A Comparative Study of Urban Mobility Patterns Using Large-Scale Spatio-Temporal Data

Chiam, Jodi; Liu, Ying

doi:10.1109/icdmw.2018.00089

Cited by 3 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research study in [16] defines a mobility index by combining the frequency and geographical range of movements in order to capture temporal and spatial aspects of mobility behaviours. This index implies the calculation of several features such as the number of trips, the number of unique regions where users lingered for a significant amount of time (also called stay points ), the specific sequences of visited PoIs, the total distance and travel time.…”

Section: Related Workmentioning

confidence: 99%

A multi‐agent system for itinerary suggestion in smart environments

Paola

Gaglio

Giammanco

et al. 2021

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

Modern smart environments pose several challenges, among which the design of intelligent algorithms aimed to assist the users. When a variety of points of interest are available, for instance, trajectory recommendations are needed to suggest users the most suitable itineraries based on their interests and contextual constraints. Unfortunately, in many cases, these interests must be explicitly requested and their lack causes the so-called cold-start problem. Moreover, lengthy travelling distances and excessive crowdedness of specific points of interest make itinerary planning more difficult. To address these aspects, a multi-agent itinerary suggestion system that aims at assisting the users in an online and collaborative way is proposed. A profiling agent is responsible for the detection of groups of users whose movements are characterised by similar semantic, spatial and temporal features; then, a recommendation agent leverages contextual information and dynamically associates the current user with the trajectory clusters according to a Multi-Armed Bandit policy. Framing the trajectory recommendation as a reinforcement learning problem permits to provide high-quality suggestions while avoiding both cold-start and preference elicitation issues. The effectiveness of the approach is demonstrated by some deployments in real-life scenarios, such as smart campuses and theme parks.

show abstract

Section: Related Workmentioning

confidence: 99%

A multi‐agent system for itinerary suggestion in smart environments

Paola

Gaglio

Giammanco

et al. 2021

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…These subgraphs are motifs. For example, Davies and Marchione (2015), Atluri et al (2018), Pasquaretta et al (2021), Oberoi and Del Mondo (2021), Dang et al (2018) are an excellent selection of papers related with methods for detecting patterns using subgraphs and its properties. Jazayeri and Yang (2020) present a complete and updated review of motif discovery algorithms.…”

Section: Introductionmentioning

confidence: 99%

Identification of patterns for space-time event networks

et al. 2022

View full text Add to dashboard Cite

This paper provides new tools for analyzing spatio-temporal event networks. We build time series of directed event networks for a set of spatial distances, and based on scan-statistics, the spatial distance that generates the strongest change of event network connections is chosen. In addition, we propose an empirical random network event generator to detect significant motifs throughout time. This generator preserves the spatial configuration but randomizes the order of the occurrence of events. To prevent the large number of links from masking the count of motifs, we propose using standardized counts of motifs at each time slot. Our methodology is able to detect interaction radius in space, build time series of networks, and describe changes in its topology over time, by means of identification of different types of motifs that allows for the understanding of the spatio-temporal dynamics of the phenomena. We illustrate our methodology by analyzing thefts occurred in Medellín (Colombia) between the years 2003 and 2015.

show abstract

Analysis of human mobility behavior in Smart Cities and Smart Environments: A systematic literature review and taxonomy

Borges,

Aranda,

Barbosa

2024

SCS

View full text Add to dashboard Cite

The analysis of human mobility behavior through computational techniques finds applications in various domains and provides valuable insights for urban planning, transportation services, and a deeper understanding of human interactions in Smart Cities and Smart Environments. In this scenario, this study presents a Systematic Literature Review (SLR) with the following main question: How are computational techniques being used to analyse human mobility behavior in Smart Cities and Smart Environments? A total of 5989 articles were initially found and filtered, resulting in 56 articles reviewed. As the main contributions, this study provides responses to 19 research questions. A list of the challenges and the computational techniques identified is provided. The algorithms, machine learning techniques and data-sources used by the reviewed studies are also presented and organized through taxonomies. A comprehensive discussion of the identified techniques is conducted, finishing with a compilation of challenges, open issues and research opportunities. To the best of our knowledge, this is the first study that reviewed human mobility behavior covering a wide range of scenarios, including urban mobility, public transport, points and regions of interest, ridesharing, bike-sharing, traffic analysis, driving behavior, electric vehicle charging stations planning, mobility on demand, crowd analysis and others.

show abstract

A Comparative Study of Urban Mobility Patterns Using Large-Scale Spatio-Temporal Data

Cited by 3 publications

References 29 publications

A multi‐agent system for itinerary suggestion in smart environments

A multi‐agent system for itinerary suggestion in smart environments

Identification of patterns for space-time event networks

Analysis of human mobility behavior in Smart Cities and Smart Environments: A systematic literature review and taxonomy

Contact Info

Product

Resources

About