This paper presents a data-driven method for transit network design that relies on a large sample of user location data available from mobile phone telecommunication networks. Such data provide opportunistic sensing and the means for transit operators to match supply with mobility demand inferred from mobile phone locations. In contrast to previous methods of transit network design, the proposed method is entirely data driven, leveraging the large-sample properties of disaggregate mobile phone network data and mobility pattern mining. The method works by deriving frequent patterns of movements from anonymized mobile phone location data and merging them to generate candidate route designs. Additional routines for optimal route selection and service frequency setting are then employed to select a network configuration made up of routes that maximizes systemwide traveler utility. Using data from half a million mobile phone users in Abidjan from the telco operator Orange, we demonstrated to provide resource-neutral system improvement of 27% in terms of end-user journey times.

The deep penetration of mobile phones offers cities the ability to opportunistically monitor citizens' mobility and use data-driven insights to better plan and manage services. With large scale data on mobility patterns, operators can move away from the costly, mostly survey based, transportation planning processes, to a more data-centric view, that places the instrumented user at the center of development. In this framework, using mobile phone data to perform transit analysis and optimization represents a new frontier with significant societal impact, especially in developing countries. In this paper we present AllAboard, an intelligent tool that analyses cellphone data to help city authorities in visually exploring urban mobility and optimizing public transport. This is performed within a self contained tool, as opposed to the current solutions which rely on a combination of several distinct tools for analysis, reporting, optimisation and planning. An interactive user interface allows transit operators to visually explore the travel demand in both space and time, correlate it with the transit network, and evaluate the quality of service that a transit network provides to the citizens at very fine grain. Operators can visually test scenarios for transit network improvements, and compare the expected impact on the travellers' experience. The system has been tested using real telecommunication data for the city of Abidjan, Ivory Coast, and evaluated from a data mining, optimisation and user prospective.

Telecommunications operators (telcos) traditional sources of income, voice and SMS, are shrinking due to customers using over-the-top (OTT) applications such as WhatsApp or Viber. In this challenging environment it is critical for telcos to maintain or grow their market share, by providing users with as good an experience as possible on their network. But the task of extracting customer insights from the vast amounts of data collected by telcos is growing in complexity and scale everey day. How can we measure and predict the quality of a user's experience on a telco network in real-time? That is the problem that we address in this paper. We present an approach to capture, in (near) real-time, the mobile customer experience in order to assess which conditions lead the user to place a call to a telco's customer care center. To this end, we follow a supervised learning approach for prediction and train our Restricted Random Forest model using, as a proxy for bad experience, the observed customer transactions in the telco data feed before the user places a call to a customer care center. We evaluate our approach using a rich dataset provided by a major African telecommunication's company and a novel big data architecture for both the training and scoring of predictive models. Our empirical study shows our solution to be effective at predicting user experience by inferring if a customer will place a call based on his current context. These promising results open new possibilities for improved customer service, which will help telcos to reduce churn rates and improve customer experience, both factors that directly impact their revenue growth.

In this paper we address the use of mobile phone location data to build urban sensing applications. In the past decade, several research works have proposed the use of different types of location data from the telecommunication network to characterise people mobility in the city. Thus, several applications to infer urban dynamics where proposed. However, different papers have used different types of mobile phone location data, making it is difficult to understand whether a particular dataset provided by a telecom operator is indeed effective for a specific urban sensing application. In this paper we address this issue by comparing the quality of the insights extracted from different types of mobile phone location data, with specific reference to two urban sensing applications: people count by location, and people flow between locations. Experiments executed on a real dataset provided by a telecom operator in Belgium show the advantages of using network-driven mobile phone location data (collected regardless on whether people are using their phone) compared to the widely used Call Detail Records.

Together with the availability of new mobility data, the development of new intelligent transport systems (ITS) have increased, in order to provide new key performance indicators toward the improvement of the management of traffic awareness in cities. ITS rely on accurate transit infrastructure data that often contain erroneous information (e.g., inconsistencies or is out of date). In this paper, we propose a new methodology that makes use of GPS traces to automatically detect or correct bus stop locations, reconstruct bus route shapes, and estimate time schedules. The methodology performs different steps: 1) data cleaning and detection of trips; 2) bus stop extraction through data mining techniques; 3) route shape reconstruction; and 4) time schedule estimation. A case study using real GPS data from the City of Dublin, Ireland, is performed.

The deep penetration of mobile phones offers cities the ability to opportunistically monitor citizensfi mobility and use data-driven insights to better plan and manage services. In this context, transit operators can leverage pervasive mobile sensing to better match observed demand for travel with their service offerings. In this paper we present AllAboard, an intelligent tool that analyses cellphone data to helps city authorities in exploring urban mobility and optimizing public transport. An interactive user interface allows transit operators to explore the travel demand in both space and time, evaluate the quality of service that a transit network provides to the citizens, and test scenarios for transit network improvements. The system has been tested using real telecommunication data for the city of Abidjan, Ivory Coast, and evaluated from a data mining, optimization and user prospective.

In this paper, we present a personal journey advisor application for helping people to navigate the city using the available bike-sharing system. For a given origin and destination, the application suggests the best pair of stations to be used to take and return a city-bike, in order to minimize the overall walking and biking travel time as well as maximizing the probability to find available bikes at the first station and returning slots at the second one. To solve the journey advisor optimization problem, we modeled real mobile bikers' behavior in terms of travel time, and used the predicted availability at every bike station to choose the pair of stations which maximizes a measure of optimality. To develop the application, we built a spatio-temporal prediction system able to estimate the number of available bikes for each station in short and long term, outperforming already developed solutions. The prediction system is based on an underlying spatial interaction network among the bike stations, and takes into account the temporal patterns included in the data. The City ride application was tested with real data from the Dublin bike-sharing system.

Social and Community Intelligence (SCI) represents an emerging area that aims at revealing individual/group behaviors, social interactions as well as community dynamics by mining the digital traces left by people while interacting with cyber-physical spaces. The digital traces are generated mainly from three information sources: Internet and Web applications, static infrastructure, mobile devices and wearable sensors. In this workshop we hope to get people from different disciplines together to share their visions and insights on how to tackle the challenges faced by SCI, such as participatory sensing, heterogeneous data fusion, intelligence extraction, privacy issues, and so on.

This paper describes a new real-time urban monitoring system. The system uses the Localizing and Handling Network Event Systems (LocHNESs) platform developed by Telecom Italia for the real-time evaluation of urban dynamics based on the anonymous monitoring of mobile cellular networks. In addition, data are supplemented based on the instantaneous positioning of buses and taxis to provide information about urban mobility in real time, ranging from traffic conditions to the movements of pedestrians throughout the city. This system was exhibited at the Tenth International Architecture Exhibition of the Venice Biennale. It marks the unprecedented monitoring of a large urban area, which covered most of the city of Rome, in real time using a variety of sensing systems and will hopefully open the way to a new paradigm of understanding and optimizing urban dynamics.

A city offers thousands of social events a day, and it is difficult for dwellers to make choices. The combination of mobile phones and recommender systems can change the way one deals with such abundance. Mobile phones with positioning technology are now widely available, making it easy for people to broadcast their whereabouts, recommender systems can now identify patterns in people's movements in order to, for example, recommend events. To do so, the system relies on having mobile users who share their attendance at a large number of social events: cold-start users, who have no location history, cannot receive recommendations. We set out to address the mobile cold-start problem by answering the following research question: how can social events be recommended to a cold-start user based only on his home location? To answer this question, we carry out a study of the relationship between preferences for social events and geography, the first of its kind in a large metropolitan area. We sample location estimations of one million mobile phone users in Greater Boston, combine the sample with social events in the same area, and infer the social events attended by 2,519 residents. Upon this data, we test a variety of algorithms for recommending social events. We find that the most effective algorithm recommends events that are popular among residents of an area. The least effective, instead, recommends events that are geographically close to the area. This last result has interesting implications for location-based services that emphasize recommending nearby events.

In this paper, we propose a tool to explore human movement dynamics in a Metropolitan Area. By analyzing a mass of individual cell phone traces, we build a Human-City Interaction System for understanding urban mobility patterns at different user-controlled temporal and geographic scales. We solve the problems that are found in available tools for spatio-temporal analysis, by allowing seamless manipulability and introducing a simultaneous\multi-scale visualization of individual and aggregate flows. Our tool is built to support the exploration and discovery of urban mobility patterns and the daily interactions of millions of people. Moreover, we implement an intelligent algorithm to evaluate the level of mobility homophily of people moving from place to place.

Over the past decade there has been an explosion in the deployment of pervasive systems like cell phone networks and content aggregators on the Internet that produce massive amounts of data as by-products of their interaction with users. This data is related to the actions and opinions of people and thereby to the overall dynamics of cities, how they function and evolve over time.

Recent developments in the technology industry and academia show that users are increasingly becoming the focus in technology development. Not only new applications capable of performing certain tasks are brought to potential users, but also technical innovations that are built around people in order to best fit their needs. Beyond standard web and desktop applications, an ideal use of this principle would be in a digital representation of a real-world city which allows city inhabitants to access, add, and modify information and thus use the city in a more informed way. The term WikiCity represents this concept. The development of a WikiCity requires answers to questions such as: how can a city be sensed, modeled, and actuated in realtime? How can inhabitants interact with information in the city? The paper presents this idea through a use-case scenario in which users can inform themselves about certain events in the city. They can retrieve real-time information related to the events such as nearby happenings, availability of public transportation, and traffic conditions, or create a personalized event route. Users can also feed information back into the WikiCity system before, during, and after attending events. This requires new developments in modeling of human behavior, flexible back-end infrastructures, and semantic-web. The architecture of such system including the relations between data sources and user services, and data flow procedures are explained in the paper. A first implementation of the WikiCity concept was presented at the White Night event in Rome, Italy on September 8, 2007.

STEP-NC is a new model for data transfer between CAD/CAM and CNC that allows specifying machining process rather that tool motion with respect to the machine axes. STEP-NC CNC controllers are a new breed of CNC controllers that allow using this high-level model for a seamless integration in the CAD-CAM-CNC chain. Various prototypes of STEP-NC CNC controllers have been developed so far, mainly based on industrial PCs. In this paper we instead show how it is possible to realize a STEP-NC CNC controller using a low cost microcontroller, embedded in the CNC machine; this goal has been obtained by accurately parallelizing the tasks involved in the machining process. More precisely, this paper presents the design and realization of an embedded system able to control a CNC machine with two degrees of freedom, to machine STEP-NC part programs and to perform also other high level activities.