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Implementation of a smart parking system providing predictions about real-time parking occupancy is considered to be crucial when managing limited parking resources. In this study, we present a methodology based on machine-learning regression models for predicting parking availability. We use traffic congestion information and garage occupancy as i...
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... This typically is the composition of a baseline model's feature set. A few studies incorporated traffic data in their parking prediction models [6], [15], [24], [25] this can be in the form of speed or their own engineered features to get traffic congestion indices. Some studies also have used parking-specific influencing factors such as parking pricing to understand changes in parking occupancy [26], [27]. ...
... Another one used logistic probability distribution and aggregating over all the observations [16]. XGBoost [37], one of the currently popular algorithms in various fields that uses a type of gradient tree boosting system that resembles an ensemble tree model, was employed by several studies [3], [7], [24] that showed the most promise in the use case of our proposed system as well. Google's research team used a single layer regression and feed forward deep neural network [31] for estimating difficult of parking using mainly google maps travel data. ...
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.
... Rather than using live-data of available slots in parking facilities, Ivan et al. [31] predict the availability of parking facilities using traffic congestion information -an indirect method to produce data using indicators. Using this work as motivation, we noted that the volume of the context queries in search of car parks would also follow the variation of the traffic volume. ...
The rapid growth in Internet of Things (IoT) has ushered in the way for better context-awareness enabling more smarter applications. Although for the growth in the number of IoT devices, Context Management Platforms (CMPs) that integrate different domains of IoT to produce context information lacks scalability to cater to a high volume of context queries. Research in scalability and adaptation in CMPs are of significant importance due to this reason. However, there is limited methods to benchmarks and validate research in this area due to the lack of sizable sets of context queries that could simulate real-world situations, scenarios, and scenes. Commercially collected context query logs are not publicly accessible and deploying IoT devices, and context consumers in the real-world at scale is expensive and consumes a significant effort and time. Therefore, there is a need to develop a method to reliably generate and simulate context query loads that resembles real-world scenarios to test CMPs for scale. In this paper, we propose a context query simulator for the context-aware smart car parking scenario in Australia's Melbourne Central Business District. We present the process of generating context queries using multiple real-world datasets and publicly accessible reports, followed by the context query execution process. The context query generator matches the popularity of places with the different profiles of commuters, preferences, and traffic variations to produce a dataset of context query templates containing 898,050 records. The simulator is executable over a seven-day profile which far exceeds the simulation time of any IoT system simulator. The context query generation process is also generic and context query language independent.
... MDVRP used a Time-Dependent Traveling Salesman algorithm, which looks for the fastest path to the next destination and then assigns the empty curbside parking spots in order to reduce the travel time for all drivers. In [38], Klandev, Tolevska et al. ...
... Previous research solved the parking problem as an optimization problem [24,26,27,33], game theoretic problem [28,30,31,34,35], queueing problem [39], and machine learning problem [38]. Different preferences were considered in this research, such as driving distance to the parking area, parking cost, physical positions, availability of parking resources, traffic congestion on the streets to the parking area, and driving time to the parking area. ...
Citation: Abdeen, M.A.R.; Nemer, I.A.; Sheltami, T.R. A Balanced Algorithm for In-City Parking Allocation: A Case Study of Al Madinah City. Sensors 2021, 21, 3148.
We worked on one of the most significant research directions in Smart City, i.e., Intelligent Transportation System (ITS). ITS encapsulates several domains, such as electronic vehicles notification systems, traffic information, smart parking, and environment. However, in this thesis, we target two of its important domains; i) Smart Parking, and ii) Road Traffic. We started our research with Smart Parking use case. Performing literature review, we realized that different Machine Learning (ML) and Deep Learning (DL) approaches have been used for smart parking solutions. In most of these proposed approaches, enclosed parking areas were targeted with different feature sets to predict the "occupancy rate" in parking areas. It inspired us to conduct a comparative analysis to answer following questions; Given the parking prediction use case, how do the traditional ML models perform as compared to complex DL models? Provided big data, can less complex, traditional ML models outperform complex DL models? How well these models can perform to predict the availability of the individual on-street parking spots rather than predicting the overall occupancy rate of an enclosed parking area. To answer these questions, we choose five well-known classical ML algorithms (K-Nearest Neighbours, Random Forest, Decision Tree) and DL algorithm (Multilayer Perceptron). To take our investigation into depth, we train Ensemble Learning Model, in which we combine all the above-mentioned ML and DL models. A huge parking dataset of city of Santander, Spain, has been used which consists of around 25 million records. We also propose to recommend available parking spots based on the current location of the driver. Moving forward with our research goals, we performed literature review on road traffic and found road traffic associated with air pollution and noise pollution often. However, to the best of our knowledge, air pollution & noise pollution have never been use d in traffic prediction problem. In this part of our research, firstly we used air pollution (CO, NO, NO2, NOx, and O3) along with the atmospheric variables, such as wind speed, wind direction, temperature, and pressure to improve the traffic forecasting in the city of Madrid. This successful experiment motivated us to extend our investigation to another factor, which is also strongly correlated with road traffic i.e., noise pollution. Hence, as an extension of our previous work, in this part of our research, we use noise pollution to improve the traffic prediction in the city of Madrid.
Searching for a free parking space can lead to traffic congestion, increasing fuel consumption, and greenhouse gas pollution in urban areas. With an efficient parking infrastructure, the cities can reduce carbon emissions caused by additional fuel combustion, waiting time, and traffic congestion while looking for a free parking slot. A potential solution to mitigating parking search is the provision of parking-related data and prediction. Previously many external data sources have been considered in prediction models; however, the underlying impact of contextual data points and prediction has not received due attention. In this work, we integrated parking occupancy, pedestrian, weather, and traffic data to analyze the impact of external factors on on-street parking prediction. A comparative analysis of well-known Machine (ML) Learning and Deep Learning (DL) techniques, including Multilayer Perceptron (MLP), Random Forest (RF), Decision Trees (DT), K-Nearest Neighbors (KNN), Gradient Boosting (GA), Adaptive Boosting (AB), and linear SVC for the prediction of
On-Street parking space availability has been conducted. The results show that RF outperformed other techniques evaluated with an average accuracy of 81% and an AUC of 0.18. The comparative analysis shows that less complex algorithms like RF, DT, and KNN outperform complex algorithms like MLP
in terms of prediction accuracy. All four data sources have positively impacted the prediction, and the proposed solution can determine the best possible parking slot based on weather conditions, traffic flow, and pedestrian volume. The experiments on live prediction showed an ingest rate of 0.1 and throughput of 0.3 events per second, demonstrating a fast and reliable prediction approach for available slots within a 5–10 min time frame. The study is scalable for larger time frames and faster predictions that can be implemented for IoT-based big data-driven environments for on-street and off-street parking.
