Residency and worker status identification based on mobile device location data

Pan, Yixuan; Sun, Qianqian; Yang, Mofeng; Darzi, Aref; Zhao, Guangchen; Kabiri, Aliakbar; Xiong, Chenfeng; Zhang, Lei

doi:10.1016/j.trc.2022.103956

Cited by 4 publications

(8 citation statements)

References 49 publications

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“…The study follows the behavior-based method from Pan ( 62 ) and Pan et al ( 68 ) to identify the home and fixed workplace that is different from home on a monthly basis. As suggested in the literature, people spend most of their time, especially during nighttime, at home, and they spend some fixed and regular hours during daytime at their workplace.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, a distance threshold of 984 ft (300 m) and a dwell time threshold of 5 min are jointly used to identify a valid trip end. The detailed algorithm can be found in Pan et al ( 68 ).…”

Section: Methodsmentioning

confidence: 99%

“…This study utilizes trip-level information to identify additional employment labels and complements the trip purpose imputation (Figure 6). In addition to the identified normal commuters (workers with fixed workplaces), professional drivers (such as taxi drivers and truck drivers) and other mobile workers without a fixed workplace (such as plumbers) are identified by heuristic rules based on their driving trip statistics ( 68 ). The parameters in the heuristic rules are calibrated against the 2020 Occupational Employment and Wage Statistics (OEWS) estimates at the metropolitan statistical area (MSA) level ( 71 ).…”

Section: Methodsmentioning

confidence: 99%

“…The framework evaluates the temporal patterns of geohash candidates observed for every device and favors the top-ranked candidates for most frequently visited location (68). For each device, multiple temporal patterns are examined stepwise, first at level-6 geohash and later level-7 geohash: (i) minimum number of observed days/ workdays required; (ii) number of observed days/nights/ workdays, observed daily/night/workday hours, and hourly observation volumes used to rank the top home/ work geohash; and (iii) additional checks on the temporal similarity ratio for work geohash candidates.…”

Section: Daily Life Center Identificationmentioning

confidence: 99%

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National-Level Multimodal Origin–Destination Estimation Based on Passively Collected Location Data and Machine Learning Methods

Pan,

Darzi,

Yang

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Along with the development of information and positioning technologies, there emerges passively collected location data that contain location observations with time information from various types of mobile devices. Passive location data are known for their large sample size and continuous behavior observations. However, they also require careful and comprehensive data processing and modeling algorithms for privacy protection and practical applications. In the meantime, the travel demand estimation of origin–destination (OD) tables is fundamental in transportation planning and analysis. There is a lack of national OD estimation that provides time-dependent travel behaviors for all travel modes. Passively collected location data appeal to researchers for their potential of serving as the data source for estimation and monitoring of large-scale multimodal travel demand. This research proposes a comprehensive set of methods for passive location data processing including data cleaning, activity location and purpose identification, trip-level information identification, social demographic imputation, sample weighting and expansion, and demand validation. For each task, the paper evaluates the state-of-the-practice and state-of-the-art algorithms and develops an applicable method jointly considering different features of various passive location data sources, imputation accuracy, and computation efficiency. The paper further examines the viability of the method kit in a national-level case study and successfully derives the multimodal national-level OD estimates with additional data products, such as trip rate and vehicle miles traveled, at different geographic levels and temporal resolutions.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Daily Life Center Identificationmentioning

confidence: 99%

See 2 more Smart Citations

National-Level Multimodal Origin–Destination Estimation Based on Passively Collected Location Data and Machine Learning Methods

Pan,

Darzi,

Yang

et al. 2023

Transportation Research Record: Journal of the Transportation R

Self Cite

View full text Add to dashboard Cite

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“…-People tend to spend most of their time, especially nighttime, at home. The nighttime hours are chosen to be 9 p.m. to 6 a.m. (19). -Home location identification ensures that only devices with a satisfied quality will remain in the dataset.…”

Section: Assumptionsmentioning

confidence: 99%

Elaborated Framework for Duplicate Device Detection from Multisourced Mobile Device Location Data

Kabiri,

Darzi,

Pan

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Mobile device location data (MDLD) have been popularly utilized in various fields. Yet large-scale applications are limited because of either biased or insufficient spatial coverage of the data from individual data vendors. One approach to improve the data coverage is to leverage the data from different data vendors and integrate them to build a more representative dataset. To extract reliable statistics from MDLD, certain data preprocessing steps are crucial to ensure the accuracy of the analysis. One of these steps is the development of a framework to remove duplicated devices or several devices that belong to the same data subject. This treatment is especially necessary when using a multiplicity of data sources, as the same device may be captured by more than one data provider. We propose a data integration methodology for multisourced data to investigate the feasibility of integrating data from several sources. By leveraging the uniqueness of travel pattern of each device, duplicate devices are identified. The proposed methodology is shown to be cost-effective through a national-level analysis. The method is successfully applied to a dataset from January 2020 consisting of more than 270 million raw devices nationwide. Our findings suggest that devices sharing the same imputed home location and the same top-five most-visited locations during a month can represent the same user in the MDLD. It is shown that more than 99.6% of the sample devices having the aforementioned attribute in common are observed at the same location simultaneously.

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Using Geographically Weighted Models to Explore Temporal and Spatial Varying Impacts on Commute Trip Change Resulting from COVID-19

Saleh Namadi,

Tahmasbi,

Mehditabrizi

et al. 2024

Transportation Research Record: Journal of the Transportation R

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COVID-19 deeply affected people’s daily life and travel behaviors. Comprehending changes in travel behavior holds significant importance, making it imperative to investigate the influential factors of sociodemographics and socioeconomics on such behavior. This study used large-scale mobile device location data at the U.S. county level in the Washington, D.C., Maryland, and Virginia (DMV) area, U.S., to reveal the impacts of demographic and socioeconomic variables on commute trip change. The study investigated the impact of these variables on commuter trips over time and space. It reflected the short- and long-term impact of COVID-19 on travel behavior via linear regression and geographically weighted regression (GWR) models. The findings indicated that counties with a higher percentage of people using walking and biking (active mode) for commuting during the initial phase of COVID-19 experienced a greater reduction in their commute trips compared with others. Conversely, for the long-term effect of COVID-19 in November 2020, we can see the impact of using active mode on trip change is not significant any more and, instead, results showed people who were using bus and rail (public mode) for commuting decreased their trips more than others. Additionally, a positive correlation was observed between median income levels and the reduction in commute trips. On the other hand, sectors that necessitated ongoing outdoor operations during the pandemic, such as manufacturing, wholesale trade, and food services, showed a substantial negative correlation with trip change. Moreover, in the DMV area, counties with a higher proportion of Democrat voters experienced less trip reduction than others. Notably, by applying the GWR and multiscale GWR models, the local spatial relationships of variables and commuting behaviors were captured. The results showed the emergence of local correlations as the pandemic evolved, suggesting a geographical impact pattern. At the onset of COVID-19, the pandemic’s impact on commuting behaviors was global. However, as time passed, travel behavior became more influenced by spatial factors and started to show localized effects.

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Residency and worker status identification based on mobile device location data

Cited by 4 publications

References 49 publications

National-Level Multimodal Origin–Destination Estimation Based on Passively Collected Location Data and Machine Learning Methods

National-Level Multimodal Origin–Destination Estimation Based on Passively Collected Location Data and Machine Learning Methods

Elaborated Framework for Duplicate Device Detection from Multisourced Mobile Device Location Data

Using Geographically Weighted Models to Explore Temporal and Spatial Varying Impacts on Commute Trip Change Resulting from COVID-19

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