A trip to work: Estimation of origin and destination of commuting patterns in the main metropolitan regions of Haiti using CDR

Zagatti, Guilherme Augusto; Gonzalez, Miguel; Avner, Paolo; Lozano‐Gracia, Nancy; Brooks, Christopher J.; Albert, Maximilian; Gray, Jonathan; Antos, Sarah Elizabeth; Burci, Priya; Erbach-Schoenberg, Elisabeth zu; Tatem, Andrew J.; Wetter, Erik; Bengtsson, Linus

doi:10.1016/j.deveng.2018.03.002

Cited by 38 publications

(35 citation statements)

References 14 publications

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“…Commuting flows account for a large share of total trips, therefore they attract more attention. For example, Zagatti et al (2018) use CDRs to estimate an OD matrix of commuting flows. For social media data, some data sources have trip purposes (activity types), such as Foursquare, while Twitter data do not directly provide this information.…”

Section: Commuting Travel Demand Estimationmentioning

confidence: 99%

Feasibility of estimating travel demand using geolocations of social media data

2021

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Travel demand estimation, as represented by an origin–destination (OD) matrix, is essential for urban planning and management. Compared to data typically used in travel demand estimation, the key strengths of social media data are that they are low-cost, abundant, available in real-time, and free of geographical partition. However, the data also have significant limitations: population and behavioural biases, and lack of important information such as trip purpose and social demographics. This study systematically explores the feasibility of using geolocations of Twitter data for travel demand estimation by examining the effects of data sparsity, spatial scale, sampling methods, and sample size. We show that Twitter data are suitable for modelling the overall travel demand for an average weekday but not for commuting travel demand, due to the low reliability of identifying home and workplace. Collecting more detailed, long-term individual data from user timelines for a small number of individuals produces more accurate results than short-term data for a much larger population within a region. We developed a novel approach using geotagged tweets as attraction generators as opposed to the commonly adopted trip generators. This significantly increases usable data, resulting in better representation of travel demand. This study demonstrates that Twitter can be a viable option for estimating travel demand, though careful consideration must be given to sampling method, estimation model, and sample size.

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Section: Commuting Travel Demand Estimationmentioning

confidence: 99%

Feasibility of estimating travel demand using geolocations of social media data

2021

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“…A user's home and work locations were identified as the base station that the user was connected to for the longest time during resting and working hours. e second algorithm, called the HomeWorkCluster algorithm [20], clustered the base stations that had interacted with the user. en, the clusters were scored according to the time of records in the clusters.…”

Section: Validation On Volunteer Data Setmentioning

confidence: 99%

“…It was validated on data from 19 volunteers and achieved median errors of 0.9 and 0.83 miles for home and work location detection, respectively. Inspired by this study [19], Zagatti et al [20] also clustered the user's traces using the Hartigan algorithm. e clusters were then scored depending on the occurrence hours and days of events.…”

Section: Introductionmentioning

confidence: 99%

Detecting Home and Work Locations from Mobile Phone Cellular Signaling Data

Yang

Xiong

Zhuo

et al. 2021

Mobile Information Systems

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Obtaining the distribution of home and work locations is essential for city planning, as it defines the structure and mobility pattern of a city. With the development of telecommunication networks, mobile network data, having the advantages of large coverage and strong followability, have produced large amounts of information about human activities. Thus, it has become a popular research subject for human position detection. In this study, we proposed a new method to detect home and work locations based on the extraction of focal points in traces, identifying an individual’s working and resting hours, and analyzing the characteristics of city grids using mobile phone cellular signaling data (CSD). At the individual level, we validated the algorithm on ground-truth volunteer data and achieved a small deviation of under 500 and 565 m for home and work location detection 85% of the time. At the aggregate level, we tested it on a city-wide anonymized CSD set and found a high Pearson correlation between our result and the census data of 0.93. Compared to existing studies, this study improved the granularity and location accuracy of home and work location detection, as well as validated the method using both individually labeled ground-truth data and aggregate data for the first time. Applying the algorithm in a city, we captured the population distribution, commuting patterns, and job-housing balance of the city and demonstrated the potential in using mobile network data for urban planning and policy formulation.

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“…This factor is a critical constraint to use of Location Based Data in urban analytics in most African cities. On the other side, because of high penetration of phones both in rural and urban areas (estimated at 77% of population in Sub-Saharan Africa [12]), CDR data are still the most reliable method to gain understanding of urban and human mobility in cities in the global South [13][14]. In this context, CDR data have been proven to allow planners, academic and government stakeholders to get a deep insight in mobility patterns and even extract information on demographic dynamics such as gender differentiated mobility [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Using Mobile Data to Understand Urban Mobility Patterns in Freetown, Sierra Leone

Matekenya¹,

Alegre²,

Arroyo³

et al. 2021

Policy Research Working Papers

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The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

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A trip to work: Estimation of origin and destination of commuting patterns in the main metropolitan regions of Haiti using CDR

Cited by 38 publications

References 14 publications

Feasibility of estimating travel demand using geolocations of social media data

Feasibility of estimating travel demand using geolocations of social media data

Detecting Home and Work Locations from Mobile Phone Cellular Signaling Data

Using Mobile Data to Understand Urban Mobility Patterns in Freetown, Sierra Leone

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