Inferring origin-destination flows using mobile phone data: A case study of Senegal

Demissie, Merkebe Getachew; Antunes, Francisco; Bento, Carlos; Phithakkitnukoon, Santi; Sukhvibul, Titipat

doi:10.1109/ecticon.2016.7561328

Cited by 20 publications

(5 citation statements)

References 12 publications

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“…Nevertheless, it is possible to obtain BTS cell-to-cell OD matrices from cellular network data. In the transportation literature, several studies have focused on extracting OD matrices from cellular network data for different regions around the world (Caceres et al 2013;Caceres et al 2007;Calabrese et al 2011;Demissie et al 2016;Iqbal et al 2014;Larijani et al 2015;Mellegard et al 2011;Nanni et al 2014;Pucci et al 2015;Zhang et al 2010). The extracted OD matrices are then used for different purposes, such as optimizing public transport network service (Berlingerio et al 2013) or estimating traffic flows (Gundlegård et al 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling cellphone trace travel mode with neural networks using transit smartcard and home interview survey data

Vaughan

Faghih-Imani

Yusuf³

et al. 2020

European Journal of Transport and Infrastructure Research

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This study proposes a framework to impute travel mode for trips identified from cellphone traces by developing a deep neural network model. In our framework, we use the trips from a home interview survey and transit smartcard data, for which the travel mode is known, to create a set of artificial pseudo-cellphone traces. The generated artificial pseudo-cellphone traces with known mode are then used to train a deep neural network classifier. We further apply the trained model to infer travel modes for the cellphone traces from cellular network data. The empirical case study region is Montevideo, Uruguay, where high-quality data are available for all three types of data used in the analysis: a large dataset of cellphone traces, a large dataset of public transit smartcard transactions, and a small household travel survey. The results can be used to create an enhanced representation of origin-destination trip-making in the region by time of day and travel mode.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling cellphone trace travel mode with neural networks using transit smartcard and home interview survey data

Vaughan

Faghih-Imani

Yusuf³

et al. 2020

European Journal of Transport and Infrastructure Research

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“…The use of mobile phone data has been explored for the development of large scale mobility sensing since the early 2000s (Caceres et al, 2008). The data have been used to investigate various aspects of transportation issues: large-scale urban sensing (Calabrese et al, 2011a;Ratti et al, 2005); traffic parameter estimation ( Bar-Gera, 2007;Demissie et al, 2013a;Demissie et al, 2013b;Liu et al, 2008); origin-destination trip estimation (Calabrese et al, 2011b;Demissie et al, 2016a;Demissie et al, 2018;White and Wells, 2002); land use inference (Demissie et al, 2015;Toole et al, 2012); travel demand estimation (Alexander et al, 2015;Colak et al, 2015;Demissie et al, 2016b;Demissie et al, 2018;Gundlegård et al, 2016;Phithakkitnukoon et al, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Understanding Human Mobility Patterns in a Developing Country Using Mobile Phone Data

Demissie

Phithakkitnukoon

Kattan

et al. 2019

Data Science Journal

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This study demonstrates the use of mobile phone data to derive country-wide mobility patterns. We identified significant locations of users such as home, work, and other based on a combined measure of frequency, duration, time, and day of mobile phone interactions. Consecutive mobile phone records of users are used to identify stay and pass-by locations. A stay location is where users spend a significant amount of their time measured through their mobile phone usage. Trips are constructed for each user between two consecutive stay locations in a day and then categorized by purpose and time of the day. Three measures of entropy are used to further understand the regularity of user's spatiotemporal mobility patterns. The results show that user's in a high entropy cluster has high percentage of non-home based trips (77%), and user's in a low entropy cluster has high percentage of commuting trips (49%), indicating high regularity. A set of doubly constrained trip distribution models is estimated. To measure travel cost, the concept of a centroid point that assumes the origins and destinations of all trips are concentrated at an arbitrary location such as the centroid of a zone is replaced by multiple origins and destinations represented by cell tower locations. Note that a cell tower location can only be used as trips origin/destination location when a stay is detected. The travel cost measured between cell tower locations has resulted in shorter trip distances and the model estimation shows less sensitivity to the distance-decay effect.

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“…Each time the mobile phone user connects to a cellular network by making or receiving a phone call or using internet, the locations of connected (nearest) cellular towers of both ends are recorded along with communication information such as timestamp, call duration, and user identification. Collectively, these location records of individual users can be explored systematically to investigate various aspects of human mobility and transportation such as social network influenced mobility [16], public transit demand [17], traffic volume [18][19], trip generation [20], trip distribution [21][22], route choice [23], transport mode [24], and migration [25] [26]. However, an effort to apply CDR data to infer and analyze temporary migration trips has not yet been made.…”

Section: Introductionmentioning

confidence: 99%

Temporary Migration Flow Inference and Analysis From Perspective of Mobile Phone Network Data

et al. 2022

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Inferring origin-destination flows using mobile phone data: A case study of Senegal

Cited by 20 publications

References 12 publications

Modelling cellphone trace travel mode with neural networks using transit smartcard and home interview survey data

Modelling cellphone trace travel mode with neural networks using transit smartcard and home interview survey data

Understanding Human Mobility Patterns in a Developing Country Using Mobile Phone Data

Temporary Migration Flow Inference and Analysis From Perspective of Mobile Phone Network Data

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