Making pervasive sensing possible: Effective travel mode sensing based on smartphones

Zhou, Xiaolu; Yu, Wei; Sullivan, W. C.

doi:10.1016/j.compenvurbsys.2016.03.001

Cited by 44 publications

(26 citation statements)

References 24 publications

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“…We have used Random Forests (RF), a supervised learning method for classification and regression that works by creating a number of decision trees on random subsets of data, and uses averaging to improve the predictive accuracy and control over-fitting (Breiman, 2001). It has been used in remote sensing and GIS for various purposes (Mutanga et al, 2012), for instance, for classifying movement trajectories (Zhou et al, 2016), for assessing fire risks (Conedera et al, 2015), and for identifying the typology of buildings (Hecht et al, 2015).…”

Section: Overview and Considerationsmentioning

confidence: 99%

Generating 3D city models without elevation data

Biljecki

Ledoux

Stoter

2017

Computers, Environment and Urban Systems

122

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Elevation datasets (e.g. point clouds) are an essential but often unavailable ingredient for the construction of 3D city models. We investigate in this paper to what extent can 3D city models be generated solely from 2D data without elevation measurements. We show that it is possible to predict the height of buildings from 2D data (their footprints and attributes available in volunteered geoinformation and cadastre), and then extrude their footprints to obtain 3D models suitable for a multitude of applications. The predictions have been carried out with machine learning techniques (random forests) using 10 different attributes and their combinations, which mirror different scenarios of completeness of real-world data. Some of the scenarios resulted in surprisingly good performance (given the circumstances): we have achieved a mean absolute error of 0.8 m in the inferred heights, which satisfies the accuracy recommendations of CityGML for LOD1 models and the needs of several GIS analyses. We show that our method can be used in practice to generate 3D city models where there are no elevation data, and to supplement existing datasets with 3D models of newly constructed buildings to facilitate rapid update and maintenance of data.

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Section: Overview and Considerationsmentioning

confidence: 99%

Generating 3D city models without elevation data

Biljecki

Ledoux

Stoter

2017

Computers, Environment and Urban Systems

122

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“…More recently, community participatory design has been enhanced with the development of social media related technologies that can gather information about residents and their daily habits and activities to inform design. Zhou et al [51] use smartphones to determine travel behavior. Campagna (2014) introduces Social Media Geographic Information (SMGI) to gather social media information from diverse sources and Spatial-Temporal Textual Analysis (STTx) to explore perceptions of the environment both spatially and temporally.…”

Section: Geodesign As Community Participatory Planningmentioning

confidence: 99%

Geodesign Processes and Ecological Systems Thinking in a Coupled Human-Environment Context: An Integrated Framework for Landscape Architecture

Deal

Larsen

2018

Sustainability

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Scholars from a variety of disciplines have been working to unravel the complexities of geodesign as an approach to tackling a host of problems. We argue that a mature understanding of geodesign requires a systemic perspective to organize the interconnections between ecological, social and economic conditions at multiple spatial and temporal scales. We reviewed definitions and perspectives of geodesign and key concepts of ecological systems thinking to develop a new framework for landscape architecture. We provide the state-of-the-art in geodesign within the context of systems thinking and coupled human-environmental resilience. We show that geodesign is capable to encourage public participation and interdisciplinary collaboration through its systemic planning processes and synergetic technologies. The thrust of geodesign-related research is the emerging paradigm of landscape-based sustainability. While landscape architecture is complex in many aspects, the integrated framework promotes our understanding about its social-ecological potential, spatial-temporal association and resilience of coupled human-environment systems. Based on the findings, we outline key contributions, implications, challenges and recommendations for future research.

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“…To overcome the influence of data noise, Thierry, Chaix, and Kestens () employed a kernel density‐based approach to identify activity locations. The concept of trajectory segmentation used in these approaches has also been extended to support many other studies, such as travel mode detection and driver behavior analysis (Vlahogianni & Barmpounakis, ; Zhou, Yu, & Sullivan, ). However, the performance of these approaches depends on the data quality of trajectories.…”

Section: Related Workmentioning

confidence: 99%

Identifying stops from mobile phone location data by introducing uncertain segments

Zhao

Yin²,

Shaw

et al. 2018

Transactions in GIS

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Identifying stops is a primary step in acquiring activity‐related information from mobile phone location data to understand the activity patterns of individuals. However, signal jumps in mobile phone location data may create “fake moves,” which will generate fake activity patterns of “stops‐and‐moves.” These “fake moves” share similar spatiotemporal features with real short‐distance moves, and the stops and moves of trajectories (SMoT), which is the most extensively used stop identification model, often fails to distinguish them when the dataset has coarse temporal resolution. This study proposes the stops, moves, and uncertainties of trajectories (SMUoT) model to address this issue by introducing uncertain segment analysis to distinguish “fake moves” and real short‐distance moves. A real mobile phone location dataset collected in Shenzhen, China is used to evaluate the performance of SMUoT. We find that SMUoT improves the performance (i.e., 15 and 19% increase in accuracy and recall rate for a one‐hour temporal resolution dataset, respectively) of stop identification and exhibits high robustness to parameter settings. With a better reliability of “stops‐and‐moves” pattern identification, the proposed SMUoT can benefit various individual activity‐related research based on mobile phone location data for many fields, such as urban planning, traffic analysis, and emergency management.

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Making pervasive sensing possible: Effective travel mode sensing based on smartphones

Cited by 44 publications

References 24 publications

Generating 3D city models without elevation data

Generating 3D city models without elevation data

Geodesign Processes and Ecological Systems Thinking in a Coupled Human-Environment Context: An Integrated Framework for Landscape Architecture

Identifying stops from mobile phone location data by introducing uncertain segments

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