HappyParking

There are estimations that indicate that about half of the vehicles on the move are searching for parking and that more than 40% of the total fuel consumption is spent while looking for an available parking space. This also contributes to significant urban traffic congestion. So, it would be interesting to have software tools that can help drivers to park easily. For the application challenge, our group proposed a HappyParking application, which would offer some interesting benefits: It ackowledges the importance of considering parking in the context of a displacement between a source location and a target location. This implies that the final target location has to be considered when deciding an appropriate parking space. Moreover, the application can be integrated into existing GPS-based navigation applications. It considers multimodality, that is, that parking a car could be just a leg within a longer trip using different modes of transportation. It exploits real-time constraints (e.g., time-based parking restrictions). It can accommodate a variety of methods to capture information about available parking spaces (e.g., magnetic sensors on the parkings, crowdsourcing information provided by drivers releasing a spot, cars with different types of sensors able to detect free places, etc.). It supports different types of parking spaces: on-street parking, private parkings and garages, home parking available for rental during specific time periods, etc...

Parkkeerausten määrän ennustaminen tunneittain kausittaisesta datasta

Forecasting parking occupancy in city areas has become increasingly important to give the city and drivers a way to predict the available parking spaces. The city can use this information for planning and the drivers can predict where to park their car and avoiding the need of searching for a parking space. In this paper we introduce various prediction models for forecasting parking occupancy on an hourly level and compare their forecasting performance with a dataset of parking instances. The tested models include linear regression, gradient boosting, SARIMAX, TBATS, Facebook Prophet, and two neural network classes: long short-term memory and gated recurrent unit. The experimental model results were compared against each other, and the evaluated results suggest that gradient boosting is the best performing model for our dataset. The results are evaluated both in the error metrics and training times of the models.Parkkipaikkojen käyttöasteen ennustaminen kaupunkialueilla on tullut yhä tärkeämmäksi, jotta kaupungilla ja kuljettajilla on tapa ennakoida vapaana olevia pysäköintipaikkoja. Kaupunki voi käyttää ennusteita liikenteen suunnitteluun ja kuljettajat voivat ennakoida, mihin pysäköidä autonsa ja välttää pysäköintipaikan etsimisen tuomia haittapuolia, kuten bensan- ja ajankulutusta. Tässä työssä esittelemme erilaisia ennustemalleja pysäköintien käyttöasteen ennustamiseksi tunnin välein ja vertaamme niiden ennustekykyä pysäköintitapahtuma tietoaineistoa käyttäen. Testattuihin malleihin sisältyvät lineaarinen regressio, gradient boosting, SARIMAX, TBATS, Facebook Prophet sekä kaksi neuroverkkoluokkaa: long short-term memory ja gated recurrent unit. Mallien alustavat tulokset viittaavat siihen, että gradient boosting antaa parhaat tulokset työn aineistoa käytettäessä. Mallien vertailun perusteena käytettiin sekä suorituskykyä, että koulutusaikoja

Harnessing Intelligence in Implementing Smart Parking Systems Integrated with Interactive Guidance System in Distinctive Shindagha Historical Neighborhood to Enhance and Regulate Mass Experiences

Under the light of the importance of finding smart solutions for cities strategic issues and in conjunction with the broad race for urban development in order to provide the best living environment and the most appropriate tourist destination, Mobility Management occupies the first place among these issues. Due to the variation in the urban fabric of cities, especially the old historical neighborhoods that are unique in their nature, composition, and conditions for their preservation, challenges related to mobility management raised in these critical areas. Therefore, in this study, we present a smart suggestion to solve the problem of providing smart parking system in Shindagha Historical District in Dubai, that has recently been revived to be the largest open museum in world. This project includes the provision of a smart parking system, studied thoughtfully across the urban and logistical conditions, determinants of urban fabric with respecting the identity of this region, which is linked to a smart tourism guidance system based on a live data platform derived from the museums and facilities that exist in this neighborhood. Moreover, this project contains a smart proposal of current public parking, in order to provide smooth and fast parking in areas with reducing the needed time to find a parking. The proposed solution will be a source of income for the city in the parking management sector. The objectives of the proposed study revolve around shortening the time spent to find a parking in the crowded areas, limiting traffic congestion, planning dynamic path of vehicle movement, reducing unnecessary energy consumption, reducing environmental pollution and gases emissions, maximizing the exploitation of the resources and facilities available in it, and organizing the movement of masses under conditions maintaining security and safety, as proposed models have been suggested for the studied area, with consideration of suitability of the design to the main purpose of the solution, best methods of construction, logistical elements and special conditions. Data have been drawn from the relevant official authorities in Dubai and according to a work methodology based on algorithms of possible occurrences, estimated queue lengths in transport impact analysis and SWOT analysis, in order to evaluate the effectiveness of those proposed smart solutions to lead to the best, smartest and most sustainable model for managing parking lots and masses congestions in such distinctive neighborhoods

Sistema de reconocimiento sobre la disponibilidad de zonas para parqueo mediante redes neuronales convolucionales con imágenes en tiempo real en el Campus Sur de la Universidad Politécnica Salesiana.

El presente trabajo tiene como objetivo desarrollar una aplicación informática de reconocimiento de imágenes, sobre la disponibilidad de espacios de estacionamiento, de diferentes zonas o áreas, en base a redes neuronales convolucionales. En la red neuronal se implemento la arquitectura mAlexnet debido a su precisión y tiempo de respuesta sobre el set de datos objetivo CN- RPark+Ext. Las imágenes sobre las zonas de parqueo se extraen en tiempo real mediante el software que provee HIKVISION, fabricante de cámaras de monitoreo. Posteriormente las imágenes son segmentadas, pre procesadas, clasificadas y almacenadas en una base de datos a través de algoritmos codificados en PYTHON. Finalmente, se aplico programación en paralelo, para la clasificación de imágenes de los espacios de estacionamiento mediante la librería MULTIPROCESSING de Python. Los resultados obtenidos de la clasificación de imágenes fueron del 97.37% ´ de exactitud sobre el set de datos CNRPark+Ext y 95.91% sobre el set de datos local objetivo. Las pruebas de rendimiento en el procesamiento en paralelo permitieron concluir que este enfoque solo toma ventaja sobre el enfoque secuencial cuando se posee una red neuronal con un largo tiempo de respuesta o gran cantidad de espacios de estacionamiento por procesar a la vez, esto debido al tiempo de inicialización necesario para cada ejecución en paralelo.The objective of this work was to develop an image recognition application about parking spot availability detection, based on convolutional neural networks. The neural network was implemented through mAlexnet architecture due its accuracy and response time the objective data set CNRPark-Ext. Parking zone images are extracted in real time through HIKVISION software, manufacturer of the surveillance cameras. Subsequently images are segmented, preprocessed, classified and stored in a database through algorithms codified in PYTHON. Finally, parallel computing was applied for parking spot classification through the PYTHON MULTIPROCESSING library. Output results from image classification showed a 97.37% accuracy with the dataset CNRPark+Ext and 95.91% with the target dataset. Performance tests with parallel computing allowed us to conclude that this approach only takes advantage from the sequential approach when the processing scenario possess a neural network with a long response time or a high amount of parking spots to process concurrently, this owing to the initialization time needed to begin a parallel executio

Understanding and predicting trends in urban freight transport

Among different components of urban mobility, urban freight transport is usually considered as the least sustainable. Limited traffic infrastructures and increasing demands in dense urban regions lead to frequent delivery runs with smaller freight vehicles. This increases the traffic in urban areas and has negative impacts upon the quality of life in urban populations. Data driven optimizations are essential to better utilize existing urban transport infrastructures and to reduce the negative effects of freight deliveries for the cities. However, there is limited work and data driven research on urban delivery areas and freight transportation networks. In this paper, we collect and analyse data on urban freight deliveries and parking areas towards an optimized urban freight transportation system. Using a new check-in based mobile parking system for freight vehicles, we aim to understand and optimize freight distribution processes. We explore the relationship between areas' availability patterns and underlying traffic behaviour in order to understand the trends in urban freight transport. By applying the detected patterns we predict the availabilities of loading/unloading areas, and thus open up new possibilities for delivery route planning and better managing of freight transport infrastructures. © 2017 IEEE

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Experiences with GreenGPS – Fuel-Efficient Navigation using Participatory Sensing

Participatory sensing services based on mobile phones constitute an important growing area of mobile computing. Most services start small and hence are initially sparsely deployed. Unless a mobile service adds value while sparsely deployed, it may not survive conditions of sparse deployment. The paper offers a generic solution to this problem and illustrates this solution in the context of GreenGPS; a navigation service that allows drivers to find the most fuel-efficient routes customized for their vehicles between arbitrary end-points. Specifically, when the participatory sensing service is sparsely deployed, we demonstrate a general framework for generalization from sparse collected data to produce models extending beyond the current data coverage. This generalization allows the mobile service to offer value under broader conditions. GreenGPS uses our developed participatory sensing infrastructure and generalization algorithms to perform inexpensive data collection, aggregation, and modeling in an end-to-end automated fashion. The models are subsequently used by our backend engine to predict customized fuel-efficient routes for both members and non-members of the service. GreenGPS is offered as a mobile phone application and can be easily deployed and used by individuals. A preliminary study of our green navigation idea was performed in [1], however, the effort was focused on a proof-of-concept implementation that involved substantial offline and manual processing. In contrast, the results and conclusions in the current paper are based on a more advanced and accurate model and extensive data from a real-world phone-based implementation and deployment, which enables reliable and automatic end-to-end data collection and route recommendation. The system further benefits from lower cost and easier deployment. To evaluate the green navigation service efficiency, we conducted a user subject study consisting of 22 users driving different vehicles over the course of several months in Urbana-Champaign, IL. The experimental results using the collected data suggest that fuel savings of 21.5% over the fastest, 11.2% over the shortest, and 8.4% over the Garmin eco routes can be achieved by following GreenGPS green routes. The study confirms that our navigation service can survive conditions of sparse deployment and at the same time achieve accurate fuel predictions and lead to significant fuel savings.This research was sponsored in part by IBM Research and NSF Grants CNS 10-59294, CNS 10-40380 and CNS 13-45266.Ope

Intelligent Parking Assist for Trucks with Prediction

Truck parking has been identified as a major issue both in the USA and E.U. and has been selected by the American Transportation Research Institute (ATRI) as the most important research need for the trucking industry in 2015 [1]\u2013[5]. The lack of appropriate and convenient parking locations has been the cause of several safety issues over the past years as drivers might be forced to either drive while tired and increase the risk of accidents or park illegally in unsafe locations, which might also pose a safety hazard to them and other drivers. Additionally, the parking shortage also impacts the shipment costs and the environment as the drivers might spend more fuel looking for parking or idling for power when parked in inappropriate locations. The project\u2019s objective is to study the truck parking problem, generate useful information and parking assist algorithms that could assist truck drivers in better planning their trips. By providing information about parking availability to truck drivers, the authors expect to induce them to better distribute themselves among existing rest areas. This would decrease the peak demand in the most popular truck stops and attenuate the problems caused by the parking shortage. In this project, several parking availability prediction algorithms are tested using data from a company\u2019s private truck stops reservation system. The prediction MSE (mean squared error) and classification (full/available) sensitivity and specificity plots are evaluated for different experiments. It is shown that none of the tested algorithms is absolutely better than the others and has superior performance in all situations. The results presented show that a more efficient way would be to combine them and use the most appropriate one according to the situation. A model assignment according to current time of the day and target time for prediction is proposed based on the experiment data

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