances better and enable adaptivity to disruptions. The PE-based features are advantageous as data are automatically gathered daily. For the PBCD model, impacts by construction events can be detected as validation. The methodology proves that it is possible to create a reliable OSPI system with predominantly PE-based features and aggregated parking capacity features.

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“…Examining travelers' parking intentions offers crucial insights into the perceived attractiveness of SPSs. Past research has predominantly focused on key determinants affecting parking intentions, such as accessibility [9][10][11][12], parking convenience [13][14][15], parking cost [16,17], and parking availability [18][19][20][21].…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating Factors for Travelers’ Parking Behavior Intentions in Changchun, China, under the Influence of Smart Parking Systems

Zhang,

Song,

Tao

et al. 2023

Sustainability

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Unraveling the determinants of travelers’ parking behavior intentions is critical to the widespread adoption of smart parking systems (SPSs), which hold the promise of greatly enhancing parking efficiency and optimizing resource allocation within urban spaces. Our study pioneers the use of an integrated methodology combining structural equation modeling (SEM) and hierarchical regression modeling (HRM) to dissect the complex interplay of these determinants. We found that, in the structural equation model, social influence notably stood out as having the most significant impact on the intention to utilize SPSs. Notably, while perceived privacy concerns may have ranked lower in terms of influence among these factors, their role was relatively crucial, particularly given the contemporary emphasis on data security. Moreover, within the hierarchical regression model, driving experience was found to play a crucial role in determining the intention to use SPSs. Equally important, our research revealed a divergence in parking intentions between individuals with children and those without. This points towards the imperative need for personalized strategies that can cater to the diverse requirements of different user demographics. This research offers guidance for operators of SPSs aiming to formulate targeted approaches.

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Statistical Analysis and Prediction of Parking Behavior

Cited by 11 publications

References 27 publications

Car Parking Availability Prediction: A Comparative Study of LSTM and Random Forest Regression Approaches

Car Parking Availability Prediction: A Comparative Study of LSTM and Random Forest Regression Approaches

Development of a Data-Driven On-Street Parking Information System Using Enhanced Parking Features

Investigating Factors for Travelers’ Parking Behavior Intentions in Changchun, China, under the Influence of Smart Parking Systems

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