Forecasting Parking Lots Availability: Analysis from a Real-World Deployment

Smart parking technologies are rapidly being deployed in cities and public/private places around the world for the sake of enabling users to know in real time the occupancy of parking lots and offer applications and services on top of that information. In this work, we detail a real-world deployment of a full-stack smart parking system based on industrial-grade components. We also propose innovative forecasting models (based on CNN-LSTM) to analyze and predict parking occupancy ahead of time. Experimental results show that our model can predict the number of available parking lots in a ±3% range with about 80% accuracy over the next 1-8 hours. Finally, we describe novel applications and services that can be developed given such forecasts and associated analysis

Comparing deep learning and statistical methods in forecasting crowd distribution from aggregated mobile phone data

Accurately forecasting how crowds of people are distributed in urban areas during daily activities is of key importance for the smart city vision and related applications. In this work we forecast the crowd density and distribution in an urban area by analyzing an aggregated mobile phone dataset. By comparing the forecasting performance of statistical and deep learning methods on the aggregated mobile data we show that each class of methods has its advantages and disadvantages depending on the forecasting scenario. However, for our time-series forecasting problem, deep learning methods are preferable when it comes to simplicity and immediacy of use, since they do not require a time-consuming model selection for each different cell. Deep learning approaches are also appropriate when aiming to reduce the maximum forecasting error. Statistical methods instead show their superiority in providing more precise forecasting results, but they require data domain knowledge and computationally expensive techniques in order to select the best parameters

Analisis Perubahan Penggunaan Lahan Berdasarkan Hasil Interpretasi Visual Citra Satelit Untuk Penerimaan Pbb (Studi Kasus : Kecamatan Semarang Utara)

Pesatnya pembangunan menyebabkan tingginya Perubahan pola penggunaan lahan. Lahan yang dulunya merupakan lahan kosong atau lahan tidak terbangun, banyak mengalami Perubahan fungsi menjadi lahan terbangun. Perubahan penggunaan lahan dapat di monitoring menggunakan data spasial remot sensing. Akusisi data remote sensing secara berseri dari waktu ke waktu memungkinkan untuk melakukan analisis Perubahan lahan. Citra yang dipakai dalam penelitian adalah Citra Ikonos tahun 2007, sedangkan pembandingnya merupakan peta penggunaan lahan kecamatan Semarang Utara tahun 2009. Software yang digunakan adalah E.R. Mapper 7.0 dan Arc.GIS 10. Proses rektifikasi menggunakan metode Map to Image dimana titik GCP diperoleh berdasarkan data sekunder dari peta yang mempunyai liputan yang sama dengan citra yang akan dikoreksi. Berdasarkan pengolahan citra Ikonos tahun 2007 dan peta penggunaan lahan tahun 2009 didapatkan Perubahan luas penggunaan lahan sebesar 62,656 Ha. Dengan adanya Perubahan luas tersebut dapat mempengaruhi Perubahan harga NJOP, Perubahan harga NJOP yang terjadi sebesar 21,6 %

Simplicial temporal networks from Wi-Fi data in a university campus: The effects of restrictions on epidemic spreading

Wireless networks are commonly used in public spaces, universities, and public institutions and provide accurate and easily accessible information to monitor the mobility and behavior of users. Following the application of containment measures during the recent pandemic, we analyzed extensive data from the Wi-Fi network in a university campus in Italy during three periods, corresponding to partial lockdown, partial opening, and almost complete opening. We measured the probability distributions of groups and link activations at Wi-Fi access points, investigating how different areas are used in the presence of restrictions. We ranked the hotspots and the area they cover according to their crowding and to the probability of link formation, which is the relevant variable in determining potential outbreaks. We considered a recently proposed epidemic model on simplicial temporal networks, and we used the measured distributions to infer the change in the reproduction number in the three phases. Our data show that additional measures are necessary to limit the spread of epidemic in the total opening phase due to the dramatic increase in the number of contacts

Towards Smart Cities for Tourism: the POLIS-EYE Project

Novel and widespread ICT and Internet of Things (IoT) technology can provide fine-grained real-time information to the tourist sector, both to support the demand side (tourists) and the supply side (managers and organizers). We present the POLIS-EYE project that aims to build decision-support systems helping tourist-managers to organize and optimize policies and resources. In particular, we focus on a service to monitor and forecast people presence in tourist areas by combining heterogeneous datasets with a special focus on data collected from the mobile phone network

Resilient distributed collection through information speed thresholds

Part 6: Large-Scale Decentralised SystemsInternational audienceOne of the key coordination problems in physically-deployed distributed systems, such as mobile robots, wireless sensor networks, and IoT systems in general, is to provide notions of “distributed sensing” achieved by the strict, continuous cooperation and interaction among individual devices. An archetypal operation of distributed sensing is data summarisation over a region of space, by which several higher-level problems can be addressed: counting items, measuring space, averaging environmental values, and so on. A typical coordination strategy to perform data summarisation in a peer-to-peer scenario, where devices can communicate only with a neighbourhood, is to progressively accumulate information towards one or more collector devices, though this typically exhibits problems of reactivity and fragility, especially in scenarios featuring high mobility. In this paper, we propose coordination strategies for data summarisation involving both idempotent and arithmetic aggregation operators, with the idea of controlling the minimum information propagation speed, so as to improve the reactivity to input changes. Given suitable assumptions on the network model, and under the restriction of no data loss, these algorithms achieve optimal reactivity. By empirical evaluation via simulation, accounting for various sources of volatility, and comparing to other existing implementations of data summarisation algorithms, we show that our algorithms are able to retain adequate accuracy even in high-variability scenarios where all other algorithms are significantly diverging from correct estimations

A guided tour of asynchronous cellular automata

Research on asynchronous cellular automata has received a great amount of attention these last years and has turned to a thriving field. We survey the recent research that has been carried out on this topic and present a wide state of the art where computing and modelling issues are both represented.Comment: To appear in the Journal of Cellular Automat