4,877 research outputs found
BuSCOPE: Fusing individual & aggregated mobility behavior for “Live” smart city services
While analysis of urban commuting data has a long and demonstrated history of
providing useful insights into human mobility behavior, such analysis has been
performed largely in offline fashion and to aid medium-to-long term urban
planning. In this work, we demonstrate the power of applying predictive
analytics on real-time mobility data, specifically the smart-card generated
trip data of millions of public bus commuters in Singapore, to create two novel
and "live" smart city services. The key analytical novelty in our work lies in
combining two aspects of urban mobility: (a) conformity: which reflects the
predictability in the aggregated flow of commuters along bus routes, and (b)
regularity: which captures the repeated trip patterns of each individual
commuter. We demonstrate that the fusion of these two measures of behavior can
be performed at city-scale using our BuScope platform, and can be used to
create two innovative smart city applications. The Last-Mile Demand Generator
provides O(mins) lookahead into the number of disembarking passengers at
neighborhood bus stops; it achieves over 85% accuracy in predicting such
disembarkations by an ingenious combination of individual-level regularity with
aggregate-level conformity. By moving driverless vehicles proactively to match
this predicted demand, we can reduce wait times for disembarking passengers by
over 75%. Independently, the Neighborhood Event Detector uses outlier measures
of currently operating buses to detect and spatiotemporally localize dynamic
urban events, as much as 1.5 hours in advance, with a localization error of 450
meters.Comment: ACM MobiSys 201
Estimating Movement from Mobile Telephony Data
Mobile enabled devices are ubiquitous in modern society. The information gathered by
their normal service operations has become one of the primary data sources used in the
understanding of human mobility, social connection and information transfer. This thesis
investigates techniques that can extract useful information from anonymised call detail records
(CDR). CDR consist of mobile subscriber data related to people in connection with the network
operators, the nature of their communication activity (voice, SMS, data, etc.), duration of the
activity and starting time of the activity and servicing cell identification numbers of both the
sender and the receiver when available.
The main contributions of the research are a methodology for distance measurements
which enables the identification of mobile subscriber travel paths and a methodology for
population density estimation based on significant mobile subscriber regions of interest. In
addition, insights are given into how a mobile network operator may use geographically located
subscriber data to create new revenue streams and improved network performance. A range of
novel algorithms and techniques underpin the development of these methodologies. These
include, among others, techniques for CDR feature extraction, data visualisation and CDR data
cleansing.
The primary data source used in this body of work was the CDR of Meteor, a mobile
network operator in the Republic of Ireland. The Meteor network under investigation has just
over 1 million customers, which represents approximately a quarter of the country’s 4.6 million
inhabitants, and operates using both 2G and 3G cellular telephony technologies.
Results show that the steady state vector analysis of modified Markov chain mobility
models can return population density estimates comparable to population estimates obtained
through a census. Evaluated using a test dataset, results of travel path identification showed
that developed distance measurements achieved greater accuracy when classifying the routes
CDR journey trajectories took compared to traditional trajectory distance measurements.
Results from subscriber segmentation indicate that subscribers who have perceived similar
relationships to geographical features can be grouped based on weighted steady state mobility
vectors. Overall, this thesis proposes novel algorithms and techniques for the estimation of
movement from mobile telephony data addressing practical issues related to sampling, privacy
and spatial uncertainty
Bikesharing and Bicycle Safety
The growth of bikesharing in the United States has had a transformative impact on urban transportation. Major cities have established large bikesharing systems, including Boston, Chicago, Denver, Minneapolis-Saint Paul, New York City, Salt Lake City, the San Francisco Bay Area, Seattle, Washington DC, and others. These systems began operating as early as 2010, and no fatalities have occurred within the US as of this writing. However, three have happened in North America—two in Canada and one in Mexico. Bikesharing has some qualities that appear inherently unsafe for bicyclists. Most prominently, helmet usage is documented to be quite low in most regions. Bikesharing is also used by irregular bicyclists who are less familiar with the local terrain. In this study, researchers take a closer look at bikesharing safety from qualitative and quantitative perspectives. Through a series of four focus groups, they discussed bikesharing usage and safety with bikesharing members and nonmembers in the Bay Area. They further engaged experts nationwide from a variety of fields to evaluate their opinions and perspectives on bikesharing and safety. Finally, researchers conducted an analysis of bicycle and bikesharing activity data, as well as bicycle and bikesharing collisions to evaluate injury rates associated with bikesharing when compared with benchmarks of personal bicycling. The data analysis found that collision and injury rates for bikesharing are lower than previously computed rates for personal bicycling. Experts and focus group participants independently pointed to bikesharing rider behavior and bikesharing bicycle design as possible factors. In particular, bikesharing bicycles are generally designed in ways that promote stability and limited speeds, which mitigate the conditions that contribute to collisions. Data analysis also explored whether there was evidence of a “safety in numbers benefit” that resulted from bikesharing activity. However, no significant impact from bikesharing activity on broader bicycle collisions could be found within the regions in which they operate. Discussion and recommendations are presented in the conclusion
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Modeling, Predicting and Capturing Human Mobility
Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility
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