Unveiling large-scale commuting patterns based on mobile phone cellular network data

Hadachi, Amnir; Pourmoradnasseri, Mozhgan; Khoshkhah, Kaveh

doi:10.1016/j.jtrangeo.2020.102871

Cited by 35 publications

(35 citation statements)

References 38 publications

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“…This way, all the existing information are exploited for the assignment, with the gravitational hypothesis coming into play only when no other relevant information is available. A final comparison is performed against [37], a recent study investigating commuting patterns in Tallinn by exploiting mobile phone cellular network data. The reference period of the study was May 2018 and the degree of precision was at district level.…”

Section: Resultsmentioning

confidence: 99%

“…Note that the two studies are built on very different datasets and the raw data from the mobile phone network were not available to the authors. Table 2 provides the results obtained by both the proposed workplace assignment method and the ones reported in [37].…”

Section: Resultsmentioning

confidence: 99%

“…The comparison is carried out by both comparing the visual pattern arising in Table 2 and by observing the delta arising from the two datasets. Only the distributions and not the absolute values could be compared since in [37] no absolute value is reported. [37] (bottom).…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data

Agriesti

Roncoli

Nahmias-Biran

2022

IJGI

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Agent-based modeling has the potential to deal with the ever-growing complexity of transport systems, including future disrupting mobility technologies and services, such as automated driving, Mobility as a Service, and micromobility. Although different software dedicated to the simulation of disaggregate travel demand have emerged, the amount of needed input data, in particular the characteristics of a synthetic population, is large and not commonly available, due to legit privacy concerns. In this paper, a methodology to spatially assign a synthetic population by exploiting only publicly available aggregate data is proposed, providing a systematic approach for an efficient treatment of the data needed for activity-based demand generation. The assignment of workplaces exploits aggregate statistics for economic activities and land use classifications to properly frame origins and destination dynamics. The methodology is validated in a case study for the city of Tallinn, Estonia, and the results show that, even with very limited data, the assignment produces reliable results up to a 500 × 500 m resolution, with an error at district level generally around 5%. Both the tools needed for spatial assignment and the resulting dataset are available as open source, so that they may be exploited by fellow researchers.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data

Agriesti

Roncoli

Nahmias-Biran

2022

IJGI

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“…Cellular Network-Based Data. At present, there are already many types of cellular data based on mobile phone collection [15]. According to different switching methods of the core network, cellular network-based data can be divided into two categories: circuit-switched (CS) domain and packet-switched (PS) domain.…”

Section: Dataset Characteristicsmentioning

confidence: 99%

“…Unlike traditional road-based point surveys and community-wide small sample censuses, the cellular network-based data are a full sample with a higher penetration rate and temporal resolution. A considerable amount of papers has conducted empirical studies to demonstrate the capability of cellular network-based data for capturing human movement patterns, identifying activity locations, monitoring transport patterns, inferring travel purposes, deriving OD matrices, and so on [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Changes in Intercity Highway Traffic Travel Patterns under the Impact of COVID-19

Sun

Jian

et al. 2021

Journal of Advanced Transportation

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The unprecedented COVID-19 pandemic impacts negatively on the security and development of human society. Comparison and analysis of intercity highway travel patterns before and during the COVID-19 pandemic can bring vital insights for the prevention and control of the pandemic. Empirical studies are conducted using cellular network-based datasets associated with two groups of city pairs in China heavily affected by COVID-19. Spatial matching, full-sample extrapolation, and trajectory feature analysis are adopted to attain travel volumes of intercity highways during four different periods. The reliability of origin-destination (OD) matrices calculated based on the cellular network-based dataset is demonstrated by comparing with the fluctuation trend of traffic count data. The empirical studies show that the OD flows associated with passenger cars on intercity highways in China decreased significantly during COVID-19. With the effective implementation of the pandemic prevention control policy and the orderly promotion of the recovery to work and production, the volumes of intercity highway OD flows returned to the pre-pandemic level in mid-April 2020. Besides, the peak of passenger car trips decreases and the time span for truck trips gets longer owing to implemented control measures in dealing with COVID-19. The results can be applied to the calculation of OD flows between most adjacent cities and analyze the intercity highway traffic travel patterns changes, which provide insightful implications for making intercity travel safety prevention and control policies under epidemic conditions.

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Transport planning and big data

Šemanjski

2023

Smart Urban Mobility

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Unveiling large-scale commuting patterns based on mobile phone cellular network data

Cited by 35 publications

References 38 publications

Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data

Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data

Analysis of Changes in Intercity Highway Traffic Travel Patterns under the Impact of COVID-19

Transport planning and big data

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