Data Driven Spatio-Temporal Modeling of Parking Demand

Fiez, Tanner; Ratliff, Lillian J.; Dowling, Chase; Zhang, Baosen

doi:10.23919/acc.2018.8431681

Cited by 15 publications

(9 citation statements)

References 12 publications

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“…In contrast to our results, most prediction methods only provide a point-wise prediction of parking occupancy. This literature can be classified into two main categories : (i) a class of model-based studies that consider a queuing model or a variation of it for analysis or prediction [17,18,19,20,21,22,23], and (ii) a class of studies that use a generic machine learning algorithm for prediction [24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43].…”

Section: Related Literaturementioning

confidence: 99%

“…The work in [26] uses spatio-temporal clustering to predict parking availability using data from San Fransisco. The authors in [27] use a Gaussian mixture model to predict parking demand in Seattle. The authors in [20] investigate four parking prediction methods, namely, autoregressive integrated moving average (ARIMA), linear regression, support vector regression (SVR), and feed-forward neural network.…”

Section: Related Literaturementioning

confidence: 99%

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A Queuing Approach to Parking: Modeling, Verification, and Prediction

Tavafoghi¹,

Poolla²,

Varaiya³

2019

Preprint

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We present a queuing model of parking dynamics and a model-based prediction method to provide realtime probabilistic forecasts of future parking occupancy. The queuing model has non-homogeneous arrival rate and time-varying service time distribution. All statistical assumptions of the model are verified using data from 29 truck parking locations, each with between 55 and 299 parking spots. For each location and each spot the data specifies the arrival and departure times of a truck, for 16 months of operation. The modeling framework presented in this paper provides empirical support for queuing models adopted in many theoretical studies and policy designs. We discuss how our framework can be used to study parking problems in different environments. Based on the queuing model, we propose two prediction methods, a microscopic method and a macroscopic method, that provide a real-time probabilistic forecast of parking occupancy for an arbitrary forecast horizon. These model-based methods convert a probabilistic forecast problem into a parameter estimation problem that can be tackled using classical estimation methods such as regressions or pure machine learning algorithms. We characterize a lower bound for an arbitrary realtime prediction algorithm. We evaluate the performance of these methods using the truck data comparing the outcomes of their implementations with other model-based and model-free methods proposed in the literature.

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Section: Related Literaturementioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

A Queuing Approach to Parking: Modeling, Verification, and Prediction

Tavafoghi¹,

Poolla²,

Varaiya³

2019

Preprint

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show abstract

“…This lays a heavier burden on road traffic and further contributes to congestion [15]. Parking demand prediction can help adjust the parking services dynamically to improve the overall efficiency and has the potential to optimise the parking space finding process [3]. Therefore, the problem is significantly important and has attracted growing attention.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal Parking Behaviour Forecasting and Analysis Before and During COVID-19

Gong¹,

Mo²,

Cao³

et al. 2021

Preprint

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Parking demand forecasting and behaviour analysis have received increasing attention in recent years because of their critical role in mitigating traffic congestion and understanding travel behaviours. However, previous studies usually only consider temporal dependence but ignore the spatial correlations among parking lots for parking prediction. This is mainly due to the lack of direct physical connections or observable interactions between them. Thus, how to quantify the spatial correlation remains a significant challenge. To bridge the gap, in this study, we propose a spatial-aware parking prediction framework, which includes two steps, i.e. spatial connection graph construction and spatio-temporal forecasting. A case study in Ningbo, China is conducted using parking data of over one million records before and during COVID-19. The results show that the approach is superior on parking occupancy forecasting than baseline methods, especially for the cases with high temporal irregularity such as during COVID-19. Our work has revealed the impact of the pandemic on parking behaviour and also accentuated the importance of modelling spatial dependence in parking behaviour forecasting, which can benefit future studies on epidemiology and human travel behaviours. CCS CONCEPTS• Applied computing → Forecasting.

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“…Supposing vehicles enter a network of parking queues in an effort to find a space, park, and then exit the network after some amount of time, queueing networks with seperable state-spaces may not be suited to describing the state space of these parking queues; this is supported by evidence of the probabilistic dependence of adjacent block-faces of curbside parking [9] (i.e. a block-face of curbside parking that is full is unlikely to be adjacent to an empty block-face), in addition to a number of other factors we will describe.…”

Section: Introductionmentioning

confidence: 99%

Modeling Curbside Parking as a Network of Finite Capacity Queues

Dowling

Ratliff

Zhang

2020

IEEE Trans. Intell. Transport. Syst.

Self Cite

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With the increasing availability of transaction data collected by digital parking meters, paid curbside parking can be advantageously modeled as a network of interdependent queues. In this article we introduce methods for analyzing a special class of networks of finite capacity queues, where tasks arrive from an exogenous source, join the queue if there is an available server or are rejected and move to another queue in search of service according to the network topology. Such networks can be useful for modeling curbside parking since queues in the network perform the same function and drivers searching for an available server are under combinatorial constraints and jockeying is not instantaneous. Further, we provide a motivating example for such networks of finite capacity queues in the context of drivers searching for parking in the neighborhood of Belltown in Seattle, Washington, USA. Lastly, since the stationary distribution of such networks used to model parking are difficult to satisfactorily characterize, we also introduce a simulation tool for the purpose of testing the assumptions made to estimate interesting performance metrics. Our results suggest that a Markovian relaxation of the problem when solving for the mean rate metrics is comparable to deterministic service times reflective of a driver's tendency to park for the maximum allowable time.

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Data Driven Spatio-Temporal Modeling of Parking Demand

Cited by 15 publications

References 12 publications

A Queuing Approach to Parking: Modeling, Verification, and Prediction

A Queuing Approach to Parking: Modeling, Verification, and Prediction

Spatio-temporal Parking Behaviour Forecasting and Analysis Before and During COVID-19

Modeling Curbside Parking as a Network of Finite Capacity Queues

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