Cluster analysis of parking behaviour: A case study in Munich

Gomari, Syrus; Knoth, Christoph; Antoniou, Constantinos

doi:10.1016/j.trpro.2021.01.057

Cited by 14 publications

(14 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using the Gaussian mixture model (GMM), Fiez and Ratliff [40] studied the parking patterns of the Belltown neighborhood in Seattle. The study from Gomari et al [41] used parking events to model the parking behaviors in Munich. Different clustering methods can be applied to the smart parking data, and a comparative analysis is conducted by Piovesan et al [42].…”

Section: Data-driven Clustering Methodsmentioning

confidence: 99%

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

2022

Sustainability

View full text Add to dashboard Cite

Parking plays an essential role in urban mobility systems across the globe, especially in metropolises. Hong Kong is a global financial center, international shipping hub, fast-growing tourism city, and major aviation hub, and it thus has a high demand for parking. As one of the initiatives for smart city development, the Hong Kong government has already taken action to install new on-street parking meters and release real-time parking occupancy information to the public. The data have been released for months, yet, to the best of our knowledge, there has been no study analyzing the data and identifying their unique characteristics for Hong Kong. In view of this, we examined the spatio-temporal patterns of on-street parking in Hong Kong using the data from the new meters. We integrate the t-SNE and k-means methods to simultaneously visualize and cluster the parking occupancy data. We found that the average on-street parking occupancy in Hong Kong is over 80% throughout the day, and three parking patterns are consistently identified by direct data visualization and clustering results. Additionally, the parking patterns in Hong Kong can be explained using land-use factors. Overall, this study can help the government better understand the unique characteristics of on-street parking and develop smart management strategies for Hong Kong.

show abstract

Section: Data-driven Clustering Methodsmentioning

confidence: 99%

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

2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…This strategy builds on the previous PSS by aggregating similar neighbourhoods. The logic behind neighbourhood clustering, as explained in [36], is to group based on same temporal trend of parking dynamics (TTPD) (see Section 2.5). The proposed method of [36] suggests using hierarchical clustering and determining the optimum number of clusters based on the silhouette score metric and the analysing its dendrogram.…”

Section: Pss 4: Based On Neighbourhood Clusters and Timementioning

confidence: 99%

“…The logic behind neighbourhood clustering, as explained in [36], is to group based on same temporal trend of parking dynamics (TTPD) (see Section 2.5). The proposed method of [36] suggests using hierarchical clustering and determining the optimum number of clusters based on the silhouette score metric and the analysing its dendrogram. Applying this for the use case of on-street parking in Munich generates 7 neighbourhood clusters, where 2 (i.e.…”

Section: Pss 4: Based On Neighbourhood Clusters and Timementioning

confidence: 99%

“…clusters 3 and 5) of them occurring at peripheries have negligible importance for BMW as they have low volume shares. Having 5 valid clusters in the study area is sufficient, as also validated in the study of [36], since the neighbourhoods within central Munich are quite similar based on the BMW PE dataset. The PSS was only applied on zoom level 14 as the considered optimal size for modelling The importance weights of this PSS slices are shown in Fig- ure 6.…”

Section: Pss 4: Based On Neighbourhood Clusters and Timementioning

confidence: 99%

See 1 more Smart Citation

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

Gomari

Knoth

Antoniou

2022

IET Intelligent Trans Sys

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…On-street parking availability data in San Francisco, from stationary sensors and high-mileage vehicle probes were studied by Bock [18]. Incentive Parking Design in Regions -Conception, Technology and Pricing Policy researched by Hanzl [19] Cluster analysis of parking behavior: A case study in Munich researched by Gomari [20].…”

Section: Introductionmentioning

confidence: 99%

Vehicle Parking Security System with Face Recognition Detection Based on Eigenface Algorithm

Raharja

Fathansyah

Chamim

2021

Journal of Robotics and Control

View full text Add to dashboard Cite

RFID (Radio Frequency Identification) card technology is used for intelligent parking systems. Officers no longer need to record and check manually and repeatedly every vehicle that comes in and out with this system. The system has a weakness, namely if the RFId card is dropped or lost, the person who finds it can use the card. For the purpose of increasing security for users, one of the latest technologies is the use of facial recognition methods. The eigenface algorithm system is an algorithm used for face recognition. The algorithm is used as a training process for the previously inputted employee faces. The results of the training data are stored in a database which is then used as a key to recognize the face of the owner of the motorized vehicle who took the motorcycle. In the trial results of the data application, motorized vehicle owners enter when the facial recognition level is above 70%.

show abstract

Cluster analysis of parking behaviour: A case study in Munich

Cited by 14 publications

References 11 publications

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

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

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

Vehicle Parking Security System with Face Recognition Detection Based on Eigenface Algorithm

Contact Info

Product

Resources

About