Routing for on-street parking search using probabilistic data

Friedrich, Tobias; Krejca, Martin S.; Rothenberger, Ralf; Arndt, Tobias; Hafner, Danijar; Kellermeier, Thomas; Krogmann, Simon; Razmjou, Armin

doi:10.3233/aic-180574

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…Vehicles cruising for parking are estimated to contribute to 30% congestion within a transport network [1]. This causes noise, air pollution, and travel time delays.…”

Section: A Backgroundmentioning

confidence: 99%

“…Another study used on-street parked out events to classify legal and illegal parking spots in the city [28]. Floating car data is another indirect source to infer parking behavior [29], [30]. Weather data has been proven by many studies to either help make prediction models or understand parking behavior [6], [9], [25].…”

Section: B Features In Parking Behavior and Parking Prediction Studiesmentioning

confidence: 99%

“…Weather data has been proven by many studies to either help make prediction models or understand parking behavior [6], [9], [25]. Some other features that are also incorporated include map-related features such as street length, landuse, and points-of-interest (POI) data regarding shops, parking facilities, [1], [5], [7], [15], [19]. A few studies also included special events [5], [6].…”

Section: B Features In Parking Behavior and Parking Prediction Studiesmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Vehicles cruising for parking are estimated to contribute to 30% congestion within a transport network [1]. This causes noise, air pollution, and travel time delays.…”

Section: A Backgroundmentioning

confidence: 99%

Section: B Features In Parking Behavior and Parking Prediction Studiesmentioning

confidence: 99%

Section: B Features In Parking Behavior and Parking Prediction Studiesmentioning

confidence: 99%

See 1 more Smart Citation

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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show abstract

“…However, it is computationally intensive and data redundant, which enlarges the network latency. Friedrich et al [29] studied heuristics and used algorithmic optimization to find parking spaces. However, its randomness might affect the accuracy of the results.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Aware Fuzzy Skyline Parking Recommendation Using Edge Traffic Facilities

Fan

Zhang

et al. 2021

IEEE Trans. Veh. Technol.

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Drivers have always been confronted with real-time parking difficulties when driving on urban roads, especially in crowded downtown or beauty spots. On the other hand, privacy leakage risks on users' private parking preferences and the sensitive data of parking lots have triggered increasing worries. Some literatures endeavor to improve parking service qualities through multi-consideration parking decision optimization on edge sides or cloud computing based on outsourced data storage. And some other literatures propose a number of privacy-preserving methods, such as cryptography and authentication, but these privacy strategies are at the expense of other qualities of parking services, especially the real-time performance. In this paper, we propose a fuzzy skyline parking recommendation scheme for realtime parking recommendation based on roadside traffic facilities. Linguistic parking information instead of raw parking-related data is used in fuzzy skyline fusion. We evaluated our solution with real-world data sets collected from parking facilities in Wulin downtown, Hangzhou city, China. The evaluation results show that our approaches achieve an average accuracy of parking recommendation over 91%, low communication cost, and quick response time with privacy protection.

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“…Vehicles cruising for on‐street parking contribute to a significant amount of congestion within a city's inner urban area [5, 6]. Based on 22 studies in different cities ranging from 1927 to 2015 as discussed in [6], the average cruising traffic share in a city is around 34% and drivers spent around 8 min searching for parking.…”

Section: Introductionmentioning

confidence: 99%

Prioritization‐based subsampling quality assessment methodology for mobility‐related information systems

Gomari

Knoth

Antoniou

2022

IET Intelligent Trans Sys

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Mobility‐related information systems, such as on‐street parking information (OSPI) systems have become more popular in the original equipment manufacturer (OEM) industry over the last decade. However, there is a lack of methods to assess their quality at a large scale. This paper introduces a data‐driven methodology to measure the true quality by fleet data prioritization‐based subsampling strategies (PSSs). It is applied to the use case of OSPI using parking events (PE), but is applicable to other mobility‐related information systems utilizing their respective fleet data. PSSs are defined based on neighbourhoods and time periods. Each PSS generates a unique set of spatio‐temporally important areas at different quadkey zoom levels over 168 week‐hours, called slices. The importance weight in each slice depends on the volume of PE within them. The algorithm for each PSS automatically selects important areas and time frames that are vital to be observed. Sample prediction models are used for the benefits assessment of the methodology by comparing it against non‐prioritized randomized selection of ground truth. It is proven that the methodology can lessen the effort of ground truth collection, while maintaining the amount of information necessary to assess the true quality of a prediction model.

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Routing for on-street parking search using probabilistic data

Cited by 6 publications

References 12 publications

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

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

Privacy-Aware Fuzzy Skyline Parking Recommendation Using Edge Traffic Facilities

Prioritization‐based subsampling quality assessment methodology for mobility‐related information systems

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