We are witnessing how urban areas are reclaiming road space, before devoted exclusively to cars, for pedestrians. With the increase of pedestrian activity, we need to update our existing transportation forecasting models by focusing more on people walking. The first step of extending the current models is to start with collecting information on pedestrians needed for the trip generation phase. This article discusses opportunities and limitations of tracking pedestrian activity by utilizing information provided by cellular networks. In order to track people, regardless of the underlying wireless media, two qualifications must be met: first, unique and anonymous identification, and second, geospatial visibility through time. While the latter requirement can be achieved with techniques that are similar for different wireless media, how to uniquely identify a pedestrian using a cellular network is domain-specific. We show that tracking of pedestrians using cellular networks can be done not only without their constant active participation, but also without disrupting normal cellular service. However, although this method is technically feasible, one should be very careful when wanting to implement it by keeping in mind a very important thing: how to protect people’s privacy.

 

Opportunities and Challenges of Trip Generation Data Collection Techniques Using Cellular Networks

Iva Bojic ; Yuji Yoshimura ; Carlo Ratti

IEEE Communications Magazine > Volume: 55 Issue: 3

Source: ieeexplore.ieee.org

Computational physicist Sharon Glotzer is uncovering the rules by which complex collective phenomena emerge from simple building blocks.

Source: www.wired.com

Sensors, coupled with transceivers, have quickly evolved from technologies purely confined to laboratory test beds to workable solutions used across the globe. These mobile and connected devices form the nuts and bolts required to fulfill the vision of the so-called internet of things (IoT). This idea has evolved as a result of proliferation of electronic gadgets fitted with sensors and often being uniquely identifiable (possible with technological solutions such as the use of Radio Frequency Identifiers). While there is a growing need for comprehensive modeling paradigms as well as example case studies for the IoT, currently there is no standard methodology available for modeling such real-world complex IoT-based scenarios. Here, using a combination of complex networks-based and agent-based modeling approaches, ​we present a novel approach to modeling the IoT. Specifically, the proposed approach uses the Cognitive Agent-Based Computing (CABC) framework to simulate complex IoT networks. We demonstrate modeling of several standard complex network topologies such as lattice, random, small-world, and scale-free networks. To further demonstrate the effectiveness of the proposed approach, we also present a case study and a novel algorithm for autonomous monitoring of power consumption in networked IoT devices. We also discuss and compare the presented approach with previous approaches to modeling. Extensive simulation experiments using several network configurations demonstrate the effectiveness and viability of the proposed approach.

 

Modeling the internet of things: a hybrid modeling approach using complex networks and agent-based models
Komal Batool and Muaz A. Niazi
Complex Adaptive Systems Modeling 2017 5:4
DOI: 10.1186/s40294-017-0043-1

Source: casmodeling.springeropen.com

As the bestsellers started piling up, from 2008’s Predictably Irrational and Nudge to 2011’s Thinking Fast, Thinking Slow, the buzz around behavioral economics — the science and practice of nudging…

Source: medium.com