An End-to-End Solution for Enabling Urban Cyclability: The Bike2Work Experience

Mobility plays a fundamental role in modern cities. How citizens experience the city, access its core services, and participate in city life, strongly depends on its mobility organization and efficiency. The challenges that municipalities face are very ambitious: on the one hand, administrators must guarantee their citizens the right to mobility and to easily access local services; on the other hand, they need to minimize the economic, social, and environmental costs of the mobility system. Municipalities are increasingly facing problems of traffic congestion, road safety, energy dependency and air pollution, and therefore encouraging a shift towards sustainable mobility habits based on active mobility is of central importance. Active modes, such as cycling, should be particularly encouraged, especially for local recurrent journeys (i.e., home-to-school, home-to-work). In this context, addressing and mitigating commuter-generated traffic requires engaging public and private stakeholders through innovative and collaborative approaches that focus not only on supply (e.g., roads and vehicles) but also on transportation demand management. In this paper, we propose an end-to-end solution for enabling urban cyclability. It supports the companies' Mobility Managers (MMs) acting on the promotion of active mobility for home-to-work commuting, helps the city administrators to understand the needed urban planning interventions, and motivates the citizens to sustainable mobility. To evaluate the effectiveness of the proposed solution we developed two analyses: the first to accurately analyze the user experience and any behaviour change related to the BIKE2WORK initiative, and the second to demonstrate how exploiting the collected data we can inform and possible guide the involved municipality (i.e., Ferrara, a city in Northern Italy) in improving the urban cyclability.Comment: 12 pages, 11 figure

Mobile Money: Understanding and Predicting its Adoption and Use in a Developing Economy

Access to financial institutions is difficult in developing economies and especially for the poor. However, the widespread adoption of mobile phones has enabled the development of mobile money systems that deliver financial services through the mobile phone network. Despite the success of mobile money, there is a lack of quantitative studies that unveil which factors contribute to the adoption and sustained usage of such services. In this paper, we describe the results of a quantitative study that analyzes data from the world's leading mobile money service, M-Pesa. We analyzed millions of anonymized mobile phone communications and M-Pesa transactions in an African country. Our contributions are threefold: (1) we analyze the customers' usage of M-Pesa and report large-scale patterns of behavior; (2) we present the results of applying machine learning models to predict mobile money adoption (AUC=0.691), and mobile money spending (AUC=0.619) using multiple data sources: mobile phone data, M-Pesa agent information, the number of M-Pesa friends in the user's social network, and the characterization of the user's geographic location; (3) we discuss the most predictive features in both models and draw key implications for the design of mobile money services in a developing country. We find that the most predictive features are related to mobile phone activity, to the presence of M-Pesa users in a customer's ego-network and to mobility. We believe that our work will contribute to the understanding of the factors playing a role in the adoption and sustained usage of mobile money services in developing economies.Comment: Accepted for publication in ACM IMWUT (Ubicomp) 201

The Rhythms of Transient Relationships: Allocating time between weekdays and weekends

A fundamental question of any new relationship is, will it last? Transient relationships, recently defined by the authors, are an ideal type of social tie to explore this question: these relationships are characterized by distinguishable starting and ending temporal points, linking the question of tie longevity to relationship finite lifetime. In this study, we use mobile phone data sets from the UK and Italy to analyze the weekly allocation of time invested in maintaining transient relationships. We find that more relationships are created during weekdays, with a greater proportion of them receiving more contact during these days of the week in the long term. The smaller group of relationships that receive more phone calls during the weekend tend to remain active for more time. We uncover a sorting process by which some ties are moved from weekdays to weekends and vice versa, mostly in the first half of the relationship. This process also carries more information about the ultimate lifetime of a tie than the part of the week when the relationship started, which suggests an early evaluation period that leads to a decision on how to allocate time to different types of transient ties.Comment: 15 pages, 4 figures. Submitted for review at Royal Society Open Science R

Investigating individual traits, network dynamics and economic behavior using mobile phone data

Recent years have witnessed a growing interest in analyzing the huge amount of human behavioral data generated by new technologies such as mobile phones, social media and credit cards. These technologies leave a trail of "digital breadcrumbs" that allow us to have new quantitative insights that may reveal patterns of individual and group behaviors. Moreover, it allows us to better understand human behavior at a fine-grained resolution and for periods of time that were previously inconceivable. Researchers can now observe human behavior, ask research questions and run experiments in ways that were simply impossible in the recent past due to qualitative methods that, despite their undeniable benefits, proved to be time and resource consuming and therefore difficult to apply to large scale studies. Studying social interaction and social networks extracted from these data sources, allow us to understand not only individual behaviors and their characteristics, but also to observe the relationships between individuals, the structure, the content and their dynamics over long periods of time. Given the capacity of mobile phones to capture real observations of communications between people, we took advantage of the data collected from these devices to further explore and investigate human behavior. Specifically, in this dissertation, we (i) present the Mobile Territorial Lab (MTL) project and illustrate the advantages of using a living lab approach to collect a longitudinal set of data from a target group of parents; (ii) investigate how the personality dispositions of an individual influence how (s)he manages her/his social network; (iii) investigate whether and how the behavior of an individual as sensed through her/his mobile phone behavior is related to the future adoption and use of the leading mobile money service M-Pesa

Personality traits and ego-network dynamics

Strong and supportive social relationships are fundamental to our well-being. However, there are costs to their maintenance, resulting in a trade-off between quality and quantity, a typical strategy being to put a lot of effort on a few high-intensity relationships while maintaining larger numbers of less close relationships. It has also been shown that there are persistent individual differences in this pattern; some individuals allocate their efforts more uniformly across their networks, while others strongly focus on their closest relationships. Furthermore, some individuals maintain more stable networks than others. Here, we focus on how personality traits of individuals affect this picture, using mobile phone calls records and survey data from the Mobile Territorial Lab (MTL) study. In particular, we look at the relationship between personality traits and the (i) persistence of social signatures, namely the similarity of the social signature shape of an individual measured in different time intervals; (ii) the turnover in egocentric networks, that is, differences in the set of alters present at two consecutive temporal intervals; and (iii) the rank dynamics defined as the variation of alter rankings in egocentric networks in consecutive intervals. We observe that some traits have effects on the stability of the social signatures as well as network turnover and rank dynamics. As an example, individuals who score highly in the Openness to Experience trait tend to have higher levels of network turnover and larger alter rank variations. On broader terms, our study shows that personality traits clearly affect the ways in which individuals maintain their personal networks

Crime, inequality and public health: a survey of emerging trends in urban data science

Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public safety; (ii) socioeconomic inequalities and segregation; and (iii) public health, with a particular focus on the city scale

Play&Go Corporate: An End-to-End Solution for Facilitating Urban Cyclability

Mobility plays a fundamental role in modern cities. How citizens experience the urban environment, access city core services, and participate in city life, strongly depends on its mobility organization and efficiency. The challenges that municipalities face are very ambitious: on the one hand, administrators must guarantee their citizens the right to mobility and to easily access local services; on the other hand, they need to minimize the economic, social, and environmental costs of the mobility system. Municipalities are increasingly facing problems of traffic congestion, road safety, energy dependency and air pollution, and therefore encouraging a shift towards sustainable mobility habits based on active mobility is of central importance. Active modes, such as cycling, should be particularly encouraged, especially for local recurrent journeys (e.g., home–to–school, home–to–work). In this context, addressing and mitigating commuter-generated traffic requires engaging public and private stakeholders through innovative and collaborative approaches that focus not only on supply (e.g., roads and vehicles) but also on transportation demand management. In this paper, we present and end-to-end solution, called Play&Go Corporate, for enabling urban cyclability and its concrete exploitation in the realization of a home-to-work sustainable mobility campaign (i.e., BIKE2 WORK) targeting employees of public and private companies. To evaluate the effectiveness of the proposed solution we developed two analyses: the first to carefully analyze the user experience and any behaviour change related to the BIKE2 WORK mobility campaign, and the second to demonstrate how exploiting the collected data we can potentially inform and guide the involved municipality (i.e., Ferrara, a city in Northern Italy) in improving urban cyclability

Mobile Money: Understanding and Predicting its Adoption and Use in a Developing Economy

Access to financial institutions is difficult in developing economies and especially for the poor. However, the widespread adoption of mobile phones has enabled the development of mobile money systems that deliver financial services through the mobile phone network. Despite the success of mobile money, there is a lack of quantitative studies that unveil which factors contribute to the adoption and sustained usage of such services. In this paper, we describe the results of a quantitative study that analyzes data from the world's leading mobile money service, M-Pesa. We analyzed millions of anonymized mobile phone communications and M-Pesa transactions in an African country. Our contributions are threefold: (1) we analyze the customers' usage of M-Pesa and report large-scale patterns of behavior; (2) we present the results of applying machine learning models to predict mobile money adoption (AUC=0.691), and mobile money spending (AUC=0.619) using multiple data sources: mobile phone data, M-Pesa agent information, the number of M-Pesa friends in the user's social network, and the characterization of the user's geographic location; (3) we discuss the most predictive features in both models and draw key implications for the design of mobile money services in a developing country. We find that the most predictive features are related to mobile phone activity, to the presence of M-Pesa users in a customer's ego-network and to mobility. We believe that our work will contribute to the understanding of the factors playing a role in the adoption and sustained usage of mobile money services in developing economies

Kernel density estimate of the distribution of the big five personality traits.

<p>The estimated probability density functions are computed using a non-parametric Gaussian kernel density estimator that employs Scott’s rule of thumb for bandwidth selection.</p

Self-distances of social signatures within subgroups.

<p>The median, the first quartile (Q1) and the third quartile (Q3) for each subgroup are reported. We performed the Kruskal-Wallis test (KW) and the Kolmogorov-Smirnov test (KS) in order to assess eventual differences between the distributions of the self distances of opposite subgroups (e.g. extroverts and introverts).</p