Agent-based modeling to simulate road travel using Big Data from smartphone GPS: An application to the continental United States

Gurram, Sashikanth; Sivaraman, Vijay; Apple, Jonathan T.; Pinjari, Abdul Rawoof

doi:10.1109/bigdata47090.2019.9006339

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…And if we increase the resolution of the data (Jimenez et al, 2019), we can also understand the causal relationships related to consumption. In terms of infrastructure load, patterns of population movement (Gurram et al, 2019) offer exciting opportunities, but can also be integrated with the condition of buildings (Gouveia and Palma, 2019), which also supports the satisfaction of urban planning tasks (Milojevic-Dupont et al, 2020) at a higher level.…”

Section: The Importance Of the System Of Systems Approachmentioning

confidence: 96%

“…Coastal tourism monitoring (Kubo et al, 2020) can be integrated with traffic data (Hu et al, 2020) to optimize traffic management and thereby reduce pollutant emissions. The effect of transport on plant damage can be included (Meineke et al, 2020) as a factor to be analyzed, or we can use it (Gurram et al, 2019) to identify patterns in population movement.…”

Section: The Importance Of the System Of Systems Approachmentioning

confidence: 99%

“…Population movements (Gurram et al, 2019) affect water consumption (Mourtzios et al, 2021), can damage plants (Meineke et al, 2020), show the popularity of coastal areas (Kubo et al, 2020), but are also suitable for improving transport planning (Hu et al, 2020). Because the movement of residents is closely related to the infrastructure (Milojevic-Dupont et al, 2020), it is a very valuable input in urban planning.…”

Section: The Importance Of the System Of Systems Approachmentioning

confidence: 99%

“…Remote sensing of water consumption (Mourtzios et al, 2021), analysis of cloud water content (He et al, 2020), and the agricultural water management model (Ismail et al, 2020) contribute to the goal of clean water and sanitation (SDG6). Planning based on the analysis of traffic data (Hu et al, 2020), studying population movements (Gurram et al, 2019) and flooding models (Avand et al, 2021) support the goal of industry, innovation and infrastructure (SDG9). Climate-friendly urban planning (Milojevic-Dupont et al, 2020), monitoring the energy demand of buildings (Gouveia and Palma, 2019), and defining disaster resilience (Sasaki et al, 2020) play an important role in achieving sustainable cities and communities (SDG11).…”

Section: The Importance Of the System Of Systems Approachmentioning

confidence: 99%

See 3 more Smart Citations

The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

Sebestyén

Czvetkó

Abonyi

2021

Front. Environ. Sci.

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The aim of this paper is to provide an overview of the interrelationship between data science and climate studies, as well as describes how sustainability climate issues can be managed using the Big Data tools. Climate-related Big Data articles are analyzed and categorized, which revealed the increasing number of applications of data-driven solutions in specific areas, however, broad integrative analyses are gaining less of a focus. Our major objective is to highlight the potential in the System of Systems (SoS) theorem, as the synergies between diverse disciplines and research ideas must be explored to gain a comprehensive overview of the issue. Data and systems science enables a large amount of heterogeneous data to be integrated and simulation models developed, while considering socio-environmental interrelations in parallel. The improved knowledge integration offered by the System of Systems thinking or climate computing has been demonstrated by analysing the possible inter-linkages of the latest Big Data application papers. The analysis highlights how data and models focusing on the specific areas of sustainability can be bridged to study the complex problems of climate change.

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Section: The Importance Of the System Of Systems Approachmentioning

confidence: 96%

Section: The Importance Of the System Of Systems Approachmentioning

confidence: 99%

Section: The Importance Of the System Of Systems Approachmentioning

confidence: 99%

Section: The Importance Of the System Of Systems Approachmentioning

confidence: 99%

See 2 more Smart Citations

The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

Sebestyén

Czvetkó

Abonyi

2021

Front. Environ. Sci.

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“…For accuracy, researchers are exploring the adoption of LBS data sources such as data from telecom carriers, smartphonebased apps, and Bluetooth devices. A few studies have fully used geospatially accurate Global Positioning System (GPS) data for the simulation [15,16]. However, they only retain the start and end point information of GPS data, and discard the key waypoint data extracted from GPS data during the research process.…”

Section: Mesoscopic Simulationmentioning

confidence: 99%

Transportation Simulation Modeling and Location-Based Services Data Completion Based on a Data and Model Dual-Driven Approach

Wang,

Shi,

Chen

et al. 2024

Applied Sciences

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The evolving economic and technological landscape has brought about significant changes in travel behaviors and traffic patterns. These changes have led to the emergence of complex, multi-modal travel demands that interact with transportation networks, posing new challenges in transportation analysis and control. The primary objective of this study is to address these challenges by improving transportation modeling and data completeness using advanced modeling tools and transportation big data. We propose a dual-driven simulation model that integrates transportation simulation and big data. The approach begins by utilizing initial Location-Based Services (LBS) data to establish a mesoscopic multi-modal simulation model, which is then calibrated. This calibrated model is then employed to complete the missing trajectories of the LBS data. The innovative aspect of this dual-driven simulation model lies in its novel approach to constructing transportation models and completing LBS data, thereby enhancing both the simulation accuracy and the results of missing path completion. We conduct tests using the urban area of Hangzhou as an example, and the results show that the Normalized Root Mean Square Error (NRMSE) between the average link speeds in the simulation model and in real world observation is reduced to 24.1%. In the LBS data completion process, our proposed method achieves a travel mode identification accuracy of 95.3% for private car travel. Compared to the two baseline methods, the average accuracy of completed trajectories increases by 6.31% and 2.46%, respectively.

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Evaluating and Validating Stay Point Detection Algorithms with different GPS Log Intervals

Kato,

Luo,

Fourie

et al. 2024

Procedia Computer Science

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Agent-based modeling to simulate road travel using Big Data from smartphone GPS: An application to the continental United States

Cited by 4 publications

References 17 publications

The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

Transportation Simulation Modeling and Location-Based Services Data Completion Based on a Data and Model Dual-Driven Approach

Evaluating and Validating Stay Point Detection Algorithms with different GPS Log Intervals

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