Giusy Di Lorenzo

AllAboard: a system for exploring urban mobility and optimizing public transport using cellphone data

This paper describes a system to leverage data on cell phones to understand mobility patterns and presents a large-scale network design model for public transit while considering existing service offerings. The work is motivated by the rapid urbanization in growth market cities across the world, where adequate resources to develop detailed travel demand models may be absent. Urban growth is coupled with high cell phone penetration which transit operators can leverage to better match observed demand for travel with service offerings. Based on call detail records from a telecommunications operator in Abidjan, Cote d’Ivoire, this paper describes analytics and optimization techniques that result in system-wide journey time improvements of 10%. The main contribution of this paper is to demonstrate how big data analytics and optimization tools can transform data from opportunistic sensing in the real-world to improve urban mobility outcomes.

Human-mobility enabled wireless networks for emergency communications during special events

Abstract During social gatherings or emergency situations, infrastructure-based communication networks have difficulty operating given either increased traffic demand or possible damage. Nevertheless, current communication networks still rely on centralized networking paradigms. The adoption of a peer-to-peer communication paradigm would be better adapted to these needs, especially if it relies on the mobile phones that people normally carry, since they are automatically distributed where the communication needs are. However a question arises: can the spatio-temporal distribution of mobile phones enable a partially-connected ad hoc network that allows emergency communications to happen with an acceptable delay? To try to answer this question, we defined a methodology composed of three steps. First, the positions of seven hundred humans, spread over a metropolitan area, have been anonymously traced during a special gathering event. Then, with a multi-disciplinary approach, we have inferred the contact events from the humans’ traces. Finally, we have assessed the effectiveness of an ad hoc network established by the mobile phones to disseminate emergency information to the population in a timely fashion. The results reveal that the humans’ mobility can effectively enable emergency communications among a significant subset of mobile phones, although the connectivity of the network strictly depends on the number of cooperating devices and on the maximum allowed delay.

Human-mobility enabled networks in urban environments: Is there any (mobile wireless) small world out there?

In the last 10 years, new paradigms for wireless networks based on human mobility have gained the attention of the research community. These paradigms, usually referred to as Pocket Switched Networks or Delay Tolerant Networks, jointly exploit human mobility and store-and-forward communications to improve the connectivity in sparse or isolated networks. Clearly, understanding the human mobility patterns is a key challenge for the design of routing protocols based on such paradigms. To this aim, we anonymously collected the positions of almost two thousand mobile phone users, spread over a metropolitan area greater than 200km^2 for roughly one month. Then, with a multi-disciplinary approach, we estimated the mobility patterns from the collected data and, assuming Wi-Fi connectivity, we inferred the contact events among the devices to evaluate the connectivity properties of a human mobility-enabled wireless network. In a nutshell, the contribution of the paper is threefold: (i) it confirms some of the results obtained in smaller environments, such as the power-law distribution for contact and inter-contact times, allowing us to estimate the distribution parameters with high statistical significance; (ii) it addresses the feasibility of the transmission opportunities provided by human mobility to build a city-wide connected network for different forwarding strategies classes; (iii) it shows uncovered characteristics of the connectivity properties of human mobility, such as the presence of the small world phenomenon in wide-scale experiments.

AllAboard: visual exploration of cellphone mobility data to optimise public transport

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.

Identifying human spatio-temporal activity patterns from mobile-phone traces

Understanding and modeling peoples mobility is a crucial component of transportation planning and management. Research in this area was originally concentrated on modeling commuting flows as they generally account for a vast majority of trips. Nowadays however, more and more trips are done to perform other activities, such as leisure. Identifying the types of places visited during a trip can be beneficial to understand the performed activities and so characterize the daily mobility of a population. In this paper we analyze a large mobile phone location dataset to monitor human locations over the course of two week time interval. We then map human locations to geographical features of the visited places and use that to characterize the daily human mobility. A limited number of visited land use patterns is found that allows describing different types of people and their daily mobility choices. The resulting patterns are characterized with peculiar trip lengths and home locations, thus showing interesting insights into modeling human travel demand, with applications to transportation activity-based models and place recommender systems.

Data-Driven Transit Network Design From Mobile Phone Trajectories

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.

SPUD—Semantic Processing of Urban Data

Abstract We present SPUD , a semantic environment for cataloging, exploring, integrating, understanding, processing and transforming urban information. A series of challenges are identified: namely, the heterogeneity of the domain and the impracticality of a common model, the volume of information and the number of data sets, the requirement for a low entry threshold to the system, the diversity of the input data, in terms of format, syntax and update frequency (streams vs static data), the complex data dependencies and the sensitivity of the information. We propose an approach for the incremental and continuous integration of static and streaming data, based on Semantic Web technologies and apply our technology to a traffic diagnosis scenario. We demonstrate our approach through a system operating on real data in Dublin and we show that semantic technologies can be used to obtain business results in an environment with hundreds of heterogeneous datasets coming from distributed data sources and spanning multiple domains.

SaferCity: A System for Detecting and Analyzing Incidents from Social Media

This paper presents a system to identify and characterise public safety related incidents from social media, and enrich the situational awareness that law enforcement entities have on potentially-unreported activities happening in a city. The system is based on a new spatio-temporal clustering algorithm that is able to identify and characterize relevant incidents given even a small number of social media reports. We present a web-based application exposing the features of the system, and demonstrate its usefulness in detecting, from Twitter, public safety related incidents occurred in New York City during the Occupy Wall Street protests.

Open Innovation Portal: A collaborative platform for open city data sharing

In recent years, many agencies and government authorities have been moving toward opening up their datasets, allowing external parties to create applications that can mash up this data. As the amount and the variety of data is increasing, it is important to create good metadata (descriptions, geographical boundaries, limitations, etc.) in order to allow individuals, who may not be domain experts, to easily search and consume data. In this paper we propose the Open Innovation Portal (OIP), a collaborative platform that allows Cities to annotate, publish and provide access to urban data from multiple sources in an intuitive, consistent and scalable way through open standards. In collaboration with Dublin City authorities and National University of Ireland Maynooth, we implemented a first prototype for Dublin. In the demo, we show how the collaborative metadata creation process works, from the raw data to the publishable information, and how the collaborative platform can be implemented both for a mobile-phone and web application.

EXSED: An Intelligent Tool for Exploration of Social Events Dynamics from Augmented Trajectories

The dynamics of social events happening in large metropolitan areas are extremely complex. Location-based user generated data could be an exceptionally rich source of information about events, however the vastness and the heterogeneity of such information makes it almost impossible for city managers to have a comprehensive view. Some events are planned and advertised only among restricted communities, for many it is problematic to estimate the number of attendees, and in general it is very difficult to have information about citizen mobility in areas nearby or where events happen. In this paper, we describe EXSED, an interactive intelligent tool to support the visual exploration of social events dynamics along the spatial, temporal and organizational dimensions. Major functionalities include: (i) studying events that are either officially/unofficially planned and with global/local scope; (ii) extract discussion topics for an event and investigate their temporal and spatial profile; (iii) estimate event attendees and correlate their mobility patterns with the event evolution. We present preliminary results of a field study with officials from Dublin City Council (Ireland), who evaluated our tool on real world user-generated tweeter data and events in the city.