Short Term Prediction of Parking Area states Using Real Time Data and Machine Learning Techniques

Public road authorities and private mobility service providers need information derived from the current and predicted traffic states to act upon the daily urban system and its spatial and temporal dynamics. In this research, a real-time parking area state (occupancy, in- and outflux) prediction model (up to 60 minutes ahead) has been developed using publicly available historic and real time data sources. Based on a case study in a real-life scenario in the city of Arnhem, a Neural Network-based approach outperforms a Random Forest-based one on all assessed performance measures, although the differences are small. Both are outperforming a naive seasonal random walk model. Although the performance degrades with increasing prediction horizon, the model shows a performance gain of over 150% at a prediction horizon of 60 minutes compared with the naive model. Furthermore, it is shown that predicting the in- and outflux is a far more difficult task (i.e. performance gains of 30%) which needs more training data, not based exclusively on occupancy rate. However, the performance of predicting in- and outflux is less sensitive to the prediction horizon. In addition, it is shown that real-time information of current occupancy rate is the independent variable with the highest contribution to the performance, although time, traffic flow and weather variables also deliver a significant contribution. During real-time deployment, the model performs three times better than the naive model on average. As a result, it can provide valuable information for proactive traffic management as well as mobility service providers.Comment: Proc. of Transportation Research Board 2020 Annual Meeting, Washington D.C., USA, January 202

Electric vehicle charging process and parking guidance app

This research work presents an information system to handle the problem of real-time guidance towards free charging slot in a city using past date and prediction and collaborative algorithmssincethereisnoreal-timesystemavailabletoprovideinformationifachargingspotisfree or occupied. We explore the prediction approach using past data correlated with weather conditions. This approach will help the driver in the daily use of his electric vehicle, minimizing the problem of range anxiety, provide guidance towards charging spots with a probability value of being available forcharginginacontextfortheappandsmartcities. Thisworkhandlestheuncertaintyofthedrivers togetasuitableandvacantplaceatachargingstationbecausemissingreal-timeinformationfromthe systemandalsoduringthedrivingprocesstowardsthefreechargingspotcanbetaken. Weintroduce a framework to allow collaboration and prediction process using past related data.info:eu-repo/semantics/publishedVersio

Algoritmos de machine learning aplicados em edifícios inteligentes com elevada penetração de veículos elétricos

A presente dissertação discute o desenvolvimento de um método de previsão de ocupação para dois parques de estacionamentos residenciais no contexto de um edifício inteligente, a fim de se conhecer, antecipadamente, qual a taxa de ocupação desses parques de estacionamentos. Para concretizar tal objetivo, utilizaram-se dados históricos realísticos coletados por observação empírica e extrapolado para um ano. O modelo de previsão desenvolvido utiliza técnicas de machine learning com diversos algoritmos testados, entre eles, Decision Tree, Extra Tree, Logistic Regression, Random Forest, Naive Bayes, K-Nearest Neighbors e Support Vector Machine. No modelo proposto foi identificado qual destes algoritmos obteve melhor desempenho. Vários tipos de modelos foram testados com o objetivo de melhorar os resultados obtidos, bem como compreender o impacto de cada um dos tratamentos dos dados utilizados. A solução final teve seu desempenho validado, com métricas de avaliação com bons resultados, exatidão e precisão superiores a 80%, e se mostrou eficaz considerando os dados analisados e ainda o horizonte temporal da previsão.This dissertation is focused on the development of a prediction method for two residential car parks in the context of an intelligent building. The aim was to know in advance the occupancy rate of these car parks, using only historical data collected by empirical observation, and extrapolate for one year. The prediction model developed uses machine learning techniques with several tested algorithms (Decision Tree, Extra Tree, Logistic Regression, Random Forest, Naive Bayes, K- Nearest Neighbors and Support Vector Machine) to identify which of these algorithms performs better. Several types of models were tested with the objective of improve the results obtained, and understand the impact of each of the data treatments used. The final solution had its performance validated, with good evaluation metrics results. Accuracy and precision were higher than 80% and, therefore, the solution proved to be effective considering the data analyzed and the temporal horizon of the forecast

Predicting space occupancy for street paid parking

This dissertation discusses how to develop a prediction method for on-street parking space availability, using only historical occupancy data collected from on-street multi-space parking meters. It is analyzed how to transform the raw data into a dataset representing the occupancy and how can this information be used to detect when the parking spaces on a street are Vacant or Full. Attributes like weather conditions and holidays are added to the data, giving them more context and comprehension. After the data preparation and analysis, a prediction model is developed using machinelearning techniques that can forecast the availability of the parking spaces on a street at a specific day and on a given moment. For that, a classification method is implemented based on decision trees and neural networks, comparing both methods regarding results and development time. Particular attention is given to the algorithm parameters, to achieve the right balance between accuracy and computational time. The developed model proved effective, correctly capturing the different behavior of each street through the different weeks, and returning results useful to drivers searching for parking and to the business owners while monitoring their parking investments and returns.Esta dissertação apresenta como pode ser desenvolvido um método para previsão de disponibilidade de lugares de estacionamento em rua, utilizando dados históricos obtidos através de parquímetros de controlo a múltiplos lugares. É analisado como os dados em bruto dos parquímetros podem ser transformados num conjunto de dados que represente qual a ocupação dos lugares, e posteriormente como esta informação pode ser utilizada para detetar se o estacionamento em uma rua está livre ou ocupado. São adicionados também mais alguns atributos, como por exemplo informação sobre as condições meteorológicas ou que dias são feriados, dando mais algum contexto e compreensão à informação já existente. Após a preparação e análise dos dados, é desenvolvido um método de previsão utilizando técnicas de aprendizagem automática de modo a que seja possível saber qual a disponibilidade de estacionamento em uma rua, a um dia específico e a um determinado momento. Para isso, foi implementado um método de classificação baseado em árvores de decisão e redes neuronais, comparando ambos os métodos do ponto de vista dos resultados e do tempo de desenvolvimento. Foi dada especial atenção aos parâmetros utilizados em cada algoritmo, de modo a que haja um balanço entre a precisão e tempo de computação. O modelo desenvolvido mostrou ser eficaz, captando corretamente o comportamento de cada rua nas diferentes semanas, devolvendo resultados uteis aos condutores que procurem lugares de estacionamento e aos proprietários do negócio por lhes permitir monitorizar o desempenho dos seus investimentos em parques de estacionamento e qual o retorno