Andrea Sassi

Coordination Infrastructures for Future Smart Social Mobility Services

In this article, the authors introduce the vision of smart social mobility services, noting that widespread deployment will require the identification and implementation of a general-purpose coordination infrastructure to support the effective realization of such services.

Crowd Steering in Public Spaces: Approaches and Strategies

Smart phones and environmental sensors make it possible to dynamically monitor the positions and patterns of movements of people in urban areas and public spaces, identify or predict possible dangerous situations (e.g., overcrowded areas) or simply recognize the profitability of a different patterns of distribution and collective movements. In this article, we focus on the problem of using such technologies also to steer the movement of people in public and urban spaces, i.e., suggesting people where to move to eventually reach some desirable global configuration in terms of crowd distribution. In particular, we overview and motivate the general problem of crowd steering, identify the technologies that can be put into play, and the possible strategies to direct people movements. Following, we present the results of a set of simulations that we have performed to assess the effectiveness of two exemplary crowd steering strategies.

Towards a General Infrastructure for Location-based Smart Mobility Services

Penetration of smart phones and localization technologies are enabling a variety of smart location-based social mobility services that can notably improve the quality life of citizens on the go. Most existing solutions are shaped from a specific set of data sources processed through predefined computational flows to provide a specific class of services, and rarely strive for generality and urban-scale goals. The contribution of this paper is to presents an architectural model for a general-purpose distributed coordination infrastructure to support the dynamic composition of a variety of smart social mobility services, by focusing on the coordination of the agents involved to address both individual mobility needs and urban-scale mobility issues. Following, it analyzes the key requirements that the implementation of such infrastructure should satisfy. Finally, to exemplify, we show how the framework can be applied to a urban ride-sharing service.

Social or Green? A Data-Driven Approach for More Enjoyable Carpooling

Carpooling, i.e. the sharing of vehicles to reach common destinations, is often performed to reduce costs and pollution. Recent works on carpooling and journey planning take into account, besides mobility match, also social aspects and, more generally, non-monetary rewards. In line with this, we present a data-driven methodology for a more enjoyable carpooling. We introduce a measure of enjoyability based on peoples interests, social links, and tendency to connect to people with similar or dissimilar interests. We devise a methodology to compute enjoyability from crowd-sourced data, and we show how this can be used on real world datasets to optimize for both mobility and enjoyability. Our methodology was tested on real data from Rome and San Francisco. We compare the results of an optimization model minimizing the number of cars, and a greedy approach maximing the enjoyability. We evaluate them in terms of cars saved, and average enjoyability of the system. We present also the results of a user study, with more than 200 users reporting an interest of 39% in the enjoyable solution. Moreoever, 24% of people declared that sharing the car with interesting people would be the primary motivation for carpooling.

Urban Crowd Steering: An Overview

Smart phones and environmental sensors make it possible to dynamically monitor the positions and patterns of movements of people in urban areas and public spaces, identify or predict possible dangerous situations e.g., overcrowded areas or simply recognize the profitability of a different patterns of distribution and collective movements. In this overview paper, we focus on the problem of using such technologies also to steer the movement of people. In particular, this paper has the goal of motivating the general problem of crowd steering, identifying the technologies that can be put to play to enforce crowd steering strategies, and presenting the possible strategies that can be adopted to steer people movements, other than the key research challenges.

On Recommending Opportunistic Rides

Research on social and mobile technologies recently provided tools to collect and mine massive amounts of mobility data. Ride sharing is one of the most prominent applications in this area. While a number of research and commercial initiatives already proposed solutions for long-distance journeys, the opportunities provided by modern pervasive systems can be used to promote local, daily ride sharing within the city. We present a set of algorithms to analyze urban mobility traces and to recognize matching rides along similar routes. These rides are amenable for ride sharing recommendations. We validate the proposed methodology using data provided by a large Italian telecom operator. Assuming the full set of considered users are willing to accept 1-km detours, experimental results on two large cities show that more than 60% of trips could be saved. These results can be used to evaluate the potential of a ride sharing system before its actual deployment and to actually support an opportunistic ride sharing recommender system.

Opportunistic Ride Sharing via Whereabouts Analysis

Smart phones and social networking tools allow to collect large-scale data about mobility habits of people. These data can support advanced forms of sharing, coordination and cooperation possibly able to reduce the overall demand for mobility. We present a methodology, based on the extraction of suitable information from mobility traces, to identify rides along the same trajectories that are amenable for ride sharing. Results on a real dataset show that, assuming users are willing to share rides and tolerate 1Km detours, about 60% of trips could be saved.

PETRA: The PErsonal TRansport Advisor Platform and Services

Smart Cities applications are fostering research in many fields including Computer Science and Engineering. Data Mining is used to support applications such as the optimization of a public urban transit network and event detection. The aim of the PErsonal TRansport Advisor (PETRA) EU FP7 project is to develop an integrated platform to supply urban travelers with smart journey and activity advices, on a multi-modal network, while taking into account uncertainty, such as delays in time of arrivals, and variations of the walking speed.

Social Collective Awareness in Socio-Technical Urban Superorganisms

Smart cities are characterized by the close integration of ICT devices and humans. However, the vast majority of current deployments of smart technologies relies on sensing devices collecting data and data mining techniques squeezing little meanings out of them. Nevertheless, we believe that citizens integrated with ICT technologies could collaboratively constitute large-scale socio-technical superorganisms supporting collective awareness and behaviours. This paper clarifies our vision on urban superorganisms, identifies the key challenges towards their actual deployment and proposes a prototype architecture supporting their development.