Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity

Making applications aware of the mobility experienced by the user can open the door to a wide range of novel services in different use-cases, from smart parking to vehicular traffic monitoring. In the literature, there are many different studies demonstrating the theoretical possibility of performing Transportation Mode Detection (TMD) by mining smart-phones embedded sensors data. However, very few of them provide details on the benchmarking process and on how to implement the detection process in practice. In this study, we provide guidelines and fundamental results that can be useful for both researcher and practitioners aiming at implementing a working TMD system. These guidelines consist of three main contributions. First, we detail the construction of a training dataset, gathered by heterogeneous users and including five different transportation modes; the dataset is made available to the research community as reference benchmark. Second, we provide an in-depth analysis of the sensor-relevance for the case of Dual TDM, which is required by most of mobility-aware applications. Third, we investigate the possibility to perform TMD of unknown users/instances not present in the training set and we compare with state-of-the-art Android APIs for activity recognition.Comment: Pre-print of the accepted version for the 14th Workshop on Context and Activity Modeling and Recognition (IEEE COMOREA 2018), Athens, Greece, March 19-23, 201

Automatic vacant parking places management system using multicamera vehicle detection

This paper presents a multicamera system for vehicles detection and their corresponding mapping into the parking spots of a parking lot. Approaches from the state-of-the-art, which work properly in controlled scenarios, have been validated using small amount of sequences and without more challenging realistic conditions (illumniation changes, different weather). On the other hand, most of them are not complete systems, but provide only parts of them, usually detectors. The proposed system has been designed for realistic scenarios considering different cases of occlussion, ilumination changes and different climatic conditions; a real scenario (the International Pittsburgh Airport parking lot) has been targeted with the condition that existing parking security cameras can be used, avoiding the deployment of new cameras or other sensors infrastructures. For design and validation, a new multicamera dataset has been recorded. The system is based on existing object detectors (the results of two of them are shown) and different proposed postprocessing stages. The results clearly show that the proposed system works correctly in challenging scenarios including almost total occlusions, illumination changes and different weather conditionsThis work has been partially supported by the Spanish Government FPU grant programme (Ministerio de Educación, Cultura y Deporte) and by the Spanish government under the project TEC2014-53176-R (HAVideo

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

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

Survey of smart parking systems

The large number of vehicles constantly seeking access to congested areas in cities means that finding a public parking place is often difficult and causes problems for drivers and citizens alike. In this context, strategies that guide vehicles from one point to another, looking for the most optimal path, are needed. Most contributions in the literature are routing strategies that take into account different criteria to select the optimal route required to find a parking space. This paper aims to identify the types of smart parking systems (SPS) that are available today, as well as investigate the kinds of vehicle detection techniques (VDT) they have and the algorithms or other methods they employ, in order to analyze where the development of these systems is at today. To do this, a survey of 274 publications from January 2012 to December 2019 was conducted. The survey considered four principal features: SPS types reported in the literature, the kinds of VDT used in these SPS, the algorithms or methods they implement, and the stage of development at which they are. Based on a search and extraction of results methodology, this work was able to effectively obtain the current state of the research area. In addition, the exhaustive study of the studies analyzed allowed for a discussion to be established concerning the main difficulties, as well as the gaps and open problems detected for the SPS. The results shown in this study may provide a base for future research on the subject.Fil: Diaz Ogás, Mathias Gabriel. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Fabregat Gesa, Ramon. Universidad de Girona; EspañaFil: Aciar, Silvana Vanesa. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentin

Trasovanie vozidla na otvorenom parkovisku na základe video detekcie

This article deals with topic of transport vehicles identification for dynamic and static transport based on video detection. It explains some of the technologies and approaches necessary for processing of specific image information (transport situation). The paper also describes a design of algorithm for vehicle detection on parking lot and consecutive record of trajectory into virtual environment. It shows a new approach to moving object detecti on (vehicles, people, and handlers) on an enclosed area with emphasis on secure parking. The create dapplication enables automatic identification of trajectory of specific objects moving within the parking area. The application was created in program language C++ with using an open source library OpenCV.Tento príspevok sa zaoberá problematikou identifikácie dopravných prostriedkov v dynamickej a statickej doprave na základe video- detekcie. Ozrejmuje niektoré technológie a postupy, ktoré sú pri spracovaní špecifickej (dopravné situácie) obrazovej informácie evyhnutné. V článku je ďalej opísaný navrhnutý algoritmus na detekciu vozidla s následným záznamom pohybovej trajektórie do virtuálneho prostredia. Práca prináša nový pohľad na detekciu ale hlavne záznam trajektórie pohybujúcich sa objektov (vozidiel, osôb, manipulátorov) na uzavretých plochách, pričom kladie hlavne dôraz aj na zvyšovanie konformity služieb otvorených parkovísk. Na základe takejto aplikácie je možné automatizovane identifikovať trajektóriu pohybu špecifického objektu po parkovisku. Aplikácia bola vytvorená v opensourse softvérovom prostriedku Microsoft Visual Studio s využitím knižnice OpenCV[1]

Exploiting Recurring Patterns to Improve Scalability of Parking Availability Prediction Systems

Parking Guidance and Information (PGI) systems aim at supporting drivers in finding suitable parking spaces, also by predicting the availability at driver’s Estimated Time of Arrival (ETA), leveraging information about the general parking availability situation. To do these predictions, most of the proposals in the literature dealing with on-street parking need to train a model for each road segment, with significant scalability issues when deploying a city-wide PGI. By investigating a real dataset we found that on-street parking dynamics show a high temporal auto-correlation. In this paper we present a new processing pipeline that exploits these recurring trends to improve the scalability. The proposal includes two steps to reduce both the number of required models and training examples. The effectiveness of the proposed pipeline has been empirically assessed on a real dataset of on-street parking availability from San Francisco (USA). Results show that the proposal is able to provide parking predictions whose accuracy is comparable to state-of-the-art solutions based on one model per road segment, while requiring only a fraction of training costs, thus being more likely scalable to city-wide scenarios