Reviewing trip purpose imputation in GPS-based travel surveys

Nguyen, Minh Hieu; Armoogum, Jimmy; Madre, Jean‐Loup; Garcia, Cédric

doi:10.1016/j.jtte.2020.05.004

Cited by 29 publications

(39 citation statements)

References 48 publications

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“…Walk detection. To detect walk activity from the mobility data, we used a methodology based on speed threshold and map-matching techniques 35,36 . Walk bouts were extracted from consecutive locations in which the speed between them did not exceed typical walking speeds (2 m/s) [36][37][38] .…”

Section: Methodsmentioning

confidence: 99%

Effect of COVID-19 response policies on walking behavior in US cities

et al. 2021

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The COVID-19 pandemic is causing mass disruption to our daily lives. We integrate mobility data from mobile devices and area-level data to study the walking patterns of 1.62 million anonymous users in 10 metropolitan areas in the United States. The data covers the period from mid-February 2020 (pre-lockdown) to late June 2020 (easing of lockdown restrictions). We detect when users were walking, distance walked and time of the walk, and classify each walk as recreational or utilitarian. Our results reveal dramatic declines in walking, particularly utilitarian walking, while recreational walking has recovered and even surpassed pre-pandemic levels. Our findings also demonstrate important social patterns, widening existing inequalities in walking behavior. COVID-19 response measures have a larger impact on walking behavior for those from low-income areas and high use of public transportation. Provision of equal opportunities to support walking is key to opening up our society and economy.

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Section: Methodsmentioning

confidence: 99%

Effect of COVID-19 response policies on walking behavior in US cities

et al. 2021

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“…The relationship between internet shopping and mobility is a popular topic in the transport science domain. Because shopping is one of the critical purposes of making trips [5][6][7][8][9], online purchasing may be accompanied by a reduction in transport activities, thanks to the significant removal of shopping-related mobility [10][11][12][13]. As a result of receiving considerable attention from multiple disciplines, the subject of online shopping has a massive body of literature [5].…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting the Growth of E-Shopping over the COVID-19 Era in Hanoi, Vietnam

2021

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In response to insufficient understanding of the determinants of change in e-shopping behaviors during the COVID-19 pandemic in developing countries, this paper used the data from 355 respondents, collected in Hanoi during the social distancing period (April 2020), to explore the factors associated with shopping online more frequently (i.e., representing the growth of e-shopping) for five product types (food, medical products, clothing, electronics, and books) in Hanoi, Vietnam. The results showed that nearly 80% of the respondents engaged in e-shopping more frequently than they did before the outbreak of COVID-19. As regards shopping online more frequently in general (i.e., for at least one product type), females were more likely to do so. In-store shopping enjoyment and a decrease in income were a facilitator and a deterrent, respectively. Regarding specific product types, completely working from home had a positive association with more frequent e-purchasing for electronics. Fear of disease encouraged higher frequencies of e-shopping for food and medical products. Notably, the shortage of physical supply was not a determinant of buying any product type online more frequently. As for the implications of our findings, supporting and encouraging low-income shoppers, older persons, and females to engage in e-shopping is necessary to limit the detrimental effects of the pandemic on their lives. The growth of internet purchasing expresses a need to manage the development of urban delivery services, to limit the uncontrolled proliferation of motorcycles. E-shopping requires delivery to complete the online-to-offline process; therefore, protecting the health of delivery drivers to ensure the safety of the whole online shopping process would be necessary.

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“…With regard to input features, geographic information system (GIS) data (land use and points of interest) hold the key to inferring purposes successfully (19), and the same have been considered for use in (6)(7)(8)(9)(10)(11)(12)(13)(14)17). Without consideration of GIS data, accuracy levels of 76% and 84.4% have been reported in (15) and (16), respectively.…”

Section: 3%mentioning

confidence: 99%

“…In extant research, the use of GIS data is limited by the geographical scope of research. As the quality of and means to store GIS data vary from one place to another ( 19 ), employing GIS data requires intensive computation. Additionally, including GIS datasets of more than one area into a single experiment is a time-consuming and labor-intensive task ( 20 ).…”

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confidence: 99%

Feature Selection for Enhancing Purpose Imputation using Global Positioning System Data without Geographic Information System Data

Nguyen

Armoogum

Adell

2020

Transportation Research Record

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This paper presents a method for enhancing purpose imputation from global positioning system data without using geographic information system data via relevant feature selection from six groups: (1) activity time; (2) user characteristics; (3) predicted travel modes; (4) actual travel modes; (5) estimated home location; and (6) estimated location of the most frequently visited non-home place (MFVP). Two datasets were collected in 2019 using TRavelVU, a smartphone application. The first one (the Hanoi dataset) comprised 652 days’ worth of data collected from 63 users in Hanoi, Vietnam, whereas the second one (the Donate dataset) comprised 932 days’ worth of information collected from 65 individuals in Denmark, Sweden, and Norway. The hyperparameters of the random forest models were tuned carefully in accordance with selected features, thereby facilitating a thorough evaluation of the improvement in prediction models. The findings of this study revealed that the addition of either actual or predicted modes resulted in improved imputation performance, albeit the former exhibited a stronger positive effect. This demonstrated the potential benefits of integrating mode detection and purpose identification into a continuous process. The newly adopted MFVP feature contributed to enhanced prediction results (around 2%). The proposed purpose-imputation models, which benefited from all features, demonstrated accuracies of the order of 75% and 85% for the Hanoi and Donate datasets, respectively. The imputation of home and work/education activities demonstrated high success, whereas reasonable prediction results with nearly all F-score levels ranging between 50% and 83% were observed for pick-up/drop-off, shopping/eating, visit/leisure, and business activities.

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Reviewing trip purpose imputation in GPS-based travel surveys

Cited by 29 publications

References 48 publications

Effect of COVID-19 response policies on walking behavior in US cities

Effect of COVID-19 response policies on walking behavior in US cities

Factors Affecting the Growth of E-Shopping over the COVID-19 Era in Hanoi, Vietnam

Feature Selection for Enhancing Purpose Imputation using Global Positioning System Data without Geographic Information System Data

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