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

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

Investigating economic activity concentration patterns of co-agglomerations through association rule mining

Economic activity tends to concentrate in particular geographic areas forming agglomerations and co-locations of firms. These agglomerations bring benefits for the firms themselves by increasing productivity, access to human resources, labor pooling, innovation, knowledge spillovers and regional growth. In this paper, we present a method for the discovery and analysis of such agglomerations. The method allows to spot patterns of co-locations in the composition of the agglomerations. Those patterns identify important relationships between the firms compounding the agglomerations thus describing the dynamics that exists inside the agglomeration itself

Data fusion for city life event detection

The automatic detection of events happening in urban areas from mobile phones’ and social networks’ datasets is an important problem that would enable novel services ranging from city management and emergency response, to social and entertainment applications. In this work we present a simple yet effective method for discovering events from spatio-temporal datasets, based on statistical anomaly detection. Our approach can combine multiple sources of information to improve results. We also present a method to automatically generate a keyword-based description of the events being detected. We run experiments in two cities with data coming from a mobile phone operator (call detail records–CDRs) and from Twitter. We show that this method gives interesting results in terms of precision and recall. We analyze the parameters of our approach and discuss its strengths and weaknesses

Forecasting Crowd Distribution in Smart Cities

In this work we present a forecasting method that can be used to predict crowd distribution across the city. Specifically, we analyze and forecast cellular network traffic and estimate crowd on such basis. Our forecasting model is based on a neural network combined with time series decomposition techniques. Our analysis shows that this approach can give interesting results in two directions. First, it creates a forecasting solution that fits all the variability in our dataset without having to create specific features and without complex search procedures for optimal parameters. Second, the method performs well, showing to be robust even in the presence of spikes in the data thus enabling better applications such as event management and detection of crowd gathering