Evaluation of Data Mining Techniques and Its Fusion with IoT Enabled Smart Technologies for Effective Prediction of Available Parking Space

After experiencing the hard times of pandemic situations we learned that if we could have a smart system that can help us in automatic parking of the vehicles then it could be a great help to society. This idea motivated us to carry out this current work. Though, nowadays, in almost every application domain, IoT techniques are the buzzword. IoT techniques can also be used to achieve efficacy in predicting free available parking space in advance. But the biggest challenge with IoT techniques is that they generate numerous data, which makes its analysis intangible. It was realized that if IoT techniques can be fused with outperforming data mining techniques, more efficient predictions can be performed. Thus, for this purpose, the main objective of our paper is to firstly, select the most appropriate data mining technique, based on performance evaluation, and then to perform prediction of available parking space in advance by fusing it with IoT techniques. Due to the busy schedule, the drivers need to get information about free parking spaces in advance by using smart phones. With the help of this information, it will be easy for the drivers to park their vehicle in the exact location without wasting their precious time and will maintain social distancing in crowded areas too. Data mining techniques can play an important role in the prediction of available parking space, by extracting only relevant and important information when applied to the given dataset. For this purpose, a comparative analysis of five data mining techniques such as the Support Vector Machine, K- Nearest approach, Decision Tree, Random Forest, and Ensemble learning approaches are applied on PK lot data set by using Python language. For calculation of result anaconda (spyder) is used as a supportive tool. The main outcome of the paper is to find the technique that will give better results for the prediction of the available space and if we fused data mining techniques with IoT technologies results are improvised. Evaluation parameters that are used for finding the best technique are precision, recall, accuracy, and F1-Score. For numerical calculation of the results, the k-fold cross-validation method is used. As the empirical results are calculated using the Pk lot dataset, the decision tree outperformed the best among all the techniques that are selected for analysis

Spartan Daily, March 26, 1965

Volume 52, Issue 95https://scholarworks.sjsu.edu/spartandaily/4710/thumbnail.jp

August 30, 2018

https://egrove.olemiss.edu/thedmonline/1072/thumbnail.jp

The Economic Impact of the Redevelopment of Georgetown, Connecticut: The Former Gilbert and Bennett Wire Mill, Main Street, and Old Mill Road

Redevelopment of old mill creates vibrant new spaces, preserves green space, creates densitydevelopment, redevelopment

A Combinatorial Miscellany: Antipodes, Parking Cars, and Descent Set Powers

In this dissertation we first introduce an extension of the notion of parking functions to cars of different sizes. We prove a product formula for the number of such sequences and provide a refinement using a multi-parameter extension of the Abel--Rothe polynomial. Next, we study the incidence Hopf algebra on the noncrossing partition lattice. We demonstrate a bijection between the terms in the canceled chain decomposition of its antipode and noncrossing hypertrees. Thirdly, we analyze the sum of the th powers of the descent set statistic on permutations and how many small prime factors occur in these numbers. These results depend upon the base expansion of both the dimension and the power of these statistics. Finally, we inspect the ƒ-vector of the descent polytope DPv, proving a maximization result using an analogue of the boustrophedon transform

The BG News September 20, 2002

The BGSU campus student newspaper September 20, 2002. Volume 91 - Issue 20https://scholarworks.bgsu.edu/bg-news/7998/thumbnail.jp

The Cowl - v.80 - n.7 - Oct 29, 2015

The Cowl - student newspaper of Providence College. Volume 80 - No. 7 - October 29, 2015. 24 pages

Parking guiding system with occupation prediction

Parking availability is an increasingly scarce and expensive resource within large cities, and this problem is considered to be one of the most critical transportation management system inside a big city. To approach this problem a proof of concept is presented as a way to guide a driver to the possible free parking lot through a prediction process using past data, correlated with traffic, weather conditions and time period features (year, month, day, holidays, and so on). A feature selection was performed by the study of data patterns, in order to understand the parking lot affluence and how certain features influence them, as well as to comprehend the sudden changes in the total occupation of the parking lot and which features really matter and have an impact on the total occupation. Those conclusions helped to create a robust and efficient predictive model in order to predict the parking lot availability rate more accurately. Three algorithms were used to build the predictive models as a way to test the most efficient and accurate one, namely Gradient Boosting Machine, Decision Random Forest and Neural Networks. Various types of models were tested with the aim of improving the results obtained, as well as understanding the impact of each of the processing of the data used. To complement this, a decision algorithm was created to guide the driver to the most optimal parking lot that presents better conditions, taking into account the location and driver characteristics, like the park more likely to have an available parking space, closer to the user’s current position or a more attractive price for the driver. Finally, these developments are integrated into a mobile application in order to work like an interface that the driver can interact.A disponibilidade de estacionamento é um recurso cada vez mais escasso e caro nas grandes cidades, e este problema é considerado um dos mais críticos nos sistemas de gestão de transportes dentro de uma grande cidade. Para abordar este problema, uma prova de conceito é apresentada como uma forma de guiar um condutor para o parque de estacionamento com lugares disponíveis através de um processo de previsão usando dados passados, correlacionados com o tráfego, condições climáticas e características do período de tempo (ano, mês, dia, feriados, e assim por diante). Uma seleção de características foi realizada pelo estudo de padrões de dados, a fim de entender a afluência do estacionamento e como certas características os influenciam, bem como para compreender as mudanças repentinas na ocupação total do estacionamento e quais características realmente importam e têm um impacto sobre a ocupação total. Essas conclusões ajudaram a criar um modelo preditivo robusto e eficiente a fim de prever a taxa de disponibilidade do estacionamento com mais precisão. Três algoritmos foram usados para construir os modelos preditivos como forma de testar o mais eficiente e preciso, a saber: Gradient Boosting Machine, Decision Random Forest e Neural Networks. Foram também testados vários tipos de modelos com o objetivo de melhorar os resultados obtidos, bem como compreender o impacto de cada um dos processamentos de dados utilizados. Para complementar, foi criado um algoritmo de decisão para orientar o condutor para o parque de estacionamento mais indicado e que apresente melhores condições, tendo em conta a localização e as características do condutor, como o mais provável de ter um lugar de estacionamento disponível, mais próximo da posição atual do utilizador ou um preço mais atrativo para o condutor. Finalmente, estes desenvolvimentos são integrados numa aplicação móvel de forma a que o utilizador consiga aceder através de uma interface